similar to: cv.glm {boot}

I apologize for posting on this question again, but unfortunately, I don't have and can't get access to MASS for at least three weeks. I have found some code on the web however which implements the prediction error algorithm in cv.glm. http://www.bioconductor.org/workshops/NGFN03/modelsel-exercise.pdf Now I've tried to adapt it to my purposes, but since I'm not deeply familiar

I've been looking at the base and Design libraries and it is unclear to me the best way to approach doing cross-validation. I'm interested in using temporal (I have five years of data), spatial (I've divided my data set up into 5 blocks that make sense and have a block variable attached to my data) and I was also thinking of doing a random cross-validation to look at general model

I was unsure of what suitable goodness-of-fit tests existed in R for logistic regression. After searching the R-help archive I found that using the Design models and resid, could be used to calculate this as follows: d <- datadist(mydataframe) options(datadist = 'd') fit <- lrm(response ~ predictor1 + predictor2..., data=mydataframe, x =T, y=T) resid(fit, 'gof'). I set up a

Hola. Llevo poco tiempo con R y estoy atascado en un problemilla, a ver si podéis ayudarme. Estoy trabajando con una base de datos sobre personalidad en diferentes especies de aves. Esta base tiene datos de más de 7000 individuos y me gustaría crear un bucle mediante el cual realizar un test de Kruskal Wallis por cada especie (quiero ver si los comportamientos son consistentes dentro de cada

Hello, Is SS7 and PRI in any way compatible in that if the interface is configured one it will work for the other (granted, it will not have any of the ISUP, etc. parameters available if the line is PRI) or are they two distince protocols that have incompatible signalling? Thanks, Elliot

This post is NOT a question, but an answer. For readers please disregard all earlier posts by myself about this question. I'm posting for two reasons. First to say thanks, especially to Dimitris, for suggesting the use of errorest in the ipred library. Second, so that the solution to this problem is in the archives in case it gets asked again. If one wants to run a k-fold cross-validation

Everything was working fine till last night. This morning I have many errors. I am using acts_as_ferret. Last updated around a month ago on linux. There are two different type of exceptions. I have over 12 exception emails but these are the two distince types. First exception: A EOFError occurred in home#event_info: End-of-File Error occured at <except.c>:79 in xraise Error occured in

Hello, I would like to use cv.tree to run a 10-fold cross-validation experiment on a tree object to help me choose a tree size. Many users seem to allow their cases to be assigned to CV groups randomly, but I have assigned each case to one of 10 cv groups, such that the data from each of my experimental units is included in only one cv-group. According to the manual for the tree Package

I have Excel files containing data that I would like to move to R. They are in the standard form of a one row header followed by rows of data, one record per row EXCEPT that there are a few rows of comments before the header. The number of rows of comments varies. For Excel files of this form without comments I have had success with: require(RODBC) z <-

Hi, all: Does anybody know how to avoid the intercept term in cv.glmnet coefficient? When I say "avoid", it does not mean using coef()[-1] to omit the printout of intercept, it means no intercept at all when doing the analysis. Thanks. [[alternative HTML version deleted]]

Dear list members, I am using a cross validation of a generalised linear model (glm). The cv.glm function (from boot package) returns an error as so-called ?delta? value. I would like to get to a (cross-validated) squared q, because I want to directly compare it to the squared correlation coefficient r. I tried to find an an equation for the raw and/or adjusted cross-validation estimate of

Hi! When I use the glm.cv function I get a value called "delta" which is explained as the "raw cross-validation estimate of prediction error". I recently found a formula for that term in literature where it is defined as: alpha = 1 / N * sum over( yi - yi,pred,CV) Well it is somehow similar to the RSS for R2 and the PRESS for Q2. But this delta value increases with

Hi, everyone When I was using cv.lm(DAAG) , I found there might be something wrong with it. The problem is that we can't use it to deal with a linear model with more than one predictor variable. But the usage documentation hasn't informed us about this. You can find it by excuting the following code: xx=matrix(rnorm(20*3),ncol=3) bb=c(1,2,0) yy=xx%*%bb+rnorm(20,0,10)

Hi, I have a question regarding the cv.glm function in the package boot. What is exactly the cost? Is it the threshold value for an estimated value to be classified as either 0 or 1? I have troubles understanding the explanation in R. Lets say I want all estimations >0.65 to be classified as 1s and <0.35 as 0s, how do I write that? And if the cost is something else, how do I set the

I have a logistic model fitted with the following R function: glmfit<-glm(formula, data, family=binomial) A reasonable cutoff value in order to get a good data classification (or confusion matrix) with the fitted model is 0.2 instead of the mostly used 0.5. And I want to use the `cv.glm` function with the fitted model: cv.glm(data, glmfit, cost, K) Since the response in the fitted

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 replacement length Can

Hello, I used the cv.lm function to validate a linear regression model fit<-lm(y ~ x1+x2+x3+x4+0, data=mydata) without intercept I tried to validate the model by performing a leave one out cross validation procedure usinfg the cvlm function: CVlm(df=mydata, fit, m=196) But how can I get the adjusted R? from the output of this function. Or is there any other function to

Here is a snippet to show what i'm trying to do. library(randomForest) library(ROCR) library(caret) data(iris) iris <- iris[(iris$Species != "setosa"),] fit <- randomForest(factor(Species) ~ ., data=iris, ntree=50) train.predict <- predict(fit,iris,type="prob")[,2]

Hi. Is there a way to get the values predicted from (leave-one-out) cv.glm? It seems like a useful diagnostic to plot observed vs. predicted values. Thanks, Jeff **************************************** Jeffrey A. Stratford, Ph.D. Postdoctoral Associate 331 Funchess Hall Department of Biological Sciences Auburn University Auburn, AL 36849 334-329-9198 FAX 334-844-9234

Hi, everyone, I have a question regarding function cv.glm in library 'boot'. Basically cv.glm can calculate the estimated K-fold cross-validation prediction error for generalized linear models. My question is this: if I am fitting a logit model, what kind of threshold will it use to calculate the prediction error (saved in 'delta')? It will use 0.5 as the threshold or pick a