Hosmer-Lemeshow Goodness of Fit Test
The HLtest function computes the classical Hosmer-Lemeshow (1980) goodness of fit test
for a binomial glm object in logistic regression
The general idea is to assesses whether or not the observed event rates match expected event rates in subgroups of the
model population. The Hosmer-Lemeshow test specifically identifies subgroups as the deciles of fitted event values,
or other quantiles as determined by the g argument.
Given these subgroups, a simple chisquare test on g-2 df is used.
In addition to print and summary methods, a plot method is
supplied to visualize the discrepancies between observed and fitted frequencies.
HosmerLemeshow(model, g = 10) HLtest(model, g = 10) ## S3 method for class 'HLtest' print(x, ...) ## S3 method for class 'HLtest' summary(object, ...) ## S3 method for class 'HLtest' plot(x, ...) ## S3 method for class 'HLtest' rootogram(x, ...)
model |
A |
g |
Number of groups used to partition the fitted values for the GOF test. |
x, object |
A |
... |
Other arguments passed down to methods |
A class HLtest object with the following components:
table |
A data.frame describing the results of partitioning the data into |
chisq |
The chisquared statistics |
df |
Degrees of freedom |
p.value |
p value |
groups |
Number of groups |
call |
|
Michael Friendly
Hosmer, David W., Lemeshow, Stanley (1980). A goodness-of-fit test for multiple logistic regression model. Communications in Statistics, Series A, 9, 1043-1069.
Hosmer, David W., Lemeshow, Stanley (2000). Applied Logistic Regression, New York: Wiley, ISBN 0-471-61553-6
Lemeshow, S. and Hosmer, D.W. (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.
rootogram, ~~~
data(birthwt, package="MASS")
# how to do this without attach?
attach(birthwt)
race = factor(race, labels = c("white", "black", "other"))
ptd = factor(ptl > 0)
ftv = factor(ftv)
levels(ftv)[-(1:2)] = "2+"
bwt <- data.frame(low = factor(low), age, lwt, race,
smoke = (smoke > 0), ptd, ht = (ht > 0), ui = (ui > 0), ftv)
detach(birthwt)
options(contrasts = c("contr.treatment", "contr.poly"))
BWmod <- glm(low ~ ., family=binomial, data=bwt)
(hlt <- HLtest(BWmod))
str(hlt)
summary(hlt)
plot(hlt)
# basic model
BWmod0 <- glm(low ~ age, family=binomial, data=bwt)
(hlt0 <- HLtest(BWmod0))
str(hlt0)
summary(hlt0)
plot(hlt0)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.