search for: trainx

Hi, I have a question about using lm on matrix, have to admit it is very trivial but I just couldn't find the answer after searched the mailing list and other online tutorial. It would be great if you could help. I have a matrix "trainx" of 492(rows) by 220(columns) that is my x, and trainy is 492 by 1. Also, I have the newdata testx which is 240 (rows) by 220 (columns). Here is what I got: py <- predict(lm(trainy ~ trainx ), data.frame(testx)) Warning message: 'newdata' had 240 rows but variable(s) found have 492...

Hi all, I would like to do cross validation in random forest using rfcv function. As the documentation for this package says: rfcv(trainx, trainy, cv.fold=5, scale="log", step=0.5, mtry=function(p) max(1, floor(sqrt(p))), recursive=FALSE, ...) however I don't know how to build trianx and trainy for my data set, and I could not understand the way trainx is built in the package documentation example for iris data set....

Hi all, I would like to do cross validation in random forest using rfcv function. As the documentation for this package says: rfcv(trainx, trainy, cv.fold=5, scale="log", step=0.5, mtry=function(p) max(1, floor(sqrt(p))), recursive=FALSE, ...) however I don't know how to build trianx and trainy for my data set, and I could not understand the way trainx is built in the package documentation example for iris data set. He...

...l package confirm that this behaviour is as expected or not - because I cannot find a way of using 'newdata' with really new data. Thanks in advance. DK > x<-matrix(rnorm(100*20),100,20) > time<-runif(100,min=0,max=7) > status<-sample(c(0,1), 100, replace = TRUE) > trainX<-x[11:100,] > trainTime<-time[11:100] > trainStatus<-status[11:100] > testX<-x[1:10,] > coxph.model<- coxph(Surv(trainTime,trainStatus)~ trainX) > sfit<- survfit(coxph.model,newdata=data.frame(testX)) > dim(sfit$surv) [1] 90 90 [[alter...

Any responds?! On Wednesday, August 23, 2017 5:50 AM, Elahe chalabi via R-help <r-help at r-project.org> wrote: Hi all, I would like to do cross validation in random forest using rfcv function. As the documentation for this package says: rfcv(trainx, trainy, cv.fold=5, scale="log", step=0.5, mtry=function(p) max(1, floor(sqrt(p))), recursive=FALSE, ...) however I don't know how to build trianx and trainy for my data set, and I could not understand the way trainx is built in the package documentation example for iris data set....

...7], method="gbm", distribution=list(name="quantile",alpha=0.5), verbose=FALSE, trControl=trainControl(method="cv",number=5), tuneGrid=gbmGrid ) Model 1: interaction.depth=1, shrinkage=0.1, n.trees=300 collapsing over other values of n.trees Error in gbm.fit(trainX, modY, interaction.depth = tuneValue$.interaction.depth, : formal argument "distribution" matched by multiple actual arguments The same error occured with distribution="laplace". I also tried the following without and success : gbm.test <- train(x.enet, y.matrix[,7],...

...ot;Coxnet: Regularized Cox Regression". Thanks in advance. DK library(survival) library(glmnet) load(system.file("doc","VignetteExample.rdata",package="glmnet")) attach(patient.data) # leave the first patient for testing # and train glmnet on all other patients trainX????? <-x[-1,] trainTime?? <-time[-1] trainStatus <- status[-1] # fit Coxnet fit <- glmnet(trainX,Surv(trainTime,trainStatus),family="cox",alpha=0.5,maxit=10000) # find lambda for which dev.ratio is max max.dev.index???? <- which.max(fit$dev.ratio) optimal.lambda <- fit...

...re quite difficult to interpret. # there's much work needed here to understand it, but for now I want to see if it's possible to impute values for another variable... # here's where I lose it, missing values, trainY, don't get it. bw.predict <- predict(bw.som, newdata=scale(bw), trainX=???, trainY=???) Ben. [[alternative HTML version deleted]]

...]) > Xtest <- scale(wines[-training, ], + center = attr(Xtraining, "scaled:center"), + scale = attr(Xtraining, "scaled:scale")) > som.wines <- som(Xtraining, grid = somgrid(5, 5, "hexagonal")) > som.prediction <- predict(som.wines, newdata = Xtest, + trainX = Xtraining, + trainY = factor(wine.classes[training])) > table(wine.classes[-training], som.prediction$prediction) 1 2 3 > som.prediction$prediction [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA...

Hi, I am trying to construct a svmpoly model using the "caret" package (please see code below). Using the same data, without changing any setting, I am just changing the seed value. Sometimes it constructs the model successfully, and sometimes I get an ?Error in indexes[[j]] : subscript out of bounds?. For example when I set seed to 357 following code produced result only for 8

I'm using caret to assess classifier performance (and it's great!). However, I've found that my results differ between R2.* and R3.* - reported accuracies are reduced dramatically. I suspect that a code change to kernlab ksvm may be responsible (see version 5.16-24 here: http://cran.r-project.org/web/packages/caret/news.html). I get very different results between caret_5.15-61 +