pgam_glmmtmb.R

library(glmmTMB)
library(mgcv)

#########################################################################
## Function to create prediction matrices from mgcv for glmmTMB
##   from ?mgcv::smooth2random
########################################################################
s2rPred <- function(sm,re,data) {
## Function to aid prediction from smooths represented as type==2
## random effects. re must be the result of smooth2random(sm,...,type=2).
  X <- PredictMat(sm,data)   ## get prediction matrix for new data
  ## transform to r.e. parameterization
  if (!is.null(re$trans.U)) X <- X%*%re$trans.U
  X <- t(t(X)*re$trans.D)
  ## re-order columns according to random effect re-ordering...
  X[,re$rind] <- X[,re$pen.ind!=0] 
  ## re-order penalization index in same way  
  pen.ind <- re$pen.ind; pen.ind[re$rind] <- pen.ind[pen.ind>0]
  ## start return object...
  r <- list(rand=list(),Xf=X[,which(re$pen.ind==0),drop=FALSE])
  for (i in 1:length(re$rand)) { ## loop over random effect matrices
    r$rand[[i]] <- X[,which(pen.ind==i),drop=FALSE]
    attr(r$rand[[i]],"s.label") <- attr(re$rand[[i]],"s.label")
  }
  names(r$rand) <- names(re$rand)
  r
} ## s2rPred


## Continuous covariate
x <- seq(1,10, length=100)
x.pred <-  seq(1,15, length=150)

## Set up penalized thin-plate regression spline for x
sm <- mgcv::smoothCon(s(x), data=as.data.frame(x))[[1]]
re <- mgcv::smooth2random(sm, "", type=2)
Xf <- re$Xf
Xr <- re$rand$Xr

## Simulated data set
set.seed(1234)
b <- rnorm(8,0,3)
beta <- c(0, 2)
lambda <- exp(Xf%*%beta + Xr%*%b)
y <- rpois(length(x),lambda)
plot(x,y)

## Fit with glmmTMB
k <- ncol(Xr)
# make a fake grouping variable
g <- rep(1, length(y)) 
## Set up TMB 'map' argument to have only 1 variance parameter for Xr 
##  This is the TPRS smoothing parameter
tmb_map <- list(theta = factor(c(rep(1, k), rep(NA, k*(k-1)/2))))

ftmb <- glmmTMB(y ~ 0+Xf +(0+Xr|g), family=poisson, map=tmb_map)
pred.obj <- s2rPred(sm, re, data.frame(x=x.pred))
newdata <- list(Xf=pred.obj$Xf, Xr=pred.obj$rand$Xr, g=rep(1, nrow(pred.obj$Xf)))

ptmb <- as.data.frame(predict(ftmb,newdata=newdata, se=T)) 


## Fit with mgcv::gam, method='REML'
fmgcv <- mgcv::gam(y~s(x), family=poisson, method="REML")
pmgcv <- as.data.frame(predict(fmgcv, newdata=data.frame(x=x.pred), se=TRUE, uncond=TRUE)) 

## Compare fits
plot(x, y, xlim=c(0,15))

lines(x.pred, exp(ptmb[,1]), col='blue', lwd=2)
lines(x.pred, exp(ptmb[,1]+1.96*ptmb[,2]), col='blue', lty=3, lwd=2)
lines(x.pred, exp(ptmb[,1]-1.96*ptmb[,2]), col='blue', lty=3, lwd=2)
text(1, 33, labels=c("glmmTMB"), col='blue', pos=4, cex=2)

lines(x.pred, exp(pmgcv[,1]), col='red', lwd=2)
lines(x.pred, exp(pmgcv[,1]+1.96*pmgcv[,2]), col='red', lty=3, lwd=2)
lines(x.pred, exp(pmgcv[,1]-1.96*pmgcv[,2]), col='red', lty=3, lwd=2)
text(1, 30, labels=c("mgcv::gam"), col='red', pos=4, cex=2)

Hi Devin, Thank you for this gist, very useful ! I like TMB very much, but I don't use it enough. You're using the pipe operator at line 37, I'd add library(magrittr) or library(tidyverse) at the beginning of the script to make it work. Also, at line 26, eta doesn't exist, I guess using x in y <- rpois(length(x),lambda) does the job. With these modifications, the code runs like a charm 😄 Cheers. Olivier

Thanks! Somethings must have slipped past while I was cleaning up the workspace ;-)

…

On Wed, Nov 25, 2020 at 12:22 PM Olivier Gimenez ***@***.***> wrote: ***@***.**** commented on this gist. ------------------------------ Hi Devin, Thank you for this gist, very useful ! I like TMB very much, but I don't use it enough. You're using the pipe operator at line 37, I'd add library(magrittr) or library(tidyverse) at the beginning of the script to make it work. Also, at line 26, eta doesn't exist, I guess using x in y <- rpois(length(x),lambda) does the job. With these modifications, the code runs like a charm 😄 Cheers. Olivier — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <https://gist.github.com/9d66aa47557ad56438aaf75dd25910ea#gistcomment-3540440>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAD5LUOPSMJ4CALBH3RQJ3DSRVRQVANCNFSM4UC3NQOA> .

-- *Devin S. Johnson, Ph.D.* Statistician (Biology) NOAA Fisheries U.S. Department of Commerce Office: 206-526-6867 devin.johnson@noaa.gov

😜 🧹