| Type: | Package |
| Title: | Linear Mixed Models - An Introduction with Applications in Veterinary Research |
| Version: | 1.0.0 |
| Maintainer: | Muhammad Yaseen <myaseen208@gmail.com> |
| Description: | R Codes and Datasets for Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998). Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute. |
| Depends: | R (≥ 3.1) |
| Imports: | ggplot2, lme4, nlme, lmerTest, multcomp |
| License: | GPL-2 |
| URL: | https://github.com/MYaseen208/VetResearchLMM |
| LazyData: | TRUE |
| RoxygenNote: | 6.0.1 |
| Note: | Department of Mathematics and Statistics, University of Agriculture Faisalabad, Faisalabad-Pakistan. |
| NeedsCompilation: | no |
| Packaged: | 2018-04-22 05:36:12 UTC; myaseen |
| Author: | Muhammad Yaseen [aut, cre], Luc Duchateau [ctb], Paul Janssen [ctb], John Rowlands [ctb] |
| Repository: | CRAN |
| Date/Publication: | 2018-04-22 05:44:50 UTC |
#' @title Examp1.3.2 from Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Description
Examp1.3.2 is used for inspecting probability distribution and to define a plausible process through linear models and generalized linear models.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
See Also
Examples
#-------------------------------------------------------------
## Example 1.3.2 p-16
#-------------------------------------------------------------
# PROC GLM DATA=ex124;
# CLASS herd dose drug;
# MODEL PCVdif=drug herd(drug) dose dose*drug;
# RANDOM herd(drug);
# RUN;
library(lme4)
str(ex124)
summary(ex124)
ex124$herd1 <- factor(ex124$herd)
ex124$drug1 <- factor(ex124$drug)
ex124$dose1 <- factor(ex124$dose)
fm1.1 <-
aov(
formula = PCVdif ~ drug1 + Error(herd1:drug1) + dose1 + dose1:drug1
, data = ex124
, projections = FALSE
, qr = TRUE
, contrasts = NULL
# , ...
)
summary(fm1.1)
Examp2.4.2.2 from Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Description
Examp2.4.2.2 is used for inspecting probability distribution and to define a plausible process through linear models and generalized linear models.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
See Also
Examples
#-------------------------------------------------------------
## Example 2.4.2.2 p-64
#-------------------------------------------------------------
# PROC MIXED DATA=ex125 METHOD=ML;
# CLASS drug dose region;
# MODEL pcv=drug dose drug*dose;
# RANDOM region drug*region;
# RUN;
#
# PROC MIXED DATA=ex125 METHOD=REML;
# CLASS drug dose region;
# MODEL pcv=drug dose drug*dose;
# RANDOM region drug*region;
# RUN;
library(lme4)
str(ex125)
fm2.4 <-
lme4::lmer(
formula = Pcv ~ dose*Drug + (1|Region/Drug)
, data = ex125
, REML = FALSE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = NULL
, devFunOnly = FALSE
# , ...
)
summary(fm2.4)
anova(fm2.4)
fm2.5 <-
lme4::lmer(
formula = Pcv ~ dose*Drug + (1|Region/Drug)
, data = ex125
, REML = TRUE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = NULL
, devFunOnly = FALSE
# , ...
)
summary(fm2.5)
anova(fm2.5)
library(lmerTest)
fm2.6 <-
lmerTest::lmer(
formula = Pcv ~ dose*Drug + (1|Region/Drug)
, data = ex125
, REML = FALSE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = NULL
, devFunOnly = FALSE
# , ...
)
summary(fm2.6)
anova(fm2.6)
fm2.7 <-
lmerTest::lmer(
formula = Pcv ~ dose*Drug + (1|Region/Drug)
