Hi,
I have a question about interpret the results from logistic regression
model. I used a dataset from the book Categorical Data Analysis (2nd
Edition) by Alan Agresti.
> summary(crabs)
color spine width satell weight psat
2:12 1: 37 Min. :21.0 Min. : 0.000 Min. :1200 Mode :logical
3:95 2: 15 1st Qu.:24.9 1st Qu.: 0.000 1st Qu.:2000 FALSE:62
4:44 3:121 Median :26.1 Median : 2.000 Median :2350 TRUE :111
5:22 Mean :26.3 Mean : 2.919 Mean :2437
3rd Qu.:27.7 3rd Qu.: 5.000 3rd Qu.:2850
Max. :33.5 Max. :15.000 Max. :5200
> crabs.glm <- glm(psat ~ color*width, family=binomial(), data=crabs)
> summary(crabs.glm)
Call:
glm(formula = psat ~ color * width, family = binomial(), data = crabs)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0546 -0.9129 0.5285 0.8140 1.9657
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.75261 11.46409 -0.153 0.878
color3 -8.28735 12.00363 -0.690 0.490
color4 -19.76545 13.34251 -1.481 0.139
color5 -4.10122 13.27532 -0.309 0.757
width 0.10600 0.42656 0.248 0.804
color3:width 0.31287 0.44794 0.698 0.485
color4:width 0.75237 0.50435 1.492 0.136
color5:width 0.09443 0.50042 0.189 0.850
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 225.76 on 172 degrees of freedom
Residual deviance: 183.08 on 165 degrees of freedom
AIC: 199.08
Number of Fisher Scoring iterations: 5
Note the predictors are mixture of continuous data and categorical
data. Here, I wonder whether there is *significant difference* among
the four interactions of color and width (say, to get a p-value). In a
two-way ANOVA, we may do a F-test. But is there an "equivalent" method
for logit model?
Thanks,
Wuming
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