This function generates predictions for merMod models, but with the ability to get standard errors as well.

predict_merMod(
object,
newdata = NULL,
se.fit = FALSE,
use.re.var = FALSE,
allow.new.levels = FALSE,
na.action = na.pass,
re.form = NULL,
boot = FALSE,
sims = 100,
prog.arg = "none",
...
)

## Arguments

object a fitted model object data frame for which to evaluate predictions. Include standard errors with the predictions? Note that these standard errors by default include only fixed effects variance. See details for more info. Default is FALSE. If se.fit is TRUE, include random effects variance in standard errors? Default is FALSE. logical if new levels (or NA values) in newdata are allowed. If FALSE (default), such new values in newdata will trigger an error; if TRUE, then the prediction will use the unconditional (population-level) values for data with previously unobserved levels (or NAs). character string - either "link", the default, or "response" indicating the type of prediction object returned. function determining what should be done with missing values for fixed effects in newdata. The default is to predict NA: see na.pass. (formula, NULL, or NA) specify which random effects to condition on when predicting. If NULL, include all random effects; if NA or ~0, include no random effects. Use bootstrapping (via lme4::bootMer()) to estimate variance for se.fit? Default is FALSE If boot is TRUE, how many simulations should be run? Default is 100. If boot and se.fit are TRUE, a character string - type of progress bar to display. Default is "none"; the function will look for a relevant *ProgressBar function, so "txt" will work in general; "tk" is available if the tcltk package is loaded; or "win" on Windows systems. Progress bars are disabled (with a message) for parallel operation. When boot and se.fit are TRUE, any additional arguments are passed to lme4::bootMer().

## Details

The developers of lme4 omit an se.fit argument for a reason, which is that it's not perfectly clear how best to estimate the variance for these models. This solution is a logical one, but perhaps not perfect. Bayesian models are one way to do better.

The method used here is based on the one described here: http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#predictions-andor-confidence-or-prediction-intervals-on-predictions