HLgof.test | R Documentation |
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.
HLgof.test(fit, obs, ngr = 10, X, verbose = FALSE)
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. |
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.
A list of test results.
Matthias Kohl Matthias.Kohl@stamats.de
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.
residuals.lrm
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))
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