search for: nfold

ear ALL, The last part of my thesis analysis is the cross validation. Right now I am having difficulty using the cross validation of gstat. Below are my commands with the tsport_ace as the variable: nfold <- 3 part <- sample(1:nfold, 69, replace = TRUE) sel <- (part != 1) m.model <- x2[sel, ] m.valid <- x2[-sel, ] t<- fit.variogram(v,vgm(0.0437, "Exp", 26, 0)) cv69 <- krige.cv(tsport_ace ~ 1, x2, t, nfold = nrow(x2)) The last line gives an error saying: Error in Spati...

Hi, I am trying to do multinomial regression using the glmnet package, but the following gives me an error (for no reason apparent to me): library(glmnet) cv.glmnet(x=matrix(c(1,2,3,4,5,6,1,2,3,4,5,6), nrow=6),y=as.factor(c(1,2,1,2,3,3)),family='multinomial',alpha=0.5, nfolds=2) The error i get is: Error in if (outlist$msg != "Unknown error") return(outlist) : argument is of length zero If i change the number of folds to 1, i get a seg fault: *** caught segfault *** address 0x0, cause 'memory not mapped' Traceback: 1: .Fortran("lognet&quo...

...all accuracy. cv.evaluate$correct <- cv.evaluate$prediction == cv.evaluate$remitter mymean<-mean(cv.evaluate$correct) retlist<-data.frame(mtry, sumvar$se, sumvar$sp, mean(cv.evaluate$correct)) return(retlist) } subloop<- function(mtry=mtry, ml.frame=ml.frame, ntrees=ntrees) { nfolds<- 10 # shuffle the numbers and divide into 10 groups numberOfRows<-dim(ml.frame)[1] lengthOfDiv<-numberOfRows/nfolds shuffled<-sample(c(1:numberOfRows), numberOfRows, replace=F) rownumber<-split(shuffled, 1:nfolds) #combine mymean into my vec myvec<-foreach (i = 1:l...

Dear R users: Recently, I tried to write a program to calculate cross-validated predicted value. My sources are as follows. However, the R reported an error. Could you please check the sources? Thanks. set.seed(100) x<-rnorm(100) y<-sample(rep(0:1,50),replace=T) dat<-data.frame(x,y) library(rms) fito<-lrm(y~x) preo<-predict(fito) pre<-matrix(NA,nrow=100,ncol=200) for (i in

...ent story). Thanks in advance for any advice. Below is my code and sample output for AUC/MSE. xc <- split(dataS$P1_retained, dataS$TotalHours_R) yc <- split(dataS$x, dataS$TotalHours_R) for (i in 1:length(yc)) { fit=cv.glmnet(as.matrix(yc[[i]]), y=xc[[i]], alpha=.05, type="mse", nfolds=10, standardize=TRUE,family="binomial") c_output = c(i,fit$cvlo[fit$lambda==fit$lambda.1se],fit$cvm[fit$lambda==fit$lambda.1se], fit$cvup[fit$lambda==fit$lambda.1se]) names(c_output) = names(output_x) output_x = rbind(output_x, t(c_output)) fit1=cv.glmnet(as.matrix(yc[[i]]), y=xc[[i]],...

Hi All,  I am trying to run cv.glmnet(x,y,family="multinomial", nfolds =4) and I only have 8 observations and the number of features I have is 1000, so my x matrix is 8 by 1000 and when I run the following, I get this error, I am not sure what is causing this problem.  Error in predmat[which, , seq(nlami)] = preds :   number of items to replace is not a multiple of r...

...ry response) but I got an error with CVbinary. Well I did this: fit <- lm(resp ~ PC1 + PC2 + PC3 + PC4 + PC5 + PC6 + PC7 + PC8 + PC9+PC10+PC11+PC12+PC13+PC14+PC15+PC16+PC17+PC18+PC19+PC20+PC21+PC22+PC23+PC24+PC25+PC26+PC27+PC28, data = dexp.cp, family=binomial()) CVbinary(fit) Error in sample(nfolds, m, replace = TRUE) : invalid 'size' argument I cannot understand this error, I was googling it, but i didn't find nothing really helpfull. Can someone help with is?...It's really important. Thank you for your time, Ana Rita -- View this message in context: http://r.789695.n4.n...

...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 / Nfold or bootstrape to permute data for external CV ### build SVM model and use mean-balanced weight to sort genes on training set ### and recursive elimination of least important genes ### author: Dr. Xin Lu, Research Scientist ### Biostatistics Department, Harvard School of Public Health library(e...

...####### > > ### example code: > library( earth ) > data( gasoline, package='pls' ) > > nir <- gasoline$NIR > dim( nir ) [1] 60 401 > class( nir ) [1] "AsIs" > > oct <- gasoline$octane > > mars <- earth( oct ~ nir, nk=300, nfold=10, degree=3, trace=0 ) > > plotmo( mars ) Error: bad index (missing column in x?) > plotmo( mars, all1=T ) Error: bad index (missing column in x?) > plotmo( mars, all1=T, all2=T ) Error: bad index (missing column in x?) > > sessionInfo() R version 2.13.1 (2011-07-08) Platf...

