Package 'AlphaPart' reference manual

Title:	Partition/Decomposition of Breeding Values by Paths of Information
Description:	A software that implements a method for partitioning genetic trends to quantify the sources of genetic gain in breeding programmes. The partitioning method is described in Garcia-Cortes et al. (2008) <doi:10.1017/S175173110800205X>. The package includes the main function AlphaPart for partitioning breeding values and auxiliary functions for manipulating data and summarizing, visualizing, and saving results.
Authors:	Gregor Gorjanc [aut, cre] , Jana Obsteter [aut] , Thiago de Paula Oliveira [aut]
Maintainer:	Gregor Gorjanc <[email protected]>
License:	GPL (>= 2)
Version:	0.9.8
Built:	2024-12-19 06:47:20 UTC
Source:	CRAN

AlphaPart.R

Description

A function to partition breeding values by a path variable. The partition method is described in García-Cortés et al., 2008: Partition of the genetic trend to validate multiple selection decisions. Animal : an international journal of animal bioscience. DOI: doi:10.1017/S175173110800205X

Usage

AlphaPart(x, pathNA, recode, unknown, sort, verbose, profile,
  printProfile, pedType, colId, colFid, colMid, colPath, colBV,
  colBy, center, scaleEBV)
AlphaPart(x, pathNA, recode, unknown, sort, verbose, profile,
  printProfile, pedType, colId, colFid, colMid, colPath, colBV,
  colBy, center, scaleEBV)

Arguments

`x`	data.frame , with (at least) the following columns: individual, father, and mother identif ication, and year of birth; see arguments `colId`, `colFid`, `colMid`, `colPath`, and `colBV`; see also details about the validity of pedigree.
`pathNA`	Logical, set dummy path (to "XXX") where path information is unknown (missing).
`recode`	Logical, internally recode individual, father and, mother identification to `1:n` codes, while missing parents are defined with `0`; this option must be used if identif ications in `x` are not already given as `1:n` codes, see also argument `sort`.
`unknown`	Value(s) used for representing unknown (missing) parent in `x`; this options has an effect only when `recode=FALSE` as it is only needed in that situation.
`sort`	Logical, initially sort `x` using `orderPed()` so that children follow parents in order to make imputation as optimal as possible (imputation is performed within a loop from the first to the last unknown birth year); at the end original order is restored.
`verbose`	Numeric, print additional information: `0` - print nothing, `1` - print some summaries about the data.
`profile`	Logical, collect timings and size of objects.
`printProfile`	Character, print profile info on the fly (`"fly"`) or at the end (`"end"`).
`pedType`	Character, pedigree type: the most common form is `"IPP"` for Individual, Parent 1 (say father), and Parent 2 (say mother) data; the second form is `"IPG"` for Individual, Parent 1 (say father), and one of Grandparents of Parent 2 (say maternal grandfather).
`colId`	Numeric or character, position or name of a column holding individual identif ication.
`colFid`	Numeric or character, position or name of a column holding father identif ication.
`colMid`	Numeric or character, position or name of a column holding mother identif ication or maternal grandparent identif ication if `pedType="IPG"` .
`colPath`	Numeric or character, position or name of a column holding path information.
`colBV`	Numeric or character, position(s) or name(s) of column(s) holding breeding Values.
`colBy`	Numeric or character, position or name of a column holding group information (see details).
`center`	Logical, if `center=TRUE` detect a shift in base population mean and attributes it as parent average effect rather than Mendelian sampling effect, otherwise, if center=FALSE, the base population values are only accounted as Mendelian sampling effect. Default is `center = TRUE`.
`scaleEBV`	a list with two arguments defining whether is appropriate to center and/or scale the `colBV` columns in respect to the base population. The list may contain the following components: `center`: a logical value `scale`: a logical value. If `center = TRUE` and `scale = TRUE` then the base population is set to has zero mean and unit variance.

Details

Pedigree in x must be valid in a sense that there are:

no directed loops (the simplest example is that the individual identification is equal to the identification of a father or mother)
no bisexuality, e.g., fathers most not appear as mothers
father and/or mother can be unknown (missing) - defined with any "code" that is different from existing identifications

Unknown (missing) values for breeding values are propagated down the pedigree to provide all available values from genetic evaluation. Another option is to cut pedigree links - set parents to unknown and remove them from pedigree prior to using this function - see pedSetBase function. Warning is issued in the case of unknown (missing) values.

In animal breeding/genetics literature the model with the underlying pedigree type "IPP" is often called animal model, while the model for pedigree type "IPG" is often called sire - maternal grandsire model. With a combination of colFid and colMid mother - paternal grandsire model can be accomodated as well.

Argument colBy can be used to directly perform a summary analysis by group, i.e., summary(AlphaPart(...), by="group"). See summary.AlphaPart for more. This can save some CPU time by skipping intermediate steps. However, only means can be obtained, while summary method gives more flexibility.

Value

An object of class AlphaPart, which can be used in further analyses - there is a handy summary method (summary.AlphaPart works on objects of AlphaPart class) and a plot method for its output (plot.summaryAlphaPart works on objects of summaryAlphaPart class). Class AlphaPart is a list. The first length(colBV) components (one for each trait and named with trait label, say trt) are data frames. Each data.frame contains:

x columns from initial data x
trt_pa parent average
trt_wMendelian sampling term
⁠trt_path1, trt_path2, ...⁠ breeding value partitions

The last component of returned object is also a list named info with the following components holding meta information about the analysis:

path column name holding path information
nP number of paths
lP path labels
nT number of traits
lT trait labels
warn potential warning messages associated with this object

If colBy!=NULL the resulting object is of a class summaryAlphaPart, see summary.AlphaPart for details.

