similar to: optFederov/AlgDesign - help avail?

Hi, I have some problems when using AlgDesign->optFederov() generating designs. I have 6 variables, all factors. 3^2 and 4^4, I want to have a design that can take care of main effects and two interactions within 2 pair of variables v3-v4 and v5-v6, the following is the code ################ require(AlgDesign) set.seed(1) levels = c(v1=3,v2=3, v3=4,v4=4,v5=4,v6=4)

Hi, I am wondering how I should interpreate the output of optFederov() in AlgDesign. Specially I want to know what is $D, $A, $Ge and $Dea, which one I can use as an efficiency to say how good the optimal design is. I only know when a orthogonal design comes, $D = 1. I red the pdf document -- vignette("AlgDesign") [Just type: vignette("AlgDesign") in R, you will get

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

Hi I am trying to generate a design using Algdesign and came across terms of "exact design" and "approxiamte theory design", I did not find a reference to explain what they are, could some one shed some light about this on me? Another question is, I want to measure the main effects and at least two interactions, variables are factors, how do I ensure this in formula,

Okay, been working with tripack, seems the most mature package for this. Got it to work well with their test data set - data(tritest). When i tried random numbers to explore further, i am getting some results that don't reconcile. example run this: library(tripack) y <- runif(100) x <- runif(100) vm <- voronoi.mosaic(x,y) plot(vm) par(new=T) plot(x,y,col='blue') when

Hi All, recently I used Design Expert for some Design Of Experiment work. I was happy with the interface to select which effects I want to see in my experiment, and which not. For example: I can select of course my main effects, but also if I want to see interaction A:B, B:C, A:B:C,but not A:C. This was very interessting as you can end up with fewer runs, especially in cases of 10 factors with

Hello, trying to locate all the string commands in the base version of R, can't seem to find an area that describes them. I am in need to do some serious parsing of text data to create my dataset. Is there a summary link to all the character operators? string manipulations that would help in parsing text.

the R developer site has been down for some time, what gives? http://developer.r-project.org/

Hi all, Does anybody know if it is possible to build a fractional factorial design in R? That is, suppose that we want do design an experiment with 3 factors with 2, 3 and 3 levels, respectivly. However we want to consider, let's say, only 6 from all possible level combinations. Does R design such experiment? Thanks in advance, Caio [[alternative HTML version deleted]]

Hello, does R estimate belief networks to estimate chains of causality. Anyone point me to the right direction, or the most developed library? looking at DEAL>

Hello, writing some R code to cleanse a data set, if the following set of symbols are identified then perform some actions. trying to write the minimum code to do this. tname = "VIX" checkticker = c("VIX", "TYX", "TNX", "IRX") if (tname == checkticker) { //perform some operations } result i get is > tname == checkticker

Hello, not understanding the output of prcomp, I reduce the number of components and the output continues to show cumulative 100% of the variance explained, which can't be the case dropping from 8 components to 3. How do i get the output in terms of the cumulative % of the total variance, so when i go from total solution of 8 (8 variables in the data set), to a reduced number of

Dear r-helpers, Could you please help me solve the following problem: When I run require(AlgDesign) trt <- LETTERS[1:5] blk <- 10 trtblk <- 3 BIB <- optBlock(~., withinData = trt, blocksizes = rep(trtblk, blk)) In response to the last command, R complains: Error in optBlock(~., withinData = trt, blocksizes = rep(trtblk, blk)) : object ".Random.seed" not found The

Dear DevelopeRs, in continuing with my suite of packages on experimental design, I am stuck with an issue that appears to be related to package AlgDesign - I have tried to get it solved by Bob Wheeler, but he seems to be stuck as well. Whenever AlgDesign is loaded, some of my code does not work any more. For example, in a fresh R session: require(DoE.base) fac.design(nlevels=c(2,6,2))

Hello, we are writing rich internet user interfaces and like to call R for some of the computational needs on the data, as well as some creation of image files. Our objects communicate via the SOAP interface. We have been researching the various packages to expose R as a SOAP service. No current CRAN SOAP packages however. Found 3 to date: RSOAP (http://sourceforge.net/projects/rsoap/)

hello, i just got introduced to R - WOW its beautiful.. I am presently a SAS user and wanted to configure R to work in a multi-user enteprise environment. Client - Server. Where we have a strong LINUX server supporting about 10 statisticians with R. Anyone have any backround or information they can share to help me get jump-started on setting up R in this environment? Does each user have

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Hi, i need to create a fractional factorial design sufficient to estimate the main effects. The factors may have any number of levels, let's say any number from 2 to 6. I've tried to use the library conf.design , but i cannot figure out how to write the code. For example, what is the code for a design with 5 factors (2x3x3x5x2) and only main effects not confounded? thanks in advance!

Hello, does anyone know of any R routines capable of whats called Hierarchical Forecasting, reconciling the different hierarchies. Example: A top down forecast where the corporate forecast is created and then all the regions within the corporate entity are also forecasted, with the constraint they sum to the corporate forecast.

Hello, running a mixed model in the package LME4, lmer() Panel data, have about 322 time periods and 50 states, total data set is approx 15K records and about 20 explanatory variables. Not a very large data set. We run random intercepts as well as random coefficients for about 10 of the variables, the rest come in as fixed effects. We are running into a wall of time to execute these models.