| Title: | Full Factorial Breeding Analysis |
|---|---|
| Description: | We facilitate the analysis of full factorial mating designs with mixed-effects models. The package contains six vignettes containing detailed examples. |
| Authors: | Aimee Lee Houde [aut, cre], Trevor Pitcher [aut] |
| Maintainer: | Aimee Lee Houde <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.5.2 |
| Built: | 2024-12-01 08:22:02 UTC |
| Source: | CRAN |
Full factorial breeding designs are useful for quantifying the amount of additive genetic, nonadditive genetic, and maternal variance that explain phenotypic traits. Such variance estimates are important for examining evolutionary potential. Traditionally, full factorial mating designs have been analyzed using a two- way analysis of variance, which may produce negative variance values and is not suited for unbalanced designs. Mixed-effects models do not produce negative variance values and are suited for unbalanced designs. However, extracting the variance components, calculating significance values, and estimating confidence intervals and/or power values for the components are not straightforward using traditional analytic methods.
In this package we address these issues and facilitate the analysis of full factorial mating designs with mixed-effects models. The observed data functions extract the variance explained by random and fixed effects and provide their significance. We then calculate the additive genetic, nonadditive genetic, and maternal variance components explaining the phenotype. In particular, we integrate nonnormal error structures for estimating these components for nonnormal data types. The resampled data functions are used to produce bootstrap confidence intervals, which can then be plotted using a simple function. This package will facilitate the analyses of full factorial mating designs in R, especially for the analysis of binary, proportion, and/or count data types and for the ability to incorporate additional random and fixed effects and power analyses.
The package contains six vignettes containing detailed examples: browseVignettes(package="fullfact").
The paper associated with the package including worked examples is: Houde ALS, Pitcher TE. 2016. fullfact: an R package for the analysis of genetic and maternal variance components from full factorial mating designs. Ecology and evolution 6 (6), 1656-1665. doi: 10.1002/ece3.1943.
The DESCRIPTION file:
| Package: | fullfact |
| Type: | Package |
| Title: | Full Factorial Breeding Analysis |
| Version: | 1.5.2 |
| Date: | 2024-02-04 |
| Author: | Aimee Lee Houde [aut, cre], Trevor Pitcher [aut] |
| Maintainer: | Aimee Lee Houde <[email protected]> |
| Depends: | R (>= 3.6) |
| Imports: | lme4, afex |
| VignetteBuilder: | knitr |
| Suggests: | knitr, rmarkdown |
| Description: | We facilitate the analysis of full factorial mating designs with mixed-effects models. The package contains six vignettes containing detailed examples. |
| License: | GPL (>= 2) |
| NeedsCompilation: | no |
| Packaged: | 2024-02-04 23:49:47 UTC; Aimee Lee |
| Repository: | CRAN |
| Date/Publication: | 2024-02-05 00:20:02 UTC |
| Config/pak/sysreqs: | cmake make libicu-dev |
Index of help topics:
JackGlmer Jackknife components for non-normal data JackGlmer2 Jackknife components for non-normal data 2 JackGlmer3 Jackknife components for non-normal data 3 JackLmer Jackknife components for normal data JackLmer2 Jackknife components for normal data 2 JackLmer3 Jackknife components for normal data 3 barMANA Bargraph of confidence intervals boxMANA Boxplot of resampled results buildBinary Convert to a binary data frame buildMulti Convert to a multinomial frame chinook_bootL Chinook salmon length, bootstrap calculations chinook_bootS Chinook salmon survival, bootstrap data chinook_jackL Chinook salmon length, jackknife data chinook_jackS Chinook salmon survival, jackknife data chinook_length Chinook salmon length, raw data chinook_resampL Chinook salmon length, bootstrap resampled chinook_resampS Chinook salmon survival, bootstrap resampled chinook_survival Chinook salmon survival, raw data ciJack Jackknife confidence intervals ciJack2 Jackknife confidence intervals 2 ciJack3 Jackknife confidence intervals 3 ciMANA Bootstrap confidence intervals ciMANA2 Bootstrap confidence intervals 2 ciMANA3 Bootstrap confidence intervals 3 fullfact-package Full Factorial Breeding Analysis observGlmer Variance components for non-normal data observGlmer2 Variance components for non-normal data 2 observGlmer3 Variance components for non-normal data 3 observLmer Variance components for normal data observLmer2 Variance components for normal data 2 observLmer3 Variance components for normal data 3 powerGlmer Power analysis for non-normal data powerGlmer2 Power analysis for non-normal data 2 powerGlmer3 Power analysis for non-normal data 3 powerLmer Power analysis for normal data powerLmer2 Power analysis for normal data 2 powerLmer3 Power analysis for normal data 3 resampFamily Bootstrap resample within families resampGlmer Bootstrap components for non-normal data resampGlmer2 Bootstrap components for non-normal data 2 resampGlmer3 Bootstrap components for non-normal data 3 resampLmer Bootstrap components for normal data resampLmer2 Bootstrap components for normal data 2 resampLmer3 Bootstrap components for normal data 3 resampRepli Bootstrap resample within replicates
Further information is available in the following vignettes:
v1_simple_normal |
Simple Normal Data Example (source, pdf) |
v2_advanced_normal |
Advanced Normal Data Example (source, pdf) |
v3_expert_normal |
Expert Normal Data Example (source, pdf) |
v4_simple_non_normal |
Simple Non-Normal Data Example (source, pdf) |
v5_advanced_non_normal |
Advanced Non-Normal Data Example (source, pdf) |
v6_expert_non_normal |
Expert Non-Normal Data Example (source, pdf) |
Aimee Lee Houde [aut, cre], Trevor Pitcher [aut]
Maintainer: Aimee Lee Houde <[email protected]>
Traditional full factorial breeding design analysis:
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
Residual variance component values for generalized linear mixed-effects models:
Nakagawa S, Schielzeth H. 2010. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biological Reviews 85(4): 935-956. DOI: 10.1111/j.1469-185X.2010.00141.x
Fixed effect variance component values for mixed-effects models:
Nakagawa S, Schielzeth H. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4(2): 133-142. DOI: 10.1111/j.2041-210x.2012.00261.x
Confidence intervals (bootstrap resampling, bias and acceleration correction, jackknife resampling):
Efron B, Tibshirani R. 1993. An introduction to the Bootstrap. Chapman and Hall, New York.
Martin, H., Westad, F. & Martens, H. (2004). Imporved Jackknife Variance Estimates of Bilinear Model Parameters. COMPSTAT 2004 – Proceedings in Computational Statistics 16th Symposium Held in Prague, Czech Republic, 2004 (ed J. Antoch), pp. 261-275. Physica-Verlag HD, Heidelberg.
Data sources:
Pitcher TE, Neff BD. 2007. Genetic quality and offspring performance in Chinook salmon: implications for supportive breeding. Conservation Genetics 8(3):607-616. DOI: 10.1007/s10592-006-9204-z
data(chinook_length) #Chinook salmon offspring length ## Standard additive genetic, non-additive genetic, and maternal variance analysis length_mod1<- observLmer(observ=chinook_length,dam="dam",sire="sire",response="length") length_mod1 ## Confidence intervals ##Bootstrap resampling of data: replicates within family ## Not run: resampRepli(dat=chinook_length,copy=c(3:8),family="family",replicate="repli", iter=1000) ## End(Not run) #saves the files in working directory: one for each replicate and #one final (combined) file "resamp_datR.csv" ##Import file #length_datR<- read.csv("resamp_datR.csv") data(chinook_resampL) #same as length_datR, 5 iterations ##Models for the resampled data: standard analysis ## Not run: length_rcomp<- resampLmer(resamp=length_datR,dam="dam",sire="sire", response="length",start=1,end=1000) ## End(Not run) ## 1. Uncorrected Bootstrap 95% confidence interval #ciMANA(comp=length_rcomp) data(chinook_bootL) #similar to length_rcomp, but 1,000 models ciMANA(comp=chinook_bootL) ## 2. Bias and accelerated corrected Bootstrap 95% confidence interval ##Jackknife resampling of data, delete-one: for acceleration estimate ## Not run: length_jack<- JackLmer(observ=chinook_length,dam="dam",sire="sire", response="length") ## End(Not run) #ciMANA(comp=length_rcomp,bias=c(0,0.7192,0.2030),accel=length_jack) data(chinook_jackL) #similar to length_jack, but all observations ciMANA(comp=chinook_bootL,bias=c(0,0.7192,0.2030),accel=chinook_jackL) ##3. Jackknife 95% confidence interval #ciJack(comp=length_jack,full=c(0,0.7192,0.2030,1.0404)) ciJack(comp=chinook_jackL,full=c(0,0.7192,0.2030,1.0404))data(chinook_length) #Chinook salmon offspring length ## Standard additive genetic, non-additive genetic, and maternal variance analysis length_mod1<- observLmer(observ=chinook_length,dam="dam",sire="sire",response="length") length_mod1 ## Confidence intervals ##Bootstrap resampling of data: replicates within family ## Not run: resampRepli(dat=chinook_length,copy=c(3:8),family="family",replicate="repli", iter=1000) ## End(Not run) #saves the files in working directory: one for each replicate and #one final (combined) file "resamp_datR.csv" ##Import file #length_datR<- read.csv("resamp_datR.csv") data(chinook_resampL) #same as length_datR, 5 iterations ##Models for the resampled data: standard analysis ## Not run: length_rcomp<- resampLmer(resamp=length_datR,dam="dam",sire="sire", response="length",start=1,end=1000) ## End(Not run) ## 1. Uncorrected Bootstrap 95% confidence interval #ciMANA(comp=length_rcomp) data(chinook_bootL) #similar to length_rcomp, but 1,000 models ciMANA(comp=chinook_bootL) ## 2. Bias and accelerated corrected Bootstrap 95% confidence interval ##Jackknife resampling of data, delete-one: for acceleration estimate ## Not run: length_jack<- JackLmer(observ=chinook_length,dam="dam",sire="sire", response="length") ## End(Not run) #ciMANA(comp=length_rcomp,bias=c(0,0.7192,0.2030),accel=length_jack) data(chinook_jackL) #similar to length_jack, but all observations ciMANA(comp=chinook_bootL,bias=c(0,0.7192,0.2030),accel=chinook_jackL) ##3. Jackknife 95% confidence interval #ciJack(comp=length_jack,full=c(0,0.7192,0.2030,1.0404)) ciJack(comp=chinook_jackL,full=c(0,0.7192,0.2030,1.0404))
A simple bargraph function for confidence intervals of additive genetic, non-additive genetic, and maternal variance components. Also, plots the median for the bootstrap resampling method or mean of the pseudo-values for the jackknife resampling method.
barMANA(ci_dat, type = "perc", bar_len = 0.1, ymax = NULL, ymin = NULL, yunit = NULL, leg = "topright", cex_ylab = 1, cex_yaxis = 1, cex_names = 1)barMANA(ci_dat, type = "perc", bar_len = 0.1, ymax = NULL, ymin = NULL, yunit = NULL, leg = "topright", cex_ylab = 1, cex_yaxis = 1, cex_names = 1)
ci_dat |
Data frame of a confidence interval function. |
type |
Default is "perc" for percentage values of variance components. Other option is "raw" for raw values of variance components. |
bar_len |
Length of error bar in inches. |
ymax |
Maximum value of the y-axis. |
ymin |
Minimum value of the y-axis. |
yunit |
Unit increment of the y-axis. |
leg |
Position of the simple legend. |
cex_ylab |
Magnification of the y-axis label. |
cex_yaxis |
Magnification of the y-axis units. |
cex_names |
Optional magnification of trait labels. |
Plots a bargraph with the median or mean as the top of the shaded bar and error bars covering the range of the confidence interval. Uses an object produced by any of the bootstrap resampling CI functions, i.e. ciMANA, ciMANA2, and ciMANA3 or jackknife resampling functions, i.e. ciJack, ciJack2, and ciJack3. The median is plotted for bootstrap resampling and the mean of pseudo-value for jackknife resampling. Produces a simple legend. The function can plot several bar graphs grouped by label to visualize several phenotypic traits.
##Import jackknife resampling results data(chinook_jackL) #Chinook salmon length length_ci<- ciJack(comp=chinook_jackL,full=c(0,0.7192,0.2030,1.0404)) barMANA(ci_dat=length_ci) #default plot barMANA(ci_dat=length_ci,bar_len=0.3,yunit=20,ymax=100,cex_ylab=1.3) ##Group length and survival together in the same plot data(chinook_bootS) #Chinook salmon survival (bootstrap resampling) length_ci<- ciJack(comp=chinook_jackL,full=c(0,0.7192,0.2030,1.0404),trait="length") survival_ci<- ciMANA(comp=chinook_bootS,trait="survival") colnames(length_ci$raw)[3]<- "median"; colnames(length_ci$percentage)[3]<- "median" comb_bar<- list(raw=rbind(length_ci$raw,survival_ci$raw), percentage=rbind(length_ci$percentage,survival_ci$percentage)) # barMANA(ci_dat=comb_bar) #default plot barMANA(ci_dat=comb_bar,bar_len=0.3,yunit=20,ymax=100,cex_ylab=1.3)##Import jackknife resampling results data(chinook_jackL) #Chinook salmon length length_ci<- ciJack(comp=chinook_jackL,full=c(0,0.7192,0.2030,1.0404)) barMANA(ci_dat=length_ci) #default plot barMANA(ci_dat=length_ci,bar_len=0.3,yunit=20,ymax=100,cex_ylab=1.3) ##Group length and survival together in the same plot data(chinook_bootS) #Chinook salmon survival (bootstrap resampling) length_ci<- ciJack(comp=chinook_jackL,full=c(0,0.7192,0.2030,1.0404),trait="length") survival_ci<- ciMANA(comp=chinook_bootS,trait="survival") colnames(length_ci$raw)[3]<- "median"; colnames(length_ci$percentage)[3]<- "median" comb_bar<- list(raw=rbind(length_ci$raw,survival_ci$raw), percentage=rbind(length_ci$percentage,survival_ci$percentage)) # barMANA(ci_dat=comb_bar) #default plot barMANA(ci_dat=comb_bar,bar_len=0.3,yunit=20,ymax=100,cex_ylab=1.3)
A simple boxplot function for bootstrap and jackknife resampled results of additive genetic, non-additive genetic, and maternal variance components.
boxMANA(comp, type = "perc", ymax = NULL, ymin = NULL, yunit = NULL, leg = "topright", cex_ylab = 1, cex_yaxis = 1, cex_names = 1)boxMANA(comp, type = "perc", ymax = NULL, ymin = NULL, yunit = NULL, leg = "topright", cex_ylab = 1, cex_yaxis = 1, cex_names = 1)
comp |
Data frame of bootstrap or jackknife resampling results. |
type |
Default is "perc" for percentage values of variance components. Other option is "raw" for raw values of variance components. |
ymax |
Maximum value of the y-axis. |
ymin |
Minimum value of the y-axis. |
yunit |
Unit increment of the y-axis. |
leg |
Position of the simple legend. |
cex_ylab |
Magnification of the y-axis label. |
cex_yaxis |
Magnification of the y-axis units. |
cex_names |
Optional magnification of trait labels. |
Plots an R boxplot. Uses an object produced by any of the bootstrap resampling functions, i.e. resampLmer, resampLmer2, resampLmer3, resampGlmer, resampGlmer2, and resampGlmer3. Or any of the jackknife resampling functions, i.e. JackLmer, JackLmer2, JackLmer3, JackGlmer, JackGlmer2, and JackGlmer3. Produces a simple legend.
##Import bootstrap resampled data model results data(chinook_bootL) #Chinook salmon length boxMANA(comp=chinook_bootL) #Default plot boxMANA(comp=chinook_bootL,yunit=20,ymax=100,cex_ylab=1.3,leg="topleft") ##Group length and survival together in the same plot data(chinook_bootS) #Chinook salmon survival chinook_bootL$trait<- "length"; chinook_bootS$trait<- "survival" comb_boot<- rbind(chinook_bootL[,-2],chinook_bootS) #remove 'tray' comb_boot$trait<- as.factor(comb_boot$trait) # boxMANA(comp=comb_boot) #Default plot boxMANA(comp=comb_boot,yunit=20,ymax=100,cex_ylab=1.3)##Import bootstrap resampled data model results data(chinook_bootL) #Chinook salmon length boxMANA(comp=chinook_bootL) #Default plot boxMANA(comp=chinook_bootL,yunit=20,ymax=100,cex_ylab=1.3,leg="topleft") ##Group length and survival together in the same plot data(chinook_bootS) #Chinook salmon survival chinook_bootL$trait<- "length"; chinook_bootS$trait<- "survival" comb_boot<- rbind(chinook_bootL[,-2],chinook_bootS) #remove 'tray' comb_boot$trait<- as.factor(comb_boot$trait) # boxMANA(comp=comb_boot) #Default plot boxMANA(comp=comb_boot,yunit=20,ymax=100,cex_ylab=1.3)
Assign a binary number (i.e. '0' or '1') to two columns containing the number of offspring. Copy information by the number of times equal to the number of offspring.
buildBinary(dat, copy, one, zero)buildBinary(dat, copy, one, zero)
dat |
Data frame to convert. |
copy |
Column numbers to copy. |
one |
Column name of counts to assign a '1' value. |
zero |
Column name of counts to assign a '0' value. |
Replicate-level data should be converted to the individual-level to not underestimate phenotypic variance, which can influence genetic and maternal estimates (see Puurtinen et al. 2009).