If profile=TRUE, profiling info is printed on screen to spot any computational bottlenecks.

References

Garcia-Cortes, L. A. et al. (2008) Partition of the genetic trend to validate multiple selection decisions. Animal, 2(6):821-824. doi:10.1017/S175173110800205X

Examples

## Small pedigree with additive genetic (=breeding) values
ped <- data.frame(  id=c(  1,   2,   3,   4,   5,   6),
                   fid=c(  0,   0,   2,   0,   4,   0),
                   mid=c(  0,   0,   1,   0,   3,   3),
                   loc=c("A", "B", "A", "B", "A", "A"),
                   gen=c(  1,   1,   2,   2,   3,   3),
                  trt1=c(100, 120, 115, 130, 125, 125),
                  trt2=c(100, 110, 105, 100,  85, 110))

## Partition additive genetic values
tmp <- AlphaPart(x=ped, colBV=c("trt1", "trt2"))
print(tmp)

## Summarize by generation (genetic mean)
summary(tmp, by="gen")

## Summarize by generation (genetic variance)
summary(tmp, by="gen", FUN = var)



## There are also two demos
  demo(topic="AlphaPart_deterministic", package="AlphaPart",
       ask=interactive())
  demo(topic="AlphaPart_stochastic",     package="AlphaPart",
       ask=interactive())

## Small pedigree with additive genetic (=breeding) values
ped <- data.frame(  id=c(  1,   2,   3,   4,   5,   6),
                   fid=c(  0,   0,   2,   0,   4,   0),
                   mid=c(  0,   0,   1,   0,   3,   3),
                   loc=c("A", "B", "A", "B", "A", "A"),
                   gen=c(  1,   1,   2,   2,   3,   3),
                  trt1=c(100, 120, 115, 130, 125, 125),
                  trt2=c(100, 110, 105, 100,  85, 110))

## Partition additive genetic values
tmp <- AlphaPart(x=ped, colBV=c("trt1", "trt2"))
print(tmp)

## Summarize by generation (genetic mean)
summary(tmp, by="gen")

## Summarize by generation (genetic variance)
summary(tmp, by="gen", FUN = var)



## There are also two demos
  demo(topic="AlphaPart_deterministic", package="AlphaPart",
       ask=interactive())
  demo(topic="AlphaPart_stochastic",     package="AlphaPart",
       ask=interactive())

Sample pedigree for partition.

Description

A dataset containing pedigree information and breeding values for six individuals.

Usage

AlphaPart.ped
AlphaPart.ped

Format

A data frame with 6 rows and 8 variables:

IId: individual's ID
FId: Father's ID
MId: Mother's ID
gen: Generation
country: Country
gender: Individual's sex
bv1: Breeding value for trait 1
bv2: Breeding value for trait 1

Source

Simulation.

AlphaPartSubset.R

Description

A function to choose the partition paths to keep.

Usage

AlphaPartSubset(x, paths = NULL)
AlphaPartSubset(x, paths = NULL)

Arguments

`x`	AlphaPart or summaryAlphaPart, object from the `AlphaPart(...)` or `summary(AlphaPart(...), ...)` call.
`paths`	Character, names of paths to be kept.

Details

Displaying results of partitions for many paths is often confusing. This function helps in selecting only paths of interest. Unspecified paths are removed from the input object x. Meta information is modified accordingly. Default setting does nothing.

Value

An object of class AlphaPart or summaryAlphaPart with only some paths. Meta information in slot "info" is modified as well.

Examples

## Small pedigree with additive genetic (=breeding) values
ped <- data.frame(  id=c(  1,   2,   3,   4,   5,   6),
                  fid=c(  0,   0,   2,   0,   4,   0),
                  mid=c(  0,   0,   1,   0,   3,   3),
                  loc=c("A", "B", "A", "B", "A", "A"),
                  gen=c(  1,   1,   2,   2,   3,   3),
                 trt1=c(100, 120, 115, 130, 125, 125),
                 trt2=c(100, 110, 105, 100,  85, 110))

## Partition additive genetic values
(tmp <- AlphaPart(x=ped, colBV=c("trt1", "trt2")))

## Keep some partitions (working on object of class AlphaPart)
(tmp2 <- AlphaPartSubset(x=tmp, paths="A"))

## Summarize by generation
(tmpS <- summary(tmp, by="gen"))

## Keep some partitions (working on object of class summaryAlphaPart)
(tmpS2 <- AlphaPartSubset(x=tmpS, paths="A"))

## ... must be equal to
(tmpS3 <- summary(tmp2, by="gen"))
## Small pedigree with additive genetic (=breeding) values
ped <- data.frame(  id=c(  1,   2,   3,   4,   5,   6),
                  fid=c(  0,   0,   2,   0,   4,   0),
                  mid=c(  0,   0,   1,   0,   3,   3),
                  loc=c("A", "B", "A", "B", "A", "A"),
                  gen=c(  1,   1,   2,   2,   3,   3),
                 trt1=c(100, 120, 115, 130, 125, 125),
                 trt2=c(100, 110, 105, 100,  85, 110))

## Partition additive genetic values
(tmp <- AlphaPart(x=ped, colBV=c("trt1", "trt2")))

## Keep some partitions (working on object of class AlphaPart)
(tmp2 <- AlphaPartSubset(x=tmp, paths="A"))

## Summarize by generation
(tmpS <- summary(tmp, by="gen"))

## Keep some partitions (working on object of class summaryAlphaPart)
(tmpS2 <- AlphaPartSubset(x=tmpS, paths="A"))

## ... must be equal to
(tmpS3 <- summary(tmp2, by="gen"))

AlphaPartSum.R

Description

A function to sum partitions of several paths.

