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Dear experts, I am trying to obtain a function from a model, so that I could further manipulate it, plot it, etc. I can get model estimates and manually construct a function, but this gets tedious when trying out different functions to fit the data. There must be a better way of doing it, no? x <- c(1:10) y <- c(1:10) fit <- lm(y ~ x) f <- function(x){fit$coef[1] + fit$coef[2]*x}

DDear developeRs, about a year ago, Alex Stolpovsky posted an issue with predict.lm on a fit generated using poly with the raw=TRUE option and too few new data (slightly modified reproducible example below). Alex did not get any reply. I have just stumbled on the same problem, and I think that this is a bug of function poly, which arises from the check whether the polynomial degree is

Dear R experts, I am observing undesired behavior of predict(fit, newdata), in case when fit object is produced by lm() involving a poly(). Here is how to reproduce: x <- c(1:10) y <- sin(c(1:10)) fit <- lm(formula=y~poly(x, 5, raw=TRUE)) predict(fit, newdata=data.frame(x=c(1:10))) ## this works predict(fit, newdata=data.frame(x=c(1:1))) ## this is broken, error below Error in poly(x,