predict.wbm: Predictions and simulations from within-between models

View source: R/utils.R

predict.wbmR Documentation

Predictions and simulations from within-between models


These methods facilitate fairly straightforward predictions and simulations from wbm models.


## S3 method for class 'wbm'
  newdata = NULL, = FALSE,
  raw = FALSE, = FALSE,
  re.form = NULL,
  type = c("link", "response"), = TRUE,
  na.action = na.pass,

## S3 method for class 'wbm'
  nsim = 1,
  seed = NULL,
  use.u = FALSE,
  newdata = NULL,
  raw = FALSE,
  newparams = NULL,
  re.form = NA,
  type = c("link", "response"), = FALSE,
  na.action = na.pass,



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.


Is newdata a merMod model frame or panel_data? TRUE indicates a merMod-style newdata, with all of the extra columns created by wbm.

If is TRUE, include random effects variance in standard errors? Default is FALSE.


(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.


character string - either "link", the default, or "response" indicating the type of prediction object returned.

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).


function determining what should be done with missing values for fixed effects in newdata. The default is to predict NA: see na.pass.


When boot and are TRUE, any additional arguments are passed to lme4::bootMer().


positive integer scalar - the number of responses to simulate.


an optional seed to be used in set.seed immediately before the simulation so as to generate a reproducible sample.


(logical) if TRUE, generate a simulation conditional on the current random-effects estimates; if FALSE generate new Normally distributed random-effects values. (Redundant with re.form, which is preferred: TRUE corresponds to re.form = NULL (condition on all random effects), while FALSE corresponds to re.form = ~0 (condition on none of the random effects).)


new parameters to use in evaluating predictions, specified as in the start parameter for lmer or glmer – a list with components theta and beta and (for LMMs or GLMMs that estimate a scale parameter) sigma


wages <- panel_data(WageData, id = id, wave = t)
model <- wbm(lwage ~ lag(union) + wks, data = wages)
# By default, assumes you're using the processed data for newdata

panelr documentation built on Aug. 22, 2023, 5:08 p.m.