Usage

AlphaPartSum(
  x,
  map = NULL,
  remove = TRUE,
  zeroPath = TRUE,
  call = "AlphaPartSum"
)
AlphaPartSum(
  x,
  map = NULL,
  remove = TRUE,
  zeroPath = TRUE,
  call = "AlphaPartSum"
)

Arguments

`x`	summaryAlphaPart, object from the `AlphaPart(...)` or `summary(AlphaPart(...), ...)` call.
`map`	List, a map of summing paths; see details and examples.
`remove`	Logical, remove original paths or not.
`zeroPath`	Logical, set called path to zero if it does not exist.
`call`	character, for internal use with `AlphaPartSubset`).

Details

Sometimes partitions of particular paths are very small or we want to sum paths that have some similarity. These actions are easy to achive manually but this functions provides a way to do this consistently with the given object x.

Arguments map must be a list of vectors of length at least two. Vectors of length one are skipped. The idea is that the first element is the new or existing path into which we add up all the remaining specified paths, say list(c("A", "B"), c("X", "X", "Y"), c("Z", "X")) would imply A = B, X = X + Y, and Z = X = X + Y. Note that once X is changed its changed value is used in further calculations. Specify different (new) names for new targets if you want to avoid this.

Be carefull with remove=TRUE, which is the default setting, as all partitions defined after the first (target/new) partition in vector in list will be removed, for example with list(c("A", "B"), c("X", "X", "Y"), c("Z", "X")) partitions B and Y will be removed, while X will not be removed as it is defined as a target/new partition.

Value

An object of class AlphaPart or summaryAlphaPart with modified partitions. Meta information in slot "info" is modified as well.

Examples

## Small pedigree with additive genetic (=breeding) values
ped <- data.frame(  id=c(  1,   2,   3,   4,   5,   6),
                  fid=c(  0,   0,   2,   0,   4,   0),
                  mid=c(  0,   0,   1,   0,   3,   3),
                  loc=c("A", "B", "A", "B", "A", "A"),
                  gen=c(  1,   1,   2,   2,   3,   3),
                 trt1=c(100, 120, 115, 130, 125, 125),
                 trt2=c(100, 110, 105,  140,  85, 110))

## Partition additive genetic values
(tmp <- AlphaPart(x=ped, colBV=c("trt1", "trt2")))

## Sum some partitions (working on object of class AlphaPart)
(tmp2 <- AlphaPartSum(x=tmp, map=list(c("X", "A", "B"), c("A", "B"))))

## Summarize by generation
(tmpS <- summary(tmp, by="gen"))

## Sum some partitions (working on object of class summaryAlphaPart)
(tmpS2 <- AlphaPartSum(x=tmpS, map=list(c("X", "A", "B"), c("A", "B"))))

## ... must be equal to
(tmpS3 <- summary(tmp2, by="gen"))
## Small pedigree with additive genetic (=breeding) values
ped <- data.frame(  id=c(  1,   2,   3,   4,   5,   6),
                  fid=c(  0,   0,   2,   0,   4,   0),
                  mid=c(  0,   0,   1,   0,   3,   3),
                  loc=c("A", "B", "A", "B", "A", "A"),
                  gen=c(  1,   1,   2,   2,   3,   3),
                 trt1=c(100, 120, 115, 130, 125, 125),
                 trt2=c(100, 110, 105,  140,  85, 110))

## Partition additive genetic values
(tmp <- AlphaPart(x=ped, colBV=c("trt1", "trt2")))

## Sum some partitions (working on object of class AlphaPart)
(tmp2 <- AlphaPartSum(x=tmp, map=list(c("X", "A", "B"), c("A", "B"))))

## Summarize by generation
(tmpS <- summary(tmp, by="gen"))

## Sum some partitions (working on object of class summaryAlphaPart)
(tmpS2 <- AlphaPartSum(x=tmpS, map=list(c("X", "A", "B"), c("A", "B"))))

## ... must be equal to
(tmpS3 <- summary(tmp2, by="gen"))

pedFixBirthYear.R

Description

A function to fix (impute) missing birth years in pedigree.

Usage

pedFixBirthYear(
  x,
  interval,
  down = FALSE,
  na.rm = TRUE,
  sort = TRUE,
  direct = TRUE,
  report = TRUE,
  colId = 1,
  colFid = 2,
  colMid = 3,
  colBY = 4
)
pedFixBirthYear(
  x,
  interval,
  down = FALSE,
  na.rm = TRUE,
  sort = TRUE,
  direct = TRUE,
  report = TRUE,
  colId = 1,
  colFid = 2,
  colMid = 3,
  colBY = 4
)

Arguments

`x`	data.frame , with (at least) the following columns: individual, father, and mother identification, and year of birth; see arguments `colId`, `colFid`, `colMid`, and `colBY`
`interval`	Numeric, a value for generation interval in years.
`down`	Logical, the default is to impute birth years based on the birth year of children starting from the youngest to the oldest individuals, while with `down=TRUE` birth year is imputed based on the birth year of parents in the opposite order.
`na.rm`	Logical, remove `NA` values when searching for the minimal (maximal) year of birth in children (parents); setting this to `FALSE` can lead to decreased success of imputation
`sort`	Logical, initially sort `x` using `orderPed()` so that children follow parents in order to make imputation as optimal as possible (imputation is performed within a loop from the first to the last unknown birth year); at the end original order is restored.
`direct`	Logical, insert inferred birth years immediately so they can be used for successive individuals within the loop.
`report`	Logical, report success.
`colId`	Numeric or character, position or name of a column holding individual identification.
`colFid`	Numeric or character, position or name of a column holding father identification.
`colMid`	Numeric or character, position or name of a column holding mother identification.
`colBY`	Numeric or character, position or name of a column holding birth year.

