similar to: Winsorisation function

Hello to all. My question is very simple... Let's say we have a data frame with three variables (columns): X, W and F. X is a numeric variable (e.g. like income) F is a factor (e.g. with 2 levels) and W is a case weight (data are from household sample but an individual was interviewed, weights are functions of number of persons in the house hold). I wanted to compute a means of X weighted

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, I am trying to write my own user defined split function for rpart. I read the example in the tests directory and I understand the general idea of the how to implement user defined splitting functions. However, I am having troubles with addressing the data frame used in calling rpart in my split functions. For example, in the evaluation function that is called once per node,

Dear useRs, I have a list, each entry of which is a matrix of constant dimensions. Is there a good way (i.e., not using a for loop) to apply a mean to each matrix entry *across list entries*? Example: foo <- list(rbind(c(1,2,3),c(4,5,6)),rbind(c(7,8,9),c(10,11,12))) some.sort.of.apply(foo,FUN=mean) I'm looking for a componentwise mean across the two entries of foo, i.e., the

HI, I am trying to figure out an efficient way to calculate group means and associate each entry with it. I made up an example: A = rep(rep(0:1,each=2),3) B = rep(rep(0:1,4),3) C = rep(rep(c(0,0,1,1),2),3) X =cbind(rnorm(24,0,1),runif(24,0,1),A,B,C) A B C [1,] -1.92926469 0.32213127 0 0 0 [2,] -0.83935617 0.77794096 0 1 0 [3,] -1.27799751

Hi I have a big problems on my Asterisk systems : I have one Asterisk Server with static IP (no nat) and 6 Linksys SPA941. All SPA are after a router with NAT: * SPA-1 and SPA-2 are on the same network, we have a pat 5060 => SPA-1 and 5061=> SPA-2 on the internet router * SPA-3, we have a pat 5062 => SPA-3 * SPA-4, we have a pat 5063 =>

Folks, I have a personal package which used to build fine. Today when I tried to build the package again, some errors popped up. Could you please help? When I paste the offending function into an R it works correctly. But the package build breaks. $ R CMD check ansecon * checking for working pdflatex ... OK * using log directory '/Users/ajayshah/L/R/packages/ansecon.Rcheck' * using R

Hi all , I have a dataframe of 200 columns and 2 rows. The first row in each column contains the frequency of cases in group I . The second row in each column contains the frequency of cases in group II. The frequency of trails is a fixed value for group I(e.g.200) and it is also another fixed values for group II (e.g. 100). The dataset looks like this :- > Mydata

Hi anonymous, ?prop.test states that it returns a list. And one of the element is 'p.value'. str() on the output of prop.test() reveals that too. So prop.test()$p.value or prop.test()["p.value"] should work. Best regards, ir. Thierry Onkelinx Statisticus / Statistician Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR

Thank you for clarifying this point but my main question was about how to modify my code to do the analysis correctly. The code I mentioned :- MyResults <- apply(Mydata, 2, function(x)prop.test(Mydata,c(200,100)) Results in this error : 'x' and 'n' must have the same length in the prop.test(x,n). How can I modify "x' or "n" arguments so the analysis

Hi, this is both a statistical and a R question... what would the best way / test to detect an outlier value among a series of 10 to 30 values ? for instance if we have the following dataset: 10,11,12,15,20,22,25,30,500 I d like to have a way to identify the last data as an outlier (only one direction). One way would be to calculate abs(mean - median) and if elevated (to what extent ?) delete the

(I see this in both R-patched r43124 and R-devel r43233.) In the Argument section of ?mean: trim the fraction (0 to 0.5) of observations to be trimmed from each end of x before the mean is computed. Values outside that range are taken as the nearest endpoint. Then in the Value section: If trim is non-zero, a symmetrically trimmed mean is computed with a fraction of trim observations

Hi All, I want to take a matrix (or data frame) and winsorize each variable. So I can, for example, correlate the winsorized variables. The code below will winsorize a single vector, but when applied to several vectors, each ends up sorted independently in ascending order so that a given observation is no longer on the same row for each vector. So I need to winsorize the variable but

I'm trying to obtain the mean of the middle 95% of the values from each row of a matrix (that is, the highest and lowest 2.5% of values in each row are removed before calculating the mean). I am having all sorts of problems with this; for example the command: apply(matrix1,1,function(x) quantile(c(.05,.90),na.rm=T)) returns the exact same quantile values for each row, which is clearly

Hello, Well, first let me say - I've looked in the APP database for this game. I've tried to trouble shoot everything I've read on the site. Here's my system: Ubuntu 64-bit 8.04 AMD 5800+ 4gb of Winsor Ram Nvidia 8500 GT Intel onboard sound Here's what's happening: Whenever I load up a game on CSS, it crashes after a gun shot. You can walk around, and everything just

Dear all I would like to superpose some smoothing line through boxplot in ggplot > dput(ad) structure(list(konc.f = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 7L, 7L, 7L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,

Hi everyone! #I need a loop or a function that creates a X