A converted data frame with a number of row matching the total number of individuals.
Puurtinen M, Ketola T, Kotiaho JS. 2009. The good-genes and compatible-genes benefits of mate choice. The American Naturalist 174(5): 741-752. DOI: 10.1086/606024
data(chinook_survival) chinook_survival2<- buildBinary(dat=chinook_survival,copy=c(1:6,9),one="alive",zero="dead")data(chinook_survival) chinook_survival2<- buildBinary(dat=chinook_survival,copy=c(1:6,9),one="alive",zero="dead")
Assign multiple numbers to multiple columns containing the number of offspring. Copy information by the number of times equal to the number of offspring.
buildMulti(dat, copy, multi)buildMulti(dat, copy, multi)
dat |
Data frame to convert. |
copy |
Column numbers to copy. |
multi |
A list containing the numbers to assign and matching column names, e.g. list(c(2,0,1),c("two","zero","one")). |
Replicate-level data should be converted to the individual-level to not underestimate phenotypic variance, which can influence genetic and maternal estimates (see Puurtinen et al. 2009).
A converted data frame with a number of row matching the total number of individuals.
Puurtinen M, Ketola T, Kotiaho JS. 2009. The good-genes and compatible-genes benefits of mate choice. The American Naturalist 174(5): 741-752. DOI: 10.1086/606024
data(chinook_survival) chinook_survival$total<- chinook_survival$alive + chinook_survival$dead #create total column chinook_survival3<- buildMulti(dat=chinook_survival,copy=c(1:6,9),multi=list(c(2,1,0), c("total","alive","dead")))data(chinook_survival) chinook_survival$total<- chinook_survival$alive + chinook_survival$dead #create total column chinook_survival3<- buildMulti(dat=chinook_survival,copy=c(1:6,9),multi=list(c(2,1,0), c("total","alive","dead")))
Bootstrap resampled Chinook salmon fork length (mm) at hatch with the amount of additive genetic, non-additive genetic, and maternal variance calculations.
data("chinook_bootL")data("chinook_bootL")
A data frame with 1000 observations on the following 9 variables.
dam.sire,a numeric vector.
tray,a numeric vector.
sire,a numeric vector.
dam,a numeric vector.
Residual,a numeric vector.
Total,a numeric vector.
additive,a numeric vector.
maternal,a numeric vector.
nonadd,a numeric vector.
Also includes the calculations for the amount of variance explained by position (tray), dam by sire, sire, dam, residual,and total.
http://link.springer.com.proxy1.lib.uwo.ca/article/10.1007
Pitcher TE, Neff BD. 2007. Genetic quality and offspring performance in Chinook salmon: implications for supportive breeding. Conservation Genetics 8(3):607-616. DOI: 10.1007/s10592-006-9204-z
data(chinook_bootL) ## Extract bootstrap confidence interval ciMANA(comp=chinook_bootL)data(chinook_bootL) ## Extract bootstrap confidence interval ciMANA(comp=chinook_bootL)
Bootstrap resampled Chinook salmon binary survival to hatch (1 is alive, 0 is dead) with the amount of additive genetic, non-additive genetic, and maternal variance calculations.
data("chinook_bootS")data("chinook_bootS")
A data frame with 1000 observations on the following 8 variables.
dam.sire,a numeric vector.
sire,a numeric vector.
dam,a numeric vector.
Residual,a numeric vector.
Total,a numeric vector.
additive,a numeric vector.
maternal,a numeric vector.
nonadd,a numeric vector.
Also includes the calculations for the amount of variance explained by dam by sire, sire, dam, residual, and total.
http://link.springer.com.proxy1.lib.uwo.ca/article/10.1007
Pitcher TE, Neff BD. 2007. Genetic quality and offspring performance in Chinook salmon: implications for supportive breeding. Conservation Genetics 8(3):607-616. DOI: 10.1007/s10592-006-9204-z
data(chinook_bootS) ## Extract bootstrap confidence interval ciMANA(comp=chinook_bootS)data(chinook_bootS) ## Extract bootstrap confidence interval ciMANA(comp=chinook_bootS)
Jackknife resampled Chinook salmon fork length (mm) at hatch with the amount of additive genetic, non-additive genetic, and maternal variance calculations. Jackknife resampling was leave-out-one.
data("chinook_jackL")data("chinook_jackL")
A data frame with 1210 observations on the following 9 variables.
dam.sire,a numeric vector.
tray,a numeric vector.
sire,a numeric vector.
dam,a numeric vector.
Residual,a numeric vector.
Total,a numeric vector.
additive,a numeric vector.
nonadd,a numeric vector.
maternal,a numeric vector.
Also includes the calculations for the amount of variance explained by position (tray), dam by sire, sire, dam, residual, and total.
http://link.springer.com.proxy1.lib.uwo.ca/article/10.1007
Pitcher TE, Neff BD. 2007. Genetic quality and offspring performance in Chinook salmon: implications for supportive breeding. Conservation Genetics 8(3):607-616. DOI: 10.1007/s10592-006-9204-z
data(chinook_jackL) ## Extract jackknifed confidence interval ciJack(comp=chinook_jackL,full=c(0,0.7192,0.2030,1.0404))data(chinook_jackL) ## Extract jackknifed confidence interval ciJack(comp=chinook_jackL,full=c(0,0.7192,0.2030,1.0404))
Jackknife resampled Chinook salmon survival with the amount of additive genetic, non-additive genetic, and maternal variance calculations. Jackknife resampling was leave-out-30.
data("chinook_jackS")data("chinook_jackS")
A data frame with 1210 observations on the following 9 variables.
dam.sire,a numeric vector.
sire,a numeric vector.
dam,a numeric vector.
Residual,a numeric vector.
Total,a numeric vector.
additive,a numeric vector.
nonadd,a numeric vector.
maternal,a numeric vector.
Also includes the calculations for the amount of variance explained by dam by sire, sire, dam, residual, and total.
http://link.springer.com.proxy1.lib.uwo.ca/article/10.1007
Pitcher TE, Neff BD. 2007. Genetic quality and offspring performance in Chinook salmon: implications for supportive breeding. Conservation Genetics 8(3):607-616. DOI: 10.1007/s10592-006-9204-z
data(chinook_jackS) ## Extract jackknifed confidence interval ciJack(comp=chinook_jackS,full=c(0.6655,0.6692,0.6266,4.4166))data(chinook_jackS) ## Extract jackknifed confidence interval ciJack(comp=chinook_jackS,full=c(0.6655,0.6692,0.6266,4.4166))
Raw Chinook salmon fork length (mm) at hatch for offspring produced using an 11 x 11 full factorial breeding design.
data("chinook_length")data("chinook_length")
A data frame with 1210 observations on the following 8 variables.
family,a factor with levels: f1 f10 f100 f101 f102 f103 f104 f105 f106 f107 f108 f109 f11 f110 f111 f112 f113 f114 f115 f116 f117 f118 f119 f12 f120 f121 f13 f14 f15 f16 f17 f18 f19 f2 f20 f21 f22 f23 f24 f25 f26 f27 f28 f29 f3 f30 f31 f32 f33 f34 f35 f36 f37 f38 f39 f4 f40 f41 f42 f43 f44 f45 f46 f47 f48 f49 f5 f50 f51 f52 f53 f54 f55 f56 f57 f58 f59 f6 f60 f61 f62 f63 f64 f65 f66 f67 f68 f69 f7 f70 f71 f72 f73 f74 f75 f76 f77 f78 f79 f8 f80 f81 f82 f83 f84 f85 f86 f87 f88 f89 f9 f90 f91 f92 f93 f94 f95 f96 f97 f98 f99
repli,a factor with levels: r1 r2
dam,a factor with levels: d1 d10 d11 d2 d3 d4 d5 d6 d7 d8 d9
sire,a factor with levels: s1 s10 s11 s2 s3 s4 s5 s6 s7 s8 s9
tray,a factor with levels: t1 t10 t11 t12 t13 t14 t15 t16 t2 t3 t4 t5 t6 t7 t8 t9
cell,a factor with levels: 1A 1B 1C 1D 2A 2B 2C 2D 3A 3B 3C 3D 4A 4B 4C 4D
length,a numeric vector.
egg_size,a numeric vector.
Also includes family identity, family replicate, incubator position (tray and cell), and average female egg size (mm) information.
http://link.springer.com.proxy1.lib.uwo.ca/article/10.1007
Pitcher TE, Neff BD. 2007. Genetic quality and offspring performance in Chinook salmon: implications for supportive breeding. Conservation Genetics 8(3):607-616. DOI: 10.1007/s10592-006-9204-z
data(chinook_length) ## Standard additive genetic, non-additive genetic, and maternal variance analysis length_mod1<- observLmer(observ=chinook_length,dam="dam",sire="sire",response="length") length_mod1data(chinook_length) ## Standard additive genetic, non-additive genetic, and maternal variance analysis length_mod1<- observLmer(observ=chinook_length,dam="dam",sire="sire",response="length") length_mod1
Bootstrap resampled Chinook salmon fork length (mm) at hatch. Number of iterations was 5.
data("chinook_resampL")data("chinook_resampL")
A data frame with 1210 observations on the following 30 variables.
dam1,a numeric vector
sire1,a numeric vector
tray1,a numeric vector
cell1,a numeric vector
length1,a numeric vector
egg_size1,a numeric vector
dam2,a numeric vector
sire2,a numeric vector
tray2,a numeric vector
cell2,a numeric vector
length2,a numeric vector
egg_size2,a numeric vector
dam3,a numeric vector
sire3,a numeric vector
tray3,a numeric vector
cell3,a numeric vector
length3,a numeric vector
egg_size3,a numeric vector
dam4,a numeric vector
sire4,a numeric vector
tray4,a numeric vector
cell4,a numeric vector
length4,a numeric vector
egg_size4,a numeric vector
dam5,a numeric vector
sire5,a numeric vector
tray5,a numeric vector
cell5,a numeric vector
length5,a numeric vector
egg_size5,a numeric vector
Pitcher TE, Neff BD. 2007. Genetic quality and offspring performance in Chinook salmon: implications for supportive breeding. Conservation Genetics 8(3):607-616. DOI: 10.1007/s10592-006-9204-z
data(chinook_resampL) #the five models length_rcomp1<- resampLmer(resamp=chinook_resampL,dam="dam",sire="sire",response="length", start=1,end=5) #full analysis should use 1,000 modelsdata(chinook_resampL) #the five models length_rcomp1<- resampLmer(resamp=chinook_resampL,dam="dam",sire="sire",response="length", start=1,end=5) #full analysis should use 1,000 models
Bootstrap resampled Chinook salmon binary survival to hatch (1 is alive, 0 is dead). Number of iterations was 5.
data("chinook_resampS")data("chinook_resampS")
A data frame with 36300 observations on the following 30 variables.
status1,a numeric vector
dam1,a numeric vector
sire1,a numeric vector
tray1,a numeric vector
cell1,a numeric vector
egg_size1,a numeric vector
status2,a numeric vector
dam2,a numeric vector
sire2,a numeric vector
tray2,a numeric vector
cell2,a numeric vector
egg_size2,a numeric vector
status3,a numeric vector
dam3,a numeric vector
sire3,a numeric vector
tray3,a numeric vector
cell3,a numeric vector
egg_size3,a numeric vector
status4,a numeric vector
dam4,a numeric vector
sire4,a numeric vector
tray4,a numeric vector
cell4,a numeric vector
egg_size4,a numeric vector
status5,a numeric vector
dam5,a numeric vector
sire5,a numeric vector
tray5,a numeric vector
cell5,a numeric vector
egg_size5,a numeric vector
Pitcher TE, Neff BD. 2007. Genetic quality and offspring performance in Chinook salmon: implications for supportive breeding. Conservation Genetics 8(3):607-616. DOI: 10.1007/s10592-006-9204-z
data(chinook_resampS) ## Not run: survival_rcomp<- resampGlmer(resamp=chinook_resampS,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit"),start=1,end=1000) ## End(Not run)data(chinook_resampS) ## Not run: survival_rcomp<- resampGlmer(resamp=chinook_resampS,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit"),start=1,end=1000) ## End(Not run)
Raw Chinook salmon numbers alive and dead to hatching of offspring produced using an 11 x 11 full factorial breeding design.
data("chinook_survival")data("chinook_survival")
A data frame with 242 observations on the following 9 variables.
family,a factor with levels: f1 f10 f100 f101 f102 f103 f104 f105 f106 f107 f108 f109 f11 f110 f111 f112 f113 f114 f115 f116 f117 f118 f119 f12 f120 f121 f13 f14 f15 f16 f17 f18 f19 f2 f20 f21 f22 f23 f24 f25 f26 f27 f28 f29 f3 f30 f31 f32 f33 f34 f35 f36 f37 f38 f39 f4 f40 f41 f42 f43 f44 f45 f46 f47 f48 f49 f5 f50 f51 f52 f53 f54 f55 f56 f57 f58 f59 f6 f60 f61 f62 f63 f64 f65 f66 f67 f68 f69 f7 f70 f71 f72 f73 f74 f75 f76 f77 f78 f79 f8 f80 f81 f82 f83 f84 f85 f86 f87 f88 f89 f9 f90 f91 f92 f93 f94 f95 f96 f97 f98 f99
repli,a factor with levels: r1 r2
dam,a factor with levels: d1 d10 d11 d2 d3 d4 d5 d6 d7 d8 d9
sire,a factor with levels: s1 s10 s11 s2 s3 s4 s5 s6 s7 s8 s9
tray,a factor with levels: t1 t10 t11 t12 t13 t14 t15 t16 t2 t3 t4 t5 t6 t7 t8 t9
cell,a factor with levels: 1A 1B 1C 1D 2A 2B 2C 2D 3A 3B 3C 3D 4A 4B 4C 4D
alive,a numeric vector.
dead,a numeric vector.
egg_size,a numeric vector.
Also includes family identity, family replicate, incubator position (tray and cell), and average female egg size (mm) information.
http://link.springer.com.proxy1.lib.uwo.ca/article/10.1007
Pitcher TE, Neff BD. 2007. Genetic quality and offspring performance in Chinook salmon: implications for supportive breeding. Conservation Genetics 8(3):607-616. DOI: 10.1007/s10592-006-9204-z
data(chinook_survival) ## Convert replicate-level recorded data to individual-level (binary) data chinook_survival2<- buildBinary(dat=chinook_survival,copy=c(2:6,9),one="alive",zero="dead") ## Standard additive genetic, non-additive genetic, and maternal variance analysis ## Not run: survival_mod1<- observGlmer(observ=chinook_survival2,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit")) survival_mod1 ## End(Not run)data(chinook_survival) ## Convert replicate-level recorded data to individual-level (binary) data chinook_survival2<- buildBinary(dat=chinook_survival,copy=c(2:6,9),one="alive",zero="dead") ## Standard additive genetic, non-additive genetic, and maternal variance analysis ## Not run: survival_mod1<- observGlmer(observ=chinook_survival2,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit")) survival_mod1 ## End(Not run)
Extracts jackknife confidence intervals for additive genetic, non-additive genetic, and maternal variance components.
ciJack(comp, full, level = 95, rnd_r = 3, rnd_p = 1, trait = NULL)ciJack(comp, full, level = 95, rnd_r = 3, rnd_p = 1, trait = NULL)
comp |
Data frame of jackknife resampling results. |
full |
A vector of raw observed additive, non-additive, maternal, and total variance component values for from the full observed data set, i.e. c(additive, non-additive, maternal, total). |
level |
Confidence level, as a percentage. Default is 95. |
rnd_r |
Number of decimal places to round the confidence interval of raw values. |
rnd_p |
Number of decimal places to round the confidence interval of percentage values. |
trait |
Optional label for the phenotypic trait. |
Used for jackknife resampling results produced using JackLmer for normal data or JackGlmer for non-normal data. Jackknife confidence intervals, using pseudo-values are described by Efron and Tibshirani (1993). The standard errors are calculated from the pseudo-values and the Student's t distribution is used to provide the lower and upper confidence values. For delete-d jackknife resampling, M degrees of freedom are used for producing the confidence interval (Martin et al. 2004): M = N / d, where N is the total number of observations and d is the number of deleted observations. That is, M is the number of row in the jackknife resampling results. Large values of M, such as 1,000, can translate to the delete-d jackknife resampling method approaching bootstrap resampling expectations (Efron & Tibshirani 1993).
Prints a data frame containing the lower, median, and upper values of the jackknife confidence interval for additive genetic, non-additive genetic, and maternal variance components. Values are presented as raw and percentages of the total variance value within each row.
Efron B, Tibshirani R. 1993. An introduction to the Bootstrap. Chapman and Hall, New York.
