| Title: | Easy Regression |
|---|---|
| Description: | Performs analysis of regression in simple designs with quantitative treatments, including mixed models and non linear models. |
| Authors: | Emmanuel Arnhold |
| Maintainer: | Emmanuel Arnhold <[email protected]> |
| License: | GPL-2 |
| Version: | 4.0 |
| Built: | 2024-11-23 06:22:19 UTC |
| Source: | CRAN |
Performs analysis of regression in simple designs with quantitative treatments, including mixed models ans non linear models
| Package: | easyreg |
| Type: | Package |
| Version: | 4.0 |
| Date: | 2019-10-13 |
| License: | GPL (>= 2) |
Emmanuel Arnhold <[email protected]>
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
SAMPAIO, I. B. M. Estatistica aplicada a experimentacao animal. 3nd Edition. Belo Horizonte: Editora FEPMVZ, Fundacao de Ensino e Pesquisa em Medicina Veterinaria e Zootecnia, 2010. 264p.
# analysis in completely randomized design data(data1) r1=er2(data1) names(r1) r1 r1[1] # analysis in randomized block design data(data2) r2=er2(data2, design=2) r2 # analysis in latin square design data(data3) r3=er2(data3, design=3) r3 # analysis in several latin squares data(data4) r4=er2(data4, design=4) r4 # the growth of Zagorje turkeys (Kaps and Lamberson, 2009) weight=c(44,66,100,150,265,370,455,605,770) age=c(1,7,14,21,28,35,42,49,56) data2=data.frame(age,weight) # two linear regplot(data2, model=5, start=c(25,6,10,20)) regplot(data2, model=5, start=c(25,6,10,20), digits=2) # in other function bl(data2)# analysis in completely randomized design data(data1) r1=er2(data1) names(r1) r1 r1[1] # analysis in randomized block design data(data2) r2=er2(data2, design=2) r2 # analysis in latin square design data(data3) r3=er2(data3, design=3) r3 # analysis in several latin squares data(data4) r4=er2(data4, design=4) r4 # the growth of Zagorje turkeys (Kaps and Lamberson, 2009) weight=c(44,66,100,150,265,370,455,605,770) age=c(1,7,14,21,28,35,42,49,56) data2=data.frame(age,weight) # two linear regplot(data2, model=5, start=c(25,6,10,20)) regplot(data2, model=5, start=c(25,6,10,20), digits=2) # in other function bl(data2)
The function performs analysis of broken line regression
bl(data, model=1, alpha=0.05, xlab = "Explanatory Variable", ylab = "Response Variable", position = 1, digits = 6, mean = TRUE, sd=FALSE, legend = TRUE, lty=2, col="dark blue", pch=20, xlim="default.x",ylim="default.y", ...)bl(data, model=1, alpha=0.05, xlab = "Explanatory Variable", ylab = "Response Variable", position = 1, digits = 6, mean = TRUE, sd=FALSE, legend = TRUE, lty=2, col="dark blue", pch=20, xlim="default.x",ylim="default.y", ...)
data |
data is a data.frame The first column contain the treatments (explanatory variable) and the second column the response variable |
model |
model for analysis: 1=two linear; 2=linear plateau (LRP); 3= model 1 with blocks random; 4 = model 2 with blocks random |
alpha |
significant level for cofidence intervals (parameters estimated) |
xlab |
name of explanatory variable |
ylab |
name of response variable |
position |
position of equation in the graph top=1 bottomright=2 bottom=3 bottomleft=4 left=5 topleft=6 (default) topright=7 right=8 center=9 |
digits |
number of digits (default=6) |
mean |
mean=TRUE (plot mean of data) mean=FALSE (plot all data) |
sd |
sd=FALSE (plot without standard deviation) sd=TRUE (plot with standard deviation) |
legend |
legend=TRUE (plot legend) legend=FALSE (not plot legend) |
lty |
line type |
col |
line color |
pch |
point type |
xlim |
limits for x |
ylim |
limits for y |
... |
others graphical parameters (see par) |
Returns coefficients of the models, t test for coefficients, knot (break point), R squared, adjusted R squared, AIC, BIC, residuals and shapiro-wilk test for residuals.
