I do exactly the same thing. The difficulty here is of course if the models have different number of coefficients - then you would have different number of columns, which is impossible in data.frame. You need to have the same number of columns for each model.
I normally use it for glm (these code snippets are commented out) but I modified it for lm for you:
models <- c()
for (i in 1:10) {
y <- rnorm(100) # generate some example data for lm
x <- rnorm(100)
m <- lm(y ~ x)
# in case of glm:
#m <- glm(y ~ x, data = data, family = "quasipoisson")
#overdispersion <- 1/m$df.residual*sum((data$count-fitted(m))^2/fitted(m))
coef <- summary(m)$coef
v.coef <- c(t(coef))
names(v.coef) <- paste(rep(rownames(coef), each = 4), c("coef", "stderr", "t", "p-value"))
v.model_info <- c(r.squared = summary(m)$r.squared, F = summary(m)$fstatistic[1], df.res = summary(m)$df[2])
# in case of glm:
#v.model_info <- c(overdisp = summary(m)$dispersion, res.deviance = m$deviance, df.res = m$df.residual, null.deviance = m$null.deviance, df.null = m$df.null)
v.all <- c(v.coef, v.model_info)
models <- rbind(models, cbind(data.frame(model = paste("model", i, sep = "")), t(v.all)))
}
I prefer to take data from summary(m). To bundle the data into data.frame, you use the cbind (column bind) and rbind (row bind) functions.
