similar to: interpretation of summary.lm() for ANOVA and ANCOVA when dealing with 2 or more factors

Have anybody been able to run ICQ2000b with wine? I need this because I'm behand a firewall that won't accept UDP, and as far as I know no ICQ-clone for linux can do only tcp/ip as ICQ2000b -- Jan-Fredrik Braseth www.ifi.uio.no/~janfbr/ 2B|!2B

Hi everyone! I meet one problem when embedding R in C code, when I run the the R code in one child thread , it always print error info: Error: C stack usage is too close to the limit I also try to set R_CStackLimit = (uintptr_t)-1 to disable the C stack check as the R-ext doc say, but it still does not work. it is interesting that if i put the R code in the main

Hi everyone! I meet one problem when embedding R in C code, when I run the the R code in one child thread , it always print error info: Error: C stack usage is too close to the limit I also try to set R_CStackLimit = (uintptr_t)-1 to disable the C stack check as the R-ext doc say, but it still does not work. it is interesting that if i put the R code in the main

Hi everyone! I meet one problem when embedding R in C code, when I run the the R code in one child thread , it always print error info: Error: C stack usage is too close to the limit I also try to set R_CStackLimit = (uintptr_t)-1 to disable the C stack check as the R-exts doc say, but it still does not work, the error info still exist. Besides it is interesting that if i

Hi everyone, I'm sorry if this turns out to be more a statistical question than one specifically about R - but would greatly appreciate your advice anyway. I've been using a logistic regression model to look at the relationship between a binary outcome (say, the odds of picking n white balls from a bag containing m balls in total) and a variety of other binary parameters:

Hi, the function TukeyHSD gives incorrect results for balanced incomplete block designs, as the example below shows, but I can only half fix it. There are two problems, 1. It uses model.tables to estimate treatment means, 2. It uses the wrong standard error The first problem can be fixed using dummy.coef, if the lines > TukeyHSD.aov function (x, which = seq(along = tabs), ordered = FALSE,

Hallo, I have this dataset with repeated measures. There are two within-subject factors, "formant" (2 levels: 1 and 2) and "f2 Ref" (25 levels: 670, 729, 788, 846, 905, 1080, 1100, 1120, 1140, 1170, 1480, 1470, 1450, 1440, 1430, 1890, 1840, 1790, 1740, 1690, 2290, 2210, 2120, 2040, 1950), and one between-subject factor, lang (2 levels:1 and 2). The response variable