Emmanuel Arnhold <[email protected]>
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
lm, ea1(easyanova package), er1
# the growth of Zagorje turkeys (Kaps and Lamberson, 2009) weight=c(44,66,100,150,265,370,455,605) age=c(1,7,14,21,28,35,42,49) data2=data.frame(age,weight) # two linear regplot(data2, model=5, start=c(25,6,10,20)) bl(data2, digits=2) #linear and quadratic plateau x=c(0,1,2,3,4,5,6) y=c(1,2,3,6.1,5.9,6,6.1) data=data.frame(x,y) bl(data,model=2, lty=1, col=1, digits=2, position=8) # effect os blocks x=c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8) y=c(4,12,9,20,16,25,21,31,28,42,33,46,33,46,34,44) blocks=rep(c(1,2),8) dat=data.frame(x,blocks,y) bl(dat, 3) bl(dat,4, sd=TRUE) bl(dat,4, mean=FALSE)# the growth of Zagorje turkeys (Kaps and Lamberson, 2009) weight=c(44,66,100,150,265,370,455,605) age=c(1,7,14,21,28,35,42,49) data2=data.frame(age,weight) # two linear regplot(data2, model=5, start=c(25,6,10,20)) bl(data2, digits=2) #linear and quadratic plateau x=c(0,1,2,3,4,5,6) y=c(1,2,3,6.1,5.9,6,6.1) data=data.frame(x,y) bl(data,model=2, lty=1, col=1, digits=2, position=8) # effect os blocks x=c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8) y=c(4,12,9,20,16,25,21,31,28,42,33,46,33,46,34,44) blocks=rep(c(1,2),8) dat=data.frame(x,blocks,y) bl(dat, 3) bl(dat,4, sd=TRUE) bl(dat,4, mean=FALSE)
Quantitative treatments in completely randomized design.
data(data1)data(data1)
A data frame with 24 observations on the following 2 variables.
treatmenta numeric vector
gaina numeric vector
SAMPAIO, I. B. M. Estatistica aplicada a experimentacao animal. 3nd Edition. Belo Horizonte: Editora FEPMVZ, Fundacao de Ensino e Pesquisa em Medicina Veterinaria e Zootecnia, 2010. 264p.
data(data1) summary(data1)data(data1) summary(data1)
Quantitative treatments in randomizad block design.
data(data2)data(data2)
A data frame with 25 observations on the following 3 variables.
protein_levela numeric vector
littera factor with levels l1 l2 l3 l4 l5
feed_conversiona numeric vector
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
data(data2) summary(data2)data(data2) summary(data2)
Quantitative treatments in latin square design.
data(data3)data(data3)
A data frame with 25 observations on the following 4 variables.
treatmenta numeric vector
animala factor with levels a1 a2 a3 a4 a5
perioda factor with levels p1 p2 p3 p4 p5
milk_fata numeric vector
data(data3) summary(data3)data(data3) summary(data3)
Quantitative treatments in several latin squares design.
data(data4)data(data4)
A data frame with 50 observations on the following 5 variables.
treatmenta numeric vector
squarea numeric vector
animala factor with levels a1 a2 a3 a4 a5
perioda factor with levels p1 p2 p3 p4 p5
milk_fata numeric vector
data(data4) summary(data4)data(data4) summary(data4)
Quantitative treatments and three response variable.
data(data5)data(data5)
A data frame with 24 observations on the following 4 variables.
