similar to: How does rpart computes "improve" for split="information"?? (which seems to be different then the "gini" case)

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 everyone, I'm a newbie to R and to CART so I hope my questions don't seem too stupid. 1.) My first question concerns the rpart() method. Which method does rpart use in order to get the best split - entropy impurity, Bayes error (min. error) or Gini index? Is there a way to make it use the entropy impurity? The second and third question concern the output of the printcp() function.

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

If I use the Ineq library and the Gini function in this way: >Gini(c(100,0,0,0)) I obtain the result 0.75 instead of 1 (that is the perfect inequality). I think Gini's formula in Ineq is based on a formula as reported here: http://mathworld.wolfram.com/GiniCoefficient.html but in the case of perfect inequality: x_1=.......=x_n-1 =0 x_n>0 these formula are equal to 1 - 1/n, not to

Hello, Thank you very much for your help. How can I draw a Lorenz curve with several replications ? Here is an example with 4 replications: hosts=c(23,31,19,10,7,7,3, 39,40,8,3,6,2,2, 47,17,8,10,6,11,1, 30,30,10,0,15,15,0) parasites=rep(seq(from=0,to=6,by=1),4) replications=c(rep(1,7),rep(2,7),rep(3,7),rep(4,7)) test <- cbind(parasites,hosts,replications) Should I

Hey there, I was wondering if someone could tell me if there's a package or command that allows me to compute a GINI coefficient using a vector of weights. Also the coefficient should be bias corrected. Diego Rojas [[alternative HTML version deleted]]

Dear all, I am working with randomForest package and I am interested in examining the "Gini importance" measures that are used as a general indicator of feature relevance. Is there a possibility of getting the Gini measure that is being estimated in each tree by the output of the getTree() function? Thanks a lot, Chrysanthi [[alternative HTML version deleted]]

Hi there, I am trying to compute Gini coefficients for vectors containing income classes. The data I possess look loke this: yit <- c(135, 164, 234, 369) piit <- c(367, 884, 341, 74 ) where yit is the vector of income classes, and fit is the vector of associated frequencies.(This data is from Rustichini, Ichino and Checci (Journal of Public Economics, 1999) ). In ineq pacakge, Gini( )

Dear List, I am unsure if this is specifically a R question or a stats question? I thought i would ask here and if i get no replies it will answer that! I am trying to calculate Gini coefficients in R, based on a slight modification of the typical equation that i have seen in a paper. -------------- next part -------------- A non-text attachment was scrubbed... Name: PastedGraphic-2.pdf Type:

I have a data set that contains income data and a group identifier. Sort of like: DATA Group,Income A,2300 B,6776 A,6668 A,6768 B,9879 C,5577 A,7867 (etc),(etc) I am trying to compute the gini coefficient for each group. I have tried the following and none seem to do the trick: 1) attach(DATA) by(DATA, group, function(x) gini(income)) 2) attach(data) tapply(income, group,

Hi. I have some data from a survey w/c include income data. Was not quite sure how to generate the gini index on these data. Jose Ramon Albert Statistical Research and Training Center www.srtc.gov.ph -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

Dear R-users, I'm working with the Rpart package and trying to understand how the procedure select the best split in the case the method "class" and the splitting index "Gini" are used. In particular I'd like to have look to the source code that works out the best split for un unordered predictor. Does anyone can suggest me which functions in the sources I should

Dear All, I have developed a programme the anable the decomposition of the Gini index, it complets tha valuable work of Achim Zeileis, the author of the ineq package. I would like to make it to be part of all R package. How should I proceed. Must I sent it to the the Core developement team ? The proogramme is written in R. Many thanks for your advice, Best regards, Souleymane

Dear All, I wish to calculate the Gini index (ineq from same package) and some other indices for the diameter distribution of each plot (df dgtot). dgtot: IDPlot Diameter(cm) 1 4 34.0 2 4 23.0 3 4 38.0 ... 51 5 16.0 52 5 8.0 53 5 9.0 ... 5301 140 25.0 5302 140 12.0 5303 140 7.0 I use: >

Dear R helpers: I have another question regarding "R" command. It is also measuring Credit Scoring. I need to measure the classifying method for customer's credit score. (ex. regression line by Fisher) If credit card company already classify their customers credit : Good customers / Bad customers [by using regression line; ex) inside of line: Good/ outside: Bad] And I found

Hello, I would like to build a Lorenz curve and calculate a Gini coefficient in order to find how much parasites does the top 20% most infected hosts support. Here is my data set: Number of parasites per host: parasites = c(0,1,2,3,4,5,6,7,8,9,10) Number of hosts associated with each number of parasites given above: hosts = c(18,20,28,19,16,10,3,1,0,0,0) To represent the Lorenz curve: I

Hi listers, Does it necessary to install any package in order to use the GINI or INEQ functions. If I use the following command the R tells me that didn't find the GINI function. x<-c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261) G<-gini(x) Thanks in advance, Marcio -- View this message in context:

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

I have written the following function to calculate the weighted mean difference for univariate data (see http://www.xycoon.com/gini_mean_difference.htm for a related formula). Unsurprisingly, the function is slow (compared to sd or mad) for long vectors. I wonder if there's a way to make the function faster, short of creating an external C function. Thanks very much for your advice. gmd

Hola, paso el mini programita q estoy viendo, lo q me llama la atencion es una parte donde se definen las funciones. Probe primero meter adentro del boots la estadistica a estimar usando directamente gini(varible, pesos) pero no me dejo. Vi q en el ej del manual de boots, siempre define antes la funcion, entonces probe definir antes una funcion haciendo grini<-function(x) {gini(variable,