Hello,
Actually, Bert is right, c() seems better. Chris wants a table with one
row per dataset and columns like he wrote in a previous post.
Something like this could do it.
#------------------------ this was already posted
x1 <- 1:100
x2 <- log(x1^2)
y1 <- x1 + x2 + rnorm(100)
y2 <- x1*runif(100, 0.5, 1.0) + x2 + rnorm(100)
predictors <- c("x1", "x2")
responses <- c("y1", "y2")
fmla <- paste(responses, paste(predictors, collapse="+"), sep="~")
model <- vector("list", length(fmla))
for(i in seq_along(fmla))
model[[i]] <- lm(as.formula(fmla[i]))
#------------------------ make the table
nr <- length(model)
nc <- nrow(coef(summary(model[[1]])))
tbl <- matrix(nrow=nr, ncol=2*nc + 1, dimnames=list(seq_len(nr),
seq_len(2*nc + 1)))
for(i in seq_along(model)){
smry <- summary(model[[i]])
tbl[i, ] <- c(t(coef(smry)[ , c(1, 4)]), smry$r.squared) # uses c()
}
colnames(tbl)[2*1:nc - 1] <- rownames(coef(smry))
colnames(tbl)[2*1:nc] <- paste("p.value", 0:(nc - 1), sep=".") # see below
colnames(tbl)[2*nc + 1] <- "R2"
tbl
If the only purpose is a printout, I would skip the 'paste', it would be
obvious which regressor the p.value corresponds to. But if the matrix is
to be used for other ends, duplicate names are a bad idea.
Rui Barradas
Em 27-05-2012 19:55, Özgür Asar escreveu:
Dear Bert Gunter,
Doesn't the summary(lm1)$coef have a matrix form? Then, while working with
columns of a matrix, can't one use c/rbind functions?
But list seem to better for what Chris asked. Nonetheless, c does not.
Best
Ozgur
-----
************************************
Ozgur ASAR
Research Assistant
Middle East Technical University
Department of Statistics
06531, Ankara Turkey
Ph: 90-312-2105309
http://www.stat.metu.edu.tr/people/assistants/ozgur/
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