R: Predicted Probabilties for GLM Fits

predprob.glm {pscl} | R Documentation

## Predicted Probabilties for GLM Fits

### Description

Obtains predicted probabilities from a fitted generalized linear model object.

### Usage

    ## S3 method for class 'glm'
    predprob(obj, newdata = NULL, at = NULL, ...)

### Arguments

`obj` | a fitted object of class inheriting from `"glm"`
`newdata` | optionally, a data frame in which to look for variables with which to predict. If omitted, the fitted linear predictors are used.
`at` | an optional numeric vector at which the probabilities are evaluated. By default `0:max(y)` where `y` is the original observed response.
`...` | arguments passed to or from other methods

### Details

This method is only defined for glm objects with `family=binomial` or `family=poisson`, or negative binomial count models fit with the `glm.nb` function in `library(MASS)`.

### Value

A matrix of predicted probabilities. Each row in the matrix is a vector of probabilities, assigning predicted probabilities over the range of responses actually observed in the data. For instance, for models with `family=binomial`, the matrix has two columns for the "zero" (or failure) and "one" (success) outcomes, respectively, and trivially, each row in the matrix sums to 1.0. For counts fit with `family=poisson` or via `glm.nb`, the matrix has `length(0:max(y))` columns. Each observation used in fitting the model generates a row to the returned matrix; alternatively, if `newdata` is supplied, the returned matrix will have as many rows as in `newdata`.

### Author(s)

Simon Jackman simon.jackman@sydney.edu.au

### See Also

`predict.glm`

### Examples

    data(bioChemists)
    glm1 <- glm(art ~ .,
                data=bioChemists,
                family=poisson,
                trace=TRUE)  ## poisson GLM
    phat <- predprob(glm1)
    apply(phat,1,sum)                    ## almost all 1.0

[Package pscl version 1.5.2 Index]