similar to: logistic model cross validation resolved

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

Dear all, Does anybody can explain this: different results got when all the same parameters are used in the errorest() in library ipred, as the following? errorest(Species ~ ., data=iris, model=randomForest, estimator = "cv", est.para=control.errorest(k=3), mtry=2)$err [1] 0.03333333 > errorest(Species ~ ., data=iris, model=randomForest, estimator = "cv",

Hi, I am using errorest function from ipred package. I am hoping to perform "bootstrap 0.632+" and "bootstrap leave one out". According to the manual page for errorest, i use the following command: ce632[i]<-errorest(ytrain ~., data=mydata, model=lda, estimator=c("boot","632plus"), predict=mypredict.lda)$error It didn't work. I then tried the

Dear List, I have a question regarding "ipred" package. Under 10-fold cv, for different knn ( = 1,3,...25), I am getting same misclassification errors: ############################################# library(ipred) data(iris) cv.k = 10 ## 10-fold cross-validation bwpredict.knn <- function(object, newdata) predict.ipredknn(object, newdata, type="class") for (i in

I have a need to call and pass arguments to nlme() from within another function. I use R version 1.8. I have found an apparent way to make this work, but I would appreciate some comments on whether this fix is really appropriate, or there is another way to do it that does not involve changing the source code. I don't have enough experience to start changing the sorurce code of a library

Hello! I'm Taeho, a graduate student in South Korea. In order to get .632+ bootstrap error using random forest, I have tried to use 'ipred' package; more specifically the function 'errorest' has been used. Following the guidelines, I made a simple command line like below: error<-errorest(class ~ ., data=data, model=randomForest, estimator = "632plus")$err

Full_Name: Noel O'Boyle Version: 2.1.0 OS: Debian GNU/Linux Sarge Submission from: (NULL) (131.111.8.96) (1) Description of error The 10-fold CV option for the svm function in e1071 appears to give incorrect results for the rmse. The example code in (3) uses the example regression data in the svm documentation. The rmse for internal prediction is 0.24. It is expected the 10-fold CV rmse

Hi, I'm using metafor to perform a mixed-effects meta-analysis. I'd like to test whether the effect is different for animals and plants/whether "group" (animal/plant) influences the effect size, but am having trouble interpreting the results I get. I've read previous posts about QM in metafor, but I'm still a bit confused. I've dummy-coded the factors:

1. This is _not_ a bug in R itself. Please don't use R's bug reporting system for contributed packages. 2. This is _not_ a bug in svm() in `e1071'. I believe you forgot to take sqrt. 3. You really should use the `tot.MSE' component rather than the mean of the `MSE' component, but this is only a very small difference. So, instead of spread[i] <- mean(mysvm$MSE), you

am trying to produce two dot plot figures in ggplot2. So far the first one (p) in the program below is working fine. However when I want to move to a faceted plot (p1) I seem to lose my ordering or, more likely, I'm just getting an ordering I am not expecting and I always have trouble understanding ordering in R! What I would like is the 2011 panel to be ordered in descending order as is

Hi, I'm trying to fit a multinomial logistic regression to my data which consists of 5 discrete variables (scales 1:10) and 1000 observations. I get the following error: Error in `row.names<-.data.frame`(`*tmp*`, value = c("NA.NA", "NA.NA", : duplicate 'row.names' are not allowed In addition: Warning message: non-unique value when setting

Hi, I am using the Random Forest package to classify observations into one of two classes. My data is unbalanced with the minority class accounting for 7% of total data set. I have heard the 10-Fold Cross validation can help me with improving classification. But being new at most of this it's not something I can do from scratch on my own. So I have spent all this morning trying to find a

Hi everyone, I'm trying to encode/decode with speex, when I do not, the audio is loud and clear, but when I encode/decode to test audio quality, I get a really poor audio quality and a robotic sound. Here's my init audio method : #define AUDIO_QUALITY 10 - (void) initAudio { try { //SPEEX CONFIG speex_bits_init(&bits_in);

Greetings, I am having trouble calculating artificial neural network misclassification errors using errorest() from the ipred package. I have had no problems estimating the values with randomForest() or svm(), but can't seem to get it to work with nnet(). I believe this is due to the output of the predict.nnet() function within cv.factor(). Below is a quick example of the problem I'm

Hi, I am trying to get an error estimation for a classification done using lda. The examples work fine, however I don't get my own code to work. The data is in object d > d class hydrophobicity charge geometry 1 2 6490.0400 1434.9700 610.99902 2 2 1602.0601 400.6030 -5824.00000 3 2 969.0060 260.1360 -415.00000 4 1

Hello all I have a mail store on a volume replica 3 with no arbiter. A while ago the disk of one of the bricks failed and I was several days late to notice it. When I did, I removed that brick from the volume, replaced the failed disk, updated the OS on that machine from el8 to el9 and gluster on all three nodes from 10.3 to 11.1, added back the brick and started a heal. Things appeared to work

*I have the following function:* /plot_mi_time = function(mdata, miname) { mdata2 = mdata[row.names(hakat) == miname, ] print(mdata2) xcoords <- c(1,1,2,2,3,3,4,4,5,5,6,6) plot(c(xcoords), mdata2, xaxt="n", ylab="Expression", xlab="Time(h)", , main=miname) axis(1, at=xcoords,

I am using the following function to plot columns from a CSV-file: plot_mi_time = function(mdata, miname) { mdata2 = mdata[row.names(mir_test) == miname, ] # print(mdata2) plot(c(1:3), mdata2, xaxt="n", ylab="Expression", xlab="Time(h)", , main=miname, pch=16) axis(1, at=c(1,2,3),labels=c("a","b","c")) } No I have a CSV file

I'm using the "Match" package to do propensity score matching. Here's some example code that shows the problem that I'm having (much of this code is taken from the Match package documentation): *data(lalonde) glm1 <- glm(treat~age + I(age^2) + educ + I(educ^2) + black + hisp + married + nodegr + re74 + I(re74^2) + re75 + I(re75^2) + u74 + u75,

Dear all, I recently upgraded my clients to 10.4, while I left the servers (distrubuted only) on glusterfs 9. I'm seeing a strange effect when I do a "mv filename1 filename2": filename2 is uplicated, one time with zero size and sticky bit set. In generally, I know that glusterfs creates link files (size zero and sticky bit set) when the new filename is hashed to a different