treatmentsa numeric vector
variable1a numeric vector
variable2a numeric vector
variable3a numeric vector
data(data5) summary(data5)data(data5) summary(data5)
The function performs analysis of some linear and nonlinear models
er1(data, model = 1, start = c(a = 1, b = 1, c = 1, d = 1, e = 1), mixed=FALSE, digits=6, alpha=0.05)er1(data, model = 1, start = c(a = 1, b = 1, c = 1, d = 1, e = 1), mixed=FALSE, digits=6, alpha=0.05)
data |
data is a data.frame The first column should contain the treatments (explanatory variable) and the remaining columns the response variables. |
model |
define the model 1 = "y~a+b*x" linear 2 = "y~a+b*x+c*x^2" quadratic 3 = "y ~ a + b * (x - c) * (x <= c)" linear plateau 4 = "y ~ (a + b * x + c * I(x^2)) * (x <= -0.5 * b/c) + (a + I(-b^2/(4 * c))) * (x > -0.5 * b/c)" quadratic plateau 5 = "ifelse(x>=d,(a-c*d)+(b+c)*x, a+b*x)" two linear 6 = "y~a*exp(b*x)" exponential 7 = "y~a*(1+b*(exp(-c*x)))^-1" logistic 8 = "y~a*(1-b*(exp(-c*x)))^3" van bertalanffy 9 = "y~a*(1-b*(exp(-c*x)))" brody 10 = "y~a*exp(-b*exp(-c*x)" gompertz 11 = "y~(a*x^b)*exp(-c*x)" lactation curve 12 = "y ~ a + b * (1 - exp(-c * x))" ruminal degradation curve 13 = "y~(a/(1+exp(2-4*c*(x-e))))+(b/(1+exp(2-4*d*(x-e))))" logistic bi-compartmental 14 = "y~a*(x^b)" exponential (allometric model) 15 = "y~a+b*x+c*x^2+d*x^3" cubic 16 = "y~a/(1+b*(exp(-c*x)))^d" richards 17 = "y~(a^d+ ((b^d)-(a^d) )*((1-exp(-c*(x-t1)))/ (1-exp(-c*(t2-t1)))))^(1/d)" schnute |
start |
start values of the iteration process |
mixed |
FALSE/defalt for fixed model or TRUE for mixed model |
digits |
number of digits in results (default=6) |
alpha |
significant level of the confident intervals for parameters in the models |
Returns coefficients of the models, t test for coefficients, R squared, adjusted R squared, AIC, BIC, and residuals of the model
Emmanuel Arnhold <[email protected]>
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
TERRANCE J. QUINN II and RICHARD B. DERISO. Quantitative Fish Dynamics, New York, Oxford, Oxford University Press, 1999.
nls, nls2
# weights of an Angus cow at ages from 8 to 108 months (Kaps and Lamberson, 2009) weight=c(280,340,430,480,550,580,590,600,590,600) age=c(8,12,24,36,48,60,72,84,96,108) data1=data.frame(age, weight) # linear er1(data1, model=1) # quadratic er1(data1, model=2) # linear plateau er1(data1, model=3) # quadratic plateau er1(data1, model=4) # two linear er1(data1, model=5, start=c(250,6,2,50)) # exponential er1(data1, model=6, start=c(250,0.05)) # logistic er1(data1, model=7, start=c(600,4,0.05)) # van bertalanffy er1(data1, model=8, start=c(600,2,0.05)) # brody er1(data1, model=9, start=c(600,4,0.05)) # gompertz er1(data1, model=10, start=c(600,4,0.05)) # richards er1(data1, model=16, start=c(600,2,0.05,1.4)) # allometric er1(data1, model=14) # cubic er1(data1, model=15) # growth of Zagorje turkeys (Kaps and Lamberson, 2009) weight=c(44,66,100,150,265,370,455,605,770) age=c(1,7,14,21,28,35,42,49,56) data2=data.frame(age,weight) # two linear er1(data2, model=5, start=c(25,6,10,20)) # gain weight measurements of turkey poults (Kaps and Lamberson, 2009) methionine=c(80,85,90,95,100,105,110,115,120) gain=c(102,115,125,133,140,141,142,140,142) data3=data.frame(methionine, gain) # linear er1(data3, model=1) # quadratic er1(data3, model=2) # linear plateau er1(data3, model=3) # quadratic plateau er1(data3, model=4) # lactation curve milk=c(25,24,26,28,30,31,27,26,25,24,23,24,22,21,22, 