add_ci.glm {ciTools} R Documentation

## Confidence Intervals for Generalized Linear 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 glm. The default method calculates confidence intervals by making an interval on the scale of the linear predictor, then applying the inverse link function from the model fit to transform the linear level confidence intervals to the response level. Alternatively, confidence intervals may be calculated through a nonparametric bootstrap method.

### Usage

## S3 method for class 'glm'
df,
fit,
alpha = 0.05,
names = NULL,
yhatName = "pred",
response = TRUE,
type = "parametric",
nSims = 2000,
...
)


### Arguments

 df A data frame of new data. fit An object of class glm. alpha A real number between 0 and 1. Controls the confidence level of the interval estimates. names NULL or character vector of length two. If NULL, confidence bounds automatically will be named by add_ci, otherwise, the lower confidence bound will be named names[1] and the upper confidence bound will be named names[2]. yhatName A character vector of length one. Name of the vector of predictions made for each observation in df response A logical. The default is TRUE. If TRUE, the confidence intervals will be determined for the expected response; if FALSE, confidence intervals will be made on the scale of the linear predictor. type A character vector of length one. Must be type = "parametric" or type = "boot". type determines the method used to compute the confidence intervals. nSims An integer. Number of simulations to perform if the bootstrap method is used. ... Additional arguments.

### Value

A dataframe, df, with predicted values, upper and lower confidence bounds attached.

add_pi.glm for prediction intervals for glm objects, add_probs.glm for conditional probabilities of glm objects, and add_quantile.glm for response quantiles of glm objects.

### Examples

# Poisson regression
fit <- glm(dist ~ speed, data = cars, family = "poisson")
# Try a different confidence level
add_ci(cars, fit, alpha = 0.5)
# Add custom names to the confidence bounds (may be useful for plotting)
add_ci(cars, fit, alpha = 0.5, names = c("lwr", "upr"))

# Logistic regression
fit2 <- glm(I(dist > 30) ~ speed, data = cars, family = "binomial")
dat <- cbind(cars, I(cars\$dist > 30))
# Form 95% confidence intervals for the fit: