Dear colleagues I have a couple of problems related with binary logistic
regression.
The first problem is how to compute Pearson and Likelihood chi-squeared
tests for grouped data.
For the same form of data set how to compute sensitivity, specificity and
related measures.
When I speak about grouped data I mean data of the following form
Alcohol.Consum Malformed NoMalformed
0 48 17066
<1 38 14464
1-2 5 788
3-5 1 126
>=6 1 37
(This data set has been taken from the book Categorical data analysis by
Agresti)
The second question is the following:is it correct to upload a grouped data
set to an ungrouped one?
The upload is achieved with the aid of the following routine
Createdataframe <- function(d){
f1 = f2 = numeric(0)
v = d[,1]
for(j in 2:3){
for(i in 1:dim(d)[1]){
f1 = c( f1, rep( levels(v)[v[i]], d[i,j] ) )
}
f2 = c( f2, rep( 3-j, sum(d[,j]) ) )
}
df=data.frame(f1,f2)
return(df)
}
My finally question is why SPSS computes the Hosmer-Lemeshow test while the
routine listed below gives NaN. (One has to put g=8 in order to get a
numeric value)
The data set is (it is also has been taken from the same book mentioned
above)
FlightNo Temp ThermalDisast
1 66 0
2 70 1
3 69 0
4 68 0
5 67 0
6 72 0
7 73 0
8 70 0
9 57 1
10 63 1
11 70 1
12 78 0
13 67 0
14 53 1
15 67 0
16 75 0
17 70 0
18 81 0
19 76 0
20 79 0
21 75 1
22 76 0
23 58 1
and the routine used is
hosmerlemeshow = function(obj, g=10) {
# first, check to see if we fed in the right kind of object
stopifnot(family(obj)$family=="binomial" && family(obj)$link=="logit")
y = obj$model[[1]]
# the double bracket (above) gets the index of items within an object
if (is.factor(y))
y = as.numeric(y)==2
yhat = obj$fitted.values
cutyhat=cut(yhat,quantile(yhat,0:g/g),include.lowest=TRUE)
obs = xtabs(cbind(1 - y, y) ~ cutyhat)
expect = xtabs(cbind(1 - yhat, yhat) ~ cutyhat)
if (any(expect < 5))
# warning("Some expected counts are less than 5. Use smaller number
of groups")
chisq = sum((obs - expect)^2/expect)
P = 1 - pchisq(chisq, g - 2)
cat("H-L2 statistic",round(chisq,4)," and its p value",P,"\n")
# by returning an object of class "htest", the function will
perform like the
# built-in hypothesis tests
return(structure(list(
method = c(paste("Hosmer and Lemeshow goodness-of-fit test with", g,
"bins", sep=" ")),
data.name = deparse(substitute(obj)),
statistic = c(X2=chisq),
parameter = c(df=g-2),
p.value = P
), class='htest'))
return(list(chisq=chisq,p.value=P))
}
I found this routine in the internet.
Thank you for your cooperation in advance. Any suggestion and/or solution
will be greatly appreciated.
With regards
Endy
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