similar to: Compute R2 and Q2 in PLS with pls.pcr package

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

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) >

Hey! How do I get the confusion matrix after performing 10-fold cross validation from SVM in R? When I try to print it, I get the confusion matrix without cross validation. I need to compute PPV. Should I report PPV without CV and total accuracy with CV? I am confused. > svmtrain <- svm(xtrain,ytrain,kernel="sigmoid",cross=10) > pred <- predict(svmtrain, xtrain) >

You need to do some extra work if you want to do classification with a regression method. One simple way to do classification with PLS is to code the classes as 0s and 1s (assuming there are only two classes) or -1s and 1s, fit the model, then threshold the prediction; e.g., those with predicted values < 0.5 (in the 0/1 coding) get labeled as 0s. There's a predict() method for mvr

I use the svm package kernlab .I have two question. In R library(kernlab) m=ksvm(xtrain,ytrain,type="C-svc",kernel=custom function, C=10) alpha(m) alphaindex(m) I can get alpha value and alpha index about package. 1. Assumption that number of sample are 20. number of support vectors are 15. then rest 5`s alphas are 0? 2. I want use kernelMatrix xtrain=as.matrix(xtrain)

Hi, I am writing to seek some guidance regarding using Lasso regression with the R package LARS. I have introductory statistics background but I am trying to learn more. Right now I am trying to duplicate the results in a paper for shRNA prediction "An accurate and interpretable model for siRNA efficacy prediction, Jean-Philippe Vert et. al, Bioinformatics" for a Bioinformatics project

I am trying to implement a simple r-svm example using the iris data (only two of the classes are taken and data is within the code). I am running into some errors. I am not an expert on svm's. If any one has used it, I would appreciate their help. I am appending the code below. Thanks../Murli ####################################################### ### R-code for R-SVM ### use leave-one-out

Hello, I have two questions. The svmpath package provides a svmpath function: --- fit <- svmpath(xtrain, ytrain, kernel.function = radial.kernel, param.kernel = 0.8) --- 1) How to get the optimal lambda value out of this result? The svmpath package also provides a predict function: --- ytest <- predict(fit, xtest) --- How to get a score (or a probability of belonging to one of the two

Hello, I'm using recursive SVM script (rSVM - http://www.stanford.edu/group/wonglab/RSVMpage/R-SVM.html ) on some microarray data. The data to be input are log2, as numeric matrix w/ attributes -- str(svm_num_mat) num [1:10, 1:12340] 13.1 13.1 13.1 13.1 13.0 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:10] "rma_log2_con_sample_1"

Hello, together I'm trying to use user defined kernel. I know that kernlab offer user defined kernel(custom kernel functions) in R. I used data spam including package kernlab. (number of variables=58 number of examples =4061) i'm user defined kernel's form, kp=function(d,e){ as=v*d bs=v*e cs=as-bs cs=as.matrix(cs) exp(-(norm(cs,"F")^2)/2) }

Dear List I am having problems to set text labels in a scatter plot. At some areas dots are close and labels overlap and texts cannot be read. I have too many dots to adjust this manually. Is there any function that will calculate the label offsets to avoid this overlapping problem? Thanks Ana -- Ana Conesa Bioinformatics and Genomics Department Centro de Investigaciones Principe Felipe

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 all, I'm trying to run a gird parameter search for a svm. Therefore I'M using the ksvm function from the kernlab package. ---- svp <- ksvm(Ktrain,ytrain,type="nu-svc",nu=C) ---- The problem is that the optimization algorithm does not return for certain parameters. I tried to use setTimeLimit() but that doesn't seem to help. I suspect that ksvm() calls c code that

Hello, I have a classification tree. I want to obtain a ROC curve for this test. What is the easiest way to obtain one? -Anjali --------------------------------- [[alternative HTML version deleted]]

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Greetings R users, I have been hoping someone would be familiar with this topic. I understand fully everything on this list is from the good graces of those who wish to help. Thanks to those who have helped in multiple circumstances. However, I wanted to post this question once more. I hope someone will read it and perhaps have some additional insight into what I may be able to do. Thank

Greetings R users, I have been working on running plsda and I would like to have the R2 and Q2 values. I know the function R2 from the 'pls' package will generate both R2 and Q2 but they are for each separate class. Is there a way to get the cumulative R2 and Q2 for the whole model? R2(pls.new, estimate="all") Response: B (Intercept) 1 comps 2 comps train

Greetings R users, My interest in the Q2cum score comes my endeavor to replicate SIMCAP PLS-DA analysis in R.? I use the exact same dataset.? After doing the analysis in R, I can get the exact same R2Ycum.? However, the Q2cum is significantly off.? Adding the Q2cum of the 1st and 2nd component comes close but that seems unconvincing and I don't understand why the 3rd component Q2cum would be

Hello, I would like to perform a Partial least square discriminate analysis (PLSDA) in R. To do this I use the package mixOmics. I could perform the PLSDA in R. however I would also like to perform a leave-one-out cross validation in order to assess the performance of my model. My supervisor told me that I should focus on the R2/Q2 ratios. However when I read the instruction for running the

Hi Samba Users, I forgot to send this mail to the samba ML, too. The question is whether anyone is using the idmap alloc config options for idmap_ldap. I would like to remove them for Samba 3.6. Details below. Thanks - Michael ----- Forwarded message from Michael Adam <obnox at samba.org> ----- Date: Wed, 10 Nov 2010 11:19:56 +0100 From: Michael Adam <obnox at samba.org> To: