Predicted values from gbm.fit and gbm differ?

Question

My intuition is that the fitted values and predicted values of a gbm object should be identical. But in this example with just one tree, the values are different:

b <- c(0,0,.8,0,0)
x <- mvrnorm(100,mu=rep(0,5),diag(5))
colnames(x) <- paste0("x",1:5)
y <- x %*% b + rnorm(10)

gbm.fit.out <- gbm.fit(y=y,x=x,shrinkage=.1,
    n.trees=1,distribution="gaussian",verbose=F)

d <- data.frame(y=y,x=x)
gbm.out <- gbm(y~.,data=d,shrinkage=.1,n.trees=1,distribution="gaussian",trainFrac=1)

p1 <- predict(gbm.fit,out,n.trees=1)
p2 <- predict(gbm.out,n.trees=1)
p1-p2

Why are they different? Does it even matter?

Answer 1

This seems to be peculiar to gbm.fit. Using gbm (and being sure to turn off bagging, and splitting the sample into training and test set) produces correct results.

require(MASS); require(gbm)
b <- c(0,0,.8,0,0)
x <- mvrnorm(100,mu=rep(0,5),diag(5))
colnames(x) <- paste0("x",1:5)
y <- x %*% b + rnorm(100)

out <-gbm(y~x1+x2+x3+x4+x5,data=data.frame(y,x),
 shrinkage=1,n.trees=1,
 distribution="gaussian",
 verbose=F,bag.fraction=1,train.fraction=1)

f <- out$fit
p <- predict(out,n.trees=1)
all(f-p == 0)