Martin, H., Westad, F. & Martens, H. (2004). Imporved Jackknife Variance Estimates of Bilinear Model Parameters. COMPSTAT 2004 – Proceedings in Computational Statistics 16th Symposium Held in Prague, Czech Republic, 2004 (ed J. Antoch), pp. 261-275. Physica-Verlag HD, Heidelberg.
data(chinook_jackL) #Chinook salmon offspring length, delete-one jackknife ciJack(chinook_jackL,c(0,0.7192,0.2030,1.0404))data(chinook_jackL) #Chinook salmon offspring length, delete-one jackknife ciJack(chinook_jackL,c(0,0.7192,0.2030,1.0404))
Extracts jackknife confidence intervals for additive genetic, non-additive genetic, and maternal variance components. Also extracts intervals for optional position and block variance components.
ciJack2(comp, full, level = 95, rnd_r = 3, rnd_p = 1, position = NULL, block = NULL, trait = NULL)ciJack2(comp, full, level = 95, rnd_r = 3, rnd_p = 1, position = NULL, block = NULL, trait = NULL)
comp |
Data frame of jackknife resampling results. |
full |
A vector of raw observed additive, non-additive, maternal, and total variance component values for from the full observed data set, i.e. c(additive, non-additive, maternal, total, position/block). If there is a position and a block c(..., total, position, block). |
level |
Confidence level, as a percentage. Default is 95. |
rnd_r |
Number of decimal places to round the confidence interval of raw values. |
rnd_p |
Number of decimal places to round the confidence interval of percentage values. |
position |
Optional column name containing position factor information. |
block |
Optional column name containing block factor information. |
trait |
Optional label for the phenotypic trait. |
Used for jackknife resampling results produced using JackLmer2 for normal data or JackGlmer2 for non-normal data. Jackknife confidence intervals, using pseudo-values are described by Efron and Tibshirani (1993). The standard errors are calculated from the pseudo-values and the Student's t distribution is used to provide the lower and upper confidence values. For delete-d jackknife resampling, M degrees of freedom are used for producing the confidence interval (Martin et al. 2004): M = N / d, where N is the total number of observations and d is the number of deleted observations. That is, M is the number of row in the jackknife resampling results. Large values of M, such as 1,000, can translate to the delete-d jackknife resampling method approaching bootstrap resampling expectations (Efron & Tibshirani 1993).
Prints a data frame containing the lower, median, and upper values of the jackknife confidence interval for additive genetic, non-additive genetic, maternal variance components, and optional position and/or block variance components. Values are presented as raw and percentages of the total variance value within each row.
Efron B, Tibshirani R. 1993. An introduction to the Bootstrap. Chapman and Hall, New York.
Martin, H., Westad, F. & Martens, H. (2004). Imporved Jackknife Variance Estimates of Bilinear Model Parameters. COMPSTAT 2004 – Proceedings in Computational Statistics 16th Symposium Held in Prague, Czech Republic, 2004 (ed J. Antoch), pp. 261-275. Physica-Verlag HD, Heidelberg.
data(chinook_jackL) #Chinook salmon offspring length, delete-one jackknife ciJack2(chinook_jackL,position="tray",c(0,0.7192,0.2030,1.0404,0.1077))data(chinook_jackL) #Chinook salmon offspring length, delete-one jackknife ciJack2(chinook_jackL,position="tray",c(0,0.7192,0.2030,1.0404,0.1077))
Extracts jackknife confidence intervals for additive genetic, non-additive genetic, and maternal variance components. Also extracts intervals for additional fixed and/or random effects.
ciJack3(comp, full, remain = NULL, level = 95, rnd_r = 3, rnd_p = 1, trait = NULL)ciJack3(comp, full, remain = NULL, level = 95, rnd_r = 3, rnd_p = 1, trait = NULL)
comp |
Data frame of jackknife resampling results |
full |
A vector of raw observed additive, non-additive, maternal, and total variance component values for from the full observed data set, i.e. c(additive, non-additive, maternal, total). Followed by any other components in the order of the vector remain, i.e. c(additive, non-additive, maternal, total, component1, component2, etc.). |
remain |
Vector of column names for additional effects |
level |
Confidence level, as a percentage. Default is 95. |
rnd_r |
Number of decimal places to round the confidence interval of raw values. |
rnd_p |
Number of decimal places to round the confidence interval of percentage values. |
trait |
Optional label for the phenotypic trait. |
Used for jackknife resampling results produced using JackLmer3 for normal data or JackGlmer3 for non-normal data. Jackknife confidence intervals, using pseudo-values are described by Efron and Tibshirani (1993). The standard errors are calculated from the pseudo-values and the Student's t distribution is used to provide the lower and upper confidence values. For delete-d jackknife resampling, M degrees of freedom are used for producing the confidence interval (Martin et al. 2004): M = N / d, where N is the total number of observations and d is the number of deleted observations. That is, M is the number of row in the jackknife resampling results. Large values of M, such as 1,000, can translate to the delete-d jackknife resampling method approaching bootstrap resampling expectations (Efron & Tibshirani 1993).
Prints a data frame containing the lower, median, and upper values of the jackknife confidence interval for additive genetic, non-additive genetic, maternal variance components, and any additional fixed effect and/or random effect variance components. Values are presented as raw and percentages of the total variance value within each row.
Efron B, Tibshirani R. 1993. An introduction to the Bootstrap. Chapman and Hall, New York.
Martin, H., Westad, F. & Martens, H. (2004). Imporved Jackknife Variance Estimates of Bilinear Model Parameters. COMPSTAT 2004 – Proceedings in Computational Statistics 16th Symposium Held in Prague, Czech Republic, 2004 (ed J. Antoch), pp. 261-275. Physica-Verlag HD, Heidelberg.
data(chinook_jackL) #Chinook salmon offspring length, delete-one jackknife ciJack3(chinook_jackL,remain=c("tray","Residual"),c(0,0.7192,0.2030,1.0404,0.1077,0.5499))data(chinook_jackL) #Chinook salmon offspring length, delete-one jackknife ciJack3(chinook_jackL,remain=c("tray","Residual"),c(0,0.7192,0.2030,1.0404,0.1077,0.5499))
Extracts bootstrap-t confidence intervals for additive genetic, non-additive genetic, and maternal variance components.
ciMANA(comp, level = 95, rnd_r = 3, rnd_p = 1, bias = NULL, accel = NULL, trait = NULL)ciMANA(comp, level = 95, rnd_r = 3, rnd_p = 1, bias = NULL, accel = NULL, trait = NULL)
comp |
Data frame of bootstrap resampling results. |
level |
Confidence level, as a percentage. Default is 95. |
rnd_r |
Number of decimal places to round the confidence interval of raw values. |
rnd_p |
Number of decimal places to round the confidence interval of percentage values. |
bias |
Optional vector of raw observed additive, non-additive, and maternal, variance component values for bias correction, i.e. c(additive, non-additive, maternal). |
accel |
Optional data frame of jackknifed data model results for acceleration correction. |
trait |
Optional label for the phenotypic trait. |
Used for bootstrap resampling results produced using resampLmer for normal data or resampGlmer for non-normal data. Bootstrap-t confidence intervals, including bias and acceleration correction methods are described by Efron and Tibshirani (1993). Jackknife data model results for acceleration correction can be produced using JackLmer, for normal data or JackGlmer for non-normal data. The 'bias fail' warning is if the bias calculation is Inf or -Inf, e.g. bias contains a zero value, so the uncorrected confidence interval is displayed.
Prints a data frame containing the lower, median, and upper values of the bootstrap-t confidence interval for additive genetic, non-additive genetic, and maternal variance components. Values are presented as raw and percentages of the total variance value within each row.
Efron B, Tibshirani R. 1993. An introduction to the Bootstrap. Chapman and Hall, New York.
#Import bootstrap resampled data model results data(chinook_bootL) #Chinook salmon offspring length #Extract un-corrected confidence interval ciMANA(comp=chinook_bootL) #Extract bias corrected confidence interval ciMANA(comp=chinook_bootL,bias=c(0,0.7192,0.2030)) #see details for 'bias' fail #Extract bias and accelerated corrected confidence interval #Import jackknife resampled data model results data(chinook_jackL) # ciMANA(comp=chinook_bootL,bias=c(0,0.7192,0.2030),accel=chinook_jackL) #see details for 'bias' fail#Import bootstrap resampled data model results data(chinook_bootL) #Chinook salmon offspring length #Extract un-corrected confidence interval ciMANA(comp=chinook_bootL) #Extract bias corrected confidence interval ciMANA(comp=chinook_bootL,bias=c(0,0.7192,0.2030)) #see details for 'bias' fail #Extract bias and accelerated corrected confidence interval #Import jackknife resampled data model results data(chinook_jackL) # ciMANA(comp=chinook_bootL,bias=c(0,0.7192,0.2030),accel=chinook_jackL) #see details for 'bias' fail
Extracts bootstrap-t confidence intervals for additive genetic, non-additive genetic, and maternal variance components. Also extracts intervals for optional position and block variance components.
ciMANA2(comp, level = 95, rnd_r = 3, rnd_p = 1, position = NULL, block = NULL, bias = NULL, accel = NULL, trait = NULL)ciMANA2(comp, level = 95, rnd_r = 3, rnd_p = 1, position = NULL, block = NULL, bias = NULL, accel = NULL, trait = NULL)
comp |
Data frame of bootstrap resampling results. |
level |
Confidence level, as a percentage. Default is 95. |
rnd_r |
Number of decimal places to round the confidence interval of raw values. |
rnd_p |
Number of decimal places to round the confidence interval of percentage values. |
position |
Optional column name containing position factor information. |
block |
Optional column name containing block factor information. |
bias |
Optional vector of raw observed additive, non-additive, maternal, position and/or block variance component values for bias correction, i.e. c(additive, non-additive, maternal, position/block). If there is a position and a block c(..., maternal, position, block). |
accel |
Optional data frame of jackknifed data model results for acceleration correction. |
trait |
Optional label for the phenotypic trait. |
Used for bootstrap resampling results produced using resampLmer2 for normal data or resampGlmer2 for non-normal data. Bootstrap-t confidence intervals, including bias and acceleration correction methods are described by Efron and Tibshirani (1993). Jackknife data model results for acceleration correction can be produced using JackLmer2, for normal data or JackGlmer2 for non-normal data. The 'bias fail' warning is if the bias calculation is Inf or -Inf, e.g. bias contains a zero value, so the uncorrected confidence interval is displayed.
Prints a data frame containing the lower, median, and upper values of the bootstrap-t confidence interval for additive genetic, non-additive genetic, maternal, and optional position and/or block variance components. Values are presented as raw and percentages of the total variance value within each row.
Efron B, Tibshirani R. 1993. An introduction to the Bootstrap. Chapman and Hall, New York.
#Import bootstrap resampled data model results data(chinook_bootL) #Chinook salmon offspring length #Extract un-corrected confidence interval ciMANA2(comp=chinook_bootL,position="tray") #Extract bias corrected confidence interval ciMANA2(comp=chinook_bootL,position="tray",bias=c(0,0.7192,0.2030,0.1077)) #see details for 'bias' fail #Extract bias and accelerated corrected confidence interval #Import jackknife resampled data model results data(chinook_jackL) # ciMANA2(comp=chinook_bootL,position="tray", bias=c(0,0.7192,0.2030,0.1077),accel=chinook_jackL) #see details for 'bias' fail#Import bootstrap resampled data model results data(chinook_bootL) #Chinook salmon offspring length #Extract un-corrected confidence interval ciMANA2(comp=chinook_bootL,position="tray") #Extract bias corrected confidence interval ciMANA2(comp=chinook_bootL,position="tray",bias=c(0,0.7192,0.2030,0.1077)) #see details for 'bias' fail #Extract bias and accelerated corrected confidence interval #Import jackknife resampled data model results data(chinook_jackL) # ciMANA2(comp=chinook_bootL,position="tray", bias=c(0,0.7192,0.2030,0.1077),accel=chinook_jackL) #see details for 'bias' fail
Extracts bootstrap-t confidence intervals for additive genetic, non-additive genetic, and maternal variance components. Also extracts intervals for additional fixed and/or random effects.
ciMANA3(comp, level = 95, rnd_r = 3, rnd_p = 1, bias = NULL, accel = NULL, remain = NULL, trait = NULL)ciMANA3(comp, level = 95, rnd_r = 3, rnd_p = 1, bias = NULL, accel = NULL, remain = NULL, trait = NULL)
comp |
Data frame of bootstrap resampling results. |
level |
Confidence level, as a percentage. Default is 95. |
rnd_r |
Number of decimal places to round the confidence interval of raw values. |
rnd_p |
Number of decimal places to round the confidence interval of percentage values. |
bias |
Optional vector of raw observed additive, non-additive, and maternal variance components for bias correction. Followed by any other components in the order of the vector remain, i.e. c(additive, non-additive, maternal, component1, component2, etc.). |
accel |
Optional data frame of jackknifed data model results for acceleration correction. |
remain |
Vector of column names for additional effects. |
trait |
Optional label for the phenotypic trait. |
Used for bootstrap resampling results produced using resampLmer3 for normal data or resampGlmer3 for non-normal data. Bootstrap-t confidence intervals, including bias and acceleration correction methods are described by Efron and Tibshirani (1993). Jackknife data model results for acceleration correction can be produced using JackLmer3, for normal data or JackGlmer3 for non-normal data. The 'bias fail' warning is if the bias calculation is Inf or -Inf, e.g. bias contains a zero value, so the uncorrected confidence interval is displayed.
Prints a data frame containing the lower, median, and upper values of the bootstrap-t confidence interval for additive genetic, non-additive genetic, maternal, and any additional fixed effect and/or random effect variance components. Values are presented as raw and percentages of the total variance value within each row.
Efron B, Tibshirani R. 1993. An introduction to the Bootstrap. Chapman and Hall, New York.
#Import bootstrap resampled data model results data(chinook_bootL) #Chinook salmon offspring length #Extract un-corrected confidence interval ciMANA3(comp=chinook_bootL,remain=c("tray","Residual")) #Extract bias corrected confidence interval ciMANA3(comp=chinook_bootL,remain=c("tray","Residual"), bias=c(0,0.7192,0.2030,0.1077,0.5499)) #see details for 'bias' fail #Extract bias and accelerated corrected confidence interval #Import jackknife resampled data model results data(chinook_jackL) # ciMANA3(comp=chinook_bootL,remain=c("tray","Residual"), bias=c(0,0.7192,0.2030,0.1077,0.5499),accel=chinook_jackL)#Import bootstrap resampled data model results data(chinook_bootL) #Chinook salmon offspring length #Extract un-corrected confidence interval ciMANA3(comp=chinook_bootL,remain=c("tray","Residual")) #Extract bias corrected confidence interval ciMANA3(comp=chinook_bootL,remain=c("tray","Residual"), bias=c(0,0.7192,0.2030,0.1077,0.5499)) #see details for 'bias' fail #Extract bias and accelerated corrected confidence interval #Import jackknife resampled data model results data(chinook_jackL) # ciMANA3(comp=chinook_bootL,remain=c("tray","Residual"), bias=c(0,0.7192,0.2030,0.1077,0.5499),accel=chinook_jackL)
Extracts additive genetic, non-additive genetic, and maternal variance components from a linear mixed-effect model using the lmer function of the lme4 package. Model random effects are dam, sire, and dam by sire.
JackGlmer(observ, dam, sire, response, fam_link, quasi = F, size = 1, first = NULL)JackGlmer(observ, dam, sire, response, fam_link, quasi = F, size = 1, first = NULL)
observ |
Data frame of observed data. |
dam |
Column name containing dam (female) parent identity information. |
sire |
Column name containing sire (male) parent identity information. |
response |
Column name containing the offspring (response) phenotype values. |
fam_link |
The family and link in family(link) format. Supported options are binomial(link="logit"), binomial(link="probit"), poisson(link="log"), and poisson(link="sqrt"). |
quasi |
Incorporate overdispersion or quasi-error structure. |
size |
Default is 1 for delete-one jackknife resampling. If size > 1, delete-d jackknife resampling occurs removing a block d equal to size. |
first |
Number of initial sub-samples to run. Useful for examing if there is variation among sub-samples before jackknife resampling the entire data set. There can be little variation for delete-one jackknife resampling with large data sets, and delete-d jackknife resampling should be considered. |
Uses delete-one jackknife resampling (Efron & Tibshirani 1993, p. 141-145). For the option of delete-d jackknife resampling, the rows of the observed data frame are shuffled and a block of observations of size d is deleted sequentially. Laplace approximation parameter estimation is used, which is a true likelihood method (Bolker et al. 2009). For the overdispersion option, an observation-level random effect is added to the model (Atkins et al. 2013). Extracts the dam, sire, dam, and dam by sire variance components. The residual variance component for the fam_links are described by Nakagawa and Schielzeth (2010, 2013). Calculates the total variance component. Calculates the additive genetic, non-additive genetic, and maternal variance components (see Lynch and Walsh 1998, p. 603).