Details

Warnings are issued when there is no information to use to impute birth years or missing values (NA) are propagated.

Arguments down and na.rm allow for repeated use of this function, i.e., with down=FALSE and with down=TRUE (both in combination with na.rm=TRUE) in order to propagate information over the pedigree until "convergence".

This function can be very slow on large pedigrees with extensive missingness of birth years.

Value

Object x with imputed birth years based on the birth year of children or parents. If report=TRUE success is printed on the screen as the number of initially, fixed, and left unknown birth years is printed.

Examples

## Example pedigree with missing (unknown) birth year for some individuals
ped0 <- data.frame(     id=c( 1, 2, 3,  4, 5, 6, 7,  8, 9, 10, 11, 12, 13, 14),
                        fid=c( 0, 0, 0,  1, 1, 1, 3,  3, 3,  5,  4,  0,  0, 12),
                        mid=c( 0, 0, 0,  2, 0, 2, 2,  2, 5,  0,  0,  0,  0, 13),
                        birth_dt=c(NA, 0, 1, NA, 3, 3, 3, 3, 4, 4, 5, NA, 6, 6) + 2000)

## First run - using information from children
ped1 <- pedFixBirthYear(x=ped0, interval=1)

## Second run - using information from parents
ped2 <- pedFixBirthYear(x=ped1, interval=1, down=TRUE)

## Third run - using information from children, but with no success
ped3 <- pedFixBirthYear(x=ped2, interval=1)
## Example pedigree with missing (unknown) birth year for some individuals
ped0 <- data.frame(     id=c( 1, 2, 3,  4, 5, 6, 7,  8, 9, 10, 11, 12, 13, 14),
                        fid=c( 0, 0, 0,  1, 1, 1, 3,  3, 3,  5,  4,  0,  0, 12),
                        mid=c( 0, 0, 0,  2, 0, 2, 2,  2, 5,  0,  0,  0,  0, 13),
                        birth_dt=c(NA, 0, 1, NA, 3, 3, 3, 3, 4, 4, 5, NA, 6, 6) + 2000)

## First run - using information from children
ped1 <- pedFixBirthYear(x=ped0, interval=1)

## Second run - using information from parents
ped2 <- pedFixBirthYear(x=ped1, interval=1, down=TRUE)

## Third run - using information from children, but with no success
ped3 <- pedFixBirthYear(x=ped2, interval=1)

pedSetBase.R

Description

A function to set the base population in the pedigree.

Usage

pedSetBase(
  x,
  keep = NULL,
  unknown = NA,
  report = TRUE,
  colId = 1,
  colFid = 2,
  colMid = 3
)
pedSetBase(
  x,
  keep = NULL,
  unknown = NA,
  report = TRUE,
  colId = 1,
  colFid = 2,
  colMid = 3
)

Arguments

`x`	data.frame , with (at least) the following columns: individual, father, and mother identification, and year of birth; see arguments `colId`, `colFid`, `colMid`, and `colBY`
`keep`	Logical, indicator that defines which individuals should stay in the the pedigree; see details.
`unknown`	Value used to represent unknown/missing identification
`report`	Logical, report success.
`colId`	Numeric or character, position or name of a column holding individual identification.
`colFid`	Numeric or character, position or name of a column holding father identification.
`colMid`	Numeric or character, position or name of a column holding mother identification.

Details

Base population in the pedigree is set by removing rows for some individuals, while their presence as parents is also removed.

This function can be very slow on large pedigrees with extensive missingness of birth years.

Value

Object x with removed rows for some individuals and their presence as parents. If report=TRUE progress is printed on the screen.

Examples

## Example pedigree
ped <- data.frame(      id=1:10,
                       fid=c(0, 0, 0, 1, 1, 1, 3, 3, 3, 5),
                       mid=c(0, 0, 0, 2, 0, 2, 2, 2, 5, 0),
                  birth_dt=c(0, 0, 1, 2, 3, 3, 3, 4, 4, 5) + 2000)

## Set base population as those individuals that were born after year 2002
pedSetBase(x=ped, keep=ped$birth_dt > 2002, unknown=0)

## Example pedigree
ped <- data.frame(      id=1:10,
                       fid=c(0, 0, 0, 1, 1, 1, 3, 3, 3, 5),
                       mid=c(0, 0, 0, 2, 0, 2, 2, 2, 5, 0),
                  birth_dt=c(0, 0, 1, 2, 3, 3, 3, 4, 4, 5) + 2000)

## Set base population as those individuals that were born after year 2002
pedSetBase(x=ped, keep=ped$birth_dt > 2002, unknown=0)

A function to plot summary of partitioned breeding values.

Description

A function to plot summary of partitioned breeding values.

Usage

## S3 method for class 'summaryAlphaPart'
plot(x, by, sortValue,
  sortValueFUN, sortValueDec, addSum, paths, xlab, ylab, xlim, ylim,
  color, lineSize, lineType, lineTypeList, useDirectLabels, method,
  labelPath, ...)
## S3 method for class 'summaryAlphaPart'
plot(x, by, sortValue,
  sortValueFUN, sortValueDec, addSum, paths, xlab, ylab, xlim, ylim,
  color, lineSize, lineType, lineTypeList, useDirectLabels, method,
  labelPath, ...)

