Nothing
params <-
list(EVAL = FALSE)
## ----load_lib,echo=FALSE------------------------------------------------------
library(glmmTMB)
knitr::opts_chunk$set(eval = if (isTRUE(exists("params"))) params$EVAL else FALSE)
## ----non-pos-def,cache=TRUE, warning=FALSE------------------------------------
# zinbm0 = glmmTMB(count~spp + (1|site), zi=~spp, Salamanders, family=nbinom2)
## ----fixef_zinbm0-------------------------------------------------------------
# fixef(zinbm0)
## ----f_zi2--------------------------------------------------------------------
# ff <- fixef(zinbm0)$zi
# round(plogis(c(sppGP=unname(ff[1]),ff[-1]+ff[1])),3)
## ----salfit2,cache=TRUE-------------------------------------------------------
# Salamanders <- transform(Salamanders, GP=as.numeric(spp=="GP"))
# zinbm0_A = update(zinbm0, ziformula=~GP)
## ----salfit2_coef,cache=TRUE--------------------------------------------------
# fixef(zinbm0_A)[["zi"]]
## ----salfit3,cache=TRUE-------------------------------------------------------
# zinbm0_B = update(zinbm0, ziformula=~(1|spp))
# fixef(zinbm0_B)[["zi"]]
# VarCorr(zinbm0_B)
## ----zinbm1,cache=TRUE--------------------------------------------------------
# zinbm1 = glmmTMB(count~spp + (1|site), zi=~mined, Salamanders, family=nbinom2)
# fixef(zinbm1)[["zi"]]
## ----zinbm1_confint,cache=TRUE------------------------------------------------
# ## at present we need to specify the parameter by number; for
# ## extreme cases need to specify the parameter range
# ## (not sure why the upper bound needs to be so high ... ?)
# cc = confint(zinbm1,method="uniroot",parm=9, parm.range=c(-20,20))
# print(cc)
## ----fatfiberglmm-------------------------------------------------------------
# ## data taken from gamlss.data:plasma, originally
# ## http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/plasma.html
# gt_load("vignette_data/plasma.rda")
# m4.1 <- glm(calories ~ fat*fiber, family = Gamma(link = "log"), data = plasma)
# m4.2 <- glmmTMB(calories ~ fat*fiber, family = Gamma(link = "log"), data = plasma)
# ps <- transform(plasma,fat=scale(fat,center=FALSE),fiber=scale(fiber,center=FALSE))
# m4.3 <- update(m4.2, data=ps)
# ## scaling factor for back-transforming standard deviations
# ss <- c(1,
# fatsc <- 1/attr(ps$fat,"scaled:scale"),
# fibsc <- 1/attr(ps$fiber,"scaled:scale"),
# fatsc*fibsc)
# ## combine SEs, suppressing the warning from the unscaled model
# s_vals <- cbind(glm=sqrt(diag(vcov(m4.1))),
# glmmTMB_unsc=suppressWarnings(sqrt(diag(vcov(m4.2)$cond))),
# glmmTMB_sc=sqrt(diag(vcov(m4.3)$cond))*ss)
# print(s_vals,digits=3)
## ----load_ss_ex,eval=TRUE-----------------------------------------------------
L <- gt_load("vignette_data/sherman.rda")
## ----ss_ex_mod1---------------------------------------------------------------
# summary(mod1)
## ----diag_1-------------------------------------------------------------------
# d1 <- diagnose(mod1)
## ----ss_mod2_up, eval=FALSE---------------------------------------------------
# mod2 <- update(mod1, ziformula=~0)
## ----ss_mod2------------------------------------------------------------------
# summary(mod2)
## ----ss_diag2-----------------------------------------------------------------
# diagnose(mod2)
## ----ss_mod2optim,eval=TRUE---------------------------------------------------
mod2_optim <- update(mod2,
control=glmmTMBControl(optimizer=optim,
optArgs=list(method="BFGS")))
## ----ss_mod2optim_comp,eval=TRUE----------------------------------------------
(parcomp <- cbind(nlminb=unlist(fixef(mod2)),
optim=unlist(fixef(mod2_optim))))
## ----comp, echo=FALSE,eval=TRUE-----------------------------------------------
zi1 <- parcomp["disp.(Intercept)","nlminb"]
zi2 <- parcomp["disp.(Intercept)","optim"]
pzi1 <- exp(zi1)
pzi2 <- exp(zi2)
## ----mod3_up, eval=FALSE------------------------------------------------------
# mod3 <- update(mod2, family=poisson)
## ----ss_mod3------------------------------------------------------------------
# summary(mod3)
## ----ss_diag3-----------------------------------------------------------------
# diagnose(mod3)
## ----checkhess----------------------------------------------------------------
# mod3$sdr$pdHess
## ----genpois_NaN,cache=TRUE---------------------------------------------------
# m1 <- glmmTMB(count~spp + mined + (1|site), zi=~spp + mined, Salamanders, family=genpois)
## ----diag_genpois-------------------------------------------------------------
# diagnose(m1)
## ----NA gradient, error=TRUE, warning=FALSE-----------------------------------
# dat1 = expand.grid(y=-1:1, rep=1:10)
# m1 = glmmTMB(y~1, dat1, family=nbinom2)
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