Relevels factors in a list of glmmTMB objects using specified reference labels.

This function re-levels factors in a list of glmmTMB objects based on the specified reference labels.

Usage

relevelling_factors(list_tmb, reference_labels)

Arguments

list_tmb: List of glmmTMB objects.
reference_labels: Vector of reference labels.

Value

A list of glmmTMB objects with re-leveled factors.

Examples

data("mtcars")
## -- specify categorical var
mtcars$vs <- factor(mtcars$vs) ## Engine (0 = V-shaped, 1 = straight)
levels(mtcars$vs) <- c("V-shaped", "straight")
mtcars$am <- factor(mtcars$am) ## Transmission (0 = automatic, 1 = manual)
levels(mtcars$am) <- c("automatic", "manual")
## -- For each group of number of cylinder,
## -- explain fuel consumption with engine shape, Gross horsepower,  and transmission type
list_tmb <- fitModelParallel(formula = mpg ~ hp + vs + am, 
               data = mtcars, group_by = "cyl", n.cores = 1 )
#> Log file location: /tmp/RtmpS86cq0/htrfit.log
#> CPU(s) number : 1
#> Cluster type : PSOCK
relevelling_factors(list_tmb , c("straight", "manual"))
#> $`6`
#> Formula:          mpg ~ hp + vs + am
#> Data: data
#>       AIC       BIC    logLik  df.resid 
#>  28.46985  28.25349 -10.23492         3 
#> 
#> Number of obs: 7
#> 
#> Dispersion estimate for gaussian family (sigma^2): 1.09 
#> 
#> Fixed Effects:
#> 
#> Conditional model:
#> (Intercept)           hp   vsstraight     ammanual  
#>    23.46038     -0.02198     -1.80247           NA  
#> 
#> $`4`
#> Formula:          mpg ~ hp + vs + am
#> Data: data
#>       AIC       BIC    logLik  df.resid 
#>  64.87146  66.86093 -27.43573         6 
#> 
#> Number of obs: 11
#> 
#> Dispersion estimate for gaussian family (sigma^2): 8.59 
#> 
#> Fixed Effects:
#> 
#> Conditional model:
#> (Intercept)           hp   vsstraight     ammanual  
#>     30.3509      -0.1033       1.2943       5.0484  
#> 
#> $`8`
#> NULL
#>