search for: nrepeat

...ir 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) dat<-gen.factorial(levels,center=FALSE,varNames=names(levels),factors= c(1,2,3,4,5,6)) model = ~.+v3:v4+v5:v6 optDgn<-optFederov(model,dat,nRepeat=5,nTrials = 32,criterion = "D", approximate = F) ---------------------------- this lead to a error msg " nTrials must be greater than or equal to the number of columns in expanded X" . I thought I do not have that many columns. if I change approximate to T, this error has gone....

...1 grammar/JMP_allowed.txt",sep="\t",header=TRUE,strip.white=TRUE) formula<-as.formula("~structure+intro + intro/intro1 + concept + concept/concept1 + prize") design<-optFederov(formula, data=file, nTrials=48, evaluateI=T, criterion="D", maxIteration=10000, nRepeats=300, approximate=F,args=T) #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ I also use the Rcmdr interface at time for ease, when using the Rcmdr interface is used the sa...

...o implement the code of the e1071 package for naive bayes, but it doens't really work, any ideas?? i am very glad about any help!! i need a naive bayes with 10-fold cross validation: code: library(e1071) model <- naiveBayes(code ~ ., mydata) tune.control <- tune.control(random = FALSE, nrepeat = 1, repeat.aggregate = min, sampling = c("cross"), sampling.aggregate = mean, cross = 10, best.model = TRUE, performances = TRUE) pred <- predict(model, mydata[,-12], type="class") tune(naiveBayes, code~., mydata, predict.fun=pred, tune.control) thx for your help! cheers,...

Hallo! I would need a code for 10-fold cross validation for the classifiers Naive Bayes and svm (e1071) package. Has there already been done something like that? I tried to do it myself by applying the tune function first: library(e1071) tune.control <- tune.control(random =F, nrepeat=1, repeat.aggregate=min.,sampling=c("cross"),sampling.aggregate=mean, cross=10, best.model=T, performances=T) model <- naiveBayes(code~., mydata, tune.control) pred <- predict(model, mydata) table(pred, mydata$code) chisq.test(code, pred) but I get the same results as without tun...

I have a set of labels arranged along a timeframe in a. Each label has a timestamp and marks a state until the next label. The dataframe a contains 5 such timestamps and 5 associated labels. This means, on a continious scale between 1-100, there are 5 markers. E.g. 'abc' marks the timestampls between 10 and 19, 'def' marks the timestamps between 20 and 32, and so on. a <-

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,

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!