similar to: rpart

Hi all, I apologies in advance if I am missing something very simple here, but since I failed at resolving this myself, I'm sending this question to the list. I would appreciate any help in understanding how the rpart function is (exactly) computing the "improve" (which is given in fit$split), and how it differs when using the split='information' vs split='gini'

Hello dear R-help members, I would appreciate any help in understanding how the rpart function computes the "improve" (which is given in fit$split) when using the split='information' parameter. Thanks to Professor Atkinson help, I was able to find how this is done in the case that split='gini'. By following the explanation here:

Dear R-helpers, I was testing the truncgof CRAN package, found something that looked like a bug, and did my job: contacted the maintainer. But he did not reply, so I am resending my query here. I installed package truncgof and run the example for function ad.test. I got the following output: set.seed(123) treshold <- 10 xc <- rlnorm(100, 2, 2) # complete sample xt <- xc[xc >=

I am trying to use the package RandomForest performing regression. The variable importance estimates are given as: "%IncMSE" and "IncNodePurity" Can anyone explain me what these refer to and how they are calculated? I found a lot of information on variable importance measures for classification problems, but nothing on regression. Thanks a lot. Mareike

Dear r-help-list: I' got a question about the computation of the improve of a split. The following is an extract of an output of the summary of a tree: Node number 1: 600 observations, complexity param=0.007272727 predicted class=0 expected loss=0.1666667 class counts: 500 100 probabilities: 0.833 0.167 left son=2 (211 obs) right son=3 (389 obs) Primary splits: x4

Hi! OK, I'm trying to select some "useful outliers" from my dataset: I defined 11 "treshold" values (1 for each level of a variable (sampling site) as follows: tresholds<-function(x) { tapply(x,mm$NAME,FUN=mean ,simplify = T, na.rm=T)->med tapply(x,mm$NAME,FUN=sd ,simplify = T, na.rm=T)->standev standev+med } tresholds(mm$chl) Now I'd like to select

Dear all In order to clearly mark values wich are larger than a treshold value, I would like to color the surface below the line given by plot (yy~xx). To color is only the surface between abline (treshold) and yy if they are larger than the specific limit. I guess I can use the function polygon, but I can not find any valuable solution. I'm grateful to you for an advice or an example.

Hi It seems, I just miss something. I defined treshold <- function(pred) { if (pred < 0.5) pred <- 0 else pred <- 1 return(pred) } and want to use apply it on a vector sapply(mylist[,,3],threshold) but I get: Error in match.fun(FUN) : Object "threshold" not found thanks for help cheers chris -- Christoph Lehmann <christoph.lehmann at gmx.ch>

I am running MD 10 (Community) as a file server on a Shuttle SB61G2. This setup worked very well under Mandrake 9.2 however, everytime I try to copy files larger than say <550 ~650MB using MD 10, my linux box freezes and must be rebooted. I can FTP the same file(s) perfectly fine to other PC 's on my home net. Small volumes of files work fine as well as ISO images, the box seems to

Dear everybody, I'm a new user of R 2.4.1 and I'm searching for information on improving the output of regression tree graphs. In the terminal nodes I am up to now able to indicate the number of values (n) and the mean of all values in this terminal node by the command > text(tree, use.n=T, xpd=T) Yet I would like to indicate automatically in the output graph of the tree some

You could use something like this x <- abs(0.95 - 1) treshold <- 0.05 x < treshold | abs(x - treshold) < 1e-6 ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance Kliniekstraat 25 1070 Anderlecht Belgium To call in the statistician after the experiment is

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, this should be an easy one, but I can't figure it out. I have a vector of tests, with their units between brackets (if they have units). eg tests <- c("pH", "Assay (%)", "Impurity A(%)", "content (mg/ml)") Now I would like to hava a function where I use a test as input, and which returns the units like: f <- function (x) sub("\\)",

Hi Suman, I think you can ascertain pureness automatically leveraging the compiler instead of manually tagging attribute to each method and call-site. It would seem like impurity should be a transitive attribute. So this would conflict with below. __attribute__((annotate("pure"))) int add(uint32_t a, uint32_t b) { // impure by calling printf... ... printf("%d + %d =

Hi, some days ago we discused here the need for an analyzer for the 1000 Hz tone, as opposite application to Milliwatt. Here it is: Mwanalyze http://planinternet.net/download/voip/asterisk/app_mwanalyze.c It performs a Fourier analysis for a fixed frequency and tells the amplitude. The frequency is not limited to 1000 Hz, but can be passed as argument. The periode duration must be a mulitple

I have been trying to write my own user defined function in Rpart.I imitated the anova splitting rule which is given as an example.In the work I am doing ,I am calculating the concentration index(ci) ,which is in between -1 and +1.So my deviance is given by abs(ci)*(1-abs(ci)).Now when I run rpart incorporating this user defined function i get the following error message: Error in

Dear R community, as stated in Breiman et.al. (1984) and De'Ath & Fabricius (2000) using sums of absolute deviations about the median as an impurity measure gives robust trees. I would like to use this method in rpart. Has somebody already tried this method in rpart? Is there maybe already a script available somewhere? I am aware of the possibility to define usersplits myself with

Is there any other mechanism than using passwd files with md5-hashed passwords created by dovecotpw that will support cram-md5 authentication? Has anyone created setups where the passwd databases reside in the individual users home directories? Is it possible to persuade dovecotpw to update the passwd databases automatically. Having to use a text editor to paste in the passwords sets a high

Thanks! On 05 Apr 2016, at 16:07, Thierry Onkelinx <thierry.onkelinx at inbo.be<mailto:thierry.onkelinx at inbo.be>> wrote: You could use something like this x <- abs(0.95 - 1) treshold <- 0.05 x < treshold | abs(x - treshold) < 1e-6 ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest team Biometrie &

Dear R community, I have recently discovered the package oblique.tree and I must admit that it was a nice surprise for me, since I have actually made my own version of a kind of a classifier which uses the idea of oblique splits (splits by means of hyperplanes). So I am now interested in comparing these two classifiers. But what I do not seem to understand is why the function