predict.glm() with three new categories in the tes

Question

I have a data set called data which has 481 092 rows.

I split data into two equal halves:

1. The first halve (row 1: 240 546) is called train and was used for the glm();
2. the second halve (row 240 547 : 481 092) is called test and should be used to validate the model;

Then I started the regression:

testreg <- glm(train$returnShipment ~ train$size + train$color + train$price + 
               train$manufacturerID + train$salutation + train$state +
               train$age + train$deliverytime, 
               family=binomial(link="logit"), data=train)

Now the prediction:

prediction <- predict.glm(testreg, newdata=test, type="response")

gives me an Error:

Error in model.frame.default(Terms, newdata, na.action=na.action, xlev=object$xlevels):
Factor 'train$manufacturerID' has new levels 125, 136, 137

Now I know that these levels were omitted in the regression because it doesn't show any coefficients for these levels.

I have tried this: predict.lm() with an unknown factor level in test data . But it somehow doesn't work for me or I maybe just don't get how to implement it. I want to predict the dependent binary variable but of course only with the existing coefficients. The link above suggests to tell R that rows with new levels should just be called /or treated as NA.

How can I proceed?

Edit-Suggested approach by Z. Li

I got problem in the first step:

xlevels <- testreg$xlevels$manufacturerID
mID125 <- xlevels[1]

but mID125 is NULL! What have I done wrong?

Answer 1

It is impossible to get estimation of new factor levels, in fixed effect modelling, including linear models and generalized linear models. glm (as well as lm) keeps records of what factor levels are presented and used during model fitting, and can be found in testreg$xlevels.

Your model formula for model estimation is:

returnShipment ~ size + color + price + manufacturerID + salutation + 
                 state + age + deliverytime

then predict complains new factor levels 125, 136, 137 for manufactureID. This means, these levels are not inside testreg$xlevels$manufactureID, therefore has no associated coefficient for prediction. In this case, we have to drop this factor variable and use a prediction formula:

returnShipment ~ size + color + price + salutation + 
                 state + age + deliverytime

However, the standard predict routine can not take your customized prediction formula. There are commonly two solutions:

1. extract model matrix and model coefficients from testreg, and manually predict model terms we want by matrix-vector multiplication. This is what the link given in your post suggests to do;
2. reset the factor levels in test into any one level appeared in testreg$xlevels$manufactureID, for example, testreg$xlevels$manufactureID[1]. As such, we can still use the standard predict for prediction.

Now, let's first pick up a factor level used for model fitting

xlevels <- testreg$xlevels$manufacturerID
mID125 <- xlevels[1]

Then we assign this level to your prediction data:

replacement <- factor(rep(mID125, length = nrow(test)), levels = xlevels)
test$manufacturerID <- replacement

And we are ready to predict:

pred <- predict(testreg, test, type = "link")  ## don't use type = "response" here!!

In the end, we adjust this linear predictor, by subtracting factor estimate:

est <- coef(testreg)[paste0(manufacturerID, mID125)]
pred <- pred - est

Finally, if you want prediction on the original scale, you apply the inverse of link function:

testreg$family$linkinv(pred)

---

update:

You complained that you met various troubles in trying the above solutions. Here is why.

Your code:

testreg <- glm(train$returnShipment~ train$size + train$color + 
               train$price + train$manufacturerID + train$salutation + 
               train$state + train$age + train$deliverytime,
               family=binomial(link="logit"), data=train)

is a very bad way to specify your model formula. train$returnShipment, etc, will restrict the environment of getting variables strictly to data frame train, and you will have trouble in later prediction with other data sets, like test.

As a simple example for such drawback, we simulate some toy data and fit a GLM:

set.seed(0); y <- rnorm(50, 0, 1)
set.seed(0); a <- sample(letters[1:4], 50, replace = TRUE)
foo <- data.frame(y = y, a = factor(a))
toy <- glm(foo$y ~ foo$a, data = foo)  ## bad style

> toy$formula
foo$y ~ foo$a  
> toy$xlevels
$`foo$a`
[1] "a" "b" "c" "d"

Now, we see everything comes with a prefix foo$. During prediction:

newdata <- foo[1:2, ]  ## take first 2 rows of "foo" as "newdata"
rm(foo)  ## remove "foo" from R session
predict(toy, newdata)

we get an error:

Error in eval(expr, envir, enclos) : object 'foo' not found

The good style is to specify environment of getting data from data argument of the function:

foo <- data.frame(y = y, a = factor(a))
toy <- glm(y ~ a, data = foo)

then foo$ goes away.

> toy$formula
y ~ a
> toy$xlevels
$a
[1] "a" "b" "c" "d"

This would explain two things:

1. You complained to me in the comment that when you do testreg$xlevels$manufactureID, you get NULL;

2. The prediction error you posted

   Error in model.frame.default(Terms, newdata, na.action=na.action, xlev=object$xlevels):
   Factor 'train$manufacturerID' has new levels 125, 136, 137
   

   complains train$manufacturerID instead of test$manufacturerID.

Answer 2

As you have divided your train and test sample based on rownumbers, some factor levels of your variables are not equally represented in both the train and test samples.

You need to do stratified sampling to ensure that both train and test samples have all factor level representations. Use stratified from the splitstackshape package.