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Dear R users, I have used the AlgDesign package to construct a D-optimal exp. design from a file containing a set of allowed runs. The code for the optFederov call is below: #Using optFederov ------------------------------ library(AlgDesign) options(contrasts=c("contr.sum","contr.poly")) file<- read.table("C:/Upstream/Designs/Vodacom Tanzania phase1

(the same mail was sent to the author) When I called the function DEoptim with control=list(strategy=1) or control=list(strategy=2) I got the error: Error in mui[rtd + 1, i] : incorrect number of dimensions Analysis of the source code of the DEoptim reveals the following fragment if (con$strategy > 5) st <- con$strategy - 5 ## binomial crossover else { st <-

Hi all, Does anybody have a workable dopt program in R to generate D-optimal designs. I downloaded dopt.zip from stat homepage of the university of oxford, whenever I load it into R and try to run, the following error comes: Error: couldn't find function "Dopt". Any information will be highly appreciated! Thank you! Harvey. --------------------------------- [[alternative HTML

Hi, I would like to have some advise about how to install Dopt on R for Windows. I can not manage to properly compile Dopt.f anf to get a suitable dll for R. Can someone explain me step by step what should I do ? Thanks in advance Isabelle Zabalza-Mezghani

Dear R users: I'm new to R and am trying to fit a mixed model Cox regression model with coxme function. I have one two-level factor (treat) and one covariate (covar) and 32 different groups (centers). I'd like to fit a random coefficients model, with treat and covar as fixed factors and a random intercept, random treat effect and random covar slope per center. I haver a couple of

Dear all, I'm having a few problems trying to reshape a data frame. I tried with reshape{stats} and melt{reshape} but I was missing something. Any help is very welcome. Please find details below: ################################# # data in its original shape: indiv <- rep(c("A","B"),c(10,10)) level.1 <- rpois(20, lambda=3) covar.1 <- rlnorm(20, 3, 1) level.2

Hi, Using package gclus in R, I have created some graphs that show the trends within subgroups of data and correlations among 9 variables (v1-v9). Being interested for more details on these data I have produced also the var-covar matrices. Question: From a pair of two subsets of data (with 9 variables each, I have two var-covar matrices for each subgroup, that differ for a treatment on one

Hi I'm running QDA on some data and calculating the discriminant function. qda.res <- qda(type ~ npreg + glu + bp + skin + bmi + ped + age) ind_yes <- c(1:N)[type == "Yes"] > ind_no <- c(1:N)[type == "No"] > cov_yes <- cov(table[ind_yes, 1:7] ) > cov_no <- cov(table[ind_no, 1:7] ) > covar<-list(cov_no, cov_yes) qdf<- function(x,

Hi all I hope anyone can help me with this. I'm trying to get vmware to play along nicely on CentOS 5.1 x64, but I get errors when I try and start a vmx image, or list them. This the the error: [root at backup vmware-mui-distrib]# vmware-cmd -l Use of uninitialized value in string eq at /usr/lib64/perl5/site_perl/5.8.8/x86_64-linux-thread-multi/VMware/VmPerl.pm line 114.

Dear R users, I am trying to make this (3 by 10) matrix A --A---------------------------------------------------- 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0.5 0.5 0 0 0 0 0 0 0 ------------------------------------------------------- from "mass.func" --mass.func------------------------------------------- > mass.func $`00` prop 5 1 $`10`

Dear All, This is not strictly R related (though I would implement the solution in R; besides, being this list so helpful for these kinds of stats questions...). I got a "strange" request from a colleage. He has a bunch (approx. 25000) subjects that belong to one of 12 possible classes. In addition, there are 8 covariates (factors) that can take as values either "absence"

Hello! I have something like this: test1 <- data.frame(intx=c(4,3,1,1,2,2,3), status=c(1,1,1,0,1,1,0), x1=c(0,2,1,1,1,0,0), x2=c(1,1,0,0,2,2,0), sex=c(0,0,0,0,1,1,1)) and I can easily fit a cox model: library(survival) coxph(Surv(intx,status) ~ x1 + x2 + strata(sex),test1) However, I want to

Hello, I finally run my measurement model in sem - successfully. Now, I am trying to print out the path diagram that is based on the results - but for some reason it's not working. Below is my script - but the problem is probably in my very last line: # ANALYSIS OF ANXIETY, DEPRESSION, AND FEAR - LISREL P.31 library(sem) # Creating the ANXIETY, DEPRESSION, AND FEAR intercorrelation matrix

Dear R-helpers, Would anybody have a compiled binary version (for Windows) of the Dopt library that is in the development site?. best regards, IOsu -------------------------------------------------------------- Jes?s Mar?a Fr?as Celayeta Dublin Institute of Technology School of Food Sci. and Env. Health. Cathal Bruha St., Dublin 1. Ireland Phone: +353 1 4024459 Fax: +353 1 4024495

> Date: Mon, 20 Feb 2006 16:43:55 -0600 > From: Aldi Kraja <aldi at wustl.edu> > > Hi, > Using package gclus in R, I have created some graphs that show the > trends within subgroups of data and correlations among 9 variables (v1-v9). > Being interested for more details on these data I have produced also the > var-covar matrices. > Question: From a pair of two

Hello! I am having trouble including covariates in a fuzzy RDD model in R with rddtools. I run the following commands: library(rddtools) data.complete2 <- as.data.frame(cbind(GDPpc.rel, GR.rate, fitval.firstStage)) colnames(data.complete2) <- c("GDPpc.rel", "GR.rate", "FitTreat") data.RDD<-rdd_data(x=data.complete2$GDPpc.rel,

Hi all, I cant' wrap my head around an error from the coxph function (package survival). Here's an example: library(survival) n = 100; set.seed(1); time = rexp(n); event = sample(c(0,1), n, replace = TRUE) covar = data.frame(z = rnorm(n)); model = coxph(Surv(time, event)~ . , data = covar) R gives the following error: > model = coxph(Surv(time, event)~ . , data = covar) Error in

Dear R users. I'm trying to fit a generalised linear spatial mode using the geoRglm package. To do so, I'm preparing my data (geodata) as follow: geoData9093 = as.geodata(data9093, coords.col= 17:18, data.col=15,* covar.col=16*) where covar.col is a factor variable (years in this case 90-91-92-93)). Then I run the model as follow: / model.5 = list(cov.pars=c(1,1),

Dear expeRts, I have a data.frame with certain covariate combinations ('group' and 'year') and corresponding values: set.seed(1) x <- data.frame(group = c(rep("A", 4), rep("B", 3)), year = c(2001, 2003, 2004, 2005, 2003, 2004, 2005), value = rexp(7)) My goal is essentially to

I am new to R. I just tried to recreate in R (using sem package and the identical input data) a solution for a simple measurment model I have found before in LISREL. LISREL had no problems and converged in just 3 iterations. In sem, I got no solution, just the warning message: "Could not compute QR decomposition of Hessian. Optimization probably did not converge. in: sem.default(ram =