Re: [R] drop1() seems to give unexpected results compare to anova()

On Sun, 3 Aug 2008, Thomas P C Chu wrote: Thanks to Mr Dalgaard for his advice and everyone else who has contributed. Inclusion of an error term at the end of sim.set$y = ... line did cure my problems with drop1() and step(). I suppose it is my own inexperience in carrying out simulations cau

Re: [R] drop1() seems to give unexpected results compare to anova()

Thanks to Mr Dalgaard for his advice and everyone else who has contributed. Inclusion of an error term at the end of sim.set$y = ... line did cure my problems with drop1() and step(). I suppose it is my own inexperience in carrying out simulations caused such gaffe. Thomas __

Re: [R] drop1() seems to give unexpected results compare to anova()

Thomas P C Chu wrote: Dear all, I have been trying to investigate the behaviour of different weights in weighted regression for a dataset with lots of missing data. As a start I simulated some data using the following: library(MASS) N <- 200 sigma <- matrix(c(1, .5, .5, 1), nrow = 2) sim.set

Re: [R] drop1() seems to give unexpected results compare to anova()

I am not sure why my messages are not threaded together. Thank you to the author of this post: https://stat.ethz.ch/pipermail/r-help/2008-August/169691.html I have tried the suggestions, but I got the same results as in my original query: https://stat.ethz.ch/pipermail/r-help/2008-August/169647

Re: [R] drop1() seems to give unexpected results compare to anova()

Thomas Chu wrote: > > Neither of those 3 lines of commands managed to drop x4 and its P value > magically decreased from 0.94 to almost 0! I am also baffled by how R > calculated those RSS. > Maybe it is using a different type of SS. If i have a lm() model, and i do: options(contrasts=c("con

Re: [R] drop1() seems to give unexpected results compare to anova()

Interestingly, if I fitted the model using glm() rather than lm(), drop1() would behave as expected: summary(model.glm <- glm(y ~ ., data = sim.set, family = 'gaussian')) summary(model.lm <- lm(y ~ ., data = sim.set)) drop1(model.glm, test = 'F') drop1(model.lm, test = 'F') model.glm <- step(mod

[R] drop1() seems to give unexpected results compare to anova()

Dear all, I have been trying to investigate the behaviour of different weights in weighted regression for a dataset with lots of missing data. As a start I simulated some data using the following: library(MASS) N <- 200 sigma <- matrix(c(1, .5, .5, 1), nrow = 2) sim.set <- as.data.frame(mvrno