Arguments

`x`	summaryAlphaPart, object from the `AlphaPart(...)` or `summary(AlphaPart(...), ...)` call.
`by`	Character, the name of a column by which summary function FUN should be applied; if `NULL` (default) summary is given for the whole table.
`sortValue`	Logical, affect legend attributes via sort of paths according to `sortValueFUN` function; if not logical, then ordered paths are given as a character vector.
`sortValueFUN`	Function, that produces single value for one vector, say `mean` or `sum`.
`sortValueDec`	Logical, sort decreasing.
`addSum`	Logical, plot the overall trend.
`paths`	Character or list or characters, name of paths to plot; if `NULL` plot all paths; see examples.
`xlab`	Character, x-axis label.
`ylab`	Character, y-axis label; can be a vector of several labels if there are more traits in `x` (recycled!).
`xlim`	Numeric, a vector of two values with x-axis limits; use a list of vectors for more traits.
`ylim`	Numeric, a vector of two values with y-axis limits; use a list of vectors for more traits.
`color`	Character, color names; by default a set of 54 colors is predefined from the RColorBrewer package; in addition a black colour is attached at the begining for the overall trend; if there are more paths than colors then recycling occours.
`lineSize`	Numeric, line width.
`lineType`	Numeric, line type (recycled); can be used only if lineTypeList=NULL.
`lineTypeList`	List, named list of numeric values that help to point out a set of paths (distinguished with line type) within upper level of paths (distinguished by, color), e.g., lineTypeList=list("-1"=1, "-2"=2, def=1) will lead to use of line 2, for paths having "-2" at the end of path name, while line type 1 (default) will, be used for other paths; specification of this argument also causes recycling of colors for the upper level of paths; if NULL all lines have a standard line type, otherwise `lineType` does not have any effect.
`useDirectLabels`	Logical, use directlabels package for legend.
`method`	List, method for direct.label.
`labelPath`	Character, legend title; used only if `useDirectLabels=FALSE`.
`...`	Arguments passed to other functions (not used at the moment).

Details

Information in summaries of partitions of breeding values can be overhelming due to a large volume of numbers. Plot method can be used to visualise this data in eye pleasing way using ggplot2 graphics.

Value

A list of ggplot objects that can be further modified or displayed. For each trait in x there is one plot visualising summarized values.

Examples



## Partition additive genetic values by country
(res <- AlphaPart(x=AlphaPart.ped, colPath="country", colBV=c("bv1", "bv2")))

## Summarize population by generation (=trend)
(ret <- summary(res, by="gen"))

## Plot the partitions
p <- plot(ret, ylab=c("bv for trait 1", "bv for trait 2"), xlab="Generation")
print(p[[1]]$abs)
print(p[[2]]$abs)
print(p)

## Partition additive genetic values by country and sex
AlphaPart.ped$country.gender <- with(AlphaPart.ped, paste(country, gender, sep="-"))
(res <- AlphaPart(x=AlphaPart.ped, colPath="country.gender", colBV=c("bv1", "bv2")))

## Summarize population by generation (=trend)
(ret <- summary(res, by="gen"))

## Plot the partitions
p <- plot(ret, ylab=c("BV for trait 1", "BV for trait 2"), xlab="Generation")
print(p)
p <- plot(ret, ylab=c("BV for trait 1", "BV for trait 2"), xlab="Generation",
        lineTypeList=list("-1"=1, "-2"=2, def=3))
print(p)
p <- plot(ret, ylab=c("BV for trait 1", "BV for trait 2"), xlab="Generation",
        lineTypeList=list("-1"=1, "-2"=2, def=3), useGgplot2=FALSE, useDirectLabels = FALSE)
print(p)

## Plot control (color and type of lines + limits)
p <- plot(ret, ylab=c("BV for trait 1", "BV for trait 2"), xlab="Generation",
        useGgplot2=TRUE, color=c("green", "gray"), lineType=c(2, 3),
        sortValue=FALSE, lineSize=4,
        xlim=c(-1, 7))
print(p)

## Partition additive genetic values by country
(res <- AlphaPart(x=AlphaPart.ped, colPath="country", colBV=c("bv1", "bv2")))

## Summarize population by generation (=trend)
(ret <- summary(res, by="gen"))

## Plot the partitions
p <- plot(ret, ylab=c("bv for trait 1", "bv for trait 2"), xlab="Generation")
print(p[[1]]$abs)
print(p[[2]]$abs)
print(p)

## Partition additive genetic values by country and sex
AlphaPart.ped$country.gender <- with(AlphaPart.ped, paste(country, gender, sep="-"))
(res <- AlphaPart(x=AlphaPart.ped, colPath="country.gender", colBV=c("bv1", "bv2")))

## Summarize population by generation (=trend)
(ret <- summary(res, by="gen"))

## Plot the partitions
p <- plot(ret, ylab=c("BV for trait 1", "BV for trait 2"), xlab="Generation")
print(p)
p <- plot(ret, ylab=c("BV for trait 1", "BV for trait 2"), xlab="Generation",
        lineTypeList=list("-1"=1, "-2"=2, def=3))
print(p)
p <- plot(ret, ylab=c("BV for trait 1", "BV for trait 2"), xlab="Generation",
        lineTypeList=list("-1"=1, "-2"=2, def=3), useGgplot2=FALSE, useDirectLabels = FALSE)
print(p)

## Plot control (color and type of lines + limits)
p <- plot(ret, ylab=c("BV for trait 1", "BV for trait 2"), xlab="Generation",
        useGgplot2=TRUE, color=c("green", "gray"), lineType=c(2, 3),
        sortValue=FALSE, lineSize=4,
        xlim=c(-1, 7))
print(p)

Print method for the output of AlphaPart function.

Description

Partitioning of breeding values if often performed on quite large datasets, which quickly fills in the whole screen. Print method therefore prints out paths, number of individuals and the first and the last few lines for each trait to quickly see what kind of data is in x.