A data frame with columns containing the raw variance components for dam, sire, dam by sire, residual, total, additive genetic, non-additive genetic, and maternal. The number of rows in the data frame matches the total number of observations (N) for delete-one jackknife resampling or M groups for delete-d jackknife resampling to the lowest integer. Each row represents a deleted single observation or deleted d observations group.
The Laplace approximation is used because there were fewer disadvantages relative to penalized quasi-likelihood and Gauss-Hermite quadrature parameter estimation (Bolker et al. 2009). That is, penalized quasi-likelihood is not recommended for count responses with means less than 5 and binary responses with less than 5 successes per group. Gauss-Hermite quadrature is not recommended for more than two or three random effects because of the rapidly declining analytical speed with the increasing number of random effects.
Atkins DC, Baldwin SA, Zheng C, Gallop RJ, Neighbors C. 2013. A tutorial on count regression and zero-altered count models for longitudinal substance use data. Psychology of Addictive Behaviors 27(1): 166-177. DOI: 10.1037/a0029508
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008
Efron B, Tibshirani R. 1993. An introduction to the Bootstrap. Chapman and Hall, New York.
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
Nakagawa S, Schielzeth H. 2010. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biological Reviews 85(4): 935-956. DOI: 10.1111/j.1469-185X.2010.00141.x
Nakagawa S, Schielzeth H. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4(2): 133-142. DOI: 10.1111/j.2041-210x.2012.00261.x
data(chinook_survival) #Chinook salmon offspring survival ## Convert replicate-level recorded data to individual-level (binary) data chinook_survival2<- buildBinary(dat=chinook_survival,copy=c(1:6,9),one="alive",zero="dead") #Delete-one ## Not run: survival_jack1<- JackGlmer(observ=chinook_survival2,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit")) ## End(Not run) #Delete-d, d=30 ## Not run: survival_jack1.2<- JackGlmer(observ=chinook_survival2,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit"),size=30) ## End(Not run)data(chinook_survival) #Chinook salmon offspring survival ## Convert replicate-level recorded data to individual-level (binary) data chinook_survival2<- buildBinary(dat=chinook_survival,copy=c(1:6,9),one="alive",zero="dead") #Delete-one ## Not run: survival_jack1<- JackGlmer(observ=chinook_survival2,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit")) ## End(Not run) #Delete-d, d=30 ## Not run: survival_jack1.2<- JackGlmer(observ=chinook_survival2,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit"),size=30) ## End(Not run)
Extracts additive genetic, non-additive genetic, and maternal variance components from a linear mixed-effect model using the lmer function of the lme4 package. Model random effects are dam, sire, and dam by sire. Options to include one random position and/or one random block effect(s).
JackGlmer2(observ, dam, sire, response, fam_link, position = NULL, block = NULL, quasi = F, size = 1, first = NULL)JackGlmer2(observ, dam, sire, response, fam_link, position = NULL, block = NULL, quasi = F, size = 1, first = NULL)
observ |
Data frame of observed data. |
dam |
Column name containing dam (female) parent identity information. |
sire |
Column name containing sire (male) parent identity information. |
response |
Column name containing the offspring (response) phenotype values |
.
fam_link |
The family and link in family(link) format. Supported options are binomial(link="logit"), binomial(link="probit"), poisson(link="log"), and poisson(link="sqrt"). |
position |
Optional column name containing position factor information. |
block |
Optional column name containing block factor information. |
quasi |
Incorporate overdispersion or quasi-error structure. |
size |
Default is 1 for delete-one jackknife resampling. If size > 1, delete-d jackknife resampling occurs removing a block d equal to size. |
first |
Number of initial sub-samples to run. Useful for examing if there is variation among sub-samples before jackknife resampling the entire data set. There can be little variation for delete-one jackknife resampling with large data sets, and delete-d jackknife resampling should be considered. |
Uses delete-one jackknife resampling (Efron & Tibshirani 1993, p. 141-145). For the option of delete-d jackknife resampling, the rows of the observed data frame are shuffled and a block of observations of size d is deleted sequentially. Laplace approximation parameter estimation is used, which is a true likelihood method (Bolker et al. 2009). For the overdispersion option, an observation-level random effect is added to the model (Atkins et al. 2013). Extracts the dam, sire, dam, and dam by sire variance components. Extracts optional position and block variance components. The residual variance component for the fam_links are described by Nakagawa and Schielzeth (2010, 2013). Calculates the total variance component. Calculates the additive genetic, non-additive genetic, and maternal variance components (see Lynch and Walsh 1998, p. 603).
A data frame with columns containing the raw variance components for dam, sire, dam by sire, residual, total, additive genetic, non-additive genetic, and maternal. Also columns containing the raw variance components for the options of position and/or block. The number of rows in the data frame matches the total number of observations (N) for delete-one jackknife resampling or M groups for delete-d jackknife resampling to the lowest integer. Each row represents a deleted single observation or deleted d observations group.
The Laplace approximation is used because there were fewer disadvantages relative to penalized quasi-likelihood and Gauss-Hermite quadrature parameter estimation (Bolker et al. 2009). That is, penalized quasi-likelihood is not recommended for count responses with means less than 5 and binary responses with less than 5 successes per group. Gauss-Hermite quadrature is not recommended for more than two or three random effects because of the rapidly declining analytical speed with the increasing number of random effects.
Atkins DC, Baldwin SA, Zheng C, Gallop RJ, Neighbors C. 2013. A tutorial on count regression and zero-altered count models for longitudinal substance use data. Psychology of Addictive Behaviors 27(1): 166-177. DOI: 10.1037/a0029508
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008
Efron B, Tibshirani R. 1993. An introduction to the Bootstrap. Chapman and Hall, New York.
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
Nakagawa S, Schielzeth H. 2010. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biological Reviews 85(4): 935-956. DOI: 10.1111/j.1469-185X.2010.00141.x
Nakagawa S, Schielzeth H. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4(2): 133-142. DOI: 10.1111/j.2041-210x.2012.00261.x
data(chinook_survival) #Chinook salmon offspring survival ## Convert replicate-level recorded data to individual-level (binary) data chinook_survival2<- buildBinary(dat=chinook_survival,copy=c(1:6,9),one="alive",zero="dead") #Delete-one ## Not run: survival_jack2<- JackGlmer2(observ=chinook_survival2,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit"),position="tray") ## End(Not run) #Delete-d, d=30 ## Not run: survival_jack2.2<- JackGlmer2(observ=chinook_survival2,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit"),position="tray",size=30) ## End(Not run)data(chinook_survival) #Chinook salmon offspring survival ## Convert replicate-level recorded data to individual-level (binary) data chinook_survival2<- buildBinary(dat=chinook_survival,copy=c(1:6,9),one="alive",zero="dead") #Delete-one ## Not run: survival_jack2<- JackGlmer2(observ=chinook_survival2,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit"),position="tray") ## End(Not run) #Delete-d, d=30 ## Not run: survival_jack2.2<- JackGlmer2(observ=chinook_survival2,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit"),position="tray",size=30) ## End(Not run)
Extracts additive genetic, non-additive genetic, and maternal variance components from a linear mixed-effect model using the lmer function of the lme4 package. Model random effects are dam, sire, dam by sire, and any additional fixed and/or random effects.
JackGlmer3(observ, dam, sire, response, fam_link, remain, quasi = F, size = 1, first = NULL)JackGlmer3(observ, dam, sire, response, fam_link, remain, quasi = F, size = 1, first = NULL)
observ |
Data frame of observed data. |
dam |
Column name containing dam (female) parent identity information. |
sire |
Column name containing sire (male) parent identity information. |
response |
Column name containing the offspring (response) phenotype values. |
fam_link |
The family and link in family(link) format. Supported options are binomial(link="logit"), binomial(link="probit"), poisson(link="log"), and poisson(link="sqrt"). |
remain |
Remaining formula using lme4 package formula. |
quasi |
Incorporate overdispersion or quasi-error structure. |
size |
Default is 1 for delete-one jackknife resampling. If size > 1, delete-d jackknife resampling occurs removing a block d equal to size. |
first |
Number of initial sub-samples to run. Useful for examing if there is variation among sub-samples before jackknife resampling the entire data set. There can be little variation for delete-one jackknife resampling with large data sets, and delete-d jackknife resampling should be considered. |
Uses delete-one jackknife resampling (Efron & Tibshirani 1993, p. 141-145). For the option of delete-d jackknife resampling, the rows of the observed data frame are shuffled and a block of observations of size d is deleted sequentially. Laplace approximation parameter estimation is used, which is a true likelihood method (Bolker et al. 2009). For the overdispersion option, an observation-level random effect is added to the model (Atkins et al. 2013). Extracts the dam, sire, dam, and dam by sire variance components. Extracts any additional fixed effect and random effect variance components. The fixed-effect variance component is as a single group using the method described by Nakagawa and Schielzeth (2013). The residual variance component for the fam_links are described by Nakagawa and Schielzeth (2010, 2013). Calculates the total variance component. Calculates the additive genetic, non-additive genetic, and maternal variance components (see Lynch and Walsh 1998, p. 603).
A data frame with columns containing the raw variance components for dam, sire, dam by sire, residual, total, additive genetic, non-additive genetic, and maternal. Also columns containing the raw variance components for remaining formula components. The number of rows in the data frame matches the total number of observations (N) for delete-one jackknife resampling or M groups for delete-d jackknife resampling to the lowest integer. Each row represents a deleted single observation or deleted d observations group.
The Laplace approximation is used because there were fewer disadvantages relative to penalized quasi-likelihood and Gauss-Hermite quadrature parameter estimation (Bolker et al. 2009). That is, penalized quasi-likelihood is not recommended for count responses with means less than 5 and binary responses with less than 5 successes per group. Gauss-Hermite quadrature is not recommended for more than two or three random effects because of the rapidly declining analytical speed with the increasing number of random effects.
Atkins DC, Baldwin SA, Zheng C, Gallop RJ, Neighbors C. 2013. A tutorial on count regression and zero-altered count models for longitudinal substance use data. Psychology of Addictive Behaviors 27(1): 166-177. DOI: 10.1037/a0029508
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008
Efron B, Tibshirani R. 1993. An introduction to the Bootstrap. Chapman and Hall, New York.
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
Nakagawa S, Schielzeth H. 2010. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biological Reviews 85(4): 935-956. DOI: 10.1111/j.1469-185X.2010.00141.x
Nakagawa S, Schielzeth H. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4(2): 133-142. DOI: 10.1111/j.2041-210x.2012.00261.x
data(chinook_survival) #Chinook salmon offspring survival ## Convert replicate-level recorded data to individual-level (binary) data chinook_survival2<- buildBinary(dat=chinook_survival,copy=c(1:6,9),one="alive",zero="dead") #Delete-one ## Not run: survival_jack3<- JackGlmer3(observ=chinook_survival2,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit""),remain="egg_size + (1|tray)") ## End(Not run) #Delete-d, d=30 ## Not run: survival_jack3.2<- JackGlmer3(observ=chinook_survival2,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit""),remain="egg_size + (1|tray)",size=30) ## End(Not run)data(chinook_survival) #Chinook salmon offspring survival ## Convert replicate-level recorded data to individual-level (binary) data chinook_survival2<- buildBinary(dat=chinook_survival,copy=c(1:6,9),one="alive",zero="dead") #Delete-one ## Not run: survival_jack3<- JackGlmer3(observ=chinook_survival2,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit""),remain="egg_size + (1|tray)") ## End(Not run) #Delete-d, d=30 ## Not run: survival_jack3.2<- JackGlmer3(observ=chinook_survival2,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit""),remain="egg_size + (1|tray)",size=30) ## End(Not run)
Extracts additive genetic, non-additive genetic, and maternal variance components from a linear mixed-effect model using the lmer function of the lme4 package. Model random effects are dam, sire, and dam by sire.
JackLmer(observ, dam, sire, response, ml = F, size = 1, first = NULL)JackLmer(observ, dam, sire, response, ml = F, size = 1, first = NULL)
observ |
Data frame of observed data. |
dam |
Column name containing dam (female) parent identity information. |
sire |
Column name containing sire (male) parent identity information. |
response |
Column name containing the offspring (response) phenotype values. |
ml |
Default is FALSE for restricted maximum likelihood. Change to TRUE for maximum likelihood. |
size |
Default is 1 for delete-one jackknife resampling. If size > 1, delete-d jackknife resampling occurs removing a block d equal to size. |
first |
Number of initial sub-samples to run. Useful for examing if there is variation among sub-samples before jackknife resampling the entire data set. There can be little variation for delete-one jackknife resampling with large data sets, and delete-d jackknife resampling should be considered. |
Uses delete-one jackknife resampling (Efron & Tibshirani 1993, p. 141-145). For the option of delete-d jackknife resampling, the rows of the observed data frame are shuffled and a block of observations of size d is deleted sequentially. Extracts the dam, sire, dam, dam by sire, and residual variance components. Calculates the total variance component. Calculates the additive genetic, non-additive genetic, and maternal variance components (see Lynch and Walsh 1998, p. 603).
A data frame with columns containing the raw variance components for dam, sire, dam by sire, residual, total, additive genetic, non-additive genetic, and maternal. The number of rows in the data frame matches the total number of observations (N) for delete-one jackknife resampling or M groups for delete-d jackknife resampling to the lowest integer. Each row represents a deleted single observation or deleted d observations group.
Maximum likelihood (ML) estimates the parameters that maximize the likelihood of the observed data and has the advantage of using all the data and accounting for non-independence (Lynch and Walsh 1998, p. 779; Bolker et al. 2009). On the other hand, ML has the disadvantage of assuming that all fixed effects are known without error, producing a downward bias in the estimation of the residual variance component. This bias can be large if there are lots of fixed effects, especially if sample sizes are small. Restricted maximum likelihood (REML) has the advantage of not assuming the fixed effects are known and averages over the uncertainty, so there can be less bias in the estimation of the residual variance component. However, REML only maximizes a portion of the likelihood to estimate the effect parameters, but is the preferred method for analyzing large data sets with complex structure.
Efron B, Tibshirani R. 1993. An introduction to the Bootstrap. Chapman and Hall, New York.
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
data(chinook_length) #Chinook salmon offspring length #Delete-one #length_jack1<- JackLmer(observ=chinook_length,dam="dam",sire="sire",response="length") length_jack1<- JackLmer(observ=chinook_length,dam="dam",sire="sire",response="length", first=2) #first 2 #Delete-d, d=5 #length_jackD<- JackLmer(observ=chinook_length,dam="dam",sire="sire",response="length", #size=5) length_jackD<- JackLmer(observ=chinook_length,dam="dam",sire="sire",response="length", size=5,first=2) #first 2data(chinook_length) #Chinook salmon offspring length #Delete-one #length_jack1<- JackLmer(observ=chinook_length,dam="dam",sire="sire",response="length") length_jack1<- JackLmer(observ=chinook_length,dam="dam",sire="sire",response="length", first=2) #first 2 #Delete-d, d=5 #length_jackD<- JackLmer(observ=chinook_length,dam="dam",sire="sire",response="length", #size=5) length_jackD<- JackLmer(observ=chinook_length,dam="dam",sire="sire",response="length", size=5,first=2) #first 2
Extracts additive genetic, non-additive genetic, and maternal variance components from a linear mixed-effect model using the lmer function of the lme4 package. Model random effects are dam, sire, and dam by sire. Options to include one random position and/or one random block effect(s).
JackLmer2(observ, dam, sire, response, position = NULL, block = NULL, ml = F, size = 1, first = NULL)JackLmer2(observ, dam, sire, response, position = NULL, block = NULL, ml = F, size = 1, first = NULL)
observ |
Data frame of observed data. |
dam |
Column name containing dam (female) parent identity information. |
sire |
Column name containing sire (male) parent identity information. |
response |
Column name containing the offspring (response) phenotype values. |
position |
Optional column name containing position factor information. |
block |
Optional column name containing block factor information. |
ml |
Default is FALSE for restricted maximum likelihood. Change to TRUE for maximum likelihood. |
size |
Default is 1 for delete-one jackknife resampling. If size > 1, delete-d jackknife resampling occurs removing a block d equal to size. |
first |
Number of initial sub-samples to run. Useful for examing if there is variation among sub-samples before jackknife resampling the entire data set. There can be little variation for delete-one jackknife resampling with large data sets, and delete-d jackknife resampling should be considered. |
Uses delete-one jackknife resampling (Efron & Tibshirani 1993, p. 141-145). For the option of delete-d jackknife resampling, the rows of the observed data frame are shuffled and a block of observations of size d is deleted sequentially. Extracts the dam, sire, dam, dam by sire, and residual variance components. Extracts optional position and block variance components. Calculates the total variance component. Calculates the additive genetic, non-additive genetic, and maternal variance components (see Lynch and Walsh 1998, p. 603).
A data frame with columns containing the raw variance components for dam, sire, dam by sire, residual, total, additive genetic, non-additive genetic, and maternal. Also columns containing the raw variance components for the options of position and/or block. The number of rows in the data frame matches the total number of observations (N) for delete-one jackknife resampling or M groups for delete-d jackknife resampling to the lowest integer. Each row represents a deleted single observation or deleted d observations group.
