Here is my code:
######Centering predictors#######
verbal.ability_C <- verbal.ability - mean(verbal.ability)
children_C <- children - mean(children)
age_C <- age - mean(age)
education_C <- education - mean(education)
work.from.home.frequency_C <- work.from.home.frequency -
mean(work.from.home.frequency)
religious.orientation_C <- religious.orientation -
mean(religious.orientation)
political.orientation_C <- political.orientation -
mean(political.orientation)
sexual.orientation_C <- sexual.orientation -mean(sexual.orientation)
########## Logistic Regression###########
logistic.model <- glm( fire.communist.teacher ~ age_C + sex + children_C +
currently.married + religious.orientation_C + political.orientation_C,
binomial(logit) )
summary( logistic.model )
exp( coefficients( logistic.model ) )
#######Probit/Binomial Regression#######
install.packages("MASS")
library(MASS)
probit.model <- polr( as.factor(verbal.ability) ~ education_C + children_C +
currently.married + work.from.home.frequency_C, method="probit")
summary( probit.model)
Here is the output with I look at my data using the str(my.data) command:
'data.frame': 2044 obs. of 13 variables:
$ sexual.orientation : int -1 -1 NA NA NA NA NA -1 NA NA ...
$ political.orientation : int 5 5 6 0 3 6 4 5 6 NA ...
$ religious.orientation : int 4 1 4 4 4 1 2 4 4 4 ...
$ weekly.hours.on.internet: int 3 20 NA NA NA NA NA NA NA 0 ...
$ verbal.ability : int 6 9 NA 3 NA NA NA 8 NA NA ...
$ work.from.home.frequency: int 3 4 NA NA NA NA 1 NA 1 1 ...
$ fire.communist.teacher : int NA NA 1 NA 0 NA 0 0 0 1 ...
$ currently.married : int -1 -1 -1 -1 1 -1 -1 -1 1 -1 ...
$ children : int 0 0 3 5 8 2 1 1 3 2 ...
$ education : int 16 16 8 10 0 6 16 15 14 14 ...
$ partnrs5 : int 6 5 -1 99 -1 -1 -1 0 -1 -1 ...
$ age : int 31 23 71 82 78 40 46 80 31 99 ...
$ sex : int 1 -1 -1 -1 -1 1 -1 -1 -1 -1 ...
I tried using the na.action command by putting right after the
'binomial(logit)' syntax, but it didn't work. I am not sure if I am using it
properly though.
So, I have tried this syntax to deal with the missing data:
logistic.model <- glm( fire.communist.teacher ~ age_C + sex + children_C +
currently.married + religious.orientation_C + political.orientation_C,
binomial(logit), na.action=na.exclude )
as well as:
logistic.model <- glm( fire.communist.teacher ~ age_C + sex + children_C +
currently.married + religious.orientation_C + political.orientation_C,
binomial(logit), na.action=na.exclude, data=na.omit(DataMiss) )
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