R: Prevent "~." in a linear mixed effect model from running an independent variable as bot-CodePudding

I seem to be running into some issues when I run the code below:

library(lme4)

columns <- c("disp", "hp", "wt", "qsec", "vs")


X <- mtcars[,c(columns, 'cyl', 'carb', 'gear')] # data


rf <- lmer(cyl~ (carb | gear)  ., data = X)

rf

### The output that I don't want (lists 'carb' and 'gear' as fixed variables):

Linear mixed model fit by REML ['lmerMod']
Formula: cyl ~ (carb | gear)   .
   Data: X
REML criterion at convergence: 76.9662
Random effects:
 Groups   Name        Std.Dev. Corr 
 gear     (Intercept) 0.2887        
          carb        0.2039   -1.00
 Residual             0.5202        
Number of obs: 32, groups:  gear, 3
Fixed Effects:
(Intercept)         carb         gear         disp           hp           wt  
  10.179140     0.025990    -0.873174     0.003883     0.008190     0.089656  
       qsec           vs  
  -0.159582    -0.779400

As you can see, it is counting 'carb' and 'gear' as fixed variables when I only need them to be used for my random effect variable.

My goal is to keep the code in a similar format and also be able to run the model without the variables 'carb' and 'gear' being taken in as fixed effects (only as random effects).

How can I prevent "~." in the first model from selecting 'carb' and 'gear' as fixed variables so that it may produce the same output as the second model below?

The output that I need: (ONLY 'carb' and 'gear' listed as random effects):

> el <- lmer(cyl~ disp   hp   wt   qsec   vs   (carb | gear), data = mtcars)

> el

Linear mixed model fit by REML ['lmerMod']
Formula: cyl ~ disp   hp   wt   qsec   vs   (carb | gear)
   Data: mtcars
REML criterion at convergence: 79.7548
Random effects:
 Groups   Name        Std.Dev. Corr 
 gear     (Intercept) 0.9932        
          carb        0.1688   -0.82
 Residual             0.5263        
Number of obs: 32, groups:  gear, 3
Fixed Effects:
(Intercept)         disp           hp           wt         qsec           vs  
   6.848103     0.004024     0.006929     0.172789    -0.169145    -0.785878

Any help at all is greatly appreciated!

CodePudding user response：

The . shortcut in R formula notation means "every variable in data except the variable(s) on the left-hand side of the formula." This is just a convenient shorthand that was originally intended for use with the lm() function. It is not very flexible as you have found out from trying to apply it in a different context than what it was intended for. The lmer() formula includes dependent variables, fixed effects, and random effects. So in this case the . shortcut will not work. To avoid hard-coding the variable names you can put together the formula by pasting strings, like this:

columns <- c("disp", "hp", "wt", "qsec", "vs")
fixed_effects <- paste(columns, collapse = ' ')
model_formula <- as.formula(paste('cyl ~ (carb|gear)  ', fixed_effects))
model_formula

# cyl ~ (carb | gear)   disp   hp   wt   qsec   vs

rf <- lmer(model_formula, data = mtcars)

Here we use paste() with the collapse argument to concatenate all elements of columns with a between each one. Then we paste() again to combine the part of the formula with the left-hand side and the random effects with the fixed effects. That gives us the full model formula as a string, which we can convert to a formula using as.formula().