search for: cofficients

I am working on my thesis in which i have couple of independent variables that are categorical in nature and the depndent variable is dichotomus. Initially I run univariate analysis and added the variables with significant p-values (p<0.25) in my full model. I have three confusions. Firstly, I am looking for confounding variables by using formula "(crude beta-cofficient - adjusted

People, help me please! How to use lm() function to defind a cofficient for 7-polinom, and what expression should I put in /formula/ -- View this message in context: http://r.789695.n4.nabble.com/lm-tp4545740p4545740.html Sent from the R help mailing list archive at Nabble.com.

Hi R community, I am new bird to R and moved recently from SAS. I am no means expert on either but very curious learner. So your help crucial for me to learn R. I have already got positive expression. I was trying to fit a mixed model in animal experiment but stuck at simple point. The following similar example is from SAS mixed model pp 212. # data genetic_evaluation <-

Hello All: I have a binomial model with one covariate, x1, treated as a factor with 3 levels. The other covariate is measured x2 <- 1:30. The response, y, is the proportion of successes out of 20 trials. glm(cbind(y, 20 - y) ~ x1 * x2, family = binomial) Now, I would like to constrain the cofficients on 2 levels of the factor, x1, to be identical and test the difference between these models by a likelihood ratio test. How can I get glm() to constrain the coefficients on 2 levels to be the same? Thanks, ANDREW

...#39;*f_1(x)+ ... + A_p'*f_p(x) with x >= C Functions f_1, f_2, ... f_p are known. Is there anything in R for that? I have tried to use nonlinear (nls package) regression, "forcing" with the "nls" function the shape of the surface, but it does not work. By the way, the cofficients A_i have to be positive, but I suppose this is another question. Thanks Casiano casiano at ull.es

Dear all, I'm trying to use the "optim" function to replicate the results from the "glm" using an example from the help page of "glm", but I could not get the "optim" function to work. Would you please point out where I did wrong? Thanks a lot. The following is the code: # Step 1: fit the glm clotting <- data.frame( u =

...da(Region ~ Mo + Ba, data = wine, prior = c(1, 1)/2) Prior probabilities of groups: 2 3 0.5 0.5 Group means: Mo Ba 2 0.1461111 0.2810000 3 0.1479167 0.1079167 Coefficients of linear discriminants: LD1 Mo -5.636024 Ba -22.069187 I am having trouble going from the cofficients derived from the spherical within group covariance to the function I am able to obtain in the other programs. The rest of the problem set up for all programs are: Region = group assignment, prior = equal priors and I do not do any data pretreatment prior to analysis. Any help would be great. Th...