similar to: Design matrix not identity

Hello, I wonder if someone could send me suggestions on how to solve the following problem: I have a vector of an arbitrary size (ex. data<-c(10,10,11,10,12,11,10,12,11,11,10,11)) and use the table function, which gives the following result 10 11 12 5 5 2 that''s fine, but what I would like to do now is: construct new classes based on the number of classes from table, 10

Hello, I wonder if someone could help me with the following: I have generated 10 000 values from rnorm and now I want to randomly replace 500 of those with NA. The problem is that values indexed between 6-10,16-20,26-30.... only should be considered for replacement. Any suggestions? Patrik Waldmann -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing

Hello, are there any good methods in R that will test if two correlation matrices (obtained in different ways) are equal? Something better than the Mantel test would be preferable. Regards, Patrik Waldmann -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

I wonder if someone could help me adjusting the following code to parallelized snow code: #Creating a data set (not needed to be parallel) n<-100 p<-100 x<-matrix(rnorm(n*p),p) y<-rnorm(n) # Bootstrapping nboot<-1000 alpha<-0.05 rhoboot <- array(0, dim=c(p,nboot)) bootranks <- array(0, dim=c(p,nboot)) bootsamples <- array( floor(runif(n*nboot)*n+1), dim=c(n,nboot)) for

Hello, are there any functions in R for estimation of the mode of a MCMC-chain? Best, Patrik Waldmann [[alternate HTML version deleted]]

Hello, I have an unbalanced data set with some factors and several variables. The missing values are spread unequally over the variables. Because of the unbalanced structure have i tried NLME, but I cannot understand how I should set up the na.omit. I would not like to use na.omit before setting up the model, as that would exclude too many observations (on the whole structure). Lets say I have

I haven't found a function that directly calculates the matrix inverse, does it exist? Otherwise what would be the fastest way to do it? Patrik Waldmann -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the

R version 2.10.1 (2009-12-14) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with

Dear R People: I have a few questions about multiple comparisons and contrasts for ANOVA, please. I've tried some things but with no success. Suppose I have a completely randomized design, and I want to have the contrast \mu_1 - 0.5 \mu_2 - 0.5 \mu_2 How do I set that up, please? I used the C command, and ran aov, but the results were identical to those with no contrasts. Also, is there

Dear All: I apologize for asking such an elementary question, but I could not find an adequate response on line. I am hoping to receive some help with the interpretation of the Intercept coefficient in the gam model below. I1 through I3 are dummy coded "Item difficulty" parameters in a data set that includes 4 items. If the Intercept is the value of Y when all other terms are 0, am I

Dear all I was willing to use argument 'exclude' in function xtabs to remove some levels of factors (xtabs help page says '"exclude: a vector of values to be excluded when forming the set of levels of the classifying factors"). I tried: > mydata <- data.frame( treatment = c("B", "A", "C", "C", "B",

Hi all Apologies for any cross posting. I have encountered a rather bizarre "problem" in Splus and R. I am using Age-Period-Cohort models to model cervical cancer and have run the same data on both R (v.1.4.1 & v1.6.2) and Splus (version 6.0). I used the same command line in both Splus and R: glm(cases~-1+as.factor(age)

Dear all, I am trying to find a R version of the S+ routine fac.design. In the help of the package conf.design fac.design is listed under "see also". Any clues as to where (which package) I can find fac.design? thanks Steve. Steven Higgins Department of Ecological Modelling UFZ - Centre for Environmental Research Leipzig-Halle Permoserstrasse 15 D-04318 Leipzig Germany Phone:

fit1<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.15,data=wbc) fit2<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.5,data=wbc) fit3<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.15,data=wbc) fit4<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.15,data=wbc)

Hi, I've searched through the contributed packages and found conf.design which supports factorial design generation - is there any way to specify the values of the levels in the functions in this library? Currently the default for 2 level designs are the levels 0 and 1. Reason I ask is I'd like to create a Central Composite design and to do this by appending to a factorial design, it

Dear R users, I am trying to draw error bars in a bar plot, I use this code (tried many others which did not work): install.packages() library(gplots) y <-c(39.02, 46.42) se <- c(7.57,7.35) plot <- barplot(y, beside=TRUE, col=0, ylim=c(0,47), axis.lty=1, main="far-red", xlab="latitude", names.arg=c("56N", "68N")) superpose.eb(plot, y, se,

Hi, I am analyzing a data set with greater than 1000 independent cases (collected in an unrestricted manner), where each case has 3 variables associated with it: one, a factor variable with 0/1 levels (called XX), another factor variable with 8 levels (X) and a third response variable with two levels (Y: 0/1). I am trying to see if X1 has an effect on the relationship between X2 and the

This is a Request For Comment, also BCCed to 390 package maintainers of reverse dependencies of the Matrix package. Most users and package authors working with our 'Matrix' package will be using it for numerical computations, and so will be using "dMatrix" (d : double precision) matrix objects M, and indirectly, e.g., for M >= c will also use "lMatrix" (l:

All, I have been reading Dr. Harrell's excellent "Regression Modeling Strategies" book and trying out the exercises. I understand that contrast( ) is used to obtain contrasts between two variables for given levels of other nuisance variables; is there a way to use contrast( ) to obtain, for example, Scheffe confidence intervals / hypothesis tests for many post hoc contrasts at

Help R list servers, I have 500 external data sets for a simulation study that I would like to bring into R to analyze. They have the names data1.dat, data2.dat, ..., dataN.dat Is there a way to automatize the bringing in of these data sets in R using the read.table function within a looping cycle? For example... for (j in 1:N){ data_"j" = read.table("data"j".dat,