20,21,19,18,17,18,18,16,17,15,16,14) days=c(15,15,15,75,75,75,135,135,135,195, 195,195,255,255,255,315,315,315,375,375,375,435,435,435,495,495,495) data4=data.frame(days,milk) er1(data4, model=11, start=c(16,0.25,0.004)) # ruminal degradation time=c(2,6,9,24,48,72,96) deg=c(20,33,46,55,66,72,76) data5=data.frame(time,deg) er1(data5, model=12) # logistic bi-compartmental (gas production) time=c(0,12,24,36,48,60,72,84,96,108,120,144,168,192) gas=c(0.002,3.8,8,14.5,16,16.5,17,17.4,17.9,18.1,18.8,19,19.2,19.3) data6=data.frame(time,gas) er1(data6, model=13, start=c(19,4,0.025,0.004,5)) # Schnute model #pacific halibut weight-age data of females (Terrance and Richard, 1999) age=c(4,5,6,7,8,9,10,11,12,13,14,15,16,17,18, 19,20,21,22,23,24,28) weight=c(1.7,2,3.9, 4.2,6.4,7.6,10.9,14.9,18.2,21.6, 25.4,28.8,30.9, 35.6,37.9,34.7,44.8,52.6,49.1,56.7,58.6,54.1) halibut=data.frame(age,weight) t1=min(halibut[,2]) t2=max(halibut[,2]) er1(halibut,model=17, start=c(a=t1,b=t2,c=0.15,d=-0.50))# weights of an Angus cow at ages from 8 to 108 months (Kaps and Lamberson, 2009) weight=c(280,340,430,480,550,580,590,600,590,600) age=c(8,12,24,36,48,60,72,84,96,108) data1=data.frame(age, weight) # linear er1(data1, model=1) # quadratic er1(data1, model=2) # linear plateau er1(data1, model=3) # quadratic plateau er1(data1, model=4) # two linear er1(data1, model=5, start=c(250,6,2,50)) # exponential er1(data1, model=6, start=c(250,0.05)) # logistic er1(data1, model=7, start=c(600,4,0.05)) # van bertalanffy er1(data1, model=8, start=c(600,2,0.05)) # brody er1(data1, model=9, start=c(600,4,0.05)) # gompertz er1(data1, model=10, start=c(600,4,0.05)) # richards er1(data1, model=16, start=c(600,2,0.05,1.4)) # allometric er1(data1, model=14) # cubic er1(data1, model=15) # growth of Zagorje turkeys (Kaps and Lamberson, 2009) weight=c(44,66,100,150,265,370,455,605,770) age=c(1,7,14,21,28,35,42,49,56) data2=data.frame(age,weight) # two linear er1(data2, model=5, start=c(25,6,10,20)) # gain weight measurements of turkey poults (Kaps and Lamberson, 2009) methionine=c(80,85,90,95,100,105,110,115,120) gain=c(102,115,125,133,140,141,142,140,142) data3=data.frame(methionine, gain) # linear er1(data3, model=1) # quadratic er1(data3, model=2) # linear plateau er1(data3, model=3) # quadratic plateau er1(data3, model=4) # lactation curve milk=c(25,24,26,28,30,31,27,26,25,24,23,24,22,21,22, 20,21,19,18,17,18,18,16,17,15,16,14) days=c(15,15,15,75,75,75,135,135,135,195, 195,195,255,255,255,315,315,315,375,375,375,435,435,435,495,495,495) data4=data.frame(days,milk) er1(data4, model=11, start=c(16,0.25,0.004)) # ruminal degradation time=c(2,6,9,24,48,72,96) deg=c(20,33,46,55,66,72,76) data5=data.frame(time,deg) er1(data5, model=12) # logistic bi-compartmental (gas production) time=c(0,12,24,36,48,60,72,84,96,108,120,144,168,192) gas=c(0.002,3.8,8,14.5,16,16.5,17,17.4,17.9,18.1,18.8,19,19.2,19.3) data6=data.frame(time,gas) er1(data6, model=13, start=c(19,4,0.025,0.004,5)) # Schnute model #pacific halibut weight-age data of females (Terrance and Richard, 1999) age=c(4,5,6,7,8,9,10,11,12,13,14,15,16,17,18, 19,20,21,22,23,24,28) weight=c(1.7,2,3.9, 4.2,6.4,7.6,10.9,14.9,18.2,21.6, 25.4,28.8,30.9, 35.6,37.9,34.7,44.8,52.6,49.1,56.7,58.6,54.1) halibut=data.frame(age,weight) t1=min(halibut[,2]) t2=max(halibut[,2]) er1(halibut,model=17, start=c(a=t1,b=t2,c=0.15,d=-0.50))
The function performs analysis of polynomial regression in simple designs with quantitative treatments.
