binaryReg: Diagnostics for Logistic Regression

In USGS-R/smwrStats: R functions to support statistical methods in water resources

Diagnostics for Logistic Regression

Description

Computes diagnostic statistics for logistic regression.

Usage

binaryReg(object, lc.max = 1000)

Arguments

object	the logistic regression model object.
lc.max	the lmaximum number of observations for the le Cessie test.

Details

Because the le Cessie test is very slow to compute for many observations, the test is not performed if there are more than lc.max observations.

Value

A list of class "binaryreg" containing these components:

regsum	the output from summary(object)
Warning	any warnings relevant to the model
Factors	information about factor explanatory variables
Profile	summary information about the coding of the response variable
Hosmer	output from the Hosmer-Lemeshow test on object
leCessie	output from the le Cessie-van Houwelingen test on object
PctCorrect	the classification table
Concordance	the concordance table
roc	output from the receiver operating characteristics test on object
diagstats	a data frame containing the response variable, the predicted response probability, the response residual, the deviance residuals, the Pearson residuals, the leverage, the value of Cook's D, and the dfits value
crit.val	the critical values for leverage, Cook's D, and dfits
flagobs	a logical value indicating which observaitons exceeded any one of the critical values
object	the object

Note

Logistic regression can be very useful alternative method for heavily censored water-quality data.
The critical values for the test criteria are computed as: leverage, 3p/n; Cook's D, median quantile for the F distribution with p+1 and n-p degrees of freedonm; and dfits, the .01 quantile of the grubbs distribution for n observations, where p is the number of parameters estiamted in the regression and n is the number of observations.
Objects of class "binaryreg" have print and plot methods.

References

Harrell, F.E., Jr., 2001, Regression modeling strategies with applications to linear models, logistic regression and survival analysis: New York, N.Y., Springer, 568 p.

Helsel, D.R., and Hirsch, R.M., 2002, Statistical methods in water resources: U.S. Geological Survey Techniques of Water-Resources Investigations, book 4, chap. A3, 522 p.

McFadden, D., 1974, Conditional logit analysis of qualitative choice behavior: p. 105-142 in Zarembka, P. (ed.), Frontiers in Econometrics. London, Academic Press, 252 p.

See Also

roc, leCessie.test, hosmerLemeshow.test

USGS-R/smwrStats documentation built on Oct. 11, 2022, 6:15 a.m.