add_quantile.lm {ciTools} R Documentation

## Quantiles for the Response of a Linear Model

### Description

This function is one of the methods of add_quantile. It is called automatically when add_quantile is called on objects of class lm.

### Usage

## S3 method for class 'lm'
df,
fit,
p,
name = 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. p A real number between 0 and 1. Sets the level of the quantiles. name NULL or a string. If NULL, quantiles automatically will be named by add_quantile, otherwise, they will be named name. yhatName A string. Name of the vector of predictions. log_response A logical. If TRUE, quantiles will be generated for the prediction made with a log-linear model: \log(Y) = X\beta + \epsilon. These quantiles will be on the scale of the original response, Y. ... Additional arguments.

### Details

Quantiles for linear models are determined parametrically, by applying a pivotal quantity to the distribution of Y|x.

### Value

A dataframe, df, with predicted values and level - p quantiles attached.

add_ci.lm for confidence intervals for lm objects, add_pi.lm for prediction intervals of lm objects, and add_probs.lm for response probabilities of lm objects.

### Examples


# Fit a linear Model
fit <- lm(dist ~ speed, data = cars)

# Find the 0.7-quantile (70th percentile) of new distances, given
# the linear model fit.
add_quantile(cars, fit, p = 0.7)

# As above, but with a custom name for the vector of quantiles
add_quantile(cars, fit, p = 0.7, name = "my_quantile")



[Package ciTools version 0.6.1 Index]