er2(data, design = 1, list = FALSE, type = 2)er2(data, design = 1, list = FALSE, type = 2)
data |
data is a data.frame data frame with two columns, treatments and response (completely randomized design) data frame with three columns, treatments, blocks and response (randomized block design) data frame with four columns, treatments, rows, cols and response (latin square design) data frame with five columns, treatments, square, rows, cols and response (several latin squares) |
design |
1 = completely randomized design 2 = randomized block design 3 = latin square design 4 = several latin squares |
list |
FALSE = a single response variable TRUE = multivariable response |
type |
type is form of obtain sum of squares 1 = a sequential sum of squares 2 = a partial sum of squares |
The response and the treatments must be numeric. Other variables can be numeric or factors.
Returns analysis of variance, models, t test for coefficients and R squared and adjusted R squared.
Emmanuel Arnhold <[email protected]>
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
SAMPAIO, I. B. M. Estatistica aplicada a experimentacao animal. 3nd Edition. Belo Horizonte: Editora FEPMVZ, Fundacao de Ensino e Pesquisa em Medicina Veterinaria e Zootecnia, 2010. 264p.
lm, lme(package nlme), ea1(package easyanova), er1
# analysis in completely randomized design data(data1) r1=er2(data1) names(r1) r1 r1[1] # analysis in randomized block design data(data2) r2=er2(data2, design=2) r2 # analysis in latin square design data(data3) r3=er2(data3, design=3) r3 # analysis in several latin squares data(data4) r4=er2(data4, design=4) r4 # data treatments=rep(c(0.5,1,1.5,2,2.5,3), c(3,3,3,3,3,3)) r1=rnorm(18,60,3) r2=r1*1:18 r3=r1*18:1 r4=r1*c(c(1:10),10,10,10,10,10,10,10,10) data6=data.frame(treatments,r1,r2,r3, r4) # use the argument list = TRUE er2(data6, design=1, list=TRUE)# analysis in completely randomized design data(data1) r1=er2(data1) names(r1) r1 r1[1] # analysis in randomized block design data(data2) r2=er2(data2, design=2) r2 # analysis in latin square design data(data3) r3=er2(data3, design=3) r3 # analysis in several latin squares data(data4) r4=er2(data4, design=4) r4 # data treatments=rep(c(0.5,1,1.5,2,2.5,3), c(3,3,3,3,3,3)) r1=rnorm(18,60,3) r2=r1*1:18 r3=r1*18:1 r4=r1*c(c(1:10),10,10,10,10,10,10,10,10) data6=data.frame(treatments,r1,r2,r3, r4) # use the argument list = TRUE er2(data6, design=1, list=TRUE)
The function plot data and equation
regplot(data, model=1, start=c(a=1,b=1,c=1,d=1,e=1), xlab="Explanatory Variable", ylab="Response Variable", position=1, digits=6, mean=TRUE, sd=FALSE, legend = TRUE, lty=2, col="dark blue", pch=20, xlim="defalt.x",ylim="defalt.y",...)regplot(data, model=1, start=c(a=1,b=1,c=1,d=1,e=1), xlab="Explanatory Variable", ylab="Response Variable", position=1, digits=6, mean=TRUE, sd=FALSE, legend = TRUE, lty=2, col="dark blue", pch=20, xlim="defalt.x",ylim="defalt.y",...)
