R: Predicting with lmer, y ~ . formula error

Question

Predicting values in new data from an lmer model throws an error when a period is used to represent predictors. Is there any way around this?

The answer to this similar question offers a way to automatically write out the full formula instead of using the period, but I'm curious if there's a way to get predictions from new data just using the period.

Here's a reproducible example:

mydata <- data.frame(
    groups = rep(1:3, each = 100),
    x = rnorm(300),
    dv = rnorm(300)
)

train_subset <- sample(1:300, 300 * .8)
train <- mydata[train_subset,]
test <- mydata[-train_subset,]

# Returns an error
mod <- lmer(dv ~ . - groups + (1 | groups), data = train)
predict(mod, newdata = test)
predict(mod) # getting predictions for the original data works

# Writing the full formula without the period does not return an error, even though it's the exact same model
mod <- lmer(dv ~ x + (1 | groups), data = train)
predict(mod, newdata = test)

Answer 1

This should be fixed in the development branch of lme4 now. You can install from GitHub (see first line below) or wait a few weeks (early April-ish) for a new version to hit CRAN.

remotes::install_github("lme4/lme4") ## you will need compilers etc.
mydata <- data.frame(
    groups = rep(1:3, each = 100),
    x = rnorm(300),
    dv = rnorm(300)
)

train_subset <- sample(1:300, 300 * .8)
train <- mydata[train_subset,]
test <- mydata[-train_subset,]

# Returns an error
mod <- lmer(dv ~ . - groups + (1 | groups), data = train)
p1 <- predict(mod, newdata = test)

mod2 <- lmer(dv ~ x + (1 | groups), data = train)
p2 <- predict(mod2, newdata = test)
identical(p1, p2) ## TRUE