add_ci.glmerMod {ciTools}  R Documentation 
Confidence Intervals for Generalized Linear Mixed Model Predictions
Description
This function is one of the methods for add_ci
, and is
called automatically when add_ci
is used on a fit
of
class glmerMod
.
Usage
## S3 method for class 'glmerMod'
add_ci(
df,
fit,
alpha = 0.05,
names = NULL,
yhatName = "pred",
response = TRUE,
type = "boot",
includeRanef = TRUE,
nSims = 500,
...
)
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 

response 
A logical. The default is 
type 
A string. If 
includeRanef 
A logical. Default is 
nSims 
A positive integer. Controls the number of bootstrap
replicates if 
... 
Additional arguments. 
Details
The default and recommended method is bootstrap. The bootstrap
method can handle many types of models and we find it to be
generally reliable and robust as it is built on the bootMer
function from lme4
. This function is experimental.
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
confidence bounds attached.
References
For general information about GLMMs http://bbolker.github.io/mixedmodelsmisc/glmmFAQ.html
See Also
add_pi.glmerMod
for prediction 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")
## Not run: add_ci(df, fit, names = c("lcb", "ucb"), nSims = 300)