similar to: Mixed Design ANOVA - singular error model

dears R-users, I'm interested in model selection problem, and i have faced some problems that i would like to ask for help. well, this is a very small example with 4 variable (just one var. is the response - z) with 100 individuals i would like to do a stepwise search, for the "best" model, and a use BIC criteria. I know when I have a lot of variables, let's say 120, I know,

Hi all, I have this figure: http://docs.google.com/Doc?id=df5zfsj4_103rjt2v4d5 created with the following steps: > x [1] 90.4 57.8 77.0 103.7 55.4 217.5 68.1 85.3 152.0 113.0 97.1 89.9 [13] 68.1 83.7 77.4 34.5 104.9 170.3 88.6 88.1 108.8 77.4 85.6 82.7 [25] 81.3 108.0 49.5 71.0 85.7 99.3 203.5 275.9 51.1 84.8 16.5 72.6 [37] 160.5 158.3 136.7 140.0 98.4 116.1

# R for Windows will not send your bug report automatically. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs at r-project.org # ###################################################### This report is joint from Richard Heiberger <rmh at temple.edu> and Burt Holland <bholland at temple.edu>. Burt Holland is the coauthor

dear all, I am a new comer to R and statistic. Now I have a little confuse about the package pls. I have to use 5 components to form a model. There are strong relationship between some of the components, which leads to the changes of the sign of each coeficeince, of course this is unwanted when using the normal regression way. So I choose the way of PLS, which is good at solve this kind of

Dear R community, Confused by some of my lab results I ask for the definition of the null hypothesis of a two-way analysis of variance in R (anova() and aov()). Starting with the following model y = a_i + b_j , i in A and j in B is the tested null hypothesis H_0: a_i = 0 for all i in A or H_0: a_m = a_n for any m and n in A? Consequently the same questions for interaction effects.

Dear Members, Greetings! I have come across a discrepancy shown by R and SAS results on same data for logistics regression.. When I processed the above csv file(1000.csv) for predicting the Action (i/c) by Age Group(1-7,Na) and Gender(M,F,Na) with GLM of R I get: R result Call: glm(formula = Action ~ Gender + AgeGroup, family = binomial, data = mydata1, na.action = na.pass) Deviance

Hello, I would like to do a partial least square discriminant analysis (PLSDA) in R using the package "ropls" Which is in R available via the R command : source("https://bioconductor.org/biocLite.R") When I try to do a PLSDA using my own data. The impact of two genders (AP,C) on 5 compounds measured in persons (samples) should be illustrated. When I try to do a PLSDA I get

Hello, I would like to do a partial least square discriminant analysis (PLSDA) in R using the package "ropls" Which is in R available via the R command : source("https://bioconductor.org/biocLite.R") I try to do a PLSDA to illustrate the impact of two genders (AP,C) on 5 compounds measured in persons (samples) should be illustrated. When I try to do a PLSDA I get the warning

Hi, I am analyzing a repeated-measures Greco-Latin Square with the aov command. I am using aov to calculate the MSs and then picking by hand the appropriate neumerator and denominator terms for the F tests. The data are the following: responseFinger mapping.code Subject.n index middle ring little ---------------------------------------------------------------------------- 1 1

Greetings. I'm trying to recreate in R some regression models I've done in SAS, but I'm not getting the same results. My advisor suspects this may be due to differences in precision between R and SAS. Does anyone know where I can find specifications for R's type double? (It doesn't seem to be in the R Language Definition.) Thanks in advance for any help anyone can

rm(list=ls()) yx.df<-read.csv("c:/MK-2-72.csv",sep=',',header=T,dec='.') dim(yx.df) #get X matrix y<-yx.df[,1] x<-yx.df[,2:643] #conver to matrix mat<-as.matrix(x) #get row number rownum<-nrow(mat) #remove the constant parameters mat1<-mat[,apply(mat,2,function(.col)!(all(.col[1]==.col[2:rownum])))] dim(yx.df) dim(mat1) #remove columns with numbers of

Hi I have a set of data with both quantitative and categorical predictors. After scaling of response variable, i looked for multicollinearity (VIF values) among the predictors and removed the predictors who were hinding some of the other significant predictors. I'm curious to know whether the predictors (who are not significant) while doing simple 'lm' will be involved in

Hi I have a set of data with both quantitative and categorical predictors. After scaling of response variable, i looked for multicollinearity (VIF values) among the predictors and removed the predictors who were hinding some of the other significant predictors. I'm curious to know whether the predictors (who are not significant) while doing simple 'lm' will be involved in

Hi, I have a question about estimating IRT models by using nlme, not just rasch model, but also other models. Behavior Research Methods <http://www.springerlink.com/content/1554-351x/> Volume 37, Number 2 <http://www.springerlink.com/content/1554-351x/37/2/>, 202-218, DOI: 10.3758/BF03192688 Using SAS PROC NLMIXED to fit item response theory models (2005). Ching-Fan