similar to: regularized discriminant function analysis using klaR: problems with predictions

Dear all, I am trying to do a n-fold cross-validation for a regularized discrimant function analysis using rda from the package klaR. However, I have problems to predict the groups from the test/validation sample. The exmaples of the R documantation and some online webpage also do not work. Does anybody know what I have done wrong? Here my code # I want to use the first 6 observations for

Hi, I run R 2.10.1 under ubuntu 10.04 LTS (Lucid Lynx) and klaR version 0.6-4. I compute a model over a 2 classes dataset (composed of 700 examples). To that aim, I use the function NaiveBayes provided in the package klaR. When I then use the prediction function : predict(my_model, new_data). I get the following warning : "In FUN(1:747[[747L]], ...) : Numerical 0 probability with

I use Windows, R version 2.5.1 When I try to run stepclass (klaR) I get an error message/warning saying: 1: error(s) in modeling/prediction step in: cv.rate(vars = c(model, tryvar), data = data, grouping = grouping, ... Actually, I look 16 warnings of this type. Can anyone tell me what this means? Also, it returns only 2 out of the 79 variables as important, however these variables

Dear all, I have a question on the shardsplot package:klaR(see the below Example). Plese tell me the meanings of " logstand <- t((t(logcount) / sdlogcount) * c(1,2,6,5,5,3))", much more. Why does this example use "c(1,2,6,5,5,3)" ? Examples: # Compute clusters and an Eight Directions Arranged Map for the # country data. Plotting the result.

Hi, a colleague ran a stepwise discriminant analysis twice in a row and got different results, suggesting some "sochasticity" in the algorithms involved. I looked at her data and found that there was a lot of collinearity, so that I reckoned that maybe "stepclass" (klaR) cannot find a clear winner when trying to include a new variable and makes a random choice. Is that true?

Hi, I'm trying to use "stepclass" to do a stepwise variable selection with method=lda. I keep getting this warning message, which shows up once for each variable added to the model during variable selection: Warning message: error(s) in modeling/prediction step in: cv.rate(vars = c(model, tryvar), data = data, grouping = grouping, I don't know how to interpret this warning. I

Hello, We have a system which creates thousands of regression/classification models and in cases where we have only one input variable NaiveBayes throws an error. Maybe I am mistaken and I shouldn't expect to have a model with only one input variable. We use R version 2.6.0 (2007-10-03). We use caret (v4.1.19), but have tested similar code with klaR (v.0.5.8), because caret relies on

Hi ! Right now I am learning to use svm functions available in R and trying to use these function with given example. I was stuck with svmlight function which is available in klaR package. Any help would be appreciated regarding this function. 1. I am unable to use svmlight( ) which is available in package: klaR. Although I have downloaded klaR_0.6-3 package from

Hello R list, I'm looking to do some stepwise discriminant function analysis (DFA) based on the minimization of Wilks' lambda in R to end up with a composite signature (of metals "Al","Sb","Bi","Cr","Ba") capable of discriminating 100% of the source factors (LANDUSE: "A","B","C"). The Wilks' lambda

Dear All, Would you please tell me how to display the sample No. on the map ? ---Below commands don't display the sample No.(from 1 to 150).--- library(som) library(klaR) iris.som3 <- som(iris[,1:4], xdim = 14,ydim = 6) library(klaR); opar<- par(xpd = NA) shardsplot(iris.som3, data.or = iris,label = TRUE) legend(3.5,14.3, col = rainbow(3), xjust =0.5, yjust = 0,legend =

When I used svmlight, I got below error: my command is: foo <- svmlight(y~., data= myData) the results: Error in file(con, "r") : unable to open connection In addition: Warning messages: 1: svm_learn not found 2: cannot open file '_model_1.txt' > myData[1:2,] y X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 1 1 63 1 0 0 145 233 1 1 0 150 0 2.3 1

Hi! I got the following runtime error when I tried to use svm method with stepclass. Error in "colnames<-"(`*tmp*`, value = c("0", "1")) : attempt to set colnames on object with less than two dimensions I repeated the same sequence of statements but this time I used the classification function used in the example, i.e., "lda" and it worked fine

Hi I want to create a nice picture about my result of k -the nearest neighbours algorithm. Here is my easy code: ################################# library(klaR) library(ipred) library(mlbench) data(PimaIndiansDiabetes2) dane=na.omit(PimaIndiansDiabetes2)[,c(2,5,9)] dane[,2]=log(dane[,2]) dane[,1:2]=scale(dane[,1:2]) zbior.uczacy=sample(1:nrow(dane),nrow(dane)/2,F)

Hi, Currently I testing the packets that contain built-in features for classification. Actually I looked packages such as: e1071, Klar, Caret, CORElearn. However, from what I noticed when building a naive Bayesian classifier, that they package use of the finite mixture model to estimate P (x | C) and using a normal distribution. In my research I use binary data and I want modeled P (x | C), eg the

On Fri, May 14, 2010 at 09:10:57PM +0200, Peter Klar wrote: > As the bug seems to be related to the SLAB allocator, the dump says ''kernel > BUG at mm/slub.c:2969!'', I also recompiled the kernel using the SLAB instead > of SLUB allocator, but this does not make any difference, the behaviour is > the same (beside the dump then reports a bug within slab.c instead of

On Fri, May 14, 2010 at 09:10:57PM +0200, Peter Klar wrote: > As the bug seems to be related to the SLAB allocator, the dump says ''kernel > BUG at mm/slub.c:2969!'', I also recompiled the kernel using the SLAB instead > of SLUB allocator, but this does not make any difference, the behaviour is > the same (beside the dump then reports a bug within slab.c instead of

Hello, I used the lda function from the MASS (VR) package and the rda function from the klaR package. I wanted to compare the result of this two functions by using the same training set. Thus, I used the rda function with lambda=1 an gamma=0, I should emulate the lda function and I should obtain the same result. But this it not the case, the two result are very different. My training set is

Hi everybody! I want to find a closer neighbourins observation. This is my code: ########################## library(klaR) library(ipred) library(mlbench) data(PimaIndiansDiabetes2) dane=na.omit(PimaIndiansDiabetes2)[,c(2,5,9)] dane[,2]=log(dane[,2]) dane[,1:2]=scale(dane[,1:2]) zbior.uczacy=sample(1:nrow(dane),nrow(dane)/2,F)

Hi, I have three questions concerning GLMMs. First, I ' m looking for a measure for the significance of the random variable in a glmm. I'm fitting a glmm (lmer) to telemetry-locations of 12 wildcat-individuals against random locations (binomial response). The individual is the random variable. Now I want to know, if the individual ("TIER") has a significant effect on the model

This is as doumented, and I think you could say the same thing of seq(). BTW, sequence() allows negative inputs, and I don't think you want sum(input) in that case. I've never seen the point of sequence(), but it has been around in R for a long time. It is used in packages eRm, extRemes, hydrosanity, klaR, seas. Who knows what people have in private code, so I don't see any