search for: ridenhour

...ou showed equations for A=0 and A=1? 2. y = A + X + A*X means you're allowing the different groups of A to have different slopes. Probably not what you intended. 3. It's probably best to provide a small sample of the data (and R code) so we know how you got what you got. Andy From: Ben Ridenhour > > Hello, > > I 'm sure there must a be a simple explanation for what I'm > doing wrong but I am stumped. I am a novice R user and this > has shaken my confidence in what I'm doing! I am trying to > run a simple ANCOVA using the model y~A*x, where y and x a...

...that while the confidence intervals are close to those provided by SAS, they are always smaller. This, in the end, makes a large difference for the application. Can anyone suggest how I might make the confidence intervals I calculate in R match up with those of SAS for nls? Thanks in advance, Ben Ridenhour ________________________________ Benjamin Ridenhour Centers for Disease Control & Prevention Atlanta, GA 30329 "If we knew what we were doing, it wouldn't be research." --Einstein [[alternative HTML version deleted]]

....074804390 2.359224e-16 0 t10* -0.007153634 1.301043e-16 0 t11* -0.018241243 -2.359224e-16 0 t12* 0.049409513 -1.200429e-15 0 Clearly the bootstrap is not working. What am I doing wrong? Thanks, Ben ------------------------------- Benjamin Ridenhour School of Biological Sciences Washigton State University P.O. Box 644236 Pullman, WA 99164-4236 Phone (509)335-7218 -------------------------------- "Nothing in biology makes sense except in the light of evolution." -T. Dobzhansky [[alternative HTML version deleted]]

..., and aov() and as expected they all give the same answer. If I do >levels(A) [1] "2" "4" which are the two levels of A. Why can't I get the same answer from JMP as in R? This is very disturbing to me! Thanks, Ben ------------------------------- Benjamin Ridenhour School of Biological Sciences Washigton State University P.O. Box 644236 Pullman, WA 99164-4236 Phone (509)335-7218 -------------------------------- "Nothing in biology makes sense except in the light of evolution." -T. Dobzhansky [[alternative HTML version deleted]]

Hello all, I'm sure I'm missing something simple here, but I can't figure out how to modify the glm.fit() function and then get R to use it (sort of). I'm doing something along the lines of: glm.fit<-edit(glm.fit) # add something trivial to the top of the glm.fit function like: print("Hello world!") #now have a modified glm.fit in position 1/.GlobalEnv

Hi all, I have a question regarding how to properly analyze a data set and then how to perform the analysis in R. First, I have data that I would like to analyze using a mixed GLM (I think this is the most appropriate method, but I am unsure). In a mixed model (y = X*beta+Z*gamma+epsilon), I would like to structure the variance matrices of gamma, G, and the error, R, to take advantage of all my