add_pi {ciTools}  R Documentation 
Add Prediction Intervals to Data Frames
Description
This is a generic function to append prediction intervals to a data
frame. A prediction interval is made for each observation in
df
with respect to the model fit
. These intervals are
then appended to df
and returned to the user as a
data frame. fit
can be a linear, loglinear, linear mixed,
generalized linear, generalized linear mixed, or accelerated
failure time model.
Usage
add_pi(df, fit, alpha = 0.05, names = NULL, yhatName = "pred", ...)
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 
A string. Name of the predictions vector. 
... 
Additional arguments 
Details
For more specific information about the arguments that are applicable in each method, consult:

add_pi.lm
for linear regression prediction intervals 
add_pi.glm
for generalized linear regression prediction intervals 
add_pi.lmerMod
for linear mixed models prediction intervals 
add_pi.glmerMod
for generalized linear mixed model prediction intervals 
add_pi.survreg
for accelerated failure time model prediction intervals
Value
A dataframe, df
, with predicted values, upper and lower
prediction bounds attached.
See Also
add_ci
for confidence intervals,
add_probs
for response level probabilities, and
add_quantile
for quantiles of the conditional
response distribution.
Examples
# Fit a linear model
fit < lm(dist ~ speed, data = cars)
# Define some new data
new_data < cars[sample(NROW(cars), 10), ]
# Add fitted values and prediction intervals to new_data
add_pi(new_data, fit)
# Fit a Poisson model
fit2 < glm(dist ~ speed, family = "poisson", data = cars)
# Add approximate prediction intervals to the fitted values of
# new_data
add_pi(new_data, fit2)
# Fit a linear mixed model
fit3 < lme4::lmer(Reaction ~ Days + (1Subject), data = lme4::sleepstudy)
# Add parametric prediction intervals for the fitted values to the
# original data
add_pi(lme4::sleepstudy, fit3, type = "parametric")