...response % variable. This matrix is then transposed tmp = rda(x, y, alpha=0, delta=0) rda.training.err = tmp$error / dim(x)[2] % The training error provided by rda.cv() is also different % from the training errors provided by lda() or rda() tmp.cv = rda.cv(tmp, x=x, y=y, nfold=10) tmp.cv$err / dim(x)[2] / 10 Thanks a lot!

...t of LASSO regression on several subsets of a big dataset. For some subsets it works well, and for some bigger subsets it does not work, with errors of type "cannot allocate vector of size 1.6Gb". The error occurs at this line of the code: example <- cv.glmnet(x=bigmatrix, y=price, nfolds=3) It also depends on the number of variables that were included in "bigmatrix". I tried on R and R64 for both Mac and R for PC but recently went onto a faster virtual machine on Linux thinking I would avoid any memory issues. It was better but still had some limits, even though memo...

...cv.glmnet(x, ly, family = "binomial", alpha = 1, # lasso penalty type.measure = "deviance", standardize = FALSE, intercept = FALSE, nfolds = 10, keep = FALSE) plot(fit.net) log(fit.net$lambda.1se) pred <- predict(fit.net, x, type = "response", s = "lambda.1se") all(coef(fit.net) == 0) all(pred ==0.5) Thanks in advance for your thoughts. Regards, Lars. [[alternative HTML...

...rdize=TRUE) coef(pena.fit2) #lasso using gamnet library(glmnet) factors<-matrix(c(CN,NoSus),ncol=2) colnames(factors)<-c("CN","NoSus") glmn.fit2<-glmnet(x=factors,y=HRLNM,family="binomial") cvglmnet<-cv.glmnet(x=factors,y=HRLNM,family="binomial",nfolds=5) plot(cvglmnet) cvglmnet$lambda.min which(cvglmnet$lambda==cvglmnet$lambda.min) glmn.fit2<-glmnet(x=factors,y=HRLNM,family="binomial",lambda=cvglmnet$lambda.min) coef(glmn.fit2) Thanks a lot btw: how to calculate the C.I. of coefs? *Yao Zhu* *Department of Urology Fudan Univer...

...t; for a Bioinformatics project that we are working on. I know that the authors of the paper are using Lasso regression and so far looking at their paper this is what I have gotten to. xtrain <- trainData > dim(trainData) [1] 18520 88 ytrain <- trainScore length(ytrain) [1] 18520 nfolds <- 100 epsilon <- exp(-10) # code from JP Vert object1 <- cv.lars(xtrain,ytrain, K=nfolds, fraction = seq(from = 0, to = 1 , length= 1000), type='lasso', eps=epsilon, plot.it=TRUE) bestfraction <- object1$fraction[min(which(object1$cv <= min(object1$cv)+ 0.01*(max(object...

...t;- list(x= mat,y=factor(actclass),geneid = geneid ,genenames=gs) mytrain <- pamr.train(mydata) new.scales <- pamr.adaptthresh(mytrain,ntries = 10, reduction.factor = 0.9) mytrain2 <- pamr.train(mydata,threshold.scale = new.scales) mycv <- pamr.cv(mytrain2,mydata,nfold = 10) res1 <- pamr.confusion(mycv, threshold = mytrain2$threshold.scale,extra = FALSE) print(res1) res2 <- pamr.confusion(mycv, threshold = 2,extra = FALSE) print(res2) ########################### END CODE ############################### ------------...

...t)) mydata <- list(x= mat,y=factor(actclass),geneid = geneid ,genenames=gs) mytrain <- pamr.train(mydata) new.scales <- pamr.adaptthresh(mytrain,ntries = 10, reduction.factor = 0.9) mytrain2 <- pamr.train(mydata,threshold.scale = new.scales) mycv <- pamr.cv(mytrain2,mydata,nfold = 10) res1 <- pamr.confusion(mycv, threshold = mytrain2$threshold.scale,extra = FALSE) print(res1) res2 <- pamr.confusion(mycv, threshold = 2,extra = FALSE) print(res2) ########################### END CODE ############################### ---...

...t;- list(x= mat,y=factor(actclass),geneid = geneid ,genenames=gs) mytrain <- pamr.train(mydata) new.scales <- pamr.adaptthresh(mytrain,ntries = 10, reduction.factor = 0.9) mytrain2 <- pamr.train(mydata,threshold.scale = new.scales) mycv <- pamr.cv(mytrain2,mydata,nfold = 10) res1 <- pamr.confusion(mycv, threshold = mytrain2$threshold.scale,extra = FALSE) print(res1) res2 <- pamr.confusion(mycv, threshold = 2,extra = FALSE) print(res2) ########################### END CODE ############################### ----------------...

can you do Logistic regression in R, if so how do you do it and how do you test the fit of a model? -- View this message in context: http://n4.nabble.com/Logistic-regression-tp1059870p1059870.html Sent from the R help mailing list archive at Nabble.com.