Nothing
library(glmmTMB)
save_image <- TRUE
data(sleepstudy, cbpp, Pastes,
package = "lme4")
fm_noRE <- glmmTMB(Reaction ~ Days , sleepstudy)
fm1 <- glmmTMB(Reaction ~ Days + (1| Subject), sleepstudy)
fm2 <- glmmTMB(Reaction ~ Days + (Days| Subject), sleepstudy)
fm2diag <- glmmTMB(Reaction ~ Days + diag(Days| Subject), sleepstudy)
fm0 <- update(fm2, . ~ . -Days)
## binomial, numeric response
fm2Bn <- update(fm2, as.numeric(Reaction>median(Reaction)) ~ .,
family=binomial)
## binomial, factor response
fm2Bf <- update(fm2, factor(Reaction>median(Reaction)) ~ ., family=binomial)
fm2P <- update(fm2, round(Reaction) ~ ., family=poisson)
fm2G <- update(fm2, family=Gamma(link="log"))
fm2NB <- update(fm2P, family=nbinom2)
## for testing sigma() against base R
fm3 <- update(fm2, . ~ Days)
fm3G <- update(fm3, family=Gamma(link="log"))
fm3NB <- update(fm3, round(Reaction) ~ ., family=nbinom2)
## z-i model
fm3ZIP <- update(fm2, round(Reaction) ~ ., family=poisson,
ziformula=~(1|Subject))
## separate-terms model
fm2diag2 <- update(fm2, . ~ Days + (1| Subject)+ (0+Days|Subject))
## model with two different grouping variables
fmP <- glmmTMB(strength ~ cask + (1|batch) + (1|sample), data=Pastes)
fm4 <- glmmTMB(Murder~Illiteracy+Population+Area+`HS Grad`,
data=as.data.frame(state.x77), REML = TRUE)
yb <- cbind(1:10,10)
ddb <- data.frame(y=I(yb))
ddb <- within(ddb, {
w <- rowSums(yb)
prop <- y[,1]/w
})
f1b <- glmmTMB(y ~ 1, family=binomial(), data=ddb)
f2b <- glm (y ~ 1, family=binomial(), data=ddb)
f3b <- glmmTMB(prop ~ 1, weights=w, family=binomial(),
data=ddb)
f4b <- glmmTMB(y[,1]/w ~ 1, weights=w, family=binomial(),
data=ddb)
gm0 <- glmmTMB(cbind(incidence, size-incidence) ~ 1 + (1|herd),
data = cbpp, family=binomial())
gm1 <- glmmTMB(cbind(incidence, size-incidence) ~ period + (1|herd),
data = cbpp, family=binomial())
## covariance structures
fsleepstudy <- transform(sleepstudy,fDays=cut(Days,c(0,3,6,10),right=FALSE),
row=factor(seq(nrow(sleepstudy))))
## two equivalent diagonal constructions
fm_diag1 <- glmmTMB(Reaction ~ Days + diag(Days| Subject), fsleepstudy)
fm_diag2 <- glmmTMB(Reaction ~ Days + ( 1 | Subject) + (0+Days | Subject),
fsleepstudy)
fm_diag2_lmer <- lme4::lmer(Reaction ~ Days + ( 1 | Subject) + (0+Days | Subject),
fsleepstudy, REML=FALSE)
fm_us1 <- glmmTMB(Reaction ~ Days + (Days| Subject), fsleepstudy)
fm_cs1 <- glmmTMB(Reaction ~ Days + cs(Days| Subject), fsleepstudy)
fm_us1_lmer <- lme4::lmer(Reaction ~ Days + ( Days | Subject),
fsleepstudy, REML=FALSE)
fm_cs2 <- glmmTMB(Reaction ~ Days + cs(fDays| Subject), fsleepstudy)
## these would be equivalent to a compound symmetry model with *homog* variance
fm_nest <- glmmTMB(Reaction ~ Days + (1| Subject/fDays), fsleepstudy)
fm_nest_lmer <- lme4::lmer(Reaction ~ Days + (1|Subject/fDays), fsleepstudy,
REML=FALSE)
## model with ~ Days + ... gives non-pos-def Hessian
fm_ar1 <- glmmTMB(Reaction ~ 1 +
(1|Subject) + ar1(row+0| Subject), fsleepstudy)
if (save_image) save.image(file="models.rda", version=2)
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