I built a logistic regression model (called 'mylogit') using the glm function in R as follows:
mylogit <- glm(answer ~ as.factor(gender) + age, data = mydata, family = "binomial")
where age is numeric and gender is categorical (male and female).
I then proceeded to make predictions with the model built.
pred <- predict(mylogit, type = "response")
I can easily make a time series plot of the predictions by doing:
plot.ts(ts(pred))
to give a plot that looks like this:
Plot of Time against Predictions
which gives a plot of the predictions.
My question is this: Is it possible to put the x axis in segments according to gender (male or female) which was specified in the glm? In other words, can I have predictions on the y axis and have gender (divided into male and female) on the x axis?
A sample of the data I want to plot from is similar to this:
I did:
bind = cbind(mydata, pred)
'bind' looks like this:
pred age gender
0.9461198 32 male
0.9463577 45 female
0.9461198 45 female
0.9461198 37 female
0.9477645 40 male
0.8304513 32 female
I don't think you need to use ts and plot.ts because the data you have is not a time series, right? Just sort pred before plotting.
# Get data
str <- "pred,age,gender
0.9461198,32,male
0.9463577,45,female
0.9461198,45,female
0.9461198,37,female
0.9477645,40,male
0.8304513,32,female"
bind <- read.csv(textConnection(str))
# Plot
bind <- bind[order(bind$gender),]
plot(bind$pred, col = bind$gender)
library(ggplot2)
ggplot(bind, aes(x = gender, y = pred)) +
geom_point(position = position_jitter(width = .3))
Or without creating bind you could do plot(pred[order(mydata$gender)]).