Maximum likelihood (ML) estimates the parameters that maximize the likelihood of the observed data and has the advantage of using all the data and accounting for non-independence (Lynch and Walsh 1998, p. 779; Bolker et al. 2009). On the other hand, ML has the disadvantage of assuming that all fixed effects are known without error, producing a downward bias in the estimation of the residual variance component. This bias can be large if there are lots of fixed effects, especially if sample sizes are small. Restricted maximum likelihood (REML) has the advantage of not assuming the fixed effects are known and averages over the uncertainty, so there can be less bias in the estimation of the residual variance component. However, REML only maximizes a portion of the likelihood to estimate the effect parameters, but is the preferred method for analyzing large data sets with complex structure.
Efron B, Tibshirani R. 1993. An introduction to the Bootstrap. Chapman and Hall, New York.
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
data(chinook_length) #Chinook salmon offspring length #Delete-one #length_jack2<- JackLmer2(observ=chinook_length,dam="dam",sire="sire",response="length", #position="tray") length_jack2<- JackLmer2(observ=chinook_length,dam="dam",sire="sire",response="length", position="tray",first=2) #first 2 #Delete-d, d=5 #length_jack2.2<- JackLmer2(observ=chinook_length,dam="dam",sire="sire",response="length", #position="tray",size=5) length_jack2.2<- JackLmer2(observ=chinook_length,dam="dam",sire="sire",response="length", position="tray",size=5,first=2) #first 2data(chinook_length) #Chinook salmon offspring length #Delete-one #length_jack2<- JackLmer2(observ=chinook_length,dam="dam",sire="sire",response="length", #position="tray") length_jack2<- JackLmer2(observ=chinook_length,dam="dam",sire="sire",response="length", position="tray",first=2) #first 2 #Delete-d, d=5 #length_jack2.2<- JackLmer2(observ=chinook_length,dam="dam",sire="sire",response="length", #position="tray",size=5) length_jack2.2<- JackLmer2(observ=chinook_length,dam="dam",sire="sire",response="length", position="tray",size=5,first=2) #first 2
Extracts additive genetic, non-additive genetic, and maternal variance components from a linear mixed-effect model using the lmer function of the lme4 package. Model random effects are dam, sire, dam by sire, and any additional fixed and/or random effects.
JackLmer3(observ, dam, sire, response, remain, ml = F, size = 1, first = NULL)JackLmer3(observ, dam, sire, response, remain, ml = F, size = 1, first = NULL)
observ |
Data frame of observed data |
dam |
Column name containing dam (female) parent identity information. |
sire |
Column name containing sire (male) parent identity information. |
response |
Column name containing the offspring (response) phenotype values. |
remain |
Remaining formula using lme4 package format. |
ml |
Default is FALSE for restricted maximum likelihood. Change to TRUE for maximum likelihood. |
size |
Default is 1 for delete-one jackknife resampling. If size > 1, delete-d jackknife resampling occurs removing a block d equal to size. |
first |
Number of initial sub-samples to run. Useful for examing if there is variation among sub-samples before jackknife resampling the entire data set. There can be little variation for delete-one jackknife resampling with large data sets, and delete-d jackknife resampling should be considered. |
Uses delete-one jackknife resampling (Efron & Tibshirani 1993, p. 141-145). For the option of delete-d jackknife resampling, the rows of the observed data frame are shuffled and a block of observations of size d is deleted sequentially. Extracts the dam, sire, dam, dam by sire, and residual variance components. Extracts any additional fixed effect and random effect variance components. The fixed-effect variance component is as a single group using the method described by Nakagawa and Schielzeth (2013). Calculates the total variance component. Calculates the additive genetic, non-additive genetic, and maternal variance components (see Lynch and Walsh 1998, p. 603).
A data frame with columns containing the raw variance components for dam, sire, dam by sire, residual, total, additive genetic, non-additive genetic, and maternal. Also columns containing the raw variance components for remaining formula components. The number of rows in the data frame matches the total number of observations (N) for delete-one jackknife resampling or M groups for delete-d jackknife resampling to the lowest integer. Each row represents a deleted single observation or deleted d observations group.
Maximum likelihood (ML) estimates the parameters that maximize the likelihood of the observed data and has the advantage of using all the data and accounting for non-independence (Lynch and Walsh 1998, p. 779; Bolker et al. 2009). On the other hand, ML has the disadvantage of assuming that all fixed effects are known without error, producing a downward bias in the estimation of the residual variance component. This bias can be large if there are lots of fixed effects, especially if sample sizes are small. Restricted maximum likelihood (REML) has the advantage of not assuming the fixed effects are known and averages over the uncertainty, so there can be less bias in the estimation of the residual variance component. However, REML only maximizes a portion of the likelihood to estimate the effect parameters, but is the preferred method for analyzing large data sets with complex structure.
Efron B, Tibshirani R. 1993. An introduction to the Bootstrap. Chapman and Hall, New York.
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
Nakagawa S, Schielzeth H. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4(2): 133-142. DOI: 10.1111/j.2041-210x.2012.00261.x
data(chinook_length) #Chinook salmon offspring length #Delete-one #length_jack3<- JackLmer3(observ=chinook_length,dam="dam",sire="sire",response="length", #remain="egg_size + (1|tray)") length_jack3<- JackLmer3(observ=chinook_length,dam="dam",sire="sire",response="length", remain="egg_size + (1|tray)",first=2) #first 2 #Delete-d, d=5 #length_jack3.2<- JackLmer3(observ=chinook_length,dam="dam",sire="sire",response="length", #remain="egg_size + (1|tray)",size=5) length_jack3.2<- JackLmer3(observ=chinook_length,dam="dam",sire="sire",response="length", remain="egg_size + (1|tray)",size=5,first=2) #first 2data(chinook_length) #Chinook salmon offspring length #Delete-one #length_jack3<- JackLmer3(observ=chinook_length,dam="dam",sire="sire",response="length", #remain="egg_size + (1|tray)") length_jack3<- JackLmer3(observ=chinook_length,dam="dam",sire="sire",response="length", remain="egg_size + (1|tray)",first=2) #first 2 #Delete-d, d=5 #length_jack3.2<- JackLmer3(observ=chinook_length,dam="dam",sire="sire",response="length", #remain="egg_size + (1|tray)",size=5) length_jack3.2<- JackLmer3(observ=chinook_length,dam="dam",sire="sire",response="length", remain="egg_size + (1|tray)",size=5,first=2) #first 2
Extracts additive genetic, non-additive genetic, and maternal variance components from a generalized linear mixed-effect model using the glmer function of the lme4 package. Model random effects are dam, sire, and dam by sire.
observGlmer(observ, dam, sire, response, fam_link, quasi = F)observGlmer(observ, dam, sire, response, fam_link, quasi = F)
observ |
Data frame of observed data. |
dam |
Column name containing dam (female) parent identity information. |
sire |
Column name containing sire (male) parent identity information. |
response |
Column name containing the offspring (response) phenotype values. |
fam_link |
The family and link in family(link) format. Supported options are binomial(link="logit"), binomial(link="probit"), poisson(link="log"), and poisson(link="sqrt"). |
quasi |
Incorporate overdispersion or quasi-error structure. |
Laplace approximation parameter estimation is used, which is a true likelihood method (Bolker et al. 2009). For the overdispersion option, an observation-level random effect is added to the model (Atkins et al. 2013). Extracts the dam, sire, dam, and dam by sire variance components. The residual variance component for the fam_links are described by Nakagawa and Schielzeth (2010, 2013). Calculates the total variance component. Calculates the additive genetic, non-additive genetic, and maternal variance components (see Lynch and Walsh 1998, p. 603). Significance values for the random effects are determined using likelihood ratio tests (Bolker et al. 2009).
A list object containing the raw variance components, the variance components as a percentage of the total variance component. Also, contains the difference in AIC and BIC, and likelihood ratio test Chi-square and p-value for all random effects.
The Laplace approximation is used because there were fewer disadvantages relative to penalized quasi-likelihood and Gauss-Hermite quadrature parameter estimation (Bolker et al. 2009). That is, penalized quasi-likelihood is not recommended for count responses with means less than 5 and binary responses with less than 5 successes per group. Gauss-Hermite quadrature is not recommended for more than two or three random effects because of the rapidly declining analytical speed with the increasing number of random effects.
Atkins DC, Baldwin SA, Zheng C, Gallop RJ, Neighbors C. 2013. A tutorial on count regression and zero-altered count models for longitudinal substance use data. Psychology of Addictive Behaviors 27(1): 166-177. DOI: 10.1037/a0029508
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
Nakagawa S, Schielzeth H. 2010. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biological Reviews 85(4): 935-956. DOI: 10.1111/j.1469-185X.2010.00141.x
Nakagawa S, Schielzeth H. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4(2): 133-142. DOI: 10.1111/j.2041-210x.2012.00261.x
data(chinook_survival) #Chinook salmon offspring survival ## Convert replicate-level recorded data to individual-level (binary) data chinook_survival2<- buildBinary(dat=chinook_survival,copy=c(2:6,9),one="alive",zero="dead") # ## Not run: survival_mod1<- observGlmer(observ=chinook_survival2,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit")) #a few minutes survival_mod1 ## End(Not run)data(chinook_survival) #Chinook salmon offspring survival ## Convert replicate-level recorded data to individual-level (binary) data chinook_survival2<- buildBinary(dat=chinook_survival,copy=c(2:6,9),one="alive",zero="dead") # ## Not run: survival_mod1<- observGlmer(observ=chinook_survival2,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit")) #a few minutes survival_mod1 ## End(Not run)
Extracts additive genetic, non-additive genetic, and maternal variance components from a generalized linear mixed-effect model using the glmer function of the lme4 package. Model random effects are dam, sire, and dam by sire. Options to include one random position and/or one random block effect(s).
observGlmer2(observ, dam, sire, response, fam_link, position = NULL, block = NULL, quasi = F)observGlmer2(observ, dam, sire, response, fam_link, position = NULL, block = NULL, quasi = F)
observ |
Data frame of observed data. |
dam |
Column name containing dam (female) parent identity information. |
sire |
Column name containing sire (male) parent identity information. |
response |
Column name containing the offspring (response) phenotype values. |
fam_link |
The family and link in family(link) format. Supported options are binomial(link="logit"), binomial(link="probit"), poisson(link="log"), and poisson(link="sqrt"). |
position |
Optional column name containing position factor information. |
block |
Optional column name containing block factor information. |
quasi |
Incorporate overdispersion or quasi-error structure. |
Laplace approximation parameter estimation is used, which is a true likelihood method (Bolker et al. 2009). For the overdispersion option, an observation-level random effect is added to the model (Atkins et al. 2013). Extracts the dam, sire, dam, and dam by sire variance components. Extracts optional position and block variance components. The residual variance component for the fam_links are described by Nakagawa and Schielzeth (2010, 2013). Calculates the total variance component. Calculates the additive genetic, non-additive genetic, and maternal variance components (see Lynch and Walsh 1998, p. 603). Significance values for the random effects are determined using likelihood ratio tests (Bolker et al. 2009).
A list object containing the raw variance components, the variance components as a percentage of the total variance component. Also, contains the difference in AIC and BIC, and likelihood ratio test Chi-square and p-value for all random effects.
The Laplace approximation is used because there were fewer disadvantages relative to penalized quasi-likelihood and Gauss-Hermite quadrature parameter estimation (Bolker et al. 2009). That is, penalized quasi-likelihood is not recommended for count responses with means less than 5 and binary responses with less than 5 successes per group. Gauss-Hermite quadrature is not recommended for more than two or three random effects because of the rapidly declining analytical speed with the increasing number of random effects.
Atkins DC, Baldwin SA, Zheng C, Gallop RJ, Neighbors C. 2013. A tutorial on count regression and zero-altered count models for longitudinal substance use data. Psychology of Addictive Behaviors 27(1): 166-177. DOI: 10.1037/a0029508
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
Nakagawa S, Schielzeth H. 2010. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biological Reviews 85(4): 935-956. DOI: 10.1111/j.1469-185X.2010.00141.x
Nakagawa S, Schielzeth H. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4(2): 133-142. DOI: 10.1111/j.2041-210x.2012.00261.x
data(chinook_survival) #Chinook salmon offspring survival ## Convert replicate-level recorded data to individual-level (binary) data chinook_survival2<- buildBinary(dat=chinook_survival,copy=c(2:6,9),one="alive",zero="dead") # ## Not run: survival_mod2<- observGlmer2(observ=chinook_survival2,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit"),position="tray") #a few minutes survival_mod2 ## End(Not run)data(chinook_survival) #Chinook salmon offspring survival ## Convert replicate-level recorded data to individual-level (binary) data chinook_survival2<- buildBinary(dat=chinook_survival,copy=c(2:6,9),one="alive",zero="dead") # ## Not run: survival_mod2<- observGlmer2(observ=chinook_survival2,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit"),position="tray") #a few minutes survival_mod2 ## End(Not run)
Extracts additive genetic, non-additive genetic, and maternal variance components from a generalized linear mixed-effect model using the glmer function of the lme4 package. Model random effects are dam, sire, dam by sire, and any additional fixed and/or random effects.
observGlmer3(observ, dam, sire, response, fam_link, remain, quasi = F, iter = 1000)observGlmer3(observ, dam, sire, response, fam_link, remain, quasi = F, iter = 1000)
observ |
Data frame of observed data. |
dam |
Column name containing dam (female) parent identity information. |
sire |
Column name containing sire (male) parent identity information. |
response |
Column name containing the offspring (response) phenotype values. |
fam_link |
The family and link in family(link) format. Supported options are binomial(link="logit"), binomial(link="probit"), poisson(link="log"), and poisson(link="sqrt"). |
remain |
Remaining formula using lme4 package format. |
quasi |
Incorporate overdispersion or quasi-error structure. |
iter |
Number of iterations for computing the parametric bootstrap significance value for any fixed effects. |
Laplace approximation parameter estimation is used, which is a true likelihood method (Bolker et al. 2009). For the overdispersion option, an observation-level random effect is added to the model (Atkins et al. 2013). Extracts the dam, sire, dam, and dam by sire variance components. Extracts any additional fixed effect and random effect variance components. The fixed-effect variance component is as a single group using the method described by Nakagawa and Schielzeth (2013). The residual variance component for the fam_links are described by Nakagawa and Schielzeth (2010, 2013). Calculates the total variance component. Calculates the additive genetic, non-additive genetic, and maternal variance components (see Lynch and Walsh 1998, p. 603). Significance values for the random effects are determined using likelihood ratio tests (Bolker et al. 2009). Significance values for any fixed effects are determined using likelihood ratio tests and a parametric bootstrap method (Bolker et al. 2009) from the mixed function of the afex package.
A list object containing the raw variance components, the variance components as a percentage of the total variance component. Contains the difference in AIC and BIC, likelihood ratio test Chi-square and p-value for random and/or fixed effects. Also contains the parametric bootstrap Chi-square and p-value for any fixed effects.
The Laplace approximation is used because there were fewer disadvantages relative to penalized quasi-likelihood and Gauss-Hermite quadrature parameter estimation (Bolker et al. 2009). That is, penalized quasi-likelihood is not recommended for count responses with means less than 5 and binary responses with less than 5 successes per group. Gauss-Hermite quadrature is not recommended for more than two or three random effects because of the rapidly declining analytical speed with the increasing number of random effects.
Atkins DC, Baldwin SA, Zheng C, Gallop RJ, Neighbors C. 2013. A tutorial on count regression and zero-altered count models for longitudinal substance use data. Psychology of Addictive Behaviors 27(1): 166-177. DOI: 10.1037/a0029508
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
Nakagawa S, Schielzeth H. 2010. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biological Reviews 85(4): 935-956. DOI: 10.1111/j.1469-185X.2010.00141.x
Nakagawa S, Schielzeth H. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4(2): 133-142. DOI: 10.1111/j.2041-210x.2012.00261.x
data(chinook_survival) #Chinook salmon offspring survival ## Convert replicate-level recorded data to individual-level (binary) data chinook_survival2<- buildBinary(dat=chinook_survival,copy=c(2:6,9),one="alive",zero="dead") #just a few iterations for the p-value of fixed effect ## Not run: survival_mod3<- observGlmer3(observ=chinook_survival2,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit"),remain="egg_size + (1|tray)",iter=5) survival_mod3 ## End(Not run)data(chinook_survival) #Chinook salmon offspring survival ## Convert replicate-level recorded data to individual-level (binary) data chinook_survival2<- buildBinary(dat=chinook_survival,copy=c(2:6,9),one="alive",zero="dead") #just a few iterations for the p-value of fixed effect ## Not run: survival_mod3<- observGlmer3(observ=chinook_survival2,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit"),remain="egg_size + (1|tray)",iter=5) survival_mod3 ## End(Not run)
Extracts additive genetic, non-additive genetic, and maternal variance components from a linear mixed-effect model using the lmer function of the lme4 package. Model random effects are dam, sire, and dam by sire.
observLmer(observ, dam, sire, response, ml = F)observLmer(observ, dam, sire, response, ml = F)
observ |
Data frame of observed data. |
dam |
Column name containing dam (female) parent identity information. |
sire |
Column name containing sire (male) parent identity information. |
response |
Column name containing the offspring (response) phenotype values. |
ml |
Default is FALSE for restricted maximum likelihood. Change to TRUE for maximum likelihood. |
Extracts the dam, sire, dam, dam by sire, and residual variance components. Calculates the total variance component. Calculates the additive genetic, non-additive genetic, and maternal variance components (see Lynch and Walsh 1998, p. 603). Significance values for the random effects are determined using likelihood ratio tests (Bolker et al. 2009).