data |
data is a data.frame The first column contain the treatments (explanatory variable) and the remaining column the response variable |
model |
define the model 1 = "y~a+b*x" linear 2 = "y~a+b*x+c*x^2" quadratic 3 = "y ~ a + b * (x - c) * (x <= c)" linear plateau 4 = "y ~ (a + b * x + c * I(x^2)) * (x <= -0.5 * b/c) + (a + I(-b^2/(4 * c))) * (x > -0.5 * b/c)" quadratic plateau 5 = "ifelse(x>=d,(a-c*d)+(b+c)*x, a+b*x)" two linear 6 = "y~a*exp(b*x)" exponential 7 = "y~a*(1+b*(exp(-c*x)))^-1" logistic 8 = "y~a*(1-b*(exp(-c*x)))^3" van bertalanffy 9 = "y~a*(1-b*(exp(-c*x)))" brody 10 = "y~a*exp(-b*exp(-c*x)" gompertz 11 = "y~(a*x^b)*exp(-c*x)" lactation curve 12 = "y ~ a + b * (1 - exp(-c * x))" ruminal degradation curve 13 = "y~(a/(1+exp(2-4*c*(x-e))))+(b/(1+exp(2-4*d*(x-e))))" logistic bi-compartmental 14 = "y~a*(x^b)" exponential (allometric model) 15 = "y~a+b*x+c*x^2+d*x^3" cubic 16 = "y~a/(1+b*(exp(-c*x)))^d" richards 17 = "y~(a^d+ ((b^d)-(a^d) )*((1-exp(-c*(x-t1)))/ (1-exp(-c*(t2-t1)))))^(1/d)" schnute |
start |
start (iterations) values of model |
xlab |
names of variable x |
ylab |
names of variable y |
position |
position of equation in the graph top=1 bottomright=2 bottom=3 bottomleft=4 left=5 topleft=6 (default) topright=7 right=8 center=9 |
digits |
number of digits (defalt=6) |
mean |
mean=TRUE (plot mean of data) mean=FALSE (plot all data) |
sd |
sd=FALSE (plot without standard deviation) sd=TRUE (plot with standard deviation) |
legend |
legend=TRUE (plot legend) legend=FALSE (not plot legend) |
lty |
line type |
col |
line color |
pch |
point type |
xlim |
limits for x |
ylim |
limits for y |
... |
others graphical parameters (see par) |
Emmanuel Arnhold <[email protected]>
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
TERRANCE J. QUINN II and RICHARD B. DERISO. Quantitative Fish Dynamics, New York, Oxford, Oxford University Press, 1999.
nls,er1,er2,bl
# weights of Angus cow at ages from 8 to 108 months (Kaps and Lamberson, 2009) weight=c(280,340,430,480,550,580,590,600,590,600) age=c(8,12,24,36,48,60,72,84,96,108) data1=data.frame(age, weight) # linear regplot(data1, model=1, digits=3, position=3, ylab="weight", xlab="age") # quadratic regplot(data1, model=2, digits=3, position=3, col=1, ylim=c(200,700)) # linear plateau regplot(data1, model=3,ylab="weight", xlab="age", lty=5, col="dark green", position=3, ylim=c(200,700), xlim=c(0,150), lwd=2) # quadratic plateau regplot(data1, model=4,ylab="weight", xlab="age") # two linear regplot(data1, model=5, start=c(250,6,2,50),digits=3, position=3 ) # exponential regplot(data1, model=6, start=c(250,0.05)) # logistic regplot(data1, model=7, start=c(600,4,0.05)) # van bertalanffy regplot(data1, model=8, start=c(600,2,0.05)) # brody regplot(data1, model=9, start=c(600,4,0.05)) # gompertz regplot(data1, model=10, start=c(600,4,0.05)) # richards regplot(data1, model=16, start=c(600,2,0.05,1.4)) # allometric regplot(data1, model=14) # cubic regplot(data1, model=15) # growth of Zagorje turkeys (Kaps and Lamberson, 2009) weight=c(44,66,100,150,265,370,455,605,770) age=c(1,7,14,21,28,35,42,49,56) data2=data.frame(age,weight) # two linear regplot(data2, model=5, start=c(25,6,10,20)) # weight gain measurements of turkey poults (Kaps and Lamberson, 