, data = ex125
, REML = TRUE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = NULL
, devFunOnly = FALSE
# , ...
)
summary(fm2.7)
anova(fm2.7)
Examp2.4.3.1 from Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Description
Examp2.4.3.1 is used for inspecting probability distribution and to define a plausible process through linear models and generalized linear models.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
See Also
Examples
#-------------------------------------------------------------
## Example 2.4.3.1 p-66
#-------------------------------------------------------------
# PROC MIXED DATA=ex127;
# CLASS sire;
# MODEL ww=;
# RANDOM sire/solution;
# RUN;
library(lme4)
str(ex127)
fm2.8 <-
lme4::lmer(
formula = Ww~(1|sire)
, data = ex127
, REML = TRUE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = NULL
, devFunOnly = FALSE
# , ...
)
summary(fm2.8)
lme4::fixef(fm2.8)
lme4::ranef(fm2.8)
Examp2.5.1.1 from Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Description
Examp2.5.1.1 is used for inspecting probability distribution and to define a plausible process through linear models and generalized linear models.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
See Also
Examples
#-------------------------------------------------------------
## Example 2.5.1.1 p-67
#-------------------------------------------------------------
# PROC MIXED DATA=ex125;
# CLASS drug dose region;
# MODEL pcv=drug dose drug*dose / solution covb;
# RANDOM region drug*region;
# RUN;
library(lme4)
str(ex125)
fm2.9 <-
lme4::lmer(
formula = Pcv ~ dose*Drug + (1|Region/Drug)
, data = ex125
, REML = TRUE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = list(dose = "contr.SAS", Drug = "contr.SAS")
, devFunOnly = FALSE
# , ...
)
summary(fm2.9)
anova(fm2.9)
summary(fm2.9)$vcov
Examp2.5.2.1 from Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Description
Examp2.5.2.1 is used for inspecting probability distribution and to define a plausible process through linear models and generalized linear models.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
See Also
Examples
#-------------------------------------------------------------
## Example 2.5.2.1 p-68
#-------------------------------------------------------------
# PROC MIXED DATA=ex125;
# CLASS drug dose region;
# MODEL pcv=drug dose drug*dose / solution covb;
# RANDOM region drug*region;
# LSMEANS drug*dose;
# RUN;
library(lmerTest)
str(ex125)
fm2.10 <-
lmerTest::lmer(
formula = Pcv ~ dose*Drug + (1|Region/Drug)
, data = ex125
, REML = TRUE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = list(dose = "contr.SAS", Drug = "contr.SAS")
, devFunOnly = FALSE
# , ...
)
summary(fm2.10)
anova(fm2.10)
summary(fm2.10)$vcov
lsmeansLT(model = fm2.10)
Examp2.5.3.1 from Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Description
Examp2.5.3.1 is used for inspecting probability distribution and to define a plausible process through linear models and generalized linear models.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
See Also
Examples
#-------------------------------------------------------------
## Example 2.5.3.1 p-70
#-------------------------------------------------------------
# PROC GLM DATA=ex125;
# CLASS drug dose region;
# MODEL pcv=region drug region*drug dose drug*dose;
# RANDOM region drug*region;
# RUN;
# PROC MIXED DATA=ex125;
# CLASS drug dose region;
# MODEL pcv=drug dose drug*dose / ddfm=satterth;
# RANDOM region drug*region;
# ESTIMATE 'drug dif' drug -1 1 drug*dose -0.5 -0.5 0.5 0.5;
# ESTIMATE 'Samorin mean' INTERCEPT 1 drug 0 1 dose 0.5 0.5
# drug*dose 0 0 0.5 0.5;
# ESTIMATE 'Samorin HvsL' dose 1 -1 drug*dose 0 0 1 -1;
# ESTIMATE 'Samorin high' INTERCEPT 1 drug 0 1 dose 1 0
# drug*dose 0 0 1 0;
# RUN;
library(lme4)
str(ex125)
ex125$Region1 <- factor(ex125$Region)