A list object containing the raw variance components, the variance components as a percentage of the total variance component. Also, contains the difference in AIC and BIC, and likelihood ratio test Chi-square and p-value for all random effects.
Maximum likelihood (ML) estimates the parameters that maximize the likelihood of the observed data and has the advantage of using all the data and accounting for non-independence (Lynch and Walsh 1998, p. 779; Bolker et al. 2009). On the other hand, ML has the disadvantage of assuming that all fixed effects are known without error, producing a downward bias in the estimation of the residual variance component. This bias can be large if there are lots of fixed effects, especially if sample sizes are small. Restricted maximum likelihood (REML) has the advantage of not assuming the fixed effects are known and averages over the uncertainty, so there can be less bias in the estimation of the residual variance component. However, REML only maximizes a portion of the likelihood to estimate the effect parameters, but is the preferred method for analyzing large data sets with complex structure.
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
data(chinook_length) #Chinook salmon offspring length length_mod1<- observLmer(observ=chinook_length,dam="dam",sire="sire",response="length") length_mod1data(chinook_length) #Chinook salmon offspring length length_mod1<- observLmer(observ=chinook_length,dam="dam",sire="sire",response="length") length_mod1
Extracts additive genetic, non-additive genetic, and maternal variance components from a linear mixed-effect model using the lmer function of the lme4 package. Model random effects are dam, sire, and dam by sire. Options to include one random position and/or one random block effect(s).
observLmer2(observ, dam, sire, response, position = NULL, block = NULL, ml = F)observLmer2(observ, dam, sire, response, position = NULL, block = NULL, ml = F)
observ |
Data frame of observed data. |
dam |
Column name containing dam (female) parent identity information. |
sire |
Column name containing sire (male) parent identity information. |
response |
Column name containing the offspring (response) phenotype values. |
position |
Optional column name containing position factor information. |
block |
Optional column name containing block factor information. |
ml |
Default is FALSE for restricted maximum likelihood. Change to TRUE for maximum likelihood. |
Extracts the dam, sire, dam, dam by sire, and residual variance components. Extracts optional position and block variance components. Calculates the total variance component. Calculates the additive genetic, non-additive genetic, and maternal variance components (see Lynch and Walsh 1998, p. 603). Significance values for the random effects are determined using likelihood ratio tests (Bolker et al. 2009).
A list object containing the raw variance components, the variance components as a percentage of the total variance component. Also, contains the difference in AIC and BIC, and likelihood ratio test Chi-square and p-value for all random effects.
Maximum likelihood (ML) estimates the parameters that maximize the likelihood of the observed data and has the advantage of using all the data and accounting for non-independence (Lynch and Walsh 1998, p. 779; Bolker et al. 2009). On the other hand, ML has the disadvantage of assuming that all fixed effects are known without error, producing a downward bias in the estimation of the residual variance component. This bias can be large if there are lots of fixed effects, especially if sample sizes are small. Restricted maximum likelihood (REML) has the advantage of not assuming the fixed effects are known and averages over the uncertainty, so there can be less bias in the estimation of the residual variance component. However, REML only maximizes a portion of the likelihood to estimate the effect parameters, but is the preferred method for analyzing large data sets with complex structure.
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
data(chinook_length) #Chinook salmon offspring length length_mod2<- observLmer2(observ=chinook_length,dam="dam",sire="sire",response="length", position="tray") length_mod2data(chinook_length) #Chinook salmon offspring length length_mod2<- observLmer2(observ=chinook_length,dam="dam",sire="sire",response="length", position="tray") length_mod2
Extracts additive genetic, non-additive genetic, and maternal variance components from a linear mixed-effect model using the lmer function of the lme4 package. Model random effects are dam, sire, dam by sire, and any additional fixed and/or random effects.
observLmer3(observ, dam, sire, response, remain, ml = F, iter = 1000)observLmer3(observ, dam, sire, response, remain, ml = F, iter = 1000)
observ |
Data frame of observed data. |
dam |
Column name containing dam (female) parent identity information. |
sire |
Column name containing sire (male) parent identity information. |
response |
Column name containing the offspring (response) phenotype values. |
remain |
Remaining formula using lme4 package format. |
ml |
Default is FALSE for restricted maximum likelihood. Change to TRUE for maximum likelihood. |
iter |
Number of iterations for computing the parametric bootstrap significance value for any fixed effects. |
Extracts the dam, sire, dam, dam by sire, and residual variance components. Extracts any additional fixed effect and random effect variance components. The fixed-effect variance component is as a single group using the method described by Nakagawa and Schielzeth (2013). Calculates the total variance component. Calculates the additive genetic, non-additive genetic, and maternal variance components (see Lynch and Walsh 1998, p. 603). Significance values for the random effects are determined using likelihood ratio tests (Bolker et al. 2009). Significance values for any fixed effects are determined using likelihood ratio tests and a parametric bootstrap method (Bolker et al. 2009) from the mixed function of the afex package.
A list object containing the raw variance components, the variance components as a percentage of the total variance component. Contains the difference in AIC and BIC, likelihood ratio test Chi-square and p-value for random and/or fixed effects. Also contains the parametric bootstrap Chi-square and p-value for any fixed effects.
Maximum likelihood (ML) estimates the parameters that maximize the likelihood of the observed data and has the advantage of using all the data and accounting for non-independence (Lynch and Walsh 1998, p. 779; Bolker et al. 2009). On the other hand, ML has the disadvantage of assuming that all fixed effects are known without error, producing a downward bias in the estimation of the residual variance component. This bias can be large if there are lots of fixed effects, especially if sample sizes are small. Restricted maximum likelihood (REML) has the advantage of not assuming the fixed effects are known and averages over the uncertainty, so there can be less bias in the estimation of the residual variance component. However, REML only maximizes a portion of the likelihood to estimate the effect parameters, but is the preferred method for analyzing large data sets with complex structure.
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
Nakagawa S, Schielzeth H. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4(2): 133-142. DOI: 10.1111/j.2041-210x.2012.00261.x
data(chinook_length) #Chinook salmon offspring length #just a few iterations for the p-value of fixed effect length_mod3<- observLmer3(observ=chinook_length,dam="dam",sire="sire",response="length", remain="egg_size + (1|tray)",iter=5) length_mod3data(chinook_length) #Chinook salmon offspring length #just a few iterations for the p-value of fixed effect length_mod3<- observLmer3(observ=chinook_length,dam="dam",sire="sire",response="length", remain="egg_size + (1|tray)",iter=5) length_mod3
Extracts the power values of dam, sire, and dam by sire variance components from a generalized linear mixed-effect model using the glmer function of the lme4 package.
powerGlmer(varcomp, nval, fam_link, alpha = 0.05, nsim = 100, poisLog = NULL)powerGlmer(varcomp, nval, fam_link, alpha = 0.05, nsim = 100, poisLog = NULL)
varcomp |
Vector of known dam, sire, and dam by sire variance components, i.e. c(dam, sire, dam x sire). |
nval |
Vector of known dam, sire, and offspring per family sample sizes, i.e. c(dam, sire, offspring). |
fam_link |
The family and link in family(link) format. Supported options are binomial(link="logit"), binomial(link="probit"), poisson(link="log"), and poisson(link="sqrt"). |
alpha |
Statistical significance value. Default is 0.05. |
nsim |
Number of simulations. Default is 100. |
poisLog |
The residual variance component value if using poisson(link="log"). |
Extracts the dam, sire, dam, and dam by sire power values. The residual variance component for the fam_links are described by Nakagawa and Schielzeth (2010, 2013). Power values are calculated by stochastically simulation data and then calculating the proportion of significance values less than alpha for each component (Bolker 2008). Significance values for the random effects are determined using likelihood ratio tests (Bolker et al. 2009).
Prints a data frame with the sample sizes, variance component inputs, variance component outputs, and power values.
The Laplace approximation is used because there were fewer disadvantages relative to penalized quasi-likelihood and Gauss-Hermite quadrature parameter estimation (Bolker et al. 2009). That is, penalized quasi-likelihood is not recommended for count responses with means less than 5 and binary responses with less than 5 successes per group. Gauss-Hermite quadrature is not recommended for more than two or three random effects because of the rapidly declining analytical speed with the increasing number of random effects.
Bolker BM. 2008. Ecological models and data in R. Princeton University Press, New Jersey.
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
Nakagawa S, Schielzeth H. 2010. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biological Reviews 85(4): 935-956. DOI: 10.1111/j.1469-185X.2010.00141.x
Nakagawa S, Schielzeth H. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4(2): 133-142. DOI: 10.1111/j.2041-210x.2012.00261.x
#100 simulations ## Not run: powerGlmer(varcomp=c(0.7930,0.1664,0.1673),nval=c(11,11,300), fam_link=binomial(link="logit)) ## End(Not run)#100 simulations ## Not run: powerGlmer(varcomp=c(0.7930,0.1664,0.1673),nval=c(11,11,300), fam_link=binomial(link="logit)) ## End(Not run)
Extracts the power values of dam, sire, and dam by sire variance components from a generalized linear mixed-effect model using the glmer function of the lme4 package. Options to include one random position and/or one random block effect(s).
powerGlmer2(varcomp, nval, fam_link, alpha = 0.05, nsim = 100, position = NULL, block = NULL, poisLog = NULL)powerGlmer2(varcomp, nval, fam_link, alpha = 0.05, nsim = 100, position = NULL, block = NULL, poisLog = NULL)
varcomp |
Vector of known dam, sire, dam by sire, and position and/or block variance components, i.e. c(dam, sire, dam x sire, position/block). If there is a position and a block c(..., dam x sire, position, block). |
nval |
Vector of known dam, sire, offspring per family, and offspring per position or number of blocks sample sizes, i.e. c(dam, sire, offspring, position/block). If there is a position and a block c(..., offspring, position, block). |
fam_link |
The family and link in family(link) format. Supported options are binomial(link="logit"), binomial(link="probit"), poisson(link="log"), and poisson(link="sqrt"). |
alpha |
Statistical significance value. Default is 0.05. |
nsim |
Number of simulations. Default is 100. |
position |
Optional number of positions. |
block |
Optional vector of dams and sires per block, e.g. c(2,2). |
poisLog |
The residual variance component value if using poisson(link="log"). |
Extracts the dam, sire, dam, dam by sire, and position and/or block power values. The residual variance component for the fam_links are described by Nakagawa and Schielzeth (2010, 2013). Power values are calculated by stochastically simulation data and then calculating the proportion of significance values less than alpha for each component (Bolker 2008). Significance values for the random effects are determined using likelihood ratio tests (Bolker et al. 2009).
Prints a data frame with the sample sizes, variance component inputs, variance component outputs, and power values.
The Laplace approximation is used because there were fewer disadvantages relative to penalized quasi-likelihood and Gauss-Hermite quadrature parameter estimation (Bolker et al. 2009). That is, penalized quasi-likelihood is not recommended for count responses with means less than 5 and binary responses with less than 5 successes per group. Gauss-Hermite quadrature is not recommended for more than two or three random effects because of the rapidly declining analytical speed with the increasing number of random effects.
Bolker BM. 2008. Ecological models and data in R. Princeton University Press, New Jersey.
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
Nakagawa S, Schielzeth H. 2010. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biological Reviews 85(4): 935-956. DOI: 10.1111/j.1469-185X.2010.00141.x
Nakagawa S, Schielzeth H. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4(2): 133-142. DOI: 10.1111/j.2041-210x.2012.00261.x
#100 simulations ## Not run: powerGlmer2(varcomp=c(0.7880,0.1667,0.1671,0.0037),nval=c(11,11,300,3300), position=11,fam_link=binomial(link="logit")) ## End(Not run)#100 simulations ## Not run: powerGlmer2(varcomp=c(0.7880,0.1667,0.1671,0.0037),nval=c(11,11,300,3300), position=11,fam_link=binomial(link="logit")) ## End(Not run)
Extracts the power values of dam, sire, and dam by sire variance components from a generalized linear mixed-effect model using the glmer function of the lme4 package. Model can include additional fixed and/or random effects.
powerGlmer3(var_rand, n_rand, design, remain, fam_link, var_fix = NULL, n_fix = NULL, alpha = 0.05, nsim = 100, poisLog = NULL, ftest = "LR", iter = NULL)powerGlmer3(var_rand, n_rand, design, remain, fam_link, var_fix = NULL, n_fix = NULL, alpha = 0.05, nsim = 100, poisLog = NULL, ftest = "LR", iter = NULL)
var_rand |
Vector of known dam, sire, dam by sire, and remaining random variance components, i.e. c(dam,sire, dam by sire, rand1, rand2, etc.). |
n_rand |
Vector of known dam, sire, family, and remaining random sample sizes, i.e. c(dam, sire, family, rand1, rand2,etc.). |
design |
A data frame of the experimental design, using only integers. First three columns must contain and be named "dam", "sire", "family". Remaining columns are the random effects followed by the fixed effects. Continuous fixed effects are a column containing the values 1:nrow(design). |
remain |
Remaining formula using lme4 package format. Must be random effects followed by fixed effects. No interactions or random slopes; formulate as intercepts in design. |
fam_link |
The family and link in family(link) format. Supported options are binomial(link="logit"), binomial(link="probit"), poisson(link="log"), and poisson(link="sqrt"). |
var_fix |
Vector of known fixed variance components, i.e. c(fix1, fix2, etc.). Continous fixed random values are sorted to match column values. |
n_fix |
Vector of known fixed sample sizes, i.e. c(fix1, fix2, etc.). Continuous fixed effects must have a sample size of 1. |
alpha |
Statistical significance value. Default is 0.05. |
nsim |
Number of simulations. Default is 100. |
poisLog |
The residual variance component value if using poisson(link="log"). |
ftest |
Default is "LR" for likelihood ratio test for fixed effects. Option "PB" is for parametric bootstrap. |
iter |
Number of iterations for computing the parametric bootstrap significance value for any fixed effects. |
Extracts the dam, sire, dam, dam by sire, and any remaining random and fixed effects power values. The residual variance component for the fam_links are described by Nakagawa and Schielzeth (2010, 2013). Power values are calculated by stochastically simulation data and then calculating the proportion of significance values less than alpha for each component (Bolker 2008). Significance values for the random effects are determined using likelihood ratio tests (Bolker et al. 2009). Significance values for any fixed effects are determined using likelihood ratio tests or parametric bootstrap method (Bolker et al. 2009) from the mixed function of the afex package.
Prints a data frame with the sample sizes, variance component inputs, variance component outputs, and power values.
The Laplace approximation is used because there were fewer disadvantages relative to penalized quasi-likelihood and Gauss-Hermite quadrature parameter estimation (Bolker et al. 2009). That is, penalized quasi-likelihood is not recommended for count responses with means less than 5 and binary responses with less than 5 successes per group. Gauss-Hermite quadrature is not recommended for more than two or three random effects because of the rapidly declining analytical speed with the increasing number of random effects.
Bolker BM. 2008. Ecological models and data in R. Princeton University Press, New Jersey.
