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

In panelr: Regression Models and Utilities for Repeated Measures and Panel Data

Predictions and simulations from within-between models

Description

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

Usage

## 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,
  ...
)

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.
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

Examples

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)

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