Usage

## S3 method for class 'AlphaPart'
print(x, n, ...)
## S3 method for class 'AlphaPart'
print(x, n, ...)

Arguments

`x`	AlphaPart, output object from `AlphaPart` function.
`n`	Integer, number of the first and last rows in `x` to print out using `head` and `tail`.
`...`	Arguments passed to `print` function.

Examples

## Small pedigree with additive genetic (=breeding) values
ped <- data.frame(  id=c(  1,   2,   3,   4,   5,   6),
                   fid=c(  0,   0,   2,   0,   4,   0),
                   mid=c(  0,   0,   1,   0,   3,   3),
                   loc=c("A", "B", "A", "B", "A", "A"),
                   gen=c(  1,   1,   2,   2,   3,   3),
                  trt1=c(100, 120, 115, 130, 125, 125),
                  trt2=c(100, 110, 105, 100,  85, 110))

## Partition additive genetic values
tmp <- AlphaPart(x=ped, colBV=c("trt1", "trt2"))
print(tmp)

## Summarize by generation (genetic mean)
summary(tmp, by="gen")

## Summarize by generation (genetic variance)
summary(tmp, by="gen", FUN = var)



## There are also two demos
  demo(topic="AlphaPart_deterministic", package="AlphaPart",
       ask=interactive())
  demo(topic="AlphaPart_stochastic",     package="AlphaPart",
       ask=interactive())

## Small pedigree with additive genetic (=breeding) values
ped <- data.frame(  id=c(  1,   2,   3,   4,   5,   6),
                   fid=c(  0,   0,   2,   0,   4,   0),
                   mid=c(  0,   0,   1,   0,   3,   3),
                   loc=c("A", "B", "A", "B", "A", "A"),
                   gen=c(  1,   1,   2,   2,   3,   3),
                  trt1=c(100, 120, 115, 130, 125, 125),
                  trt2=c(100, 110, 105, 100,  85, 110))

## Partition additive genetic values
tmp <- AlphaPart(x=ped, colBV=c("trt1", "trt2"))
print(tmp)

## Summarize by generation (genetic mean)
summary(tmp, by="gen")

## Summarize by generation (genetic variance)
summary(tmp, by="gen", FUN = var)



## There are also two demos
  demo(topic="AlphaPart_deterministic", package="AlphaPart",
       ask=interactive())
  demo(topic="AlphaPart_stochastic",     package="AlphaPart",
       ask=interactive())

Print a plot generate by the function `plotSummaryAlphaPart`

Description

Plot output object from plot.summaryAlphaPart.

Usage

## S3 method for class 'plotSummaryAlphaPart'
print(x, ask, ...)
## S3 method for class 'plotSummaryAlphaPart'
print(x, ask, ...)

Arguments

`x`	plotSummaryAlphaPart, output object from `plot.summaryAlphaPart` function
`ask`	Logical, ask before printing another plot?
`...`	Arguments passed to other functions (not used at the moment).

Examples

## Partition additive genetic values
(res <- AlphaPart(x=AlphaPart.ped, colPath="country", colBV=c("bv1", "bv2")))

## Summarize population by generation (=trend)
(ret <- summary(res, by="gen"))

## Plot the partitions
p <- plot(ret, ylab=c("BV for trait 1", "BV for trait 2"), xlab="Generation")
print(p[[1]])
print(p[[2]])
#print(p)
## Partition additive genetic values
(res <- AlphaPart(x=AlphaPart.ped, colPath="country", colBV=c("bv1", "bv2")))

## Summarize population by generation (=trend)
(ret <- summary(res, by="gen"))

## Plot the partitions
p <- plot(ret, ylab=c("BV for trait 1", "BV for trait 2"), xlab="Generation")
print(p[[1]])
print(p[[2]])
#print(p)

Print method for objects of the class summaryAlphaPart.

Description

Print method for objects of the class summaryAlphaPart (result of summary(AlphaPart(...))).

Usage

## S3 method for class 'summaryAlphaPart'
print(x, ...)
## S3 method for class 'summaryAlphaPart'
print(x, ...)

Arguments

`x`	summaryAlphaPart, output object from `summary.AlphaPart` function.
`...`	Arguments passed to other functions (not used at the moment).

Examples

## --- Partition additive genetic values by loc ---
res <- AlphaPart(x=AlphaPart.ped, colPath="country", colBV=c("bv1", "bv2"))

## Summarize whole population
ret <- summary(res)

## Summarize population by generation (=trend)
ret <- summary(res, by="gen")

## Summarize population by generation (=trend) but only for domestic location
ret <- summary(res, by="gen", subset=res[[1]]$country == "domestic")

## --- Partition additive genetic values by loc and gender ---

AlphaPart.ped$country.gender <- with(AlphaPart.ped, paste(country, gender, sep="-"))
res <- AlphaPart(x=AlphaPart.ped, colPath="country.gender", colBV=c("bv1", "bv2"))

## Summarize population by generation (=trend)
ret <- summary(res, by="gen")

## Summarize population by generation (=trend) but only for domestic location
ret <- summary(res, by="gen", subset=res[[1]]$country == "domestic")
## --- Partition additive genetic values by loc ---
res <- AlphaPart(x=AlphaPart.ped, colPath="country", colBV=c("bv1", "bv2"))

## Summarize whole population
ret <- summary(res)

## Summarize population by generation (=trend)
ret <- summary(res, by="gen")

## Summarize population by generation (=trend) but only for domestic location
ret <- summary(res, by="gen", subset=res[[1]]$country == "domestic")

## --- Partition additive genetic values by loc and gender ---

AlphaPart.ped$country.gender <- with(AlphaPart.ped, paste(country, gender, sep="-"))
res <- AlphaPart(x=AlphaPart.ped, colPath="country.gender", colBV=c("bv1", "bv2"))

## Summarize population by generation (=trend)
ret <- summary(res, by="gen")

## Summarize population by generation (=trend) but only for domestic location
ret <- summary(res, by="gen", subset=res[[1]]$country == "domestic")

Save plot method for `AlphaPart`

Description

Save plot method for AlphaPart

Usage

savePlot(...)
savePlot(...)