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
Nakagawa S, Schielzeth H. 2010. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biological Reviews 85(4): 935-956. DOI: 10.1111/j.1469-185X.2010.00141.x
Nakagawa S, Schielzeth H. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4(2): 133-142. DOI: 10.1111/j.2041-210x.2012.00261.x
##design object: 2 remaining random effects and 1 continous fixed effect block=c(2,2); blocN=4; position=16; posN=20; offN=20 dam0<- stack(as.data.frame(matrix(1:(block[1]*blocN),ncol=blocN,nrow=block[1]))) sire0<- stack(as.data.frame(matrix(1:(block[2]*blocN),ncol=blocN,nrow=block[2]))) observ0<- merge(dam0,sire0, by="ind") levels(observ0[,1])<- 1:blocN; colnames(observ0)<- c("block","dam","sire") observ0$family<- 1:nrow(observ0) #add family #expand for offspring, observ0 x offN observ1<- do.call("rbind", replicate(offN,observ0,simplify=FALSE)) observ1$position<- rep(1:position,each=posN) observ1$position<- sample(observ1$position,nrow(observ1)) #shuffle desn<- observ1[,c(2,3,4,5,1)];rm(observ0,observ1) #dam,sire,family,position,block desn$egg_size<- 1:nrow(desn) #100 simulations ## Not run: powerGlmer3(var_rand=c(1,0.15,0.11,0.5,0.3),n_rand=c(8,8,16,16,4), fam_link=binomial(link="logit"),var_fix=0.1,n_fix=1,design=desn, remain="(1|position)+(1|block)+egg_size") ## End(Not run)##design object: 2 remaining random effects and 1 continous fixed effect block=c(2,2); blocN=4; position=16; posN=20; offN=20 dam0<- stack(as.data.frame(matrix(1:(block[1]*blocN),ncol=blocN,nrow=block[1]))) sire0<- stack(as.data.frame(matrix(1:(block[2]*blocN),ncol=blocN,nrow=block[2]))) observ0<- merge(dam0,sire0, by="ind") levels(observ0[,1])<- 1:blocN; colnames(observ0)<- c("block","dam","sire") observ0$family<- 1:nrow(observ0) #add family #expand for offspring, observ0 x offN observ1<- do.call("rbind", replicate(offN,observ0,simplify=FALSE)) observ1$position<- rep(1:position,each=posN) observ1$position<- sample(observ1$position,nrow(observ1)) #shuffle desn<- observ1[,c(2,3,4,5,1)];rm(observ0,observ1) #dam,sire,family,position,block desn$egg_size<- 1:nrow(desn) #100 simulations ## Not run: powerGlmer3(var_rand=c(1,0.15,0.11,0.5,0.3),n_rand=c(8,8,16,16,4), fam_link=binomial(link="logit"),var_fix=0.1,n_fix=1,design=desn, remain="(1|position)+(1|block)+egg_size") ## End(Not run)
Extracts the power values of dam, sire, and dam by sire variance components from a linear mixed-effect model using the lmer function of the lme4 package.
powerLmer(varcomp, nval, alpha = 0.05, nsim = 100, ml = F)powerLmer(varcomp, nval, alpha = 0.05, nsim = 100, ml = F)
varcomp |
Vector of known dam, sire, dam by sire, and residual variance components, i.e. c(dam, sire, dam x sire, residual). |
nval |
Vector of known dam, sire, and offspring per family sample sizes, i.e. c(dam, sire, offspring). |
alpha |
Statistical significance value. Default is 0.05. |
nsim |
Number of simulations. Default is 100. |
ml |
Default is FALSE for restricted maximum likelihood. Change to TRUE for maximum likelihood. |
Extracts the dam, sire, dam, and dam by sire power values. Power values are calculated by stochastically simulation data and then calculating the proportion of significance values less than alpha for each component (Bolker 2008). Significance values for the random effects are determined using likelihood ratio tests (Bolker et al. 2009).
Prints a data frame with the sample sizes, variance component inputs, variance component outputs, and power values.
Maximum likelihood (ML) estimates the parameters that maximize the likelihood of the observed data and has the advantage of using all the data and accounting for non-independence (Lynch and Walsh 1998, p. 779; Bolker et al. 2009). On the other hand, ML has the disadvantage of assuming that all fixed effects are known without error, producing a downward bias in the estimation of the residual variance component. This bias can be large if there are lots of fixed effects, especially if sample sizes are small. Restricted maximum likelihood (REML) has the advantage of not assuming the fixed effects are known and averages over the uncertainty, so there can be less bias in the estimation of the residual variance component. However, REML only maximizes a portion of the likelihood to estimate the effect parameters, but is the preferred method for analyzing large data sets with complex structure.
Bolker BM. 2008. Ecological models and data in R. Princeton University Press, New Jersey.
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
#100 simulations #powerLmer(varcomp=c(0.1900,0,0.1719,0.6315),nval=c(11,11,10)) # #5 simulations powerLmer(varcomp=c(0.1900,0,0.1719,0.6315),nval=c(11,11,10),nsim=5)#100 simulations #powerLmer(varcomp=c(0.1900,0,0.1719,0.6315),nval=c(11,11,10)) # #5 simulations powerLmer(varcomp=c(0.1900,0,0.1719,0.6315),nval=c(11,11,10),nsim=5)
Extracts the power values of dam, sire, and dam by sire variance components from a linear mixed-effect model using the lmer function of the lme4 package. Options to include one random position and/or one random block effect(s).
powerLmer2(varcomp, nval, alpha = 0.05, nsim = 100, position = NULL, block = NULL, ml = F)powerLmer2(varcomp, nval, alpha = 0.05, nsim = 100, position = NULL, block = NULL, ml = F)
varcomp |
Vector of known dam, sire, dam by sire, residual, and position and/or block variance components, i.e. c(dam, sire, dam x sire, residual, position/block). If there is a position and a block c(..., residual, position, block). |
nval |
Vector of known dam, sire, offspring per family, and offspring per position or number of blocks sample sizes, i.e. c(dam, sire, offspring, position/block). If there is a position and a block c(..., offspring, position, block). |
alpha |
Statistical significance value. Default is 0.05. |
nsim |
Number of simulations. Default is 100. |
position |
Optional number of positions. |
block |
Optional vector of dams and sires per block, e.g. c(2,2). |
ml |
Default is FALSE for restricted maximum likelihood. Change to TRUE for maximum likelihood. |
Extracts the dam, sire, dam, dam by sire, and position and/or block power values. Power values are calculated by stochastically simulation data and then calculating the proportion of significance values less than alpha for each component (Bolker 2008). Significance values for the random effects are determined using likelihood ratio tests (Bolker et al. 2009).
Prints a data frame with the sample sizes, variance component inputs, variance component outputs, and power values.
Maximum likelihood (ML) estimates the parameters that maximize the likelihood of the observed data and has the advantage of using all the data and accounting for non-independence (Lynch and Walsh 1998, p. 779; Bolker et al. 2009). On the other hand, ML has the disadvantage of assuming that all fixed effects are known without error, producing a downward bias in the estimation of the residual variance component. This bias can be large if there are lots of fixed effects, especially if sample sizes are small. Restricted maximum likelihood (REML) has the advantage of not assuming the fixed effects are known and averages over the uncertainty, so there can be less bias in the estimation of the residual variance component. However, REML only maximizes a portion of the likelihood to estimate the effect parameters, but is the preferred method for analyzing large data sets with complex structure.
Bolker BM. 2008. Ecological models and data in R. Princeton University Press, New Jersey.
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
#100 simulations #position only, e.g. 8 tanks ## Not run: powerLmer2(varcomp=c(0.2030,0,0.1798,0.5499,0.1077),nval=c(8,8,20,160),position=8) #block only, e.g. four 2 x 2 ## Not run: powerLmer2(varcomp=c(0.2030,0,0.1798,0.5499,0.1077),nval=c(8,8,20,4),block=c(2,2)) #position and block ## Not run: powerLmer2(varcomp=c(0.2030,0,0.1798,0.5499,0.1077,0.1077),nval=c(8,8,20,40,4), position=8,block=c(2,2)) ## End(Not run)#100 simulations #position only, e.g. 8 tanks ## Not run: powerLmer2(varcomp=c(0.2030,0,0.1798,0.5499,0.1077),nval=c(8,8,20,160),position=8) #block only, e.g. four 2 x 2 ## Not run: powerLmer2(varcomp=c(0.2030,0,0.1798,0.5499,0.1077),nval=c(8,8,20,4),block=c(2,2)) #position and block ## Not run: powerLmer2(varcomp=c(0.2030,0,0.1798,0.5499,0.1077,0.1077),nval=c(8,8,20,40,4), position=8,block=c(2,2)) ## End(Not run)
Extracts the power values of dam, sire, and dam by sire variance components from a linear mixed-effect model using the lmer function of the lme4 package. Model can include additional fixed and/or random effects.
powerLmer3(var_rand, n_rand, design, remain, var_fix = NULL, n_fix = NULL, alpha = 0.05, nsim = 100, ml = F, ftest = "LR", iter = NULL)powerLmer3(var_rand, n_rand, design, remain, var_fix = NULL, n_fix = NULL, alpha = 0.05, nsim = 100, ml = F, ftest = "LR", iter = NULL)
var_rand |
Vector of known dam, sire, dam by sire, residual, and remaining random variance components, i.e. c(dam, sire, dam x sire, residual, rand1, rand2, etc.). |
n_rand |
Vector of known dam, sire, family, and remaining random sample sizes, i.e. c(dam, sire, family, rand1, rand2, etc.). |
design |
A data frame of the experimental design, using only integers. First three columns must contain and be named "dam", "sire", "family". Remaining columns are the random effects followed by the fixed effects. Continuous fixed effects are a column containing the values 1:nrow(design). |
remain |
Remaining formula using lme4 package format. Must be random effects followed by fixed effects. No interactions or random slopes; formulate as intercepts in design. |
var_fix |
Vector of known fixed variance components, i.e. c(fix1, fix2, etc.). Continous fixed random values are sorted to match column values. |
n_fix |
Vector of known fixed sample sizes, i.e. c(fix1, fix2, etc.). Continuous fixed effects must have a sample size of 1. |
alpha |
Statistical significance value. Default is 0.05. |
nsim |
Number of simulations. Default is 100. |
ml |
Default is FALSE for restricted maximum likelihood. Change to TRUE for maximum likelihood. |
ftest |
Default is "LR" for likelihood ratio test for fixed effects. Option "PB" is for parametric bootstrap. |
iter |
Number of iterations for computing the parametric bootstrap significance value for any fixed effects. |
Extracts the dam, sire, dam, dam by sire, and any remaining random and fixed effects power values. Power values are calculated by stochastically simulation data and then calculating the proportion of significance values less than alpha for each component (Bolker 2008). Significance values for the random effects are determined using likelihood ratio tests (Bolker et al. 2009). Significance values for any fixed effects are determined using likelihood ratio tests or parametric bootstrap method (Bolker et al. 2009) from the mixed function of the afex package.
Prints a data frame with the sample sizes, variance component inputs, variance component outputs, and power values.
Maximum likelihood (ML) estimates the parameters that maximize the likelihood of the observed data and has the advantage of using all the data and accounting for non-independence (Lynch and Walsh 1998, p. 779; Bolker et al. 2009). On the other hand, ML has the disadvantage of assuming that all fixed effects are known without error, producing a downward bias in the estimation of the residual variance component. This bias can be large if there are lots of fixed effects, especially if sample sizes are small. Restricted maximum likelihood (REML) has the advantage of not assuming the fixed effects are known and averages over the uncertainty, so there can be less bias in the estimation of the residual variance component. However, REML only maximizes a portion of the likelihood to estimate the effect parameters, but is the preferred method for analyzing large data sets with complex structure.
Bolker BM. 2008. Ecological models and data in R. Princeton University Press, New Jersey.
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
##design object: 2 remaining random effects and 1 continous fixed effect block=c(2,2); blocN=4; position=16; posN=20; offN=20 dam0<- stack(as.data.frame(matrix(1:(block[1]*blocN),ncol=blocN,nrow=block[1]))) sire0<- stack(as.data.frame(matrix(1:(block[2]*blocN),ncol=blocN,nrow=block[2]))) observ0<- merge(dam0,sire0, by="ind") levels(observ0[,1])<- 1:blocN; colnames(observ0)<- c("block","dam","sire") observ0$family<- 1:nrow(observ0) #add family #expand for offspring, observ0 x offN observ1<- do.call("rbind", replicate(offN,observ0,simplify=FALSE)) observ1$position<- rep(1:position,each=posN) observ1$position<- sample(observ1$position,nrow(observ1)) #shuffle desn<- observ1[,c(2,3,4,5,1)];rm(observ0,observ1) #dam,sire,family,position,block desn$egg_size<- 1:nrow(desn) #100 simulations ## Not run: powerLmer3(var_rand=c(0.19,0.03,0.02,0.51,0.1,0.05),n_rand=c(8,8,16,16,4), var_fix=0.1,n_fix=1,design=desn,remain="(1|position)+ (1|block)+ egg_size") ## End(Not run)##design object: 2 remaining random effects and 1 continous fixed effect block=c(2,2); blocN=4; position=16; posN=20; offN=20 dam0<- stack(as.data.frame(matrix(1:(block[1]*blocN),ncol=blocN,nrow=block[1]))) sire0<- stack(as.data.frame(matrix(1:(block[2]*blocN),ncol=blocN,nrow=block[2]))) observ0<- merge(dam0,sire0, by="ind") levels(observ0[,1])<- 1:blocN; colnames(observ0)<- c("block","dam","sire") observ0$family<- 1:nrow(observ0) #add family #expand for offspring, observ0 x offN observ1<- do.call("rbind", replicate(offN,observ0,simplify=FALSE)) observ1$position<- rep(1:position,each=posN) observ1$position<- sample(observ1$position,nrow(observ1)) #shuffle desn<- observ1[,c(2,3,4,5,1)];rm(observ0,observ1) #dam,sire,family,position,block desn$egg_size<- 1:nrow(desn) #100 simulations ## Not run: powerLmer3(var_rand=c(0.19,0.03,0.02,0.51,0.1,0.05),n_rand=c(8,8,16,16,4), var_fix=0.1,n_fix=1,design=desn,remain="(1|position)+ (1|block)+ egg_size") ## End(Not run)
Bootstrap resample observations grouped by family identities for a specified number of iterations to create a resampled data set.
resampFamily(dat, copy, family, iter)resampFamily(dat, copy, family, iter)
dat |
Data frame observed data to resample. |
copy |
Column numbers to copy. |
family |
Column name containing family identity information. |
iter |
Number of iterations for resampling. |
The resampled data can be used for producing bootstrap confidence intervals.
Because of the large file sizes that can be produced, the resampling of each family X is saved separately as a common separated (X_resampF.csv) file in the working directory. These files are merged to create the final resampled data set (resamp_datF.csv).
data(chinook_length) #Chinook salmon offspring length #resampFamily(dat=chinook_length,copy=c(3:8),family="family",iter=1000) #example with a couple iterations #resampFamily(dat=chinook_length,copy=c(3:8),family="family",iter=2)data(chinook_length) #Chinook salmon offspring length #resampFamily(dat=chinook_length,copy=c(3:8),family="family",iter=1000) #example with a couple iterations #resampFamily(dat=chinook_length,copy=c(3:8),family="family",iter=2)
Extracts additive genetic, non-additive genetic, and maternal variance components from a generalized linear mixed-effect model using the glmer function of the lme4 package. Model random effects are dam, sire, and dam by sire.
resampGlmer(resamp, dam, sire, response, fam_link, start, end, quasi = F)resampGlmer(resamp, dam, sire, response, fam_link, start, end, quasi = F)
resamp |
Data frame of bootstrap resampled data. |
dam |
Column name containing dam (female) parent identity information. |
sire |
Column name containing sire (male) parent identity information. |
response |
Column name containing the offspring (response) phenotype values. |
fam_link |
The family and link in family(link) format. Supported options are binomial(link="logit"), binomial(link="probit"), poisson(link="log"), and poisson(link="sqrt"). |
start |
Starting model number. |
end |
Ending model number. |
quasi |
Incorporate overdispersion or quasi-error structure. |
Used for bootstrap resampled data set produced using resampRepli or resampFamily. Laplace approximation parameter estimation is used, which is a true likelihood method (Bolker et al. 2009). For the overdispersion option, an observation-level random effect is added to the model (Atkins et al. 2013). Extracts the dam, sire, dam, and dam by sire variance components. The residual variance component for the fam_links are described by Nakagawa and Schielzeth (2010, 2013). Calculates the total variance component. Calculates the additive genetic, non-additive genetic, and maternal variance components (see Lynch and Walsh 1998, p. 603).
A data frame with columns containing the raw variance components for dam, sire, dam by sire, residual, total, additive genetic, non-additive genetic, and maternal. The number of rows in the data frame matches the number of iterations in the resampled data set and each row represents a model number.
The Laplace approximation is used because there were fewer disadvantages relative to penalized quasi-likelihood and Gauss-Hermite quadrature parameter estimation (Bolker et al. 2009). That is, penalized quasi-likelihood is not recommended for count responses with means less than 5 and binary responses with less than 5 successes per group. Gauss-Hermite quadrature is not recommended for more than two or three random effects because of the rapidly declining analytical speed with the increasing number of random effects.
