similar to: how to replace my double for loop which is little efficient!

I want to load many files in the R. The names of the files are "Sim1.txt", " Sim2.txt", "Sim3.txt", "Sim4.txt", "Sim5.txt" and so on. Can I read them at one time? What should I do? I can give the same names in R. Thanks. For example: > tst=paste("Sim",1:20,".txt",sep="") # the file names > tst [1]

Hi all, I met with a problem about the weighted least square regression. 1. I simulated a Normal vector (sim1) with mean 425906 and standard deviation 40000. 2. I simulated a second Normal vector with conditional mean b1*sim1, where b1 is just a number I specified, and variance proportional to sim1. Precisely, the standard deviation is sqrt(sim1)*50. 3. Then I run a WLS regression without the

Hi, Can anyone help to write a more elegant version of my code? I am sure this can be put into a loop but I am having trouble creating the objects b1,b2,b3,...,etc. b1 <- rigamma(50,1,1) theta1 <- rgamma(50,0.5,(1/b1)) sim1 <- rpois(50,theta1) b2 <- rigamma(50,1,1) theta2 <- rgamma(50,0.5,(1/b2)) sim2 <- rpois(50,theta2) b3 <- rigamma(50,1,1) theta3 <-

To whoever this may concern I am trying to write a repeat loop and can't make out from the documentation on the website how exactly to construct the repeat, break structure of the loop. Below is the function sim2.dat that I am trying to create, in which firstly I create n random uniform(0,1) numbers. From there I assign values to k, P and F. From then on for each random number u[j] of u, I

If the observations are normally distributed and the 2xk design is balanced, theory requires that the tests for interaction and row effects be independent. In my program, appended below, this would translate to cntT (approx)= cntR*cntI/N if all R routines were functioning correctly. They aren't. sim2=function(size,N,p){ cntR=0 cntC=0 cntI=0 cntT=0 cntP=0 for(i in 1:N){

Hi, I am using the FracSim library to simulate a time series. However, the simulate function ignores my attempt to set the RNG seed I need for reproducible research. The published docs and google have not yielded an answer, so any help greatly received. Thanks, Selwyn ## Example code snippet library(FracSim) ## simulate some 1d fractal data set.seed(1234) sim1 = fracsim.1d(h=0.5,k=1000,n=5000)

I try to perform a clustering using an existing dissimilarity matrix that I calculated using distance (analogue) I tried two different things. One of them worked and one not and I don`t understand why. Here the code: not working example library(cluster) library(analogue) iris2<-as.data.frame(iris) str(iris2) 'data.frame': 150 obs. of 5 variables: $ Sepal.Length: num 5.1 4.9 4.7

Hi, I'm mighty new to R. I'm using it on Windows. I'm trying to cluster using a distance matrix I created from the data on my own and called it D10.dist. I loaded the cluster package. Then tried the following command... > agnes("E:D10.dist", diss = TRUE, metric = "euclidean", stand = FALSE, > method = "average", par.method, keep.diss = n < 1000,

Sorry, I never use pam. In the help, you can see that pam require a dataframe OR a dissimilarity matrix. If diss=FALSE then "euclidean" was use.So, I interpret that a matrix of dissimilarity is generated automatically. Problems may be in your data. Indeed pam(ruspini, 4)$diss write a dissimilaty matrix while pam(MYdata,10)$diss wite NULL 2017-08-17 16:03 GMT+02:00 Sema Atasever

On Oct 21, 2011, at 12:15 AM, James Molloy wrote: > Hi Andy, > > Could you describe how this would be done? In the current ARM itineraries > (say C-A9 for example), the superscalar issue stage is modelled as taking 1 > cycle. If it were to take 2 cycles instead, as far as I can tell the hazard > analyser would stall because both FU's would be acquired. > > I would

I need to write a nonhierarchical clustering routine and I'm studying the way hclust (in the mva library) is built in R to see how things are done and what I can modify. I ran f2c on the hclust.f file (so I could read it in a language I know!) and there is one thing I don't quite understand about the way it gets called and the way it returns values. That Fortran function gets called in

Greetings, I am reasonably experienced with R but I recently tried to do some clustering using the "cluster" package, in order to see if it would help. I only tried this once with the 1.3.1 version and it worked (I don't quite remember which method I used). Now, I tried with the 1.4.0 version and no clustering function seems to work with matrices that contain NAs, even though

Hello all! I am performingsome clustering analysis on microarray data using agnes{cluster} and I have created my own dissimilarity matrix according to a distance measure different from "euclidean" or "manhattan" etc. My question is, if I choose for example method="complete", how are the distances between the elements calculated? Are they taken form the dissimilarity

Dear Germano, Thank you for your fast reply, In the above code, *MYData *is the actual data set. Do not we need to convert *MYData to *the dissimilarity matrix using *pam(as.dist(**MYData**), k = 10, diss = TRUE*)* code line?* *Regards.* On Thu, Aug 17, 2017 at 2:58 PM, Germano Rossi <germano.rossi at gmail.com> wrote: > try this > > MYdata <-

I don't know if there is some fault in compiling or a bug of the new R-1.7.0 version: cl.pam.2 <- pam(as.dist(1-cor(mel.data)),2) plot(cl.pam.2) perform a right partitioning and silhouette plot on the old R-1.6.2 instead "Error in clusplot.default(x$diss,...... ; x is not numeric" is the output on the new R-1.7.0. Same platform: RH8.0 i686. Some suggestions? A.S.

Hello, I would like to use my own dissimilarity matrix in a PAM clustering with method "pam" (cluster package) instead of a dissimilarity matrix created by daisy. I read data from a file containing the dissimilarity values using "read.csv". This creates a matrix (alternatively: an array or vector) which is not accepted by "pam": A call

Hi I have submitted a package (rioja) to CRAN. It checks OK for all R versions and OS's except r-release-macosx-ix86 where it fails when checking the examples. Specifically, it fails because R can't find the package vegan which is needed in a function. Here is the snippet from the check results: ### Begin snippet checking examples ... ERROR Running examples in 'rioja-Ex.R'

Hello list, the following approach did not work: clustersA <- pam(distances, nkA, diss=TRUE); gc(); filenameclu = paste("filenameclu", ".txt"); write.table(clustersA , file=filenameclu,sep=","); although it worked with clustersA <- hclust(distances, method="ward"); and a consecutive kclassA <- cutree(clustersA, k=nkA); filename =

Dear useRs, I want to simulate a time series (stationary; the distribution of values is skewed to the right; quite a few ARMA absolute standardized residuals above 2 - about 8% of them). Is this the right way to do it? #-------------------------------- load("rdtb") #the time series > summary(rdtb) Min. 1st Qu. Median Mean 3rd Qu. Max. -1.11800 -0.65010 -0.09091

HI R users With clara function I get a data frame (maybe this is not the exact word, I'm new to R) with the following variables: > names(myclara) [1] "sample" "medoids" "i.med" "clustering" "objective" [6] "clusinfo" "diss" "call" "silinfo" "data" I want to