search for: gcalhoun

Hi, Is there any way to estimate a DEPENDENT variable through a GLM/LM model? Suppose I have the linear model: y=a0+a1*x1+a2*x2 (a0=1, a1=0.6, a2=0.8, x1~N(1,1), x2~N(0,1)). The alphas and the auxiliary variables are given and I have to estimate y. The point is if I estimate it, let¹s say algebraically, I get high variances that do not decrease as sample sizes increases... Is the any other way

...ot; which doesn't indicate that \var should be handled any differently than any other macro, but the code makes me think that R is trying to pass the macro through to LaTeX. Thanks! --Gray -- Gray Calhoun Assistant Professor of Economics, Iowa State University http://www.econ.iastate.edu/~gcalhoun patch: Index: src/library/tools/R/Rd2latex.R =================================================================== --- src/library/tools/R/Rd2latex.R (revision 57751) +++ src/library/tools/R/Rd2latex.R (working copy) @@ -163,10 +163,7 @@ BSL = '@BSL@';...

Hi to all, I am looking for a randomisation procedure for a single matrix, including a possibility to set the number of randomisations and the to set the number of row and columns . Knut

Dear all, I tried once to create one variable called bip such that: bip <- cip + (1/f(cip))*fi(f,cip) And this was working. But now, doing the same thing I did before, the software shows me the following message: Error in f(x, ...) : unused argument(s) (subdivision = 2000) I have the variable cip and the variable bip should be created such that: Fn <- ecdf(cip) f <- function(x) {(1 -

Dear Friends, Using package Vegan, I need to calculate Shannons Diversity index and Pielou's Evenness for a set of 20 study areas. Each area is represented by a matrix of 25 sample plots x tree species. The code is as following, where data stands for the data matrix of any of the 20 areas: S <- specnumber(data) H <- diversity(data) J <- H/log(S) I indexed the 20 areas by a

myDF: d1 d2 d3 d4 d5 -0.166910351 0.022304377 -0.00825924 0.008330689 -0.000925938 -0.166910351 0.022304377 -0.00825924 0.008330689 -0.000925938 -0.166910351 0.022304377 -0.00825924 0.008330689 -0.168225938 -0.166910351 0.022304377 -0.00825924 0.008330689 -0.168225938 -0.166910351 0.022304377 -0.00825924 0.008330689 -0.168225938 -0.166910351

Hi, I have huge matrices in which the response variable is in the first column and the regressors are in the other columns. What I wanted to do now is something like this: #this is just to get an example-matrix DataMatrix <- rep(1,1000); Disturbance <- rnorm(900); DataMatrix[101:1000] <- DataMatrix[101:1000]+Disturbance; DataMatrix <- matrix(DataMatrix,ncol=10,nrow=100); #estimate

Hi, Suppose I have an arbitrary function: arbfun<-function(x) {...} Is there a robust implementation of a numerical derivative routine in R which I can use to take it's derivative ? Something a bit more than simple division by delta of the difference of evaluating the function at x and x+delta... Perhaps there is a way to do this using D or deriv but I could not figure it out.

Dear Wiza[R]ds, I am very grateful to Duncan Murdoch for his assistance with this problem. His help was invaluable. However, the problem has become a little more complicated for me. Now, in each plot, I need to plot the observed and fitted values of a supine and upright posture experiment. Here is what I have and how far I got. # tritiated (3H)-Norepinephrine(NE) disappearance from plasma #

I am feeling rather dumb right now. I created what I thought was a data.frame as follows: aaa <- lapply(split(moreinfo,list(moreinfo$m_id),drop = TRUE), fun_m_id) m_id_default_res <- do.call(rbind, aaa) print("==========================================") m_id_default_res print("==========================================") ndf <- m_id_default_res[, c('mid',