Predict() - Maybe I'm not understanding it

Question

I posted earlier today about an error I was getting with using the predict function. I was able to get that corrected, and thought I was on the right path.

Answer 1

Thanks Hong, that was exactly the problem I was running into. The error you get suggests that the number of rows is wrong, but the problem is actually that the model has been trained using a command that ends up with the wrong names for parameters.

This is really a critical detail that is entirely non-obvious for lm and so on. Some of the tutorial make reference to doing lines like lm(olive$Area@olive$Palmitic) - ending up with variable names of olive$Area NOT Area, so creating an entry using anewdata<-data.frame(Palmitic=2) can't then be used. If you use lm(Area@Palmitic,data=olive) then the variable names are right and prediction works.

The real problem is that the error message does not indicate the problem at all:

Warning message: 'anewdata' had 1 rows but variable(s) found to have X rows

Answer 2

First, you want to use

model <- lm(Total ~ Coupon, data=df)

not model <-lm(df$Total ~ df$Coupon, data=df).

Second, by saying lm(Total ~ Coupon), you are fitting a model that uses Total as the response variable, with Coupon as the predictor. That is, your model is of the form Total = a + b*Coupon, with a and b the coefficients to be estimated. Note that the response goes on the left side of the ~, and the predictor(s) on the right.

Because of this, when you ask R to give you predicted values for the model, you have to provide a set of new predictor values, ie new values of Coupon, not Total.

Third, judging by your specification of newdata, it looks like you're actually after a model to fit Coupon as a function of Total, not the other way around. To do this:

model <- lm(Coupon ~ Total, data=df)
new.df <- data.frame(Total=c(79037022, 83100656, 104299800))
predict(model, new.df)

Answer 3

instead of newdata you are using newdate in your predict code, verify once. and just use Coupon$estimate <- predict(model, Coupon) It will work.

Answer 4

To avoid error, an important point about the new dataset is the name of independent variable. It must be the same as reported in the model. Another way is to nest the two function without creating a new dataset

model <- lm(Coupon ~ Total, data=df)
predict(model, data.frame(Total=c(79037022, 83100656, 104299800)))

Pay attention on the model. The next two commands are similar, but for predict function, the first work the second don't work.

model <- lm(Coupon ~ Total, data=df) #Ok
model <- lm(df$Coupon ~ df$Total) #Ko