fm2.11 <-
aov(
formula = Pcv ~ Region1 + Drug + Error(Drug:Region1) + dose + dose:Drug
, data = ex125
, projections = FALSE
, qr = TRUE
, contrasts = NULL
# , ...
)
summary(fm2.11)
fm2.12 <-
lmerTest::lmer(
formula = Pcv ~ dose*Drug + (1|Region/Drug)
, data = ex125
, REML = TRUE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = list(dose = "contr.SAS", Drug = "contr.SAS")
, devFunOnly = FALSE
# , ...
)
summary(fm2.12)
anova(object = fm2.12, ddf = "Satterthwaite")
library(multcomp)
Contrasts1 <-
matrix(c(
1, 0.5, 0, 0
, 0, 0, -1, -0.5
, 1, 1, 0, 0
, 0, 1, 0, 0
)
, ncol = 4
, byrow = TRUE
, dimnames = list(
c("C1", "C2", "C3", "C4")
, rownames(summary(fm2.12)$coef)
)
)
Contrasts1
summary(glht(fm2.12, linfct=Contrasts1))
Examp2.5.4.1 from Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Description
Examp2.5.4.1 is used for inspecting probability distribution and to define a plausible process through linear models and generalized linear models.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
See Also
Examples
#-------------------------------------------------------------
## Example 2.5.4.1 p-74
#-------------------------------------------------------------
# PROC MIXED DATA=ex125;
# CLASS drug dose region;
# MODEL pcv=drug dose drug*dose / ddfm=satterth;
# RANDOM region drug*region;
# ESTIMATE 'Samorin mean' INTERCEPT 1 drug 0 1 dose 0.5 0.5
# drug*dose 0 0 0.5 0.5;
# RUN;
# PROC GLM DATA=ex125;
# CLASS drug dose region;
# MODEL pcv=region drug region*drug dose drug*dose;
# ESTIMATE 'Samorin mean' INTERCEPT 1 drug 0 1 dose 0.5 0.5
# drug*dose 0 0 0.5 0.5;
# RUN;
library(lme4)
str(ex125)
ex125$Region1 <- factor(ex125$Region)
fm2.13 <-
lmerTest::lmer(
formula = Pcv ~ dose*Drug + (1|Region/Drug)
, data = ex125
, REML = TRUE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = list(dose = "contr.SAS", Drug = "contr.SAS")
, devFunOnly = FALSE
# , ...
)
summary(fm2.13)
library(multcomp)
Contrasts2 <-
matrix(c(
1, 0.5, 0, 0
)
, ncol = 4
, byrow = TRUE
, dimnames = list(
c("C5")
, rownames(summary(fm2.13)$coef)
)
)
Contrasts2
summary(glht(fm2.13, linfct=Contrasts2))
Examp2.6.1 from Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Description
Examp2.6.1 is used for inspecting probability distribution and to define a plausible process through linear models and generalized linear models.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
See Also
Examples
#-------------------------------------------------------------
## Example 2.6.1 p-76
#-------------------------------------------------------------
# PROC MIXED DATA=ex125;
# CLASS drug dose region;
# MODEL pcv=drug dose drug*dose / ddfm=satterth;
# RANDOM region drug*region;
# CONTRAST 'drug dif' drug -1 1 drug*dose -0.5 -0.5 0.5 0.5;
# CONTRAST 'all' drug 1 -1 dose 0 0 drug*dose 0.5 0.5 -0.5 -0.5,
# drug 0 0 dose 1 -1 drug*dose 0.5 -0.5 0.5 -0.5,
# drug 0 0 dose 0 0 drug*dose 0.5 -0.5 -0.5 0.5;
# RUN;
library(lmerTest)
str(ex125)
ex125$Region1 <- factor(ex125$Region)
fm2.14 <-
lmerTest::lmer(
formula = Pcv ~ dose*Drug + (1|Region/Drug)
, data = ex125
, REML = TRUE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = list(dose = "contr.SAS", Drug = "contr.SAS")
