ciTools (version 0.6.1)

add_quantile.glmerMod: Response Quantiles for Generalized Linear Mixed Model Predictions

Description

This function is one of the methods for add_pi, and is called automatically when add_pi is used on a fit of class glmerMod. This function is experimental.

Usage

# S3 method for glmerMod
add_quantile(
  df,
  fit,
  p,
  name = NULL,
  yhatName = "pred",
  type = "boot",
  includeRanef = TRUE,
  nSims = 10000,
  ...
)

Arguments

df

A data frame of new data.

fit

An object of class glmerMod.

p

A real number between 0 and 1. Sets the probability level of the quantiles.

name

NULL or a string. If NULL, quantile automatically will be named by add_quantile

yhatName

NULL or a string. Name of the predictions vector.

type

A string. Must be "boot", If type = "boot", then add_ci calls lme4::simulate to calculate the confidence intervals. This method may be time consuming, but is applicable with random slope and random intercept models.

includeRanef

A logical. Default is TRUE. Set whether the predictions and intervals should be made conditional on the random effects. If FALSE, random effects will not be included.

nSims

A positive integer. Controls the number of bootstrap replicates.

...

Additional arguments.

Value

A dataframe, df, with predicted values and quantiles attached.

Details

If IncludeRanef is False, random slopes and intercepts are set to 0. Unlike in `lmer` fits, settings random effects to 0 does not mean they are marginalized out. Consider generalized estimating equations if this is desired.

See Also

add_pi.glmerMod for prediction intervals of glmerMod objects, add_probs.glmerMod for conditional probabilities of glmerMod objects, and add_ci.glmerMod for confidence intervals of glmerMod objects.

Examples

# NOT RUN {
n <- 300
x <- runif(n)
f <- factor(sample(1:5, size = n, replace = TRUE))
y <- rpois(n, lambda = exp(1 - 0.05 * x * as.numeric(f) + 2 * as.numeric(f)))
df <- data.frame(x = x, f = f, y = y)
fit <- lme4::glmer(y ~ (1+x|f), data=df, family = "poisson")

add_quantile(df, fit, name = "quant0.6", p = 0.6, nSims = 500)

# }