R: Table of Actual Outcomes against Predicted Outcomes for...

hitmiss {pscl} | R Documentation

## Table of Actual Outcomes against Predicted Outcomes for discrete data models

### Description

Cross-tabulations of actual outcomes against predicted outcomes for discrete data models, with summary statistics such as percent correctly predicted (PCP) under fitted and null models. For models with binary responses (generalized linear models with `family=binomial`), the user can specific a classification threshold for the predicted probabilities.

### Usage

    hitmiss(obj, digits = max(3, getOption("digits") - 3), ...)

    ## S3 method for class 'glm'
    hitmiss(obj,digits=max(3,getOption("digits")-3),
                ...,
                k=.5)

### Arguments

`obj` | a fitted model object, such as a `glm` with `family=binomial`, a `polr` model for ordinal responses, or a `multinom` model for unordered/multinomial outcomes
`digits` | number of digits to display in on-screen output
`...` | additional arguments passed to or from other functions
`k` | classification threshold for binary models

### Details

For models with binary responses, the user can specify a parameter 0 < `k` < 1; if the predicted probabilities exceed this threshold then the model is deemed to have predicted y=1, and otherwise to have predicted y=0. Measures like percent correctly predicted are crude summaries of model fit; the cross-tabulation of actual against predicted is somewhat more informative, providing a little more insight as to where the model fits less well.

### Value

For `hitmiss.glm`, a vector of length 3:

`pcp` | Percent Correctly Predicted
`pcp0` | Percent Correctly Predicted among y=0
`pcp1` | Percent Correctly Predicted among y=1

### Note

To-do: The `glm` method should also handle binomial data presented as two-vector success/failures counts; and count data with `family=poisson`, the `glm.nb` models and `zeroinfl` and `hurdle` etc. We should also make the output a class with prettier print methods, i.e., save the cross-tabulation in the returned object etc.

### Author(s)

Simon Jackman simon.jackman@sydney.edu.au

### See Also

`pR2` for pseudo r-squared; `predict`; `extractAIC`. See also the ROCR package and the `lroc` function in the epicalc package for ROC computations for assessing binary classifications.

### Examples

    data(admit)
    ## ordered probit model
    op1 <- MASS::polr(score ~ gre.quant + gre.verbal + ap + pt + female,
                Hess=TRUE,
                data=admit,
                method="probit")
    hitmiss(op1)

[Package pscl version 1.5.2 Index]