similar to: k-NN routines

Dear r-help, I am trying to build a new function (to process rpart objects) that will output matrix that has a row for each node and a column for each feature. With each entry in the table is a numerical property at that node for that feature (e.g. surrogate split agreement, improvement). My current trouble is that the only clue to the identity of the feature is stored as the *name* of the

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-help, Is there any available description of the components of lqs objects found in the package "lqs"? > names(slts) [1] "crit" "sing" "coefficients" "bestone" [5] "fitted.values" "residuals" "scale" "terms" [9] "call"

Do those who have had trouble finding stuff in the R-archive used the search engine? http://www.scirus.com/ And as far as threading goes. My mailler "mutt" threads the mailing list quite nicely. --Clayton -- Clayton Springer, Ph. D. Sandia National Laboratories csprin at ca.sandia.gov Biosystems Research Department (925) 294-2143 P.O. Box

Dear R, Short verse of the question: Is there R code which will calculate surrogate splits and/or delta impurity for decision trees at each node? Long Version: I have local, legacy code which I use to calculate my decision trees. I would like to switch to R, but as I understand it surrogate splits are not implemented. Surrogate splits and feature ranking are described in Breiman et al

Hi All, When I tried to do logistic regression and I got the following messages: > SampledW.glm.ALL <- glm (V1 ~ ., family = binomial, data = SampledW) Warning messages: 1: Algorithm did not converge in: (if (is.empty.model(mt)) glm.fit.null else glm.fit)(x = X, y = Y, 2: fitted probabilities numerically 0 or 1 occurred in: (if (is.empty.model(mt)) glm.fit.null else glm.fit)(x = X, y =

Dear r-devel, I am mutating rpart to do calculations on trees. I am trying to extract information from the tree. However, I got a seg. fault. This is the offending line in "rpmatrix.c": deltaI[0][0] = spl->improve; (Commenting it out cures the seg fault) I would like some advice on how to debug this. I have allocated memory with calloc and deltaI[0][0] should be

Dear R users, I am a novice user working through V & R. My first question stems from p20 of the second edition.: I perform the following actions: > mydata <- c(2.9,3.4,3.4,3.7,3.7,2.8,2.8,2.5,2.4,2.4) > names(mydata) <- c('c','j','b','e','i','h','g','d','f','a') > mydata c j b e i h

Dear All, I was trying to follow some of the examples in Venables and Ripley "Modern applied ... with S-plus" I have downloaded a copy of the iris data set and loaded into R. : however I cannot use the apply command (from p47): > apply (iris, 2 ,mean) Error in sum(..., na.rm = na.rm) : invalid "mode" of argument > apply (iris, c(2) ,mean) Error in sum(..., na.rm =

Dear r-help, I am looking for additional documentation on the "adj" column in rpart's splits matrix. The help says: adj gives the adjusted concordance for surrogate splits I am looking info about "adjusted concordance". I cannot find this phrase in either Therneau & Atkinson original RPART documentation or the CART book. This question came up in the

Hi I want to create a nice picture about my result of k -the nearest neighbours algorithm. Here is my easy code: ################################# library(klaR) library(ipred) library(mlbench) data(PimaIndiansDiabetes2) dane=na.omit(PimaIndiansDiabetes2)[,c(2,5,9)] dane[,2]=log(dane[,2]) dane[,1:2]=scale(dane[,1:2]) zbior.uczacy=sample(1:nrow(dane),nrow(dane)/2,F)

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

Hi there, is there any R-function for k-nearest neighbour agglomerative hierarchical clustering? By this I mean standard agglomerative hierarchical clustering as in hclust or agnes, but with the k-nearest neighbour distance between clusters used on the higher levels where there are at least k>1 distances between two clusters (single linkage is 1-nearest neighbour clustering)? Best regards,

Thanks, Mark. Um, how's about from the commandline or how do I get, I guess we're still using X11, windows to load. Sorry, :<}}} Clayton >-----Original Message----- >From: centos-bounces at centos.org [mailto:centos-bounces at centos.org] On >Behalf Of Mark LaPierre >Sent: Sunday, December 14, 2014 5:50 PM >To: centos at centos.org; Mark LaPierre >Subject: Re:

On Fri, Mar 10, 2023 at 10:26?AM Chris Clayton <chris2553 at googlemail.com> wrote: > > Hi. > > Is it likely that this fix will be sumbmitted to mainline during the ongoing 6.3 development cycle? > yes, it's already pushed to drm-misc-fixed, which then will go into the current devel cycle. I just don't know when it's the next time it will be pushed upwards, but it

On Mon, Feb 20, 2023 at 11:51 AM Chris Clayton <chris2553 at googlemail.com> wrote: > > > > On 20/02/2023 05:35, Ben Skeggs wrote: > > On Sun, 19 Feb 2023 at 04:55, Chris Clayton <chris2553 at googlemail.com> wrote: > >> > >> > >> > >> On 18/02/2023 15:19, Chris Clayton wrote: > >>> > >>> > >>> On

I got some R code that I don't understand. Question as comment in code //where is t comming from, what is phi inverse rAC <- function(name, n, d, theta){ #generic function for Archimedean copula simulation illegalpar <- switch(name, clayton = (theta < 0), gumbel = (theta < 1), frank = (theta < 0), BB9 = ((theta[1] < 1) | (theta[2] < 0)), GIG = ((theta[2] < 0) |

Personally, I am agnostic. I've just read thru Centos documentation that there is a big effort to remove all upstream personalities from Centos. Personally, I don't see why RH is doing this. I would think that it undermines RH. But I'm still new/old to all of this. It used to be the big argument was between Unix from Berkeley(4.? I think) and SysIII/V. Always always battles for turf.

Hello there: I got 13 variables in my training/target set, the first 12 variables are mixture of numerical and categorical variables. The last one is the one I need to predict, and it is a numerical variable. >train<-read.table("train.txt") >test<-read.table("test.txt") >cl<-factor(train[,13]) >pred<-knn(train, test, clk=3, prob=TRUE) >pred I got

I am currently doing some prediction work using the knn script in the 'class' package. Does anyone know a way of having R return the IDs (sample IDs, or column IDs of the training matrix) of the 'k' samples that are chosen by the algorithm as being nearest to a given test sample? I have searched/read everything I can about the script, however have not found anything other than the