2009) methionine=c(80,85,90,95,100,105,110,115,120) gain=c(102,115,125,133,140,141,142,140,142) data3=data.frame(methionine, gain) # linear regplot(data3, model=1) # quadratic regplot(data3, model=2) # linear plateau regplot(data3, model=3) # quadratic plateau regplot(data3, model=4) # lactation curve milk=c(25,24,26,28,30,31,27,26,25,24,23,24,22,21,22,20,21,19, 18,17,18,18,16,17,15,16,14) days=c(15,15,15,75,75,75,135,135,135,195,195,195,255,255,255,315, 315,315,375,375,375,435,435,435,495,495,495) data4=data.frame(days,milk) regplot(data4, model=11, start=c(16,0.25,0.004)) # ruminal degradation time=c(2,6,9,24,48,72,96) deg=c(20,33,46,55,66,72,76) data5=data.frame(time,deg) regplot(data5, model=12) # logistic bi-compartmental (gas production) time=c(0,12,24,36,48,60,72,84,96,108,120,144,168,192) gas=c(0.002,3.8,8,14.5,16,16.5,17,17.4,17.9,18.1,18.8,19,19.2,19.3) data6=data.frame(time,gas) regplot(data6, model=13, start=c(19,4,0.025,0.004,5)) # multiple curves time=c(0,12,24,48,64,72,96) t1=c(36,48,59,72,85,86,87) t2=c(14,25,36,49,59,65,72) t3=c(55,78,86,87,86,87,88) data=data.frame(time,t1,t2,t3) regplot(data, model=12) regplot(data, model=4) # include standard deviation in graph data(data1) regplot(data1, sd=TRUE) # Schnute model #pacific halibut weight-age data of females (Terrance and Richard, 1999) age=c(4,5,6,7,8,9,10,11,12,13,14,15,16,17, 18,19,20,21,22,23,24,28) weight=c(1.7,2,3.9, 4.2,6.4,7.6,10.9,14.9,18.2,21.6,25.4,28.8, 30.9, 35.6,37.9,34.7,44.8,52.6,49.1,56.7,58.6,54.1) halibut=data.frame(age,weight) t1=min(halibut[,2]) t2=max(halibut[,2]) regplot(halibut,model=17,start=c(t1,t2,0.22,-0.63), ylim=c(0,100))# weights of Angus cow at ages from 8 to 108 months (Kaps and Lamberson, 2009) weight=c(280,340,430,480,550,580,590,600,590,600) age=c(8,12,24,36,48,60,72,84,96,108) data1=data.frame(age, weight) # linear regplot(data1, model=1, digits=3, position=3, ylab="weight", xlab="age") # quadratic regplot(data1, model=2, digits=3, position=3, col=1, ylim=c(200,700)) # linear plateau regplot(data1, model=3,ylab="weight", xlab="age", lty=5, col="dark green", position=3, ylim=c(200,700), xlim=c(0,150), lwd=2) # quadratic plateau regplot(data1, model=4,ylab="weight", xlab="age") # two linear regplot(data1, model=5, start=c(250,6,2,50),digits=3, position=3 ) # exponential regplot(data1, model=6, start=c(250,0.05)) # logistic regplot(data1, model=7, start=c(600,4,0.05)) # van bertalanffy regplot(data1, model=8, start=c(600,2,0.05)) # brody regplot(data1, model=9, start=c(600,4,0.05)) # gompertz regplot(data1, model=10, start=c(600,4,0.05)) # richards regplot(data1, model=16, start=c(600,2,0.05,1.4)) # allometric regplot(data1, model=14) # cubic regplot(data1, model=15) # growth of Zagorje turkeys (Kaps and Lamberson, 2009) weight=c(44,66,100,150,265,370,455,605,770) age=c(1,7,14,21,28,35,42,49,56) data2=data.frame(age,weight) # two linear regplot(data2, model=5, start=c(25,6,10,20)) # weight gain measurements of turkey poults (Kaps and Lamberson, 2009) methionine=c(80,85,90,95,100,105,110,115,120) gain=c(102,115,125,133,140,141,142,140,142) data3=data.frame(methionine, gain) # linear regplot(data3, model=1) # quadratic regplot(data3, model=2) # linear plateau regplot(data3, model=3) # quadratic plateau regplot(data3, model=4) # lactation curve