add_pi.glmerMod {ciTools}  R Documentation 
Prediction Intervals 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_pi(
df,
fit,
alpha = 0.05,
names = NULL,
yhatName = "pred",
type = "boot",
includeRanef = TRUE,
nSims = 10000,
...
)
Arguments
df 
A data frame of new data. 
fit 
An object of class 
alpha 
A real number between 0 and 1. Controls the confidence level of the interval estimates. 
names 

yhatName 

type 
A string. Must be 
includeRanef 
A logical. Default is 
nSims 
A positive integer. Controls the number of bootstrap replicates. 
... 
Additional arguments. 
Details
Prediction intervals are approximate and determined by simulation
through the simulate
function distributed with lme4
.
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, upper and lower
prediction bounds attached.
See Also
add_ci.glmerMod
for confidence intervals
of glmerMod
objects, add_probs.glmerMod
for
conditional probabilities of glmerMod
objects, and
add_quantile.glmerMod
for response quantiles 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+xf), data=df, family = "poisson")
add_pi(df, fit, names = c("LPB", "UPB"), nSims = 500)