similar to: simulating future observations from heteroscedastic fits

Dear R community, I am analyzing plant communities with the function mvpart, using a dissimilarit matrix as input. The matrix is calculated with the funtion gdist. fit <- mvpart(gdist (ba12[,18:29], meth="maximum", full=TRUE, sq=F) ~ beers + slope_dem + elev_dem+ plc_dem + pr_curv+ +curv+max_depth+doc_rocks+ abandon+land_use+ca_old, data=ba12, xv="p") This

Hello, all: I have a dataset with 2 groups. I want to estimate 2 means and 2 standard deviations. I naively think I should be able to use lme to do that, e.g., lme(y~gp, random=y~1|gp, method='ML'). I think I should get the same answer as from lm(y~1, ...) within each level of group. I can get the same means, but I don't know how to extract the within-gp standard

Hallo, > fit12<-lmFit(qrg[,1:2]) > t12<-toptable(fit12,adjust="fdr",number=25,genelist=qrg$genes[,1]) > t12 ID logFC t P.Value adj.P.Val B 522 PLAU_OP -6.836144 -8.420414 5.589416e-05 0.01212520 2.054965 1555 CD44_WIZ -6.569622 -8.227938 6.510169e-05 0.01212520 1.944046 Can anyone tell me what the difference is between P.Value

R 2.8.1 windows XP I am getting an error message that I don't understand when I try to run GLM. The error only occurs when I have all independent variables in the model. When I drop one independent variable, the model runs fine. Can anyone help me understand what the error means and how I can correct it? Thank you, John > fit11<-glm(AAMTCARE~BMI+BMIsq+SEX+jPHI+jMEDICAID+factor(AgeCat)+

Hallo, how can I store a variable as a tab-delimited txt-file? I crated a variable with the following commands: > fit12<-lmFit(qrg[,1:2]) > t12<-toptable(fit12,adjust="fdr",number=25,genelist=qrg$genes[,1]) > t12 ID logFC t P.Value adj.P.Val B 522 PLAU_OP -6.836144 -8.420414 5.589416e-05 0.01212520 2.054965 1555 CD44_WIZ -6.569622

Greetings to you all, I am performing a semi parametric bootstrap in R on a Gamma Distributed data and a Binomial distributed data. The main challenge am facing is the fact that the residual variance depends on the mean (if I am correct). I strongly feel that the script below may be wrong due to mean-variance relationship #####R code####### fit1s

Hi, I wish to analyze with R the results of a perception experiment in which subjects had to recognize each stimulus among three choices (this was a forced-choice design). The experiment runs under two different conditions and the data is like the following: N1 : count of trials in condition 1 p11, p12, p13: proportions of choices 1, 2, and 3 in condition 1 N2 : count of trials in

I'm exploring the simplex algorithm with a simple transportation problem of the form: minimize: 8a + 6b + 10c + 9d + 5e + 7f subject to the constraints: a + b + c = 11 d + e + f = 14 a + d = 10 b + e = 8 c + f = 7 I've implemented this in R (1.4.0 (2001-12-19), SunOS 5.7) as: require(boot) costs = c(8,6,10,9,5,7) constraints = c( 1,1,1,0,0,0, 0,0,0,1,1,1, 1,0,0,1,0,0,