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DESCRIPTION

Computes predicted values and pointwise confidence intervals for logistic regression.

 

USAGE

pointwise.logit(glm.obj,newdata,coverage=0.99)

 

REQUIRED ARGUMENTS

glm.obj		glm object with family=binomial and default link
newdata		list holding values of the covariates for which predictions are required. See predict.lm for details.

 

OPTIONAL ARGUMENTS

coverage

confidence level for confidence intervals

 

VALUE

List with components:

upper		upper limits of confidence intervals
fit		predicted values
lower		lower limits of confidence intervals

 

DETAILS

This routine is an improvement on the S-Plus function pointwise for logistic regression objects. Confidence intervals are computed using a normal approximation on the linear predictor scale, and then mapped via the logistic transform to the probability scale. The generalized linear model dispersion parameter is assumed to be 1.

Note that the S-Plus function pointwise produces correct predicted values but its standard errors and confidence intervals are not reliable for logistic regression, or for any discrete generalized linear model.

EXAMPLES

glm.obj <- glm(y~x,family="binomial")
pw <- pointwise.logit(glm.obj,newdata="x")

 

 

Gordon Smyth. Copyright © 1996-2016. Last modified: 10 February 2004