predict.wbm: Predictions and simulations from within-between models
In panelr: Regression Models and Utilities for Repeated Measures and Panel Data

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

## S3 method for class 'wbm'
predict(
  object,
  newdata = NULL,
  se.fit = FALSE,
  raw = FALSE,
  use.re.var = FALSE,
  re.form = NULL,
  type = c("link", "response"),
  allow.new.levels = TRUE,
  na.action = na.pass,
  ...
)

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

`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.
`raw`	Is `newdata` a `merMod` model frame or `panel_data`? TRUE indicates a `merMod`-style newdata, with all of the extra columns created by `wbm`.
`use.re.var`	If `se.fit` is TRUE, include random effects variance in standard errors? Default is FALSE.
`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.
`type`	character string - either `"link"`, the default, or `"response"` indicating the type of prediction object returned.
`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).
`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`.
`...`	When `boot` and `se.fit` are TRUE, any additional arguments are passed to `lme4::bootMer()`.
`nsim`	positive integer scalar - the number of responses to simulate.
`seed`	an optional seed to be used in `set.seed` immediately before the simulation so as to generate a reproducible sample.
`use.u`	(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).)
`newparams`	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`

data("WageData")
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
predict(model)