## 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 class 'lmerMod'
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.

### 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().

### Value

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

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

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.