, devFunOnly = FALSE
# , ...
)
summary(fm2.14)
anova(object = fm2.14, ddf = "Satterthwaite")
library(multcomp)
Contrasts3 <-
matrix(c(
0, 0, -1, -0.5
)
, ncol = 4
, byrow = TRUE
, dimnames = list(
c("C1")
, rownames(summary(fm2.14)$coef)
)
)
Contrasts3
summary(glht(fm2.14, linfct=Contrasts3))
if(packageVersion("lmerTest") >= "3.0")
contest(fm2.14, Contrasts3, joint = FALSE)
Examp3.1 from Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Description
Examp3.1 is.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
See Also
Examples
#-------------------------------------------------------------
## Example 3.1 Model 1 p-80
#-------------------------------------------------------------
# PROC MIXED DATA=ex31;
# CLASS drug dose herd;
# MODEL PCV2=drug dose(drug)/solution ddfm=satterth;
# RANDOM herd(drug);
# ESTIMATE 'Mean Samorin' intercept 1 drug 0 1 dose(drug) 0 0 1;
# ESTIMATE 'Berenil 2 doses' dose(drug) 1 -1 0;
# ESTIMATE 'Ber vs Sam at dose 1' drug 1 -1 dose(drug) 1 0 -1;
# CONTRAST 'Mean Samorin' intercept 1 drug 0 1 dose(drug) 0 0 1;
# CONTRAST 'Berenil dif 2 doses' dose(drug) 1 -1 0;
# CONTRAST 'Ber vs Sam at dose 1' drug 1 -1 dose(drug) 1 0 -l;
# CONTRAST 'some difference' drug 1 -1 dose(drug) 0.5 0.5 -1,
# drug 0 0 dose(drug) 1 -1 0;
# LSMEANS dose(drug);
# RUN;
library(lmerTest)
str(ex31)
ex31$drug1 <- factor(ex31$drug)
ex31$dose1 <- factor(ex31$dose)
ex31$herd1 <- factor(ex31$herd)
fm3.1 <-
lmerTest::lmer(
formula = PCV2 ~ drug1 + dose1:drug1 + (1|herd1:drug1)
, data = ex31
, REML = TRUE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = list(dose1 = "contr.SAS", drug1 = "contr.SAS")
, devFunOnly = FALSE
# , ...
)
summary(fm3.1)
anova(object = fm3.1, ddf = "Satterthwaite")
lsmeansLT(model = fm3.1, test.effs = "dose1:drug1")
#-------------------------------------------------------------
## Example 3.1 Model 2 p-84
#-------------------------------------------------------------
# PROC MIXED DATA=ex31;
# CLASS drug dose herd;
# MODEL PCV2=PCV1 drug dose(drug)/solution ddfm=satterth;
# RANDOM herd(drug);
# RUN;
library(lmerTest)
str(ex31)
ex31$drug1 <- factor(ex31$drug)
ex31$dose1 <- factor(ex31$dose)
ex31$herd1 <- factor(ex31$herd)
fm3.2 <-
lmerTest::lmer(
formula = PCV2 ~ PCV1 + drug1 + dose1:drug1 + (1|herd1:drug1)
, data = ex31
, REML = TRUE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = list(dose1 = "contr.SAS", drug1 = "contr.SAS")
, devFunOnly = FALSE
# , ...
)
summary(fm3.2)
anova(object = fm3.2, ddf = "Satterthwaite")
lsmeansLT(model = fm3.2, test.effs = "herd1:drug1")
#-------------------------------------------------------------
## Example 3.1 Model 3 p-86
#-------------------------------------------------------------
# PROC MIXED DATA=ex31;
# CLASS drug dose herd;
# MODEL PCV2=drug dose(drug) PCV1*dose(drug)/solution ddfm=satterth;
# RANDOM herd(drug);
# RUN;
library(lmerTest)
str(ex31)
ex31$drug1 <- factor(ex31$drug)
ex31$dose1 <- factor(ex31$dose)
ex31$herd1 <- factor(ex31$herd)
fm3.3 <-
lmerTest::lmer(
formula = PCV2 ~ drug1 + PCV1*dose1:drug1 + (1|herd1:drug1)
, data = ex31
, REML = TRUE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = list(dose1 = "contr.SAS", drug1 = "contr.SAS")
