Try this:
mapply(function(y, x, z)lm(y ~ x + z), as.data.frame(y),
as.data.frame(x.a), as.data.frame(x.b), SIMPLIFY = FALSE)
On Thu, Mar 18, 2010 at 8:35 AM, Frederick Ho wrote:
> Hi everyone,
>
> I have a response matrix (y) and two predictor matrices (x.a, x.b), how
> should i proceed if i wan
try this:
y <- matrix(rnorm(100*100), 100, 100)
x.a <- matrix(rnorm(100*100), 100, 100)
x.b <- matrix(rnorm(100*100), 100, 100)
M <- ncol(y)
models <- vector("list", M)
for (m in 1:M) {
Dat <- data.frame(y = y[, m], x.a = x.a[, m], x.b = x.b[, m])
models[[m]] <- lm(y ~ ., data = Dat)
}
Hi everyone,
I have a response matrix (y) and two predictor matrices (x.a, x.b), how
should i proceed if i want to regress y on x.a and x.b column by column?
To be specific, what i want to do is:
y[,1]~x.a[,1]+x.b[,1]
y[,2]~x.a[,2]+x.b[,2]
.
.
.
I have tried lm(y~x1+x2) but it does not work as
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