lm

I have a simple polynomial regression which I do as follows attach(mtcars) fit <- lm(mpg ~ hp + I(hp^2)) Now, I plot as follows > plot(mpg~hp) > points(hp, fitted(fit), col='red', pch=20) This gives me the following I want to connect these points into a smooth curve, using lines gives me the following > lines(hp, fitted(fit), col='red', type='b') What am I missing here. I want the output to be a smooth curve which connects the points Try: lines(sort(hp), fitted(fit)[order(hp)], col='red', type='b') Because your statistical units in the dataset are not ordered, thus, when you use lines it's a

I am looking for just the value of the B1(newx) linear model coefficient, not the name. I just want the 0.5 value. I do not want the name "newx". newx <- c(0.5,1.5.2.5) newy <- c(2,3,4) out <- lm(newy ~ newx) out looks like: Call: lm(formula = newy ~ newx) Coefficients: (Intercept) newx 1.5 1.0 I arrived here. But now I am stuck. out$coefficients["newx"] newx 1.0 For a single element like this, use [[ rather than [ . Compare: coefficients(out)["newx"] # newx # 1 coefficients(out)[["newx"]] # [1] 1 More generally, use unname() : unname(coefficients(out)[c("newx", "(Intercept)")]) # [1] 1.0 1.5

I am fitting a model to factor data and predicting. If the newdata in predict.lm() contains a single factor level that is unknown to the model, all of predict.lm() fails and returns an error. Is there a good way to have predict.lm() return a prediction for those factor levels the model knows and NA for unknown factor levels, instead of only an error? Example code: foo <- data.frame(response=rnorm(3),predictor=as.factor(c("A","B","C"))) model <- lm(response~predictor,foo) foo.new <- data.frame(predictor=as.factor(c("A","B","C","D"))) predict(model,newdata=foo.new) I would like the very last