## Response Level Probabilities for Linear Models

### Description

This is the method add_probs uses if the model is of class lm. Probabilities are calculated parametrically, using a pivotal quantity.

### Usage

## S3 method for class 'lm'
df,
fit,
q,
name = NULL,
yhatName = "pred",
comparison = "<",
log_response = FALSE,
...
)


### Arguments

 df A data frame of new data. fit An object of class lm. Predictions are made with this object. q A real number. A quantile of the response distribution. name NULL or a string. If NULL, probabilities automatically will be named by add_probs, otherwise, the probabilities will be named name in the returned data frame. yhatName A character vector of length one. Names of the comparison "<", or ">". If comparison = "<", then Pr(Y|x < q) is calculated for each observation in df. Otherwise, Pr(Y|x > q) is calculated. log_response A logical. Default is FALSE. Set to TRUE if the model is log-linear: \log(Y) = X \beta + \epsilon. ... Additional arguments.

### Value

A dataframe, df, with predicted values and probabilities attached.

add_ci.lm for confidence intervals for lm objects, add_pi.lm for prediction intervals of lm objects, and add_quantile.lm for response quantiles of lm objects.

### Examples


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

# Calculate the probability that a new dist will be less than 20,
# given the model.