diagn: Computes diagnostics for generalized linear models.

In catregs: Post-Estimation Functions for Generalized Linear Mixed Models

Computes diagnostics for generalized linear models.

Description

Given a glm object, diagn returns case-level diagnostics. For logistic, probit, Poisson, and negative binomial models, it returns Pearson residuals, standardized Pearson residuals, the diagonal of the hat matrix, delta-beta (Cook's D), and deviance residuals. For zero-inflated and hurdle models, it returns the Pearson residual and the observation number.

Usage

diagn(model)

Arguments

model

A model object. The model should be regression model for limited dependent variables, such as a logistic regression.

Value

out

The output is a dataframe of diagnostic statistics. For logit, probit, Poisson, and negative binomial models, the output includes the Pearson residual (pearsonres), the diagonal of the Hat matrix (h), the standardized Pearson residual (stdpres), the delta-beta statistic (deltabeta), the observation number (obs), and the deviance residual (devres). For zero-inflated and hurdle models, the output includes the Pearson residual (pearsonres), and the observation number (obs).

Author(s)

David Melamed

Examples

data("Mize19AH")
m1 <- glm(alcB ~woman*parrole + age + race2 + race3 +
race4 + income + ed1 + ed2 + ed3 + ed4,
family="binomial",data=Mize19AH)
head(diagn(m1))

catregs documentation built on Sept. 11, 2024, 6:18 p.m.