search for: crossval

...do not match up with the comments in the help pages. $ library(pls) $ data(NIR) $ testing.plsNOCV <- plsr(y ~ X, 6, data = NIR, method="kernelpls", validation="none") $ NIR.plsCV <- plsr(y ~ X, 6, data = NIR, CV=TRUE, method="kernelpls") $ testing.plsCV <- crossval(testing.plsNOCV) $ R2(NIR.plsCV) (Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 0.0000 0.9812 0.9825 0.9964 0.9997 0.9999 6 comps 0.9999 $ R2(testing.plsCV) (Intercept) 1 comps 2 comps 3 comps 4 comps...

...to? Second, how closely does the evaluation environment in the browser/debugger match what you would get in the function at the same point? I ask because if I evaluate a statement in the browser it seems to work one way, but if I execute it it works another way. The statement is clusterEvalQ(cl, crossval.setup(x, y, groups, theta.fit, theta.predict)) This evaluates the crossval.setup function across the cluster cl. crossval.setup is a function which puts its arguments in a list g (a local variable) and then does gcv <<- g. The intent is to stuff the data into a global variable for use by l...

...checking Rd files ... WARNING Rd files without 'alias': man/parcv-internal.Rd These tags are required in an Rd file. See chapter 'Writing R documentation' in manual 'Writing R Extensions'. * checking for undocumented objects ... WARNING Undocumented code objects: [1] "crossval.fit" "crossval.outerfit" "crossval.setup" [4] "gcv" * checking for code/documentation mismatches ... OK * checking for undocumented arguments in \usage ... OK * creating parcv-Ex.R ... OK * checking examples ... OK * creating parcv-manual.tex ... OK * checkin...

The function crossval (in library bootstrap) works well for the first degree polynomial model, but in the case of the second degree model I got an error message (see below). I would be very greatfull if somebody could give some advices for the following: > library(bootstrap) > > x<-c(22,23.4,24.9,28.5,2...

...he scatterplot matrix Data attached Thanks in advance MarkM library("car") par(mfcol=c(1,2)) # Parameters to change Infile<-"kt3d_Thk1.dat" X<-"Estimated thickness (mE)" Y<-"True thickness (mE)" #load data #read the data skip then read header crossval <- read.table(Infile,skip = 9,sep = "") head<-readLines(Infile,9) #decode the header lines wanted head2<-head[3:9] colnames(crossval)=head2 #Filter out non-estimated crossval2<-crossval[crossval$Estimate>0,] # Compute the means AveEst<-mean(crossval2$Estimate) AveT...

Hi, I was trying to do cross-validation using the crossval function (bootstrap package), with the following code: --------------------------------------------------------------------------------------------------------- theta.fit <- function(x,y){ model <- svm(x,y,kernel = "linear") } theta.predict <- function(fit,x){...

...;)) data(iris) training1=iris[inTrain1,] datvars=training1[,1:4] dat.sc=scale(datvars) n=nrow(dat.sc) dat.indices=seq(1,n) timematrix=with(training1, classvec2classmat(Species[dat.indices])) pls.dat=plsr(timematrix ~ dat.sc, ncomp=3, method="oscorespls", data=training1) x=crossval(pls.dat, segments=10) summary(x) summary(plsFit2) I see two different R2 values and I cannot figure out how to get the Q2 value. Any insight as to what my errors may be would be appreciated. Regards, -- Charles [[alternative HTML version deleted]]

Hi all, Has anyone tried to do k-fold cross validation for flexible discriminant analysis ( mda library), for example, using crossval() in bootstrap? The problem is that the function crossval() requires a separate matrix for predictors and another for responses, whereas the function fda(), using the formula argument only. Is there another way of doing k-fold cross validation for functions which only have the formula argument, l...

Seems crossval from library(bootstrap) can only be used for leave-one-out and k-fold cross validation? Here is a dumb question, suppose n=80, how to do exactly leave-50-out cross validation? K-fold cross validation is not eligible for this case since n/ngroup is not an integer. Thanks!

Hi   I want to do leave-one-out cross-validation for multinomial logistic regression in R. I did multinomial logistic reg. by package nnet in R. How I do validation? by which function? response variable has 7 levels   please help me   Thanks alot Azam [[alternative HTML version deleted]]

...= FALSE, use.N2=TRUE, check.data=TRUE, lean=FALSE) # plot predicted vs. observed plot(fit) plot(fit, resid=TRUE) # Water-table reconstruction pred <- predict(fit, y) #plot the reconstruction plot(sites, pred$fit[, 1], type="b") # cross-validation model using bootstrapping fit.xv <- crossval(fit, cv.method="boot", nboot=1000) par(mfrow=c(1,2)) plot(fit) plot(fit, resid=TRUE) plot(fit.xv, xval=TRUE) plot(fit.xv, xval=TRUE, resid=TRUE) [[alternative HTML version deleted]]

...ng to follow steps in Manly, B. 1998. Randomization, bootstrap and monte carlo methods in biology. 2nd edition., p. 48. PDF of the approach/steps should be available here: https://wyocoopunit.box.com/s/9vm4vgmbx5h7um809bvg6u7wr392v6i9 If this is too statsy for the R list, I can try stackexchange or crossval, but the R list provides all kinds of great help so thought I'd try it first. I aware of boot::boot but am hoping to avoid it for the current analysis I am working on (the boot function became quite challenging, for me, for a few reasons). I cannot figure out where I'm going wrong but the...

...latex abcpar text html latex bcanon text html latex bootpred text html latex bootstrap text html latex boott text html latex crossval text html latex jackknife text html latex DONE (bootstrap) DONE (INSTALL) == This problem appears with other packages as well, and so it must be due to some cluelessness or environment variable setting problems on my part, my RHOME is...