similar to: kernlab - error message: array(0, c(n, p)) : 'dim' specifies too large an array

Hi, I am trying to perform relevance vector machines with the rvm-function from kernlab. On one dataset I get this message: Setting default kernel parameters Error in if (length(data) != vl) { : RMate stopped at line 0 of selection missing value where TRUE/FALSE needed Calls: rvm ... .local -> backsolve -> as.matrix -> chol -> diag -> array can someone explain this error

I'm hoping that someone with deeper insight into S4 than I, that is to say virtually everyone reading this list, could help resolve the following problem in SparseM. We have setGeneric("backsolve", function(r, x, k = NULL, upper.tri = NULL, transpose = NULL, twice = TRUE, ...) standardGeneric("backsolve"), useAsDefault= function(r, x,

Hi, according to ?rvm the relevance vector machine function as implemented in the kernlab-package has an argument 'cross' with which you can perform k-fold cross validation. However, when I try to add a 10-fold cross validation I get the following error message: Error in match.arg(type, c("C-svc", "nu-svc", "kbb-svc", "spoc-svc",

(To the list moderator: I just subscribed to the list. Apologies for not having done so longer before trying to post.) Hi all, I am currently using lmer to analyze data from an experiment with a single fixed factor (treatment, 6 levels) and a single random factor (block). I've been trying to follow the online guidance for estimating p-values for parameter estimates on these and other

Hi!! I am trying to figure out how to use the string kernel "stringdot" in kernlab. k <- function(x,y) { (sum(x*y) +1)*exp(-0.001*sum((x-y)^2)) } class(k) <- "kernel" data(promotergene) ## train svm using custom kernel gene.k <- ksvm(Class~.,data=promotergene,kernel=k,C=10,cross=5) # works fine in this case gene.rbf <-

I'm trying to read data a program produces in matrixmarket array format into R and its giving me fits. I've tried read.MM (below) and readMM (from the Matrix package) but neither works. One of them says array format isn't supported, the other reports something indecipherable about Fortran. Here's the file contents. Can't get much simpler than that! %%MatrixMarket matrix array

Dear R users I am trying to obtain p-values for (quasi)poisson lmer models, including Markov-chain Monte Carlo sampling and the command summary. > > My problems is that p values derived from both these methods are totally different. My question is (1) there a bug in my code and > (2) How can I proceed, left with these uncertainties in the estimations of > the p-values? > > Below

Hi All, I am using ksvm method in kernlab R package for support vector machines. I learned the multiclass one-against-one svm from training data and using it to classify new datapoints. But I want to update/finetune the 'svm weights' based on some criteria and use the updated svm weights in the predict method framework. I don't know if its possible or not, how do classify new

For example, if I call "library(spam)", I would get messages like this Package 'spam' is loaded. Spam version 0.27-0 (2011-08-17). Type demo( spam) for some demos, help( Spam) for an overview of this package. Help for individual functions is optained by adding the suffix '.spam' to the function name, e.g. 'help(chol.spam)'. Attaching package: ?spam? The

Dear all, how can I perform a repeated measures ANOVA using a covariance matrix as input? E.g., I have four repeated measures (N = 200) with mean vector tau (no BS factor): tau <- rbind(1.10, 2.51, 2.76, 3.52) and covariance matrix (sigma): sigma <- matrix(c(0.523, 0.268, 0.267, 0.211, 0.268, 0.444, 0.492, 0.571, 0.267, 0.492, 1.213,

Hi all, After trial and error by myself for a few hours, I decide to ask for your help. I have a training set which is a matrix of size 200 x 2, where the two columns denote each independent variable. I have 200 observations. ----------------- ss=data.frame(trainingSet); result=lm(trainingClass~ss$X1+ss$X2); ----------------- where trainingClass denotes the true classes of the training data.

Dear friends. I tried the session below with 10 MB in both vsize and nsize but didn't get the example work. Is this a problem in LME or in me or both or somewhere else or undefined ? R : Copyright 1999, The R Development Core Team Version 0.64.0 Patched (May 3, 1999) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type

Dear all, I am currently analyzing count data from a hierarchical design, and I?ve tried to follow the suggestions for a correct estimation of p-values as discusssed at R-Wiki (http://wiki.r-project.org/rwiki/doku.php?id=guides:lmer-tests&s=lme%20and%20aov). However, I have the problem that my model only consists of parameters with just 1 d.f. (intercepts, slopes), so that the

I am getting the above warning following loading of Geneland 3.1.5 on unix , while a simple plot sends output to the pdf file ( see attached code) no output results from Geneland functions, resulting in empty pdf files > library (Geneland) Loading required package: RandomFields Loading required package: fields Loading required package: spam Package 'spam' is loaded. Spam version

Hi, I have just installed R 2.4.0 and when I try to load SpareseM, I get the following error message library(SparseM) Package SparseM (0.71) loaded. To cite, see citation("SparseM") Error in loadNamespace(package, c(which.lib.loc, lib.loc), keep.source = keep.source) : in 'SparseM' methods specified for export, but none defined: as.matrix.csr, as.matrix.csc,

Dear R users I am trying to obtain p-values for (quasi)poisson lmer models, using Markov-chain Monte Carlo sampling and the command summary. > > My problems is that p values derived from both these methods are totally different. My question is (1) there a bug in my code and > (2) How can I proceed, left with these uncertainties in the estimations of > the p-values? > > Below is

Hi, First of all, I'm a complete rookie to R (~2 weeks). But anyway, I'm trying to use the RWeka interface for C4.5 (J48) classification. As a proof of concept I'm using the Iris data set to create a training set of 30 instances (10 per species) and use the remaining 120 instances as my test set. This is what I do: trainingIndices <- rep(1:10, 3) + rep(0:2, each=10) * 50

Dear helpers, I am a Dutch student from the Erasmus University. For my Bachelor thesis I have written a script in R using boosting by means of classification and regression trees. This script uses the function the predefined function rpart. My input file consists of about 4000 vectors each having 2210 dimensions. In the third iteration R complains of a lack of memory, although in each iteration

I am looking fpr a algo to find matrix inverse. Till time I am aware of Gauss-Jordan Elimination procedure to find the same. Are there any other algo. as well? What does R use to find the inverse? -- View this message in context: http://www.nabble.com/Algo.-for-matrix-inverse-tp20182285p20182285.html Sent from the R help mailing list archive at Nabble.com.

Can we do Cholesky Decompositon in R for any matrix --------------------------------- [[alternative HTML version deleted]]