Hi, I am running a logistic regression on a simple dataset (attached) using glm:
> dat<-read.table("dat.txt",sep='\t',header=T)
If I use summary() on a logistic model:
> summary(glm(y~x1*x2,dat,family='binomial'))
Coefficients: Estimate Std. Error z value Pr(>|z|)(Intercept)
19.57 5377.01 0.004 0.997x1 -18.59 5377.01 -0.003
0.997x2B -19.57 5377.01 -0.004 0.997x1:x2B 38.15
7604.24 0.005 0.996
As you can see, the interaction term is very insignificant (p = 0.996)!
But if I use a anova() to compare a full vs reduced model to evaluate the
interaction term:
> anova(glm(y~x1+x2,dat,family='binomial'),
> glm(y~x1*x2,dat,family='binomial'))Analysis of Deviance Table
Model 1: y ~ x1 + x2Model 2: y ~ x1 * x2 Resid. Df Resid. Dev Df Deviance1
22 27.067 2 21 21.209 1 5.8579
This follows a chi-square distribution with 1 df, so the corresponding p value
is:
> 1-pchisq(5.8679,1)[1] 0.01541944
So I get very different p value on the interaction term, can someone share
what's going wrong here?
Thanks!
Yi
"y" "x1" "x2"
"R" 1 "B"
"NR" 1 "A"
"R" 0 "B"
"R" 1 "B"
"R" 1 "A"
"R" 1 "A"
"R" 0 "A"
"R" 1 "A"
"R" 0 "B"
"R" 0 "B"
"NR" 0 "B"
"NR" 0 "B"
"R" 1 "A"
"R" 1 "A"
"NR" 1 "A"
"R" 0 "A"
"R" 0 "A"
"NR" 0 "B"
"R" 0 "A"
"R" 1 "A"
"NR" 1 "A"
"R" 1 "B"
"R" 1 "A"
"R" 1 "B"
"R" 1 "A"
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