Dear List,

I think I'm going crazy here...can anyone explain why do I get the same
predictions in train and test data sets below when the second has a missing
input?

y <- rnorm(1000)
x1 <- rnorm(1000)
x2 <- rnorm(1000)
train <- data.frame(y,x1,x2)
test <- data.frame(x1)

myfit <- glm(y ~ x1 + x2, data=train)
summary(myfit)
all(predict(myfit, test) == predict(myfit, train))

[1] TRUE



Thanks,

Axel.

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