predict_merMod: Alternative interface for 'merMod' predictions

In jtools: Analysis and Presentation of Social Scientific Data

Alternative interface for merMod predictions

Description

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

Usage

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

Arguments

object	a fitted model object
newdata	data frame for which to evaluate predictions.
se.fit	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.
use.re.var	If se.fit is TRUE, include random effects variance in standard errors? Default is FALSE.
allow.new.levels	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).
type	character string - either "link", the default, or "response" indicating the type of prediction object returned.
na.action	function determining what should be done with missing values for fixed effects in newdata. The default is to predict NA: see na.pass.
re.form	(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.
boot	Use bootstrapping (via lme4::bootMer()) to estimate variance for se.fit? Default is FALSE
sims	If boot is TRUE, how many simulations should be run? Default is 100.
prog.arg	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

jtools documentation built on Sept. 11, 2024, 7 p.m.