similar to: Confusion matrix from cross validation in R:

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

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

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

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) >

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

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)

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) }

Hi I am doing this sort of thing: POLY: > > obj = best.tune(svm, similarity ~., data = training, kernel = "polynomial") > summary(obj) Call: best.tune(svm, similarity ~ ., data = training, kernel = "polynomial") Parameters: SVM-Type: eps-regression SVM-Kernel: polynomial cost: 1 degree: 3 gamma: 0.04545455 coef.0: 0

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: In the example below (or for a censored data) using survfit.coxph, can anyone point me to a link or a pdf as to how the probabilities appearing in bold under "summary(pred$surv)" are calculated? Do these represent acumulative probability distribution in time (not including censored time)? Thanks very much, parmee *fit <- coxph(Surv(futime, fustat) ~ age, data = ovarian)*

Hi there, I don't have much experience with fitting at all and I'd like to get some advice how to use the "weights"-argument with nls correctly. I have created some data with a sigmoidal curve shape. Each y-Value was generated by the mean of three values. A standard deviation was calculated too. Now, I'd like to weight the data points respective to its standard

Dear Sir, I got a problem with my program. I would like to classify my data using bagging support vector machine ensemble. I split my data into training data and test data. For a given data sets TR(X), K replicated training data sets are first randomly generated by bootstrapping technique with replacement. Next, Support Vector Mechine (SVM) is applied for each bootstrap data sets. Finally, the

Hello, I have 12 sample each sample has got 1000 observation, i.e I have a matrix X with 1000 rows and 12 columns! m <- svm(t(X)) p <- predict (m) Can anyone tell me how to use svmtrain() in R! Many Yhanks, Samuel [[alternative HTML version deleted]]

Hi list, I was wondering how I should go about fitting a sigmoid curve to a dataset. More specifically how I estimate parameters a and b in the following equation: 1 / 1+exp(-(x-a)*b) with b the steepness of the sigmoid curve and a the shift of the center of the sigmoid curve relative to the center of your dataframe. The fit is in function of x, the location within the input vector and y, an

I fit my CDF to sum of exponentials and now I want to take the numerical derivative of this function to obtain probability density.I will really appreciate your help reagrding the error messages I am getting which I don't understand. * * > fitterma <- function(xtime) { a <- -0.09144115 b <- -0.01335756 c <- -2.368057 d <- -0.00600052

Hello, all experts, My major is computer-aied drug design ( main QSAR). Now, my paper need be reviesed, and one reviewer ask me do genetic algorithm coupled with gaussian process method (GA+GP). my data: training set: 191*106 test set: 73*106 here, I need use GA+GP to do variable selection when building the model. In R, there are not GA package like in matlab

All, Appreciate any leads on the following: In a recent blind-validation study of a depression screening instrument we used a two-stage sampling design. In stage 1, we used a broad paper-and-pencil screen to identify likely positives (say 30% of entire sample). In stage 2 we conducted in-depth interviews with the 30% of likely positives plus another 20% of the negatives as controls.