similar to: cv.lm() broken; cross validation vs. predict(interval="prediction")

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)

Dear R-list, I'm not sure what I've found about a function in DAAG package is a bug. 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. The code illustrates my discovery: > library(DAAG)

--begin inclusion -- I have a matched-case control dataset that I'm using conditional logistic regression (clogit in survival) to analyze. I'm trying to conduct k-folds cross validation on my top models but all of the packages I can find (CVbinary in DAAG, KVX) won't work with clogit models. Is there any easy way to do this in R? -end inclusion -- The clogit funciton is simply a

Dear all, I've tried a multiple regression, and now I want to try a cross-validation. I obtain this error (it must be sth related to df) that I don't understand, any help would be appreciated. cv.lm(df= dat, lm2.52f, m=3) Error en `[.data.frame`(df, , ynam) : undefined columns selected lm2.52f is my lm object, dat is a dataframe where the variables involved in .lm are I tried CVlm

Hello, I did a cross-validation using cvlm from DAAG package but wasn't sure how to assess the result. Does this result means my model is a good model? I understand that the overall ms is the mean of sum of squares. But is 0.0987 a good number? The response (i.e. gailRel5yr) has min,1st Quantile, median, mean and 3rd Quantile, and max as follows: (0.462, 0.628, 0.806, 0.896, 1.000, 2.400) ?

I would like to run a loop in R. I have never done this before, so I would be very grateful for your help ! 1. I have a sample set: 25 objects. I would like to draw 1 object from it and use it as a test set for my future external validation. The remaining 24 objects I would like to use as a training set (to select a model). I would like to repeat this process until all 25 objects are used as a

I use R on my Ubuntu 9.04 laptop, which was installed by "aptitude install" way Now i'm learning a book of R which needs "MASS" and "DAAG" installed. So i followed the instructions: >install.packages("MASS") >library("MASS") MASS works fine. But DAAG doesn't. Anyone who could help would be appreciated a lot ! Below are my error

I use R on my Ubuntu 9.04 laptop, which was installed by "aptitude install" way Now i'm learning a book of R which needs "MASS" and "DAAG" installed. So i followed the instructions: >install.packages("MASS") >library("MASS") MASS works fine. But DAAG doesn't. Anyone who could help would be appreciated a lot ! Below are my error

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

Dear R users, I'm a newbie for R and want to ask some basic questions. So, after I open the R software, I typed library(DAAG). Then, I get massive warning messages as shown below. Why does it happen? Also, here are few specific questions regarding each message. 1) Loading required package: MASS -> Does this mean that the MASS package is not included in DAAG? 2) Attaching package:

Hi, I use the lda function from the MASS package to classify some samples according to some chemical properties. If I run lda without cross validation all is ok but, if I run lda with cross validation, the R consol say: resLDA <- ldaRedOx <- lda(Activity ~ TRedOx[,1:6], CV=TRUE, data=dfDataRedOx, subset=train) predLDA <- predict(resLDA, newdata=dfDataRedOx[-train,])$class >

I am failing to get compactPDF to make any change to a pdf file that, a/c to the message from the CRAN upload site, can be very substantially compacted. Any ideas what may be wrong? I have also tried recreating the pdf file. I also tried R CMD build --resave-data --compact-vignettes DAAG The data files compact alright (but I get the 'significantly better compression' warning message

Hie I am trying to install Package DAAG this is the error I get > library(DAAG) Loading required package: randomForest Error: package 'randomForest' could not be loaded In addition: Warning message: In library(pkg, character.only = TRUE, logical.return = TRUE, lib.loc = lib.loc) : there is no package called 'randomForest' what should i do thanks The University of Fort Hare

(See DAAG book, p. 173, ex. 3) I'm a new user of R, and I'm following the DAAG text. I want to create a subset of the races2000 data frame, but get errors because of a mismatch of values in some columns: > library(DAAG) > attach(races2000) > hills2000 <- races2000[races2000$type == 'hill'] Error in as.matrix.data.frame(x) : dim<- : dims [product 770] do not

Hello, is there a special package/method to cross-validate linear fixed effects and mixed effects models (from lme)? I've tried cv.glm on an lme (hoping that it may deal with any kind of linear model ...), but it raises an error: Error in eval(expr, envir, enclos) : couldn't find function "lme.formula" so I guess it's not dealing with an lme. I've realized that

Hello, I would like to know, whether for the support vector classification function ksvm() the response values stored in object at ymatrix are cross validated outputs/predictions: Example code from package kernlab, function ksvm: library(kernlab) ## train a support vector machine filter <- ksvm(type~.,data=spam,kernel="rbfdot",kpar=list(sigma=0.05),C=5,cross=3) filter filter at

I just downloaded the windows version R version 2.4.0 alpha (2006-09-05 r39134) 1. When I downloaded the packages, the following two were not found. > utils:::menuInstallPkgs() --- Please select a CRAN mirror for use in this session --- dependency ''fCalendar'' is not available dependency ''SparseM'' is not available I am not sure which other packages

I used pause() from library(DAAG) to pasue between plots. This works when I source a script, but seems don't work when I run (ctrl + R) the script in R. Did I do something wrong? Thanks.

I used pause() from library(DAAG) to pasue between plots. This works when I source a script, but seems don't work when I run (ctrl + R) the script in R. Did I do something wrong? Thanks.