, devFunOnly = FALSE
# , ...
)
summary(fm3.3)
anova(object = fm3.3, ddf = "Satterthwaite")
lsmeansLT(model = fm3.3, test.effs = "dose1:drug1")
Examp3.2 from Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Description
Examp3.2 is used for inspecting probability distribution and to define a plausible process through linear models and generalized linear models.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
See Also
Examples
#-------------------------------------------------------------
## Example 3.3 p-88
#-------------------------------------------------------------
# PROC MIXED DATA=ex32;
# CLASS sex sire_id breed;
# MODEL ww = sex agew breed/SOLUTION DDFM=SATTERTH;
# RANDOM sire_id(breed)/SOLUTION;
# LSMEANS breed/ADJUST = TUKEY;
# RUN;
library(lmerTest)
str(ex32)
ex32$sire_id1 <- factor(ex32$sire_id)
ex32$breed1 <- factor(ex32$breed)
fm3.4 <-
lmerTest::lmer(
formula = Ww ~ sex + agew + breed1 + (1|sire_id1:breed1)
, data = ex32
, REML = TRUE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = list(sex = "contr.SAS", breed1 = "contr.SAS")
, devFunOnly = FALSE
# , ...
)
summary(fm3.4)
anova(object = fm3.4, ddf = "Satterthwaite")
lsmeansLT(model = fm3.4)
Examp3.3 from Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Description
Examp3.3 is used for inspecting probability distribution and to define a plausible process through linear models and generalized linear models.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
See Also
Examples
#-------------------------------------------------------------
## Example 3.3 Model 1 p-88
#-------------------------------------------------------------
# PROC MIXED DATA=ex33;
# CLASS breed animal_id;
# MODEL pcv = breed breed*time/SOLUTION;
# RANDOM animal_id(breed)/SOLUTION;
# RUN;
library(lme4)
options(contrasts = c(factor = "contr.SAS", ordered = "contr.poly"))
str(ex33)
fm3.5 <-
lme4::lmer(
formula = PCV ~ breed + breed:time + (1|animal_id:breed)
, data = ex33
, REML = TRUE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = list(breed = "contr.SAS")
, devFunOnly = FALSE
# , ...
)
summary(fm3.5)
anova(fm3.5)
library(lmerTest)
fm3.6 <-
lmerTest::lmer(
formula = PCV ~ breed + breed:time + (1|animal_id:breed)
, data = ex33
, REML = TRUE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = list(breed = "contr.SAS")
, devFunOnly = FALSE
# , ...
)
summary(fm3.6)
anova(object = fm3.6, ddf = "Satterthwaite")
# PROC MIXED DATA=ex33;
# CLASS breed animal_id;
# MODEL pcv = breed breed*time/SOLUTION;
# REPEATED/TYPE=CS SUB = animal_id(breed) R;
# RUN;
library(nlme)
fm3.7 <-
nlme::gls(
model = PCV ~ breed + breed:time
, data = ex33
, correlation = corCompSymm(, form = ~ 1|animal_id/breed)
, weights = NULL
# , subset =
, method = "REML" # c("REML", "ML")
, na.action = na.fail
, control = list()
)
summary(fm3.7)
anova(fm3.7)
# PROC MIXED DATA=ex33;
# CLASS breed animal_id;
# MODEL pcv = time breed breed*time/SOLUTION;
# RANDOM animal_id(breed)/SOLUTION;
# RUN;
fm3.8 <-
lme4::lmer(
formula = PCV ~ time + breed + breed:time + (1|animal_id:breed)
, data = ex33
, REML = TRUE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = list(breed = "contr.SAS")
, devFunOnly = FALSE
# , ...
)
summary(fm3.8)
anova(fm3.8)
fm3.9 <-
lmerTest::lmer(
formula = PCV ~ time + breed + breed:time + (1|animal_id:breed)
, data = ex33
, REML = TRUE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = list(breed = "contr.SAS")
