As a reproducible example, let's use the next no-sense example:
> library(glmmTMB)
> summary(glmmTMB(am ~ disp + hp + (1|carb), data = mtcars))
Family: gaussian ( identity )
Formula: am ~ disp + hp + (1 | carb)
Data: mtcars
AIC BIC logLik deviance df.resid
34.1 41.5 -12.1 24.1 27
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
carb (Intercept) 2.011e-11 4.485e-06
Residual 1.244e-01 3.528e-01
Number of obs: 32, groups: carb, 6
Dispersion estimate for gaussian family (sigma^2): 0.124
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.7559286 0.1502385 5.032 4.87e-07 ***
disp -0.0042892 0.0008355 -5.134 2.84e-07 ***
hp 0.0043626 0.0015103 2.889 0.00387 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Actually, my real model family is nbinom2. I want to make a contrast test between disp and hp. So, I try:
> glht(glmmTMB(am ~ disp + hp + (1|carb), data = mtcars), linfct = matrix(c(0,1,-1)))
Error in glht.matrix(glmmTMB(am ~ disp + hp + (1 | carb), data = mtcars), :
‘ncol(linfct)’ is not equal to ‘length(coef(model))’
How can I avoid this error?
Thank you!
The problem is actually fairly simple: linfct needs to be a matrix with the number of columns equal to the number of parameters. You specified matrix(c(0,1,-1)) without specifying numbers of rows or columns, so R made a column matrix by default. Adding nrow=1 seems to work.
library(glmmTMB)
library(multcomp)
m1<- glmmTMB(am ~ disp + hp + (1|carb), data = mtcars)
modelparm.glmmTMB <- function (model, coef. = function(x) fixef(x)[[component]],
vcov. = function(x) vcov(x)[[component]],
df = NULL, component="cond", ...) {
multcomp:::modelparm.default(model, coef. = coef., vcov. = vcov.,
df = df, ...)
}
glht(m1, linfct = matrix(c(0,1,-1),nrow=1))