HLtest  R Documentation 
The HLtest
function computes the classical HosmerLemeshow (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 HosmerLemeshow 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 g2
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 chisquare statistics 
df 
Degrees of freedom 
p.value 
p value 
groups 
Number of groups 
call 

Michael Friendly
Hosmer, David W., Lemeshow, Stanley (1980). A goodnessoffit test for multiple logistic regression model. Communications in Statistics, Series A, 9, 10431069.
Hosmer, David W., Lemeshow, Stanley (2000). Applied Logistic Regression, New York: Wiley, ISBN 0471615536
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), 92106.
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)
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