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

ToDo: 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 jackman@stanford.edu

See Also

pR2 for pseudo r-squared; predict; extractAIC. See also the lroc function in the epicalc package for ROC computations.

Examples

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

[Package pscl version 1.03 Index]