I am creating a model attempting to predict the probability someone will
reoffend after being caught for a crime. There are seven total inputs and I
planned on using a logistic regression. I started with a null deviance of
182.91 and ended up with a residual deviance of 83.40 after accounting for
different interactions and such. However, I realized after that my code is
different from that in my book. And I can't figure out what I need to put
in it's place. Here's my code:
library(foreign)
library(car)
foo = read.table("C:/Documents and
Settings/Chris/Desktop/4330/criminals.dat", header=TRUE)
reoff = foo[ ,1]
race = foo[ ,2]
age = foo[ ,3]
gender = foo[ ,4]
educ = foo[ ,5]
subst = foo[ ,6]
prior = foo[ ,7]
violence = foo[ ,8]
fit1h = glm(reoff ~ factor(subst) + factor(violence) + prior +
factor(violence):factor(subst) + factor(violence):factor(educ) +
factor(violence):factor(age) + factor(violence):factor(prior))
summary(fit1h)
If you noticed, there's no part of my code that looks like:
family=binomial(link="logit"))
If I code like my book has done, it would look like:
fit1i = glm(reoff ~ factor(subst) + factor(violence) + prior +
factor(violence):factor(subst) + factor(violence):factor(educ) +
factor(violence):factor(age) + factor(violence):factor(prior),
family=binomial(link="logit"))
summary(fit1i)
However, when I do this, my null deviance is 1104 and my residual deviance
is 23460. THIS IS A HUGE DIFFERENCE IN MODEL FIT! I'm not sure if I have
to redo my model or if my book was simply doing the
"family=binomial(link="logit")" for a specific problem/reason.
So, to my question:
Do I need to include "family=binomial(link="logit")" in my code? Do I need
to include any type of family?
Thanks for your help,
-chris
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