similar to: modifying predict.nnet() to function with errorest()

Dear All, After followed the help of nnet, I could get the networks trained and, excitedly, get the prediction for other samples. It is a two classes data set, I used "N" and "P" to label the two. My question is, how do I get the predicted numerical value for each sample? Not just give me the label(either "N" or "P")? Thanks! FYI: The nnet example I

Dear all, can anybody help me with the program below? The function predict.lda seems to be defined but cannot be used by errortest. The R version is 1.7.1 Thanks in advance, Stefan ---------------- library("MASS"); library("ipred"); data(iris3); tr <- sample(1:50, 25); train <- rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3]); test <- rbind(iris3[-tr,,1],

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

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

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

> data(iris) # iris3 is first 3 rows of iris > iris3 <- iris[1:3,] # z compares row 1 to each row of iris3 and is correctly computed > z <- c(F,F,F) > for(i in seq(z)) z[i] <- identical(iris3[1,],iris3[i,]) > z [1] TRUE FALSE FALSE # this should do the same but is incorrect > apply(iris3,1,function(x)identical(x,iris3[1,])) 1 2 3 FALSE FALSE FALSE

Dear R users, I want to do discriminant analysis on my data. I have successfully followed the discriminant analysis in V & R on the iris data: > ir <- rbind (iris3[,,1],iris3[,,2],iris3[,,3]) > ir.species <- c(rep("s",50),rep("c",50),rep("v",50)) > a <- lda(log(ir),ir.species) > a$svd^2/sum(a$svd^2) [1] 0.996498601 0.003501399 > a.x <-

Dear R-users, I'd like to estimate standard errors (for lda) and make a boxplot with the "632plus" and "boot" error estimators included in package ipred (method: errorest). The "boot" estimator returns only a standard deviation but not the whole error data. Thank you in advance, regards, Antoine

Hi !! I am trying to analyze some of my data using linear discriminant analysis. I worked out the following example code in Venables and Ripley It does not seem to be happy with it. ============================ library(MASS) library(stats) data(iris3) ir<-rbind(iris3[,,1],iris3[,,2],iris3[,,3]) ir.species<-factor(c(rep("s",50),rep("c",50),rep("v",50)))

Hi. The there is an example in nnet help which is pasted in below. I am not sure how they are generating 'targets'. What is the 'class.ind() function doing? In the help docs for it they say "Generates a class indicator function from a given factor." I tried putting a simple vector of the "classes" into test.cl (below) but I get an error of "(list) object

I am using the iris example came with nnet package to test AMORE. I can see the outcomes are similar to nnet with adaptative gradient descent. However, when I changed the method in the newff to the batch gradient descent, even by setting the epoch numbers very large, I still found all the iris expected class=2 being classified as class=3. In addition, all those records in the outcomes (y) are the

> > library(MASS) > Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]), + Sp = rep(c("s","c","v"), rep(50,3))) > train <- sample(1:150, 75) > table(Iris$Sp[train]) c s v 22 23 30 > z <- lda(Sp ~ ., Iris, prior = c(1,1,1)/3, subset = train) Error in if (targetlist[i] == stringname) { : argument is of length

Hi listers, I want to make lattice plots xyplots with the indication of legends inside each panel with only the points and the lines actually ploted inside each given panel according to the group(ing) factor. The code below shows what I have achieved so far and I hope will make clear what I want to have. It seems to me that my solution is a very "dirty hack" and there certainly is

Dear all, I would like to solve a trivial problem (I guess it is) but can't find the right way. Maybe someone can help me ? I've got a table with two factors (station = station ID, buffer = buffer size in meters) and a value for each unique combination of those two factors (S = number of habitats within each buffer around each station) like this: TABLE 1 station buffer S Abaia01 200 2

Hi Francois, Thanks for your quick response. Actually, I had already done that... sudo apt-get install libpoppler-cpp-dev Reading package lists... Done Building dependency tree Reading state information... Done libpoppler-cpp-dev is already the newest version (0.41.0-0ubuntu1.1). 0 upgraded, 0 newly installed, 0 to remove and 107 not upgraded. Therefore, I assume I have this installed. Best

dear R-users, I have to model counts where all counts above some threshold have been censored. In the same dataset I have too many zeroes for a Poisson or even a negative binomial distribution to make sense, so I would need a zero-inflated-censored negative binomial family for use in glmer (or glmmADMB?). That seems not to exist. my question is : how could I add a custom-built family of

Dear R'helpers, I am reading lines in a .txt file Each line is stocked into a n elements object, as this: [958] " 422 287 339 31 203 602 547 1026 500 366 346 227" [959] " 410 67 11 220 110 451 562 598 732 163 163 220" [960] " 179 513 95 186 102 595 333 1289 804 210 294 459" [961] " 276 153 307 138 126 233 623 739 521 421 209 75" [962] " 64

Hi, I am wondering what does "mypredict.lda<-function(object, newdata)predict(object, newdata=newdata)$class" actually do? I run a few errorest commands in the same function on the same dataset using the same classifier lda. The only difference is some use "cv", other use "boot" and "632plus". They all share one mypredict.lda. Will it cause any

This post is NOT a question, but an answer. For readers please disregard all earlier posts by myself about this question. I'm posting for two reasons. First to say thanks, especially to Dimitris, for suggesting the use of errorest in the ipred library. Second, so that the solution to this problem is in the archives in case it gets asked again. If one wants to run a k-fold cross-validation