similar to: seeking help on using LARS package

Could anybody help me out please? > cl<-as.factor(traindata[,13]) > knn(traindata[1:295,2], newdata[1:32,2], cl,k=2, prob=TRUE) Error in knn(traindata[1:295, 2], newdata[1:32, 2], cl, k = m, prob = TRUE) : Dims of test and train differ Both traindata and newdata have 13 elements. Only one of the first 12 elemnets is needed to predict the 13 element. What's the problem of

Dear list I am using the mvr function of the package pls.pcr to compute PLS resgression using a X matrix of gene expression variables and a Y matrix of medical varaibles. I would like to obtain the R2 (sum of squares captured by the model) and Q2 (proportion of total sum of squares captured in leave-one-out cross validation) of the model. I am not sure if there are specific slots in the

hi, i have problem with transactions and multiple databases. Object1 and Object2 are two different models/databases with different connections, when i use only one model: Object1.transaction do object1.value1 = "123456" object1.save do_some_error end it''s ok, error is produced, nothing is commited and database is unchanged. when i use 2 models, described at

Hi there I have a list that looks like this object1 object1 78 object1 object2 45 object1 object3 34 object1 object4 45 object2 object2 89 object2 object3 32 object2 object4 13 but i want to create a matrix like this in order to use the dist function of R object1 object2 object3 object4 object1 78 45 34 45 object2 45

On Fri, 2016-04-15 at 00:32 +0100, Jonathan Hunter wrote: > Thank you Andrew, really appreciated. > > I have now run 'samba-tool dbcheck --cross-ncs --fix' and it has > successfully fixed some errors; there were 110 previously, however > there are still 69 remaining after a second pass of dbcheck --fix. > > The remaining errors seem to be mainly of this form: >

Thank you Andrew, really appreciated. I have now run 'samba-tool dbcheck --cross-ncs --fix' and it has successfully fixed some errors; there were 110 previously, however there are still 69 remaining after a second pass of dbcheck --fix. The remaining errors seem to be mainly of this form: ERROR: duplicate attributeID values for myattrib in replPropertyMetaData on

Hi, I am using errorest function from ipred package. I am hoping to perform "bootstrap 0.632+" and "bootstrap leave one out". According to the manual page for errorest, i use the following command: ce632[i]<-errorest(ytrain ~., data=mydata, model=lda, estimator=c("boot","632plus"), predict=mypredict.lda)$error It didn't work. I then tried the

Hello, I want to use nnet package in R, to train and simulate a NN and get the value of MSE. I am reading in a file which has 19 input variables and one output variable and has a total of 2000 observations. The first column in the file is a column just for giving the serial numbers of the observations. I have already read in the file and also extracted the different values into the matrices to

Hello, I want to know if there is any way to avoid source() stopping when there is an error. Here is the content of my Main.R script: source("~/R/source/Constructor1.R") # Object1 should be constructed ifelse(exists("Object1"), # It's an S4 object print("Object1 exists"), # I can't avoid using 'validity'

Dear All I have a program that breaks at the following lines of code: bigfunction = { ... object1 = myfunction(x) object2 = strsplit(object1, ",")[[1]] ... } where myfunction is defined elsewhere outside of bigfunction. The error I get is "error in strsplit() -- object1 not found". However, when I insert browser() into my code so that the above reads, bigfunction = {

Hello, I've come across a strange error... Here is what happens: model <- svm(traindata,trainlabels, type="C-classification", kernel="radial", cost=10, class.weights=c("win"=3,"lose"=1), scale=FALSE, probability = TRUE) predictions <- predict(model, traindata) pred <- prediction(predictions, trainlabels) This returns an error: Error in

I recently updated all my plugins, and for the fun of it, I added ALL the Rcmdr Plugins to my collection to see what functionality might exist. I started Rcmdr and loaded ALL the available Plugins from to the Rcdmdr Tools menu. To my suprise Rcmdr produced a number of warnings and finally an error. The error suggests that some of the extensions are incompatable with each other because of menu

Dear R people, I am using tune.nnet from e1071 package to tune the parameters for nnet. I am using the following syntax: tuneclass <-c(rep(1,46),rep(2,15)) tunennet <-tune.nnet(x=traindata,y=tuneclass,size=c(50,75,100),decay=c(0,0.005,0.010),MaxNWts = 20000) Here traindata is the training data that I want to tune for nnet which is a matrix with 61 rows(samples) and 200

Hi all, I am using cross-validation to validate a generalized linear mixed effects model fitted using glmmPQL. i found that the predict function has a problem and i wonder if anyone has encountered the same problem? glmm1 = glmmPQL(y~aX+b,random=~1|sample,data=traindata) predict(glmm1,newdata=testdata,level=1,type="response") gives me all "NA"s. it works for level=0 (the

In that case, I can't work out why the first model fails but not the second. I would start looking at "Data" to see what it contains. if: object2 <- polr(Inc ~ Training ,Data,Hess = T,method = "logistic" ) works, the problem may be with the "Adopt" variable. Jim On Tue, Mar 20, 2018 at 10:55 AM, Willy Byamungu <wmulimbi at email.uark.edu> wrote: >

Hi all, Suppose I want to set the values in a column to the log of the values of another column like so: object$LogDist <- log10(object$Distance) How do I loop through the objects if I have object1, object2, etc to perform this function? object1$LogDist <- log10(object1$Distance) object2$LogDist <- log10(object2$Distance) object3$LogDist <- log10(object3$Distance) I was trying to

dear all, It appears that glmnet(), when "selecting" the covariates entering the model, skips from K covariates, say, to K+2 or K+3. Thus 2 or 3 variables are "added" at the same time and it is not possible to obtain a ranking of the covariates according to their importance in the model. On the other hand lars() "adds" the covariates one at a time. My question

Dear all, I've started working with R (vs 1041) a few weeks ago, and now I'm having problems with the amount of memory. I'm working on the windows-me, my computer has 128 Mb of memory. I'm using the R under the emacs (ESS-5.1.20) and it is started by the command: Rterm --min-vsize=10M --max-vsize=100M --min-nsize=500k --max-nsize=1M I've been had problems when executing a

Dear R users, I have two simple questions here, and hope someone can help me on this. Thanks in advance. 1. I have a list object lst=list(a1=matrix(rnorm(4),2,2), a2=matrix(rnorm(4),2,2),a3=matrix(rnorm(4),2,2)). Here I only use three elements for illustration, and in fact the length of lst, n, is unknown in advance. I want to define an object for each element of this lst, and the objects have

Dear members, My name is Seungho Huh. I am a statistician who tries to use the Kernel PLS method in a classification problem. I am sending this email to ask you something about the "kernelpls" function in R (pls.pcr package). I would like to obtain the predicted Y values for test data, using the Kernel PLS method. Let's take the example in the R help: > data(NIR) >