Issue with predict() for glm models

Dear Uwe, 

-----Original Message-----
From: Uwe Ligges [mailto:ligges at statistik.uni-dortmund.de] 
Sent: Thursday, September 23, 2004 8:06 AM
To: John Fox
Cc: jrausch at nd.edu; r-help at stat.math.ethz.ch
Subject: Re: [R] Issue with predict() for glm models

John Fox wrote:

Dear Uwe,

Unless I've somehow messed this up, as I mentioned 

yesterday, what you 

suggest doesn't seem to work when the predictor is a 

matrix. Here's a 

simplified example:

X <- matrix(rnorm(200), 100, 2)
y <- (X %*% c(1,2) + rnorm(100)) > 0
dat <- data.frame(y=y, X=X)
mod <- glm(y ~ X, family=binomial, data=dat) new <- data.frame(X = 
matrix(rnorm(20),2)) predict(mod, new)

Dear John,

the questioner had a 2 column matrix with 40 and one with 50 
observations (not a 100 column matrix with 2 observation) and 
for those matrices it works ...

Indeed, and in my example the matrix predictor X has 2 columns and 100 rows;
I did screw up the matrix for the "new" data to be used for predictions (in
the example I sent today but not yesterday), but even when this is done
right -- where the new data has 10 rows and 2 columns -- there are 100 (not
10) predicted values:

X <- matrix(rnorm(200), 100, 2)  # original predictor matrix with 100 rows
y <- (X %*% c(1,2) + rnorm(100)) > 0
dat <- data.frame(y=y, X=X)
mod <- glm(y ~ X, family=binomial, data=dat)

new <- data.frame(X = matrix(rnorm(20),10, 2)) # corrected -- note 10 rows
predict(mod, new) # note 100 predicted values