, devFunOnly = FALSE
# , ...
)
summary(fm3.9)
anova(object = fm3.9, ddf = "Satterthwaite", type = 3)
# PROC MIXED DATA=ex33;
# CLASS breed animal_id;
# MODEL pcv = breed breed*time/SOLUTION;
# REPEATED/TYPE=AR(1) SUBJET = animal_id(breed) R;
# RUN;
library(nlme)
fm3.10 <-
nlme::gls(
model = PCV ~ breed + breed:time
, data = ex33
, correlation = corAR1(, form = ~ 1|animal_id/breed)
, weights = NULL
# , subset =
, method = "REML" # c("REML", "ML")
, na.action = na.fail
, control = list()
)
summary(fm3.10)
anova(fm3.10)
# PROC MIXED DATA=ex33;
# CLASS breed animal_id;
# MODEL pcv = breed breed*time/SOLUTION;
# RANDOM INTERCEPT time/TYPE=UN SUBJET = animal_id(breed) SOLUTION;
# RUN;
library(nlme)
# fm3.11 <-
# nlme::gls(
# model = PCV ~ breed + breed:time
# , data = ex33
# , random = ~1|animal_id/breed
# , correlation = corAR1(, form = ~ 1|animal_id/breed)
# , weights = NULL
# # , subset =
# , method = "REML" # c("REML", "ML")
# , na.action = na.fail
# , control = list()
# )
# summary(fm3.11)
# anova(fm3.11)
ex121 from Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Description
ex121 is.
Usage
data(ex121)
Format
A data.frame with 40 rows and 4 variables.
Details
herd two treatment 0 and 1
drug unit of observation or observation ID
dose is continuous \& may be assumed Gaussian
PCVDif is the number of "successes"(N and F specify a binomial response)
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Examples
data(ex121)
ex124 from Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Description
ex124 is.
Usage
data(ex124)
Format
A data.frame with 40 rows and 4 variables.
Details
herd two treatment 0 and 1
drug unit of observation or observation ID
dose is continuous \& may be assumed Gaussian
PCVDif is the number of "successes"(N and F specify a binomial response)
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
See Also
Examples
data(ex124)
ex125 from Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Description
ex125 is.
Usage
data(ex125)
Format
A data.frame with 40 rows and 4 variables.
Details
herd two treatment 0 and 1
drug unit of observation or observation ID
dose is continuous \& may be assumed Gaussian
PCVDif is the number of "successes"(N and F specify a binomial response)
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
See Also
Examples
data(ex125)
ex127 from Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Description
ex127 is.
Usage
data(ex127)
Format
A data.frame with 40 rows and 4 variables.
Details
herd two treatment 0 and 1
drug unit of observation or observation ID
dose is continuous \& may be assumed Gaussian
PCVDif is the number of "successes"(N and F specify a binomial response)
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
See Also
Examples
data(ex127)
ex31 from Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Description
ex31 is.
Usage
data(ex31)
Format
A data.frame with 38 rows and 6 variables.
Details
herd Herds of Cattle
animal_id Animal ID
PCV1 Packed Cell Volume (PCV) determined at the time of treatment
PCV2 Packed Cell Volume (PCV) determined at a month later following treatment
dose Dose of Drugs
drug Two drugs against trypanosomosis, Berenil and Samorin, are studied
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
See Also
Examples
data(ex31)
ex32 from Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Description
ex32 is.
Usage
data(ex32)
Format
A data.frame with 40 rows and 4 variables.
Details
herd two treatment 0 and 1
drug unit of observation or observation ID
dose is continuous \& may be assumed Gaussian
PCVDif is the number of "successes"(N and F specify a binomial response)
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
See Also
Examples
data(ex32)
ex33 from Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Description
ex33 is.
Usage
data(ex33)
Format
A data.frame with 40 rows and 4 variables.
Details
herd two treatment 0 and 1
drug unit of observation or observation ID
dose is continuous \& may be assumed Gaussian
PCVDif is the number of "successes"(N and F specify a binomial response)
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
See Also
Examples
data(ex33)