similar to: function cv.glm in library 'boot'

I have a couple quick questions about the use of cv.glm for cross-validation. 1. If we have a Poisson GLM with counts Y~Poisson(mu) and ln(mu)=beta0+beta1*x1+..., is the prediction error (delta) that is output from cv.glm provided in terms of the counts (y) or the (mu)? 2. Can cv.glm be used for negative binomial models fit using glm.nb? It appears to work, but since NB models aren't

I am try to cross validate some logistic regressions. cv.glm allows me to do this randomly but since I have data over a number of years and over a number of distince areas, I would like to cross-validate temporarly and spatially. I've already attached the temporal and spatial attributes to my data, but I'm unsure as to how to achieve this, as it seems I can't use cv.glm for this

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

Hi, I've done some searching where others have had trouble with this error (or "warning" actually), but I'm unable to solve my problem. I have a data sheet with 13 columns and 36 rows. Each column has exactly the same number of rows. I've created glms and now want to do cross-validation on 2 of them. Please be gentle-- I'm new to R (and statistics, too, for that

Dear list, I am modelling fish distributions using the glm-function followed by the step-function, and then want to cross-validate the model via the cv.glm-function from the {boot} package. I am working on fish distributions on coral reefs. The code I have works for one fish species. Since I have 227 fishes, I wrote a loop. Now the cv.glm-function comes up with an error message: "Error in

Dear list members. I have problems with the usage of cv.glm from the boot package. Here are some parts of the script I wanted to use: data <- read.table("selected_2D.csv", header=TRUE, sep=",") ? glm.fitted <- glm("ydata$ y ~ 1 + density + vsurf_ID6 + vsurf_S ", data=data) error <- cv.glm(data=data, glm.fitted, K=6) ydata$y is a separate data set, where

Hello, Thank you for probably not so new question, but i am new to R. Does any of packages have something like glm+regularization? So far i see probably something close to that as a ridge regression in MASS but I think i need something like GLM, in particular binomial regularized versions of polynomial regression. Also I am not sure how some of the K-fold crossvalidation helpers out there

Hey all, I need some help with a cross validation. I'm new with R and as well with statistics. I had a group work to create a tool for remote sensing class that extracts the best bands of hyperspectral satellite images that describe vegetation. Its a regression between a linear function of using a normalized differenced index (i-j)/(i+j) while i and j are the bands (in the data these are the

Hi All, I have a fitted model called glm.fit which I used glm and data dat is my data frame pred= predict(glm.fit, data = dat, type="response") to predict how it predicts on my whole data but obviously I have to do cross-validation to train the model on one part of my data and predict on the other part. So, I searched for it and I found a function cv.glm which is in package boot.

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

I am trying to write a wrapper for the last example in help(cv.glm) that deals with leave-one-out-cross-validation (LOOCV) for a logistic model. This wrapper will be used as part of a bigger program. Here is my wrapper funtion : logistic.LOOCV.err <- function( formu=NULL, data=NULL ){ cost.fn <- function(cl, pred) mean( abs(cl-pred) > 0.5 ) glmfit <- glm(

hello, I apologize that I poste this question again but I did not receive any replies on this topic and would like to know if the mail has been read over or if there is no method of statistical prediction computation for poisson errors in glm(). I am looking for a way to compute the model prediciton error of a glm() with poisson error family. cv.glm() does not work. (it prompts values

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

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

Hello guys! I am working with some classifiers ( SVM,C4.5,RNA,etc) using 10-C.V. Once I have the model of each one, I make the validation of these models in one dataset. Then,with my model and the dataset, I extract a confusion matrix to know the capacity of prediction from the model. And finally, I extract the accuracy of this prediction based on the diagonal from the confusion matrix. The

Hi, perhaps you can help me to find out, how to find the best Lambda in a LASSO-model. I have a feature selection problem with 150 proteins potentially predicting Cancer or Noncancer. With a lasso model fit.glm <- glmpath(x=as.matrix(X), y=target, family="binomial") (target is 0, 1 <- Cancer non cancer, X the proteins, numerical in expression), I get following path (PICTURE

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

Hy, I got the following problem when trying to build a rpart model and using everything but LOOCV. Originally, I wanted to used k-fold partitioning, but every partitioning except LOOCV throws the following warning: ---- Warning message: In nominalTrainWorkflow(dat = trainData, info = trainInfo, method = method, : There were missing values in resampled performance measures. ----- Below are some

If my aim is to select a good subset of parameters for my final logit model built using glm(). What is the best way to cross-validate the results so that they are reliable? Let's say that I have a large dataset of 1000's of observations. I split this data into two groups, one that I use for training and another for validation. First I use the training set to build a model, and the the

Folks; I am having a problem with the cv.glm and would appreciate someone shedding some light here. It seems obvious but I cannot get it. I did read the manual, but I could not get more insight. This is a database containing 3363 records and I am trying a cross-validation to understand the process. When using the cv.glm, code below, I get mean of perr1 of 0.2336 and SD of 0.000139. When using a