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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