lmer failing with na.pass - r

Question

When I run a lmer model with lme4 using na.pass as the na.action, I get the following error:
R: NA/NaN/Inf in foreign function call (arg 1)
I run the model like this:
model1 <- lme4::lmer(agg_dv_singing ~ GMS.Musical.Training +
JAJ.ability + MDT.ability + MPT.ability + PDCT.ability +
PIAT.ability + agg_dv_long_note + demographics.age +
aggiv_entropy + aggiv_interval_complexity +
aggiv_rhythmic_complexity + aggiv_tonal_complexity +
log.freq + length + (1|p_id),
data = dat, na.action = na.pass)
summary(dat) indicates that there are no Inf or NaN values, although yes, there are many NA values.
Running na.pass outside of lmer on the same data set does not give an error:
na.pass(dat)
So what could be going wrong within lmer?

Answer 1

Comments to a previous question of yours attempted to explain that, in general, mixed model machinery cannot handle estimation from cases when there are missing values in the predictors; it just doesn't work that way. If you want to fit mixed models with missing data you need to do some form of imputation, i.e. filling in values for missing predictors (e.g. see the mice package, which is more or less the state of the art at least as far as the R ecosystem is concerned). Here is what the four different standard na.* actions do in the context of mixed models:
na.fail(): fail immediately if there are missing values in the data (predictors or response). This is frustrating, but alerts you immediately to the fact that you have missing data, and lets you decide what to do about it.
na.omit(): drop non-complete cases from the data before fitting.
na.exclude(): like na.omit(), but keep track of the locations of the excluded cases. When using predict() or residuals() (or any function that produces results per observation), reconstitute a complete data set with NA values for the non-complete cases in the original data set. (I usually find this setting to be the most useful default.)
na.pass: do not remove NA values, but attempt to continue with the fitting procedure. As you found out, this usually doesn't work at all! It will just pass the NA values down through the code until something goes wrong. Typically one of two things happens at this point:
if the entire estimation procedure is written using R functions that can handle and propagate missing values, then you'll usually get a fitted model object with NA/NaN for all coefficients, likelihoods, etc. etc. (because the missing values contaminate the entire fitting procedure);
if some step of the estimation procedure can't handle NA/NaN values (as in this case), you get an inscrutable error from the first point in the procedure that fails.
If you look at the source code of na.pass() (by typing na.pass at the R prompt), you'll see that in fact all it does is return the same object, unchanged. To be honest, I'm not really sure why na.pass even exists, except for completeness ... (or compatibility with S)
Your NA value was not in a parameter that is used in a random-effects term: if it had, you would have gotten a more interpretable error message:
library(lme4)
ss <- sleepstudy
ss[1,"Days"] <- NA
lmer(Reaction ~ Days + (Days|Subject), ss, na.action=na.pass)
Error in lme4::lFormula(formula = Reaction ~ Days + (Days | Subject), :
NA in Z (random-effects model matrix): please use "na.action='na.omit'" or "na.action='na.exclude'"
If I fit a model with (1|Subject), so that the NA value only affects the fixed effects
lmer(Reaction ~ Days + (1|Subject), ss, na.action=na.pass)
then we get your error message.
Error in qr.default(X, tol = tol, LAPACK = FALSE) :
NA/NaN/Inf in foreign function call (arg 1)
traceback() tells me that this happens in the internal chkRank.drop.cols() function, where R is trying to figure out if any of your fixed-effect columns are collinear. There should probably be a check for missing values there ...