add_quantile.lmerMod {ciTools}  R Documentation 
This function is one of the methods for add_quantile
and
is called automatically when add_quantile
is applied to an
object of class lmerMod
.
## S3 method for class 'lmerMod'
add_quantile(
df,
fit,
p,
name = NULL,
yhatName = "pred",
includeRanef = TRUE,
type = "boot",
nSims = 10000,
log_response = FALSE,
...
)
df 
A data frame of new data. 
fit 
An object of class 
p 
A real number between 0 and 1. Determines the probability level of the quantiles. 
name 

yhatName 
A string. Determines the name of the column of predictions. 
includeRanef 
The random effects be included or not? If

type 
A string. Options are 
nSims 
A positive integer. Set the number of bootstrap
simulations to perform. Only applied when 
log_response 
A logical. Set to 
... 
Additional arguments. 
add_qauntile.lmerMod
may use one of two different methods
for determining quantiles: a parametric method or a parametric
bootstrap method (via lme4::simulate
). The parametric method
is the default. Only use the parametric method (type =
"parametric"
) if fit
is a random intercept model,
e.g. fit = lmer(y ~ x + (1group))
. If your model of
interest is random slope and random intercept, use the parametric
bootstrap method (type = "boot"
).
A dataframe, df
, with predicted values and levelp
quantiles attached.
add_ci.lmerMod
for confidence intervals
for lmerMod
objects, add_pi.lmerMod
for
prediction intervals of lmerMod
objects, and
add_probs.lmerMod
for response probabilities of
lmerMod
objects.
dat < lme4::sleepstudy
# Fit a random intercept model
fit < lme4::lmer(Reaction ~ Days + (1Subject), data = lme4::sleepstudy)
# Using the parametric method: given the model fit, what value
# of reaction time do we expect half of new reaction times to fall
# under?
add_quantile(dat, fit, p = 0.5)
# Using the parametric method:
# as above, but we ignore the random effects.
add_quantile(dat, fit, p = 0.5, includeRanef = FALSE)