Re: [R] Student-t distributed random value generation within a confidence interval?

allvals <- rt(1000,df=11) ## 1000 samples is overkill: slightly more than ##500*(1.05) should be large enough subvals <- (allvals[abs(allvals) Thank you very much bbolker. It works very well! Best regards Thomas -- View this message in context: http://r.789695.n4.nabble.com/Student-t-

[R] Student-t distributed random value generation within a confidence interval?

Dear R-users! I´m faced with following problem: Given is a sample where the sample size is 12, the sample mean is 30, and standard deviation is 4.1. Based on a Student-t distribution i´d like to simulate randomly 500 possible mean values within a two-tailed 95% confidence interval. Calculation of

[R] Linear discriminant function analysis based median as group centroid and nonparametric scale estimators???

Dear All, I am using a specific approach for my master thesis. In essence, a supervised reclassification is used as an intermediate step to find chemical parameters which are able to reclassify defined groups. These variables will be used in a next step where location and scale estimators of the g

Re: [R] Optimization in R similar to MS Excel Solver

Dear Richard, It is funny. I have to perform the approach of sediment fingerprinting for my master thesis. Mr. Hasselman gave me the advice to take a closer look into the limSolve package a few days ago. http://cran.r-project.org/web/packages/limSolve/index.html I guess, the lsei-function of thi

Re: [R] Which package/function for solving weighted linear least squares with inequality and equality constraints?

Dear Berend, Thank you very much for proposing the limSolve-package! The lsei function of the package is exactly what I was looking for. Best regards Thomas -- View this message in context: http://r.789695.n4.nabble.com/Which-package-function-for-solving-weighted-linear-least-squares-with-ine

Re: [R] store the results of two connected and "disturbed" for-loops to data.frame

Dear Mr. Barradas , Thank you very much! It works very well, especially defining a<-0 and using a<-a+1 as some kind of loop-count was exactly what i searched for! best regards Thomas -- View this message in context: http://r.789695.n4.nabble.com/store-the-results-of-two-connected-and-disturb

[R] store the results of two connected and "disturbed" for-loops to data.frame

Dear all, here is a example of my problem: /#data# g<-c(1,1,1,2,2,2) A<-runif(6,min=1,max=5) B<-runif(6,min=100,max=1000) C<-runif(6,min=30,max=31) D<-runif(6,min=67,max=98765) var<-cbind(A,B,C,D) label<-colnames(var) store<-data.frame(matrix(ncol=2)) colnames(store)=c("usedVar","prediction") l

[R] random variable selection algorithms for lda?

Dear R-experts, I would like to find the best variable combination which are maximises the accuracy of a cross validated reclassification. My data consists of 36 samples, equal distributed to 6 groups, and each sample are characterised by 20 variables. /data<-data.frame(1:36,1:20) group<-(1,1,1,1