similar to: cv.glm error function

Dear all I used R2WinBUGS package's bugs() function to generate MCMC results. Then I tried to save the simulation draws in R, using read.bugs() function. Here is a simple test: ###################### library(coda) library(R2WinBUGS) #fake some data to test beta0=1 beta1=1.5 beta2=-1 beta3=2 N=200 x1=rnorm(N, mean=0,sd=1) x2=rnorm(N, mean=0,sd=1) x3=rnorm(N, mean=0,sd=1) lambda2= exp(beta0+

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

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, 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

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

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(

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, 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

Dear R-Helpers, I am trying to bootstrap the coefficient of variation on a suite of vectors, here I provide an example using one of the vectors in my study. When I ran this script with the vector x <-c(0.625, 0.071428571, 0.133333333, 0.125, 0), it returned CV(boot) [the second one], and stderr(boot) [the second one] without problem. However, when I ran it with the vector in the

Hi, I'm trying to get maximum likelihood estimates of \alpha, \beta_0 and \beta_1, this can be achieved by solving the following three equations: n / \alpha + \sum\limits_{i=1}^{n} ln(\psihat(i)) - \sum\limits_{i=1}^{n} ( ln(x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} 1/(psihat(i)) - (\alpha+1) \sum\limits_{i=1}^{n} ( 1 / (x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} (

Dear friends, I am testing glm as at page 514/515 of THE R BOOK by M.Crawley, that is on proportion data. I use glm(y~x1+,family=binomial) y is a proportion in (0,1), and x is a real number. I get the error: In eval(expr, envir, enclos) : non-integer #successes in a binomial glm! But that is exactly what was suggested in the book, where there is no mention of a similar warning. Where am I

The function knn.cv in the class package doesn't have error checking to ensure that the length of the classlabel argument is equal to the number of rows in the test set. If the classlabel is short, the result is often a segfault. > library(class) > dat <- matrix(rnorm(1000), nrow=10) > cl <- c(rep(1,5), rep(2,5)) > cl2 <- c(rep(1,5), rep(2,4)) > knn.cv(dat, cl) [1] 2

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 have a microarray dataset from Agilent chips. The data were really log ratio between test samples and a universal reference RNA. Because of the nature of log ratios, coefficient of variation (CV) doesn't really apply to this kind of data due to the fact that mean of log ratio is very close to 0. What kind of measurements would people use to measure the dispersion so that I can compare

Hi, Newbie question about the pls package. Setup: Mac OS 10.3.9 R: Aqua GUI 1.01, v 2.0.1 I want to get R^2 and Q^2 (LOO and Leave-10-Out) values for each component for my model. I was running into a few problems so I played with the example a little and the results do not match up with the comments in the help pages. $ library(pls) $ data(NIR) $ testing.plsNOCV <- plsr(y ~ X, 6, data =

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