Arguments

...

Arguments passed to type specific methods, say width and height for type="pdf" etc.

Value

Beside the side effect of saving plots to disk, filenames are printed on screen during the process and at the end invisibly returned.

Save plot objects on the disk for permanent storage. Function `savePlot` from the grDevices package works for current page on graphical device. This is an attempt to make this function generic so that one can define `savePlot` methods for particular needs.

Description

Save plot objects of class plotSummaryAlphaPart on the disk for permanent storage.

Usage

## S3 method for class 'plotSummaryAlphaPart'
savePlot(x, filename, type,
  device, pre.hook, traitsAsDir, ...)

## Default S3 method:
savePlot(...)
## S3 method for class 'plotSummaryAlphaPart'
savePlot(x, filename, type,
  device, pre.hook, traitsAsDir, ...)

## Default S3 method:
savePlot(...)

Arguments

`x`	Object on which to chose savePLot method.
`filename`	Character, filename to save to.
`type`	Character, file/device type.
`device`	Device, the device to save from.
`pre.hook`	Function, call some code before calling print method for plots (see examples).
`traitsAsDir`	Logical, should plots be saved within trait folders; the construction is `file.path(dirname(file), trait, basename(file))`. folders are created if they do not exist.
`...`	Arguments passed to `type` specific methods, say `width` and `height` for `type="pdf"` etc.

Value

Beside the side effect of saving plots to disk, filenames are printed on screen during the process and at the end invisibly returned.

Examples

## Partition additive genetic values
res <- AlphaPart(x=AlphaPart.ped, colPath="country", colBV=c("bv1", "bv2"))

## Summarize population by generation (=trend)
ret <- summary(res, by="gen")

## Plot the partitions
p <- plot(ret, ylab=c("BV for trait 1", "BV for trait 2"), xlab="Generation")

## Save the plots
tmp <- savePlot(x = p, filename="test", type="png")

## Remove the files
unlink(tmp)

## Partition additive genetic values
res <- AlphaPart(x=AlphaPart.ped, colPath="country", colBV=c("bv1", "bv2"))

## Summarize population by generation (=trend)
ret <- summary(res, by="gen")

## Plot the partitions
p <- plot(ret, ylab=c("BV for trait 1", "BV for trait 2"), xlab="Generation")

## Save the plots
tmp <- savePlot(x = p, filename="test", type="png")

## Remove the files
unlink(tmp)

A function to summarize AlphaPart object.

Description

Breedng values of individuals are often summarized, either by year of birth or some other classification. Function summary.AlphaPart provides a way to ease the computation of such summaries on partitions of breeding values.

Usage

## S3 method for class 'AlphaPart'
summary(object, by, FUN, labelSum, subset,
  sums, cov,  ...)
## S3 method for class 'AlphaPart'
summary(object, by, FUN, labelSum, subset,
  sums, cov,  ...)

Arguments

`object`	AlphaPart, output object from `AlphaPart` function.
`by`	Character, the name of a column by which summary function FUN should be applied; if `NULL` (default) summary is given for the whole table.
`FUN`	Function, which function should be used in summary; function should return single value per each level of by.
`labelSum`	Character, label used for the overall breeding value.
`subset`	Logical, perform summary only on a subset of `object` subsetted by this argument.
`sums`	Logical, link between `AlphaPart` and `summary.AlphaPart()` (only for internal use!).
`cov`	Logical, if FALSE returns `n` variances plus one additional column containing two times the sum of all covariances; otherwise returns `n` variance and `n(n-1)/2` covariances in the form of `2*Cov(., .)`, where `n` is the number of partitions. This argument only works when `FUN = var`. Defaut `cov = FALSE`.
`...`	Arguments passed to other functions (not used at the moment).

Value

An object of class summaryAlphaPart, which is a list of data frames with summary statistics on breeding value partitions. For each trait there a dataframe holds summary for the "whole/original" breeding value and its partitions. In addition another list is added (named info) with the following components holdinfg meta info:

path column name holding path information
nP number of paths
lP path labels
nT number of traits
lT trait labels
by column name of variable by which summary was performed
warn potential warning messages associated with this object
labelSum column name of summary for "whole/original" breeding values

There is a handy plot method (plot.summaryAlphaPart) for output.

Examples

## --- Partition additive genetic values by loc ---
res <- AlphaPart(x=AlphaPart.ped, colPath="country", colBV=c("bv1", "bv2"))

## Summarize whole population
ret <- summary(res)

## Summarize population by generation (=trend)
ret <- summary(res, by="gen")

## Summarize population by generation (=trend) but only for domestic location
ret <- summary(res, by="gen", subset=res[[1]]$country == "domestic")

## --- Partition additive genetic values by loc and gender ---

AlphaPart.ped$country.gender <- with(AlphaPart.ped, paste(country, gender, sep="-"))
res <- AlphaPart(x=AlphaPart.ped, colPath="country.gender", colBV=c("bv1", "bv2"))

## Summarize population by generation (=trend)
ret <- summary(res, by="gen")

## Summarize population by generation (=trend) but only for domestic location
ret <- summary(res, by="gen", subset=res[[1]]$country == "domestic")
## --- Partition additive genetic values by loc ---
res <- AlphaPart(x=AlphaPart.ped, colPath="country", colBV=c("bv1", "bv2"))

