I'm trying to educate myself about predictive analytics and am using
R to generate a linear model with the following data.
age <- c(23, 19, 25, 10,9, 12, 11, 8, 13) steroid <- c(27.1, 22.1, 21.9, 10.7, 7.4, 18.8, 14.7, 5.7) gpa <- c( 2.1, 2.9, 2.8, 3.5, 3.2, 3.9, 2.8, 2.6) sample <- data.frame(age, steroid, gpa) fit2 <- lm(steroid~age+gpa) summary(fit2) newdata <- data.frame(age=15, gpa=3.2) predict(fit2, newdata, interval="predict") # I want the fitted / predicted value
From the summary for the linear model, I have information on the coefficients associated with each predictor. However, I want to go further and find the predicted probability of information from this data. So for someone who is 13 and has a GPA of 3.3, what's the predicted probability of them ranking high on the steroid scale? What about for someone who is age 10? etc.
I understand explanatory modeling and have no problem implementing that in
R, I just have issues with using
R to construct meaningful predictive analytics. If you have any insights on this, feel free to share.