milk=c(25,24,26,28,30,31,27,26,25,24,23,24,22,21,22,20,21,19, 18,17,18,18,16,17,15,16,14) days=c(15,15,15,75,75,75,135,135,135,195,195,195,255,255,255,315, 315,315,375,375,375,435,435,435,495,495,495) data4=data.frame(days,milk) regplot(data4, model=11, start=c(16,0.25,0.004)) # ruminal degradation time=c(2,6,9,24,48,72,96) deg=c(20,33,46,55,66,72,76) data5=data.frame(time,deg) regplot(data5, model=12) # logistic bi-compartmental (gas production) time=c(0,12,24,36,48,60,72,84,96,108,120,144,168,192) gas=c(0.002,3.8,8,14.5,16,16.5,17,17.4,17.9,18.1,18.8,19,19.2,19.3) data6=data.frame(time,gas) regplot(data6, model=13, start=c(19,4,0.025,0.004,5)) # multiple curves time=c(0,12,24,48,64,72,96) t1=c(36,48,59,72,85,86,87) t2=c(14,25,36,49,59,65,72) t3=c(55,78,86,87,86,87,88) data=data.frame(time,t1,t2,t3) regplot(data, model=12) regplot(data, model=4) # include standard deviation in graph data(data1) regplot(data1, sd=TRUE) # Schnute model #pacific halibut weight-age data of females (Terrance and Richard, 1999) age=c(4,5,6,7,8,9,10,11,12,13,14,15,16,17, 18,19,20,21,22,23,24,28) weight=c(1.7,2,3.9, 4.2,6.4,7.6,10.9,14.9,18.2,21.6,25.4,28.8, 30.9, 35.6,37.9,34.7,44.8,52.6,49.1,56.7,58.6,54.1) halibut=data.frame(age,weight) t1=min(halibut[,2]) t2=max(halibut[,2]) regplot(halibut,model=17,start=c(t1,t2,0.22,-0.63), ylim=c(0,100))
This function performs test of models and parameters
regtest(data, model = 1, start = c(a = 1, b = 1, c = 1, d = 1,e = 1))regtest(data, model = 1, start = c(a = 1, b = 1, c = 1, d = 1,e = 1))
data |
data is a data.frame The first column contain explanatory variable, second column contain treatments and the third column contain the response variable |
model |
define the model 1 = "y~a+b*x" linear 2 = "y~a+b*x+c*x^2" quadratic 3 = "y ~ a + b * (x - c) * (x <= c)" linear plateau 4 = "y ~ (a + b * x + c * I(x^2)) * (x <= -0.5 * b/c) + (a + I(-b^2/(4 * c))) * (x > -0.5 * b/c)" quadratic plateau 5 = "ifelse(x>=d,(a-c*d)+(b+c)*x, a+b*x)" two linear 6 = "y~a*exp(b*x)" exponential 7 = "y~a*(1+b*(exp(-c*x)))^-1" logistic 8 = "y~a*(1-b*(exp(-c*x)))^3" van bertalanffy 9 = "y~a*(1-b*(exp(-c*x)))" brody 10 = "y~a*exp(-b*exp(-c*x)" gompertz 11 = "y~(a*x^b)*exp(-c*x)" lactation curve 12 = "y ~ a + b * (1 - exp(-c * x))" ruminal degradation curve 13 = "y~(a/(1+exp(2-4*c*(x-e))))+(b/(1+exp(2-4*d*(x-e))))" logistic bi-compartmental 14 = "y~a*(x^b)" exponential (allometric model) 15 = "y~a+b*x+c*x^2+d*x^3" cubic 16 = "y~a/(1+b*(exp(-c*x)))^d" richards 17 = "y~(a^d+ ((b^d)-(a^d) )*((1-exp(-c*(x-t1)))/ (1-exp(-c*(t2-t1)))))^(1/d)" schnute |
start |
start values of iterations |
Returns coefficients of the models, test for coefficients, AIC and BIC.
Emmanuel Arnhold <[email protected]>
lm, ea1(easyanova package), pr2, regplot
x=c(1,1,1,2,2,2,3,3,3,4,4,4) y=c(5,5.3,6,8,8.9,12,14,18,25,25,29,32) t=c("t1","t2","t3","t1","t2","t3","t1","t2","t3","t1","t2","t3") data=data.frame(x,t,y) # linear regtest(data, model=1) # quadratic regtest(data, model=2) # exponential regtest(data, model=6) # ... etcx=c(1,1,1,2,2,2,3,3,3,4,4,4) y=c(5,5.3,6,8,8.9,12,14,18,25,25,29,32) t=c("t1","t2","t3","t1","t2","t3","t1","t2","t3","t1","t2","t3") data=data.frame(x,t,y) # linear regtest(data, model=1) # quadratic regtest(data, model=2) # exponential regtest(data, model=6) # ... etc