ciTools (version 0.6.1)

add_pi.lmerMod: Prediction Intervals for Linear Mixed Model Fitted Values

Description

This function is one of the methods in add_pi, and is called automatically when add_pi is used on a fit of class lmerMod.

Usage

# S3 method for lmerMod
add_pi(
  df,
  fit,
  alpha = 0.05,
  names = NULL,
  yhatName = "pred",
  type = "parametric",
  includeRanef = TRUE,
  log_response = FALSE,
  nSims = 10000,
  ...
)

Arguments

df

A data frame of new data

fit

An object of class lmerMod.

alpha

A real number between 0 and 1. Controls the confidence level of the interval estimates.

names

NULL or character vector of length two. If NULL, prediction bounds automatically will be named by add_pi, otherwise, the lower prediction bound will be named names[1] and the upper prediction bound will be named names[2].

yhatName

A string. Name of the predictions vector.

type

A string, either "parametric" or "boot". Determines the method used to calculate the prediction intervals.

includeRanef

A logical. Set whether the predictions and intervals should be conditioned on the random effects. If FALSE, random effects will not be included.

log_response

A logical, indicating if the response is on log scale in the model fit. If TRUE, prediction intervals will be returned on the response scale.

nSims

A positive integer. If type = "boot", nSims will determine the number of bootstrap simulations to perform.

...

Additional arguments.

Value

A dataframe, df, with predicted values, upper and lower prediction bounds attached.

Details

It is recommended that one use parametric prediction intervals when modeling with a random intercept linear mixed model. Otherwise, prediction intervals may be simulated via a parametric bootstrap using the function lme4.simulate().

See Also

add_ci.lmerMod for confidence intervals for lmerMod objects, add_probs.lmerMod for conditional probabilities of lmerMod objects, and add_quantile.lmerMod for response quantiles of lmerMod objects.

Examples

# NOT RUN {
dat <- lme4::sleepstudy
# Fit a (random intercept) linear mixed model
fit <- lme4::lmer(Reaction ~ Days + (1|Subject), data = lme4::sleepstudy)
# Add 50% prediction intervals to the original data using the default
# method.
add_pi(dat, fit, alpha = 0.5)

# Add 50% prediction intervals to the original data using the
# parametric bootstrap method. Form prediction intervals at the population
# level (unconditional on the random effects).
add_pi(dat, fit, alpha = 0.5, type = "boot", includeRanef = FALSE)

# }