Error comparing linear mixed effects models - r

Question

I want to see whether the fixed effect Group2 in my model is significant. The model is:
Response ~ Group1 + Group2 + Gender + Age + BMI + (1 | Subject)
To check the significance I create a null model not containing the effect Group2:
Resp.null = lmer(Response~Group1+Gender+Age+BMI+(1|Subject),
data=mydata,REML=FALSE)
and the full model containing the effect Group2:
Resp.model = lmer(Response~Group1+Group2+Gender+Age+BMI+(1|Subject),
data=mydata,REML=FALSE)
Then I use anova() to compare the two, but I get an error:
anova(Resp.null, Resp.model)
## Error in anova.merMod(Resp.null, Resp.model) :
## models were not all fitted to the same size of dataset
I think that the problem is that Group1 contains NaN, but I thought that linear mixed models were robust to missing data.
How can I solve this problem and compare the two models?
Do I have to delete the rows corresponding to NaN and fit Resp.null without these rows?
The data can be downloaded here.
Please note that you should replace "<undefined>" with NaN like this:
mydata = read.csv("mydata.csv")
mydata[mydata == "<undefined>"] <- NA

Answer 1

To avoid the "models were not all fitted to the same size of dataset" error in anova, you must fit both models on the exact same subset of data.
There are two simple ways to do this, and while this reproducible example uses lm and update, for lmer objects the same approach should work:
# 1st approach
# define a convenience wrapper
update_nested <- function(object, formula., ..., evaluate = TRUE){
update(object = object, formula. = formula., data = object$model, ..., evaluate = evaluate)
}
# prepare data with NAs
data(mtcars)
for(i in 1:ncol(mtcars)) mtcars[i,i] <- NA
xa <- lm(mpg~cyl+disp, mtcars)
xb <- update_nested(xa, .~.-cyl)
anova(xa, xb)
## Analysis of Variance Table
##
## Model 1: mpg ~ cyl + disp
## Model 2: mpg ~ disp
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 26 256.91
## 2 27 301.32 -1 -44.411 4.4945 0.04371 *
## ---
## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# 2nd approach
xc <- update(xa, .~.-cyl, data=na.omit(mtcars[ , all.vars(formula(xa))]))
anova(xa, xc)
## Analysis of Variance Table
##
## Model 1: mpg ~ cyl + disp
## Model 2: mpg ~ disp
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 26 256.91
## 2 27 301.32 -1 -44.411 4.4945 0.04371 *
## ---
## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
If however you're only interested in testing a single variable (e.g. Group2), then perhaps the Anova() or linearHypothesis() in car would work as well for this usecase.
See also:
How to update `lm` or `glm` model on same subset of data?
R error which says "Models were not all fitted to the same size of dataset"

Answer 2

Fit Resp.model first, then use Resp.model#frame as data argument.
Resp.null = lmer(Response~Group1+Gender+Age+BMI+(1|Subject),
data=Resp.model#frame,REML=FALSE)