add_ci {ciTools}  R Documentation 
Add Confidence Intervals for Fitted Values to Data Frames
Description
This is a generic function to append confidence intervals for
predictions of a model fit to a data frame. A confidence interval
is generated for the fitted value of each observation in
df
. These confidence intervals are then appended to
df
and returned to the user as a data frame. The fit
may
be a linear, loglinear, linear mixed, generalized linear model,
generalized linear mixed, or accelerated failure time model.
Usage
add_ci(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 character vector of length one. Name of the
vector of the predictions made for each observation in 
... 
Additional arguments. 
Details
For more specific information about the arguments that are applicable in each method, consult:

add_ci.lm
for linear model confidence intervals 
add_ci.glm
for generalized linear model confidence intervals 
add_ci.lmerMod
for linear mixed model confidence intervals 
add_ci.glmerMod
for generalized linear mixed model confidence intervals 
add_ci.survreg
for accelerated failure time confidence intervals
Note that add_ci
calculates confidence intervals for
fitted values, not model coefficients. For confidence
intervals of model coefficients, see confint
.
Value
A dataframe, df
, with predicted values, upper and lower
confidence bounds attached.
See Also
add_pi
for prediction 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)
# Make a confidence interval for each observation in cars, and
# append to the data frame
add_ci(cars, fit)
# Make new data
new_data < cars[sample(NROW(cars), 10), ]
add_ci(new_data, fit)
# Fit a Poisson model
fit2 < glm(dist ~ speed, family = "poisson", data = cars)
# Append CIs
add_ci(cars, fit2)
# Fit a linear mixed model using lme4
fit3 < lme4::lmer(Reaction ~ Days + (1Subject), data = lme4::sleepstudy)
# Append CIs
# Generally, you should use more than 100 bootstrap replicates
add_ci(lme4::sleepstudy, fit3, nSims = 100)
# Fit a logistic model
fit4 < glm(I(dist > 20) ~ speed, family = "binomial", data = cars)
# Append CIs
add_ci(cbind(cars, I(cars$dist > 20)), fit4)