## Summarize whole population
ret <- summary(res)

## Summarize population by generation (=trend)
ret <- summary(res, by="gen")

## Summarize population by generation (=trend) but only for domestic location
ret <- summary(res, by="gen", subset=res[[1]]$country == "domestic")

## --- Partition additive genetic values by loc and gender ---

AlphaPart.ped$country.gender <- with(AlphaPart.ped, paste(country, gender, sep="-"))
res <- AlphaPart(x=AlphaPart.ped, colPath="country.gender", colBV=c("bv1", "bv2"))

## Summarize population by generation (=trend)
ret <- summary(res, by="gen")

## Summarize population by generation (=trend) but only for domestic location
ret <- summary(res, by="gen", subset=res[[1]]$country == "domestic")

write.csv.R

Description

Save summaries of partitioned breeding values to CSV files on disk for further analyses of processing with other software or just for saving (backing up) results.

Usage

write.csv(...)

## Default S3 method:
write.csv(...)

## S3 method for class 'AlphaPart'
write.csv(x, file, traitsAsDir = FALSE, csv2 = TRUE, row.names = FALSE, ...)

## S3 method for class 'summaryAlphaPart'
write.csv(x, file, traitsAsDir = FALSE, csv2 = TRUE, row.names = FALSE, ...)
write.csv(...)

## Default S3 method:
write.csv(...)

## S3 method for class 'AlphaPart'
write.csv(x, file, traitsAsDir = FALSE, csv2 = TRUE, row.names = FALSE, ...)

## S3 method for class 'summaryAlphaPart'
write.csv(x, file, traitsAsDir = FALSE, csv2 = TRUE, row.names = FALSE, ...)

Arguments

`...`	Other options passed to `write.csv2` or `write.csv`.
`x`	AlphaPart, object returned from `AlphaPart` function or summaryAlphaPart, object returned from `summary.AlphaPart` function.
`file`	Character, file name with or without .csv extension, e.g., both "file" and "file.csv" are valid.
`traitsAsDir`	Logical, should results be saved within trait folders; the construction is `file.path(dirname(file), trait, basename(file))`; folders are created if they do not exist.
`csv2`	Logical, export using `write.csv2` or `write.csv`.
`row.names`	Logical, export row names as well?

Details

Function write.csv from the utils package works when exported object is a data.frame or a matrix. This is an attempt to make this function generic so that one can define write.csv methods for other objects.

Value

It contains:

write.csv - see write.csv for details.
write.csv.AlphaPart - for each trait (list component in x) a file is saved on disk with name "AlphaPart_trait.csv", where the file will hold original data and breeding value partitions. With traitsAsDir=TRUE files are saved as "trait/file_trait.csv". File names are printed on screen during the process of export and at the end invisibly returned.
write.csv.summaryAlphaPart - for each trait (list component in x) a file partitions named "file_trait.csv" is saved on disk. With traitsAsDir=TRUE files are saved as "trait/file_trait_*.csv". File names are printed on screen during the process of export and at the end invisibly returned.

Methods (by class)

default: Default write.csv method.
AlphaPart: Save partitioned breeding values to CSV files on disk on disk for further analyses or processing with other software or just for saving (backing up) results.
summaryAlphaPart: Save summaries of partitioned breeding values to CSV files on disk for further analyses of processing with other software or just for saving (backing up) results.

Examples

## Partition additive genetic values
res <- AlphaPart(x=AlphaPart.ped, colPath="country", colBV=c("bv1", "bv2"))

## Write summary on the disk and collect saved file names
fileName <- file.path(tempdir(), "AlphaPart")
ret <- write.csv(x=res, file=fileName)
print(ret)
file.show(ret[1])

## Clean up
files <- dir(path=tempdir(), pattern="AlphaPart*")
unlink(x=files)

## Partition additive genetic values
res <- AlphaPart(x=AlphaPart.ped, colPath="country", colBV=c("bv1", "bv2"))

## Write summary on the disk and collect saved file names
fileName <- file.path(tempdir(), "AlphaPart")
ret <- write.csv(x=res, file=fileName)
print(ret)
file.show(ret[1])

## Clean up
files <- dir(path=tempdir(), pattern="AlphaPart*")
unlink(x=files)

Package 'AlphaPart'

Help Index

AlphaPart.R

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Sample pedigree for partition.

Description

Usage

Format

Source

AlphaPartSubset.R

Description

Usage

Arguments

Details

Value

See Also

Examples

AlphaPartSum.R

Description

Usage

Arguments

Details

Value

See Also

Examples

pedFixBirthYear.R

Description

Usage

Arguments

Details

Value

See Also

Examples

pedSetBase.R

Description

Usage

Arguments

Details

Value

See Also

Examples

A function to plot summary of partitioned breeding values.

Description

Usage

Arguments

Details

Value

Examples

Print method for the output of AlphaPart function.

Description

Usage

Arguments

See Also

Examples

Print a plot generate by the function plotSummaryAlphaPart

Description

Usage

Arguments

See Also

Examples

Print method for objects of the class summaryAlphaPart.

Description

Usage

Arguments

See Also

Examples

Save plot method for AlphaPart

Description

Usage

Arguments

Value

Save plot objects on the disk for permanent storage. Function savePlot from the grDevices package works for current page on graphical device. This is an attempt to make this function generic so that one can define savePlot methods for particular needs.

Description

Print a plot generate by the function `plotSummaryAlphaPart`

Save plot method for `AlphaPart`

Save plot objects on the disk for permanent storage. Function `savePlot` from the grDevices package works for current page on graphical device. This is an attempt to make this function generic so that one can define `savePlot` methods for particular needs.