similar to: what is the cost in cv.glm?

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, 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

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

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

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

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

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

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. 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

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

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 all, I am using the "svm" command in the e1071 package. Does it have an automatic way of setting the "cost" parameter? I changed a few values for the "cost" parameter but I hope there is a systematic way of obtaining the best "cost" value. I noticed that there is a "cross" (Cross validation) parameter in the "svm" function. But I

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 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

Georges Monette and I would like to announce a new package, cv, now on CRAN, which implements cross-validation of regression models. Some of the functions supplied by the package: - cv() is a generic function with a default method and computationally efficient "lm" and "glm" methods, along with a method for a list of competing models. There are also experimental

Georges Monette and I would like to announce a new package, cv, now on CRAN, which implements cross-validation of regression models. Some of the functions supplied by the package: - cv() is a generic function with a default method and computationally efficient "lm" and "glm" methods, along with a method for a list of competing models. There are also experimental

Hi there, I am trying to use predict() with an object returned by cv.glmnet(), and get the following error: no applicable method for 'predict' applied to an object of class "cv.glmnet" What's wrong? my code: x=matrix(rnorm(100*20),100,20) y=rnorm(100) cv.fit=cv.glmnet(x,y) predict(cv.fit,newx=x[1:5,]) coef(cv.fit) Thanks so much, Asaf -- View this message in context:

Hi I am using clang-6 to compile this C++ code and I see a strange temporary variable allocated at expression address 0x7ff1131536e8. If I change the ternary operator at line 483 to an if-else, the temporary is not allocated. Thanks Variables: ========= FFMPEGVideoCapture ffmpeg_video_capture_; cv::VideoCapture opencv_video_capture_; Function: ======== bool

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, 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