add_pi.lm {ciTools}  R Documentation 
Prediction Intervals for Linear Model Predictions
Description
This function is one of the methods for add_pi
and is
automatically called when an object of class lm
is passed to
to add_pi
.
Usage
## S3 method for class 'lm'
add_pi(
df,
fit,
alpha = 0.05,
names = NULL,
yhatName = "pred",
log_response = FALSE,
...
)
Arguments
df 
A data frame of new data. 
fit 
An object of class lm. Predictions are made with this object. 
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. 
log_response 
A logical. If TRUE, prediction intervals will be
generated at the response level of a loglinear model:

... 
Additional arguments. 
Details
Prediction intervals for lm
objects are calculated
parametrically. This function is essentially just a wrapper for
predict(fit, df, interval = "prediction")
if fit
is a
linear model. If log_response = TRUE
, prediction intervals
for the response are calculated parametrically, then the
exponential function is applied to transform them to the original
scale.
Value
A dataframe, df
, with predicted values, upper and lower
prediction bounds attached.
See Also
add_ci.lm
for confidence intervals for
lm
objects. add_probs.lm
for conditional
probabilities of lm
objects, and
add_quantile.lm
for response quantiles of
lm
objects.
Examples
# Fit a linear model
fit < lm(dist ~ speed, data = cars)
# Add prediction intervals and fitted values to the original data
add_pi(cars, fit)
# Try to add predictions to a data frame of new data
new_data < cars[sample(NROW(cars), 10), ]
add_pi(new_data, fit)
# Try a different confidence level
add_pi(cars, fit, alpha = 0.5)
# Add custom names to the prediction bounds.
add_pi(cars, fit, alpha = 0.5, names = c("lwr", "upr"))