HLgof.test {MKclass}

Hosmer-Lemeshow goodness of fit tests.

Description

The function computes Hosmer-Lemeshow goodness of fit tests for C and H statistic as well as the le Cessie-van Houwelingen-Copas-Hosmer unweighted sum of squares test for global goodness of fit.

Usage

HLgof.test(fit, obs, ngr = 10, X, verbose = FALSE)

Arguments

fit

numeric vector with fitted probabilities.

obs

numeric vector with observed values.

ngr

number of groups for C and H statistic.

X

covariate(s) for le Cessie-van Houwelingen-Copas-Hosmer global goodness of fit test.

verbose

logical, print intermediate results.

Details

Hosmer-Lemeshow goodness of fit tests are computed; see Lemeshow and Hosmer (1982).

If X is specified, the le Cessie-van Houwelingen-Copas-Hosmer unweighted sum of squares test for global goodness of fit is additionally determined; see Hosmer et al. (1997). A more general version of this test is implemented in function residuals.lrm in package rms.

Value

A list of test results.

Author(s)

Matthias Kohl [e-mail]

References

S. Lemeshow and D.W. Hosmer (1982). A review of goodness of fit statistics for use in the development of logistic regression models. American Journal of Epidemiology, 115(1), 92-106.

D.W. Hosmer, T. Hosmer, S. le Cessie, S. Lemeshow (1997). A comparison of goodness-of-fit tests for the logistic regression model. Statistics in Medicine, 16, 965-980.

See Also

residuals.lrm

Examples

set.seed(111)
x1 <- factor(sample(1:3, 50, replace = TRUE))
x2 <- rnorm(50)
obs <- sample(c(0,1), 50, replace = TRUE)
fit <- glm(obs ~ x1+x2, family = binomial)
HLgof.test(fit = fitted(fit), obs = obs)
HLgof.test(fit = fitted(fit), obs = obs, X = model.matrix(obs ~ x1+x2))