Atkins DC, Baldwin SA, Zheng C, Gallop RJ, Neighbors C. 2013. A tutorial on count regression and zero-altered count models for longitudinal substance use data. Psychology of Addictive Behaviors 27(1): 166-177. DOI: 10.1037/a0029508
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
Nakagawa S, Schielzeth H. 2010. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biological Reviews 85(4): 935-956. DOI: 10.1111/j.1469-185X.2010.00141.x
Nakagawa S, Schielzeth H. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4(2): 133-142. DOI: 10.1111/j.2041-210x.2012.00261.x
data(chinook_resampS) #5 iterations #survival_rcomp<- resampGlmer(resamp=survival_datR,dam="dam",sire="sire", #response="status",fam_link=binomial(link="logit"),start=1,end=1000) ## Not run: survival_rcomp<- resampGlmer(resamp=chinook_resampS,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit"),start=1,end=5) ## End(Not run)data(chinook_resampS) #5 iterations #survival_rcomp<- resampGlmer(resamp=survival_datR,dam="dam",sire="sire", #response="status",fam_link=binomial(link="logit"),start=1,end=1000) ## Not run: survival_rcomp<- resampGlmer(resamp=chinook_resampS,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit"),start=1,end=5) ## End(Not run)
Extracts additive genetic, non-additive genetic, and maternal variance components from a generalized linear mixed-effect model using the glmer function of the lme4 package. Model random effects are dam, sire, and dam by sire. Options to include one random position and/or one random block effect(s).
resampGlmer2(resamp, dam, sire, response, fam_link, start, end, position = NULL, block = NULL, quasi = F)resampGlmer2(resamp, dam, sire, response, fam_link, start, end, position = NULL, block = NULL, quasi = F)
resamp |
Data frame of bootstrap resampled data. |
dam |
Column name containing dam (female) parent identity information. |
sire |
Column name containing sire (male) parent identity information. |
response |
Column name containing the offspring (response) phenotype values. |
fam_link |
The family and link in family(link) format. Supported options are binomial(link="logit"), binomial(link="probit"), poisson(link="log"), and poisson(link="sqrt"). |
start |
Starting model number. |
end |
Ending model number. |
position |
Optional column name containing position factor information. |
block |
Optional column name containing block factor information. |
quasi |
Incorporate overdispersion or quasi-error structure. |
Used for bootstrap resampled data set produced using resampRepli or resampFamily. Laplace approximation parameter estimation is used, which is a true likelihood method (Bolker et al. 2009). For the overdispersion option, an observation-level random effect is added to the model (Atkins et al. 2013). Extracts the dam, sire, dam, and dam by sire variance components. Extracts optional position and block variance components. The residual variance component for the fam_links are described by Nakagawa and Schielzeth (2010, 2013). Calculates the total variance component. Calculates the additive genetic, non-additive genetic, and maternal variance components (see Lynch and Walsh 1998, p. 603).
A data frame with columns containing the raw variance components for dam, sire, dam by sire, residual, total, additive genetic, non-additive genetic, and maternal. Also columns containing the raw variance components for the options of position and/or block. The number of rows in the data frame matches the number of iterations in the resampled data set and each row represents a model number.
The Laplace approximation is used because there were fewer disadvantages relative to penalized quasi-likelihood and Gauss-Hermite quadrature parameter estimation (Bolker et al. 2009). That is, penalized quasi-likelihood is not recommended for count responses with means less than 5 and binary responses with less than 5 successes per group. Gauss-Hermite quadrature is not recommended for more than two or three random effects because of the rapidly declining analytical speed with the increasing number of random effects.
Atkins DC, Baldwin SA, Zheng C, Gallop RJ, Neighbors C. 2013. A tutorial on count regression and zero-altered count models for longitudinal substance use data. Psychology of Addictive Behaviors 27(1): 166-177. DOI: 10.1037/a0029508
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
Nakagawa S, Schielzeth H. 2010. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biological Reviews 85(4): 935-956. DOI: 10.1111/j.1469-185X.2010.00141.x
Nakagawa S, Schielzeth H. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4(2): 133-142. DOI: 10.1111/j.2041-210x.2012.00261.x
data(chinook_resampS) #5 iterations #survival_rcomp2<- resampGlmer2(resamp=survival_datR,dam="dam",sire="sire", #response="status",fam_link=binomial(link="logit"),position="tray",start=1,end=1000) ## Not run: survival_rcomp2<- resampGlmer2(resamp=chinook_resampS,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit"),position="tray",start=1,end=5) ## End(Not run)data(chinook_resampS) #5 iterations #survival_rcomp2<- resampGlmer2(resamp=survival_datR,dam="dam",sire="sire", #response="status",fam_link=binomial(link="logit"),position="tray",start=1,end=1000) ## Not run: survival_rcomp2<- resampGlmer2(resamp=chinook_resampS,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit"),position="tray",start=1,end=5) ## End(Not run)
Extracts additive genetic, non-additive genetic, and maternal variance components from a generalized linear mixed-effect model using the glmer function of the lme4 package. Model random effects are dam, sire, dam by sire, and any additional fixed and/or random effects.
resampGlmer3(resamp, dam, sire, response, fam_link, start, end, remain, quasi = F)resampGlmer3(resamp, dam, sire, response, fam_link, start, end, remain, quasi = F)
resamp |
Data frame of bootstrap resampled data. |
dam |
Column name containing dam (female) parent identity information. |
sire |
Column name containing sire (male) parent identity information. |
response |
Column name containing the offspring (response) phenotype values. |
fam_link |
The family and link in family(link) format. Supported options are binomial(link="logit"), binomial(link="probit"), poisson(link="log"), and poisson(link="sqrt"). |
start |
Starting model number. |
end |
Ending model number. |
remain |
Remaining formula using lme4 package format with # sign (see column names), e.g. fixed# + (1|random#). |
quasi |
Incorporate overdispersion or quasi-error structure. |
Used for bootstrap resampled data set produced using resampRepli or resampFamily. Laplace approximation parameter estimation is used, which is a true likelihood method (Bolker et al. 2009). For the overdispersion option, an observation-level random effect is added to the model (Atkins et al. 2013). Extracts the dam, sire, dam, and dam by sire variance components. Extracts any additional fixed effect and random effect variance components. The fixed-effect variance component is as a single group using the method described by Nakagawa and Schielzeth (2013). The residual variance component for the fam_links are described by Nakagawa and Schielzeth (2010, 2013). Calculates the total variance component. Calculates the additive genetic, non-additive genetic, and maternal variance components (see Lynch and Walsh 1998, p. 603).
A data frame with columns containing the raw variance components for dam, sire, dam by sire, residual, total, additive genetic, non-additive genetic, and maternal. Also columns containing the raw variance components for remaining formula components. The number of rows in the data frame matches the number of iterations in the resampled data set and each row represents a model number.
The Laplace approximation is used because there were fewer disadvantages relative to penalized quasi-likelihood and Gauss-Hermite quadrature parameter estimation (Bolker et al. 2009). That is, penalized quasi-likelihood is not recommended for count responses with means less than 5 and binary responses with less than 5 successes per group. Gauss-Hermite quadrature is not recommended for more than two or three random effects because of the rapidly declining analytical speed with the increasing number of random effects.
Atkins DC, Baldwin SA, Zheng C, Gallop RJ, Neighbors C. 2013. A tutorial on count regression and zero-altered count models for longitudinal substance use data. Psychology of Addictive Behaviors 27(1): 166-177. DOI: 10.1037/a0029508
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
Nakagawa S, Schielzeth H. 2010. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biological Reviews 85(4): 935-956. DOI: 10.1111/j.1469-185X.2010.00141.x
Nakagawa S, Schielzeth H. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4(2): 133-142. DOI: 10.1111/j.2041-210x.2012.00261.x
data(chinook_resampS) #5 iterations #survival_rcomp3<- resampGlmer3(resamp=survival_datR,dam="dam",sire="sire", #response="status",fam_link=binomial(link="logit"),remain="egg_size# + (1|tray#)", #start=1,end=1000) ## Not run: survival_rcomp3<- resampGlmer3(resamp=survival_datR,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit"),remain="egg_size# + (1|tray#)", start=1,end=5) ## End(Not run)data(chinook_resampS) #5 iterations #survival_rcomp3<- resampGlmer3(resamp=survival_datR,dam="dam",sire="sire", #response="status",fam_link=binomial(link="logit"),remain="egg_size# + (1|tray#)", #start=1,end=1000) ## Not run: survival_rcomp3<- resampGlmer3(resamp=survival_datR,dam="dam",sire="sire", response="status",fam_link=binomial(link="logit"),remain="egg_size# + (1|tray#)", start=1,end=5) ## End(Not run)
Extracts additive genetic, non-additive genetic, and maternal variance components from a linear mixed-effect model using the lmer function of the lme4 package. Model random effects are dam, sire, and dam by sire.
resampLmer(resamp, dam, sire, response, start, end, ml = F)resampLmer(resamp, dam, sire, response, start, end, ml = F)
resamp |
Data frame of bootstrap resampled data. |
dam |
Column name containing dam (female) parent identity information. |
sire |
Column name containing sire (male) parent identity information. |
response |
Column name containing the offspring (response) phenotype values. |
start |
Starting model number. |
end |
Ending model number. |
ml |
Default is FALSE for restricted maximum likelihood. Change to TRUE for maximum likelihood. |
Used for bootstrap resampled data set produced using resampRepli or resampFamily. Extracts the dam, sire, dam, dam by sire, and residual variance components. Calculates the total variance component. Calculates the additive genetic, non-additive genetic, and maternal variance components (see Lynch and Walsh 1998, p. 603).
A data frame with columns containing the raw variance components for dam, sire, dam by sire, residual, total, additive genetic, non-additive genetic, and maternal. The number of rows in the data frame matches the number of iterations in the resampled data set and each row represents a model number.
Maximum likelihood (ML) estimates the parameters that maximize the likelihood of the observed data and has the advantage of using all the data and accounting for non-independence (Lynch and Walsh 1998, p. 779; Bolker et al. 2009). On the other hand, ML has the disadvantage of assuming that all fixed effects are known without error, producing a downward bias in the estimation of the residual variance component. This bias can be large if there are lots of fixed effects, especially if sample sizes are small. Restricted maximum likelihood (REML) has the advantage of not assuming the fixed effects are known and averages over the uncertainty, so there can be less bias in the estimation of the residual variance component. However, REML only maximizes a portion of the likelihood to estimate the effect parameters, but is the preferred method for analyzing large data sets with complex structure.
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
data(chinook_resampL) #5 iterations #length_rcomp1<- resampLmer(resamp=length_datR,dam="dam",sire="sire",response="length", #start=1,end=1000) length_rcomp1<- resampLmer(resamp=chinook_resampL,dam="dam",sire="sire",response="length", start=1,end=5)data(chinook_resampL) #5 iterations #length_rcomp1<- resampLmer(resamp=length_datR,dam="dam",sire="sire",response="length", #start=1,end=1000) length_rcomp1<- resampLmer(resamp=chinook_resampL,dam="dam",sire="sire",response="length", start=1,end=5)
Extracts additive genetic, non-additive genetic, and maternal variance components from a linear mixed-effect model using the lmer function of the lme4 package. Model random effects are dam, sire, and dam by sire. Options to include one random position and/or one random block effect(s).
resampLmer2(resamp, dam, sire, response, start, end, position = NULL, block = NULL, ml = F)resampLmer2(resamp, dam, sire, response, start, end, position = NULL, block = NULL, ml = F)
resamp |
Data frame of bootstrap resampled data. |
dam |
Column name containing dam (female) parent identity information. |
sire |
Column name containing sire (male) parent identity information. |
response |
Column name containing the offspring (response) phenotype values. |
start |
Starting model number. |
end |
Ending model number. |
position |
Optional column name containing position factor information. |
block |
Optional column name containing block factor information. |
ml |
Default is FALSE for restricted maximum likelihood. Change to TRUE for maximum likelihood. |
Used for bootstrap resampled data set produced using resampRepli or resampFamily. Extracts the dam, sire, dam, dam by sire, and residual variance components. Extracts optional position and block variance components. Calculates the total variance component. Calculates the additive genetic, non-additive genetic, and maternal variance components (see Lynch and Walsh 1998, p. 603).
A data frame with columns containing the raw variance components for dam, sire, dam by sire, residual, total, additive genetic, non-additive genetic, and maternal. Also columns containing the raw variance components for the options of position and/or block. The number of rows in the data frame matches the number of iterations in the resampled data set and each row represents a model number.
Maximum likelihood (ML) estimates the parameters that maximize the likelihood of the observed data and has the advantage of using all the data and accounting for non-independence (Lynch and Walsh 1998, p. 779; Bolker et al. 2009). On the other hand, ML has the disadvantage of assuming that all fixed effects are known without error, producing a downward bias in the estimation of the residual variance component. This bias can be large if there are lots of fixed effects, especially if sample sizes are small. Restricted maximum likelihood (REML) has the advantage of not assuming the fixed effects are known and averages over the uncertainty, so there can be less bias in the estimation of the residual variance component. However, REML only maximizes a portion of the likelihood to estimate the effect parameters, but is the preferred method for analyzing large data sets with complex structure.
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
data(chinook_resampL) #5 iterations #length_rcomp2<- resampLmer2(resamp=length_datR,dam="dam",sire="sire",response="length", #start=1,end=1000,position="tray") length_rcomp2<- resampLmer2(resamp=chinook_resampL,dam="dam",sire="sire",response="length", start=1,end=5,position="tray")data(chinook_resampL) #5 iterations #length_rcomp2<- resampLmer2(resamp=length_datR,dam="dam",sire="sire",response="length", #start=1,end=1000,position="tray") length_rcomp2<- resampLmer2(resamp=chinook_resampL,dam="dam",sire="sire",response="length", start=1,end=5,position="tray")
Extracts additive genetic, non-additive genetic, and maternal variance components from a linear mixed-effect model using the lmer function of the lme4 package. Model random effects are dam, sire, dam by sire, and any additional fixed and/or random effects.
resampLmer3(resamp, dam, sire, response, start, end, remain, ml = F)resampLmer3(resamp, dam, sire, response, start, end, remain, ml = F)
resamp |
Data frame of bootstrap resampled data. |
dam |
Column name containing dam (female) parent identity information. |
sire |
Column name containing sire (male) parent identity information. |
response |
Column name containing the offspring (response) phenotype values. |
start |
Starting model number. |
end |
Ending model number. |
remain |
Remaining formula using lme4 package format with # sign (see column names), e.g. fixed# + (1|random#). |
ml |
Default is FALSE for restricted maximum likelihood. Change to TRUE for maximum likelihood. |
Used for bootstrap resampled data set produced using resampRepli or resampFamily. Extracts the dam, sire, dam, dam by sire, and residual variance components. Extracts any additional fixed effect and random effect variance components. The fixed-effect variance component is as a single group using the method described by Nakagawa and Schielzeth (2013). Calculates the total variance component. Calculates the additive genetic, non-additive genetic, and maternal variance components (see Lynch and Walsh 1998, p. 603).
A data frame with columns containing the raw variance components for dam, sire, dam by sire, residual, total, additive genetic, non-additive genetic, and maternal. Also columns containing the raw variance components for remaining formula components. The number of rows in the data frame matches the number of iterations in the resampled data set and each row represents a model number.
Maximum likelihood (ML) estimates the parameters that maximize the likelihood of the observed data and has the advantage of using all the data and accounting for non-independence (Lynch and Walsh 1998, p. 779; Bolker et al. 2009). On the other hand, ML has the disadvantage of assuming that all fixed effects are known without error, producing a downward bias in the estimation of the residual variance component. This bias can be large if there are lots of fixed effects, especially if sample sizes are small. Restricted maximum likelihood (REML) has the advantage of not assuming the fixed effects are known and averages over the uncertainty, so there can be less bias in the estimation of the residual variance component. However, REML only maximizes a portion of the likelihood to estimate the effect parameters, but is the preferred method for analyzing large data sets with complex structure.
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
Nakagawa S, Schielzeth H. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4(2): 133-142. DOI: 10.1111/j.2041-210x.2012.00261.x
data(chinook_resampL) #length_rcomp3<- resampLmer3(resamp=length_datR,dam="dam",sire="sire",response="length", #start=1,end=1000,remain="egg_size# + (1|tray#)") length_rcomp3<- resampLmer3(resamp=chinook_resampL,dam="dam",sire="sire",response="length", start=1,end=5,remain="egg_size# + (1|tray#)")data(chinook_resampL) #length_rcomp3<- resampLmer3(resamp=length_datR,dam="dam",sire="sire",response="length", #start=1,end=1000,remain="egg_size# + (1|tray#)") length_rcomp3<- resampLmer3(resamp=chinook_resampL,dam="dam",sire="sire",response="length", start=1,end=5,remain="egg_size# + (1|tray#)")
Bootstrap resample observations grouped by replicate identities within family identities for a specified number of iterations to create a resampled data set.
resampRepli(dat, copy, family, replicate, iter)resampRepli(dat, copy, family, replicate, iter)
dat |
Data frame observed data to resample. |
copy |
Column numbers to copy. |
family |
Column name containing family identity information. |
replicate |
Column name containing replicate identity information. |
iter |
Number of iterations for resampling. |
The resampled data can be used for producing bootstrap confidence intervals.
Because of the large file sizes that can be produced, the resampling of each replicate Y per family X is saved separately as a common separated (X_Y_resampR.csv) file in the working directory. These files are merged to create the final resampled data set (resamp_datR.csv).
data(chinook_length) #Chinook salmon offspring length #resampRepli(dat=chinook_length,copy=c(3:8),family="family",replicate="repli",iter=1000) #example with a couple iterations #resampRepli(dat=chinook_length,copy=c(3:8),family="family",replicate="repli",iter=2)data(chinook_length) #Chinook salmon offspring length #resampRepli(dat=chinook_length,copy=c(3:8),family="family",replicate="repli",iter=1000) #example with a couple iterations #resampRepli(dat=chinook_length,copy=c(3:8),family="family",replicate="repli",iter=2)