similar to: random numbers

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( )

Well apparently this has nothing to do with the gini() command. I cannot get it to work for something as simple as sum() Here is the little example I am playing with, maybe someone can help me find my error: a<-c("A","B","C","A","B","C","A","A","C","B")

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:

Hi, I'm using the ineq package to calculate some concentration measures (Gini, Herfindal, ...) and I was wondering if there's around also a function to calculate standard error on these measures. If not, is anybody aware of where I can find a reference on this point? Thanks. -- ======================================================== Angelo Secchi PGP Key ID:EA280337

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

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'

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:

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]]

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]]

Hello all, I would like to rigorously test whether observations in my dataset are outliers. I guess all the main tests in R (Grubbs) impose the assumption of normality. My data is surely not normal, so I would like to use something else. As far as I can tell from wikipedia, Peirce's criterion is just that. The data I am interested in testing is: 1) Continuous on the unit interval 2)

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,

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",

Friends, I have noticed that many publications that use RF report variable importance as a function of mean decrease in accuracy rather than mean decrease in gini. Am I correct that the mean decrease in accuracy is just the mean decrease in gini divided by 100? Thanks, Helen Mills Poulos Yale School of Forestry

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

Hi, I have a right-censored (positive) random variable (e.g. failure times subject to right censoring) that is observed for N subjects: Y_i, I = 1, 2, ..., N. Note that Y_i = min(T_i, C_i), where T_i is the true failure time and C_i is the censored time. Let us assume that C_i is independent of T_i. Now, I would like to generate another random variable U_i, I = 1, 2, ..., N, which is

Hi, I've a question about the RandomForest package. The package allows the extraction of a variable importance measure. As far as I could see from the documentation, the computation is based on the Gini index. Do you know if this extraction can be also based on other criteria? In particular, I'm interested in the info gain criterion. Best regards, Chris --

Dear all R users, I am building a Cox PH model on a small dataset. I am wondering how to measure the predictive power of my cox model? Normally the ROC curve or Gini value are used in logistic regression model. Is there any similar measurement suitable for Cox model? Also if I use C-index statistic to measure the predictive power, is it a time-dependent value (i.e. do I need to calculate it for

curiosity---given that vector operations are so much faster than scalar operations, would it make sense to make uniroot vectorized? if I read the uniroot docs correctly, uniroot() calls an external C routine which seems to be a scalar function. that must be slow. I am thinking a vectorized version would be useful for an example such as of <- function(x,a) ( log(x)+x+a ) uniroot( of, c(

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