add_quantile.glmerMod {ciTools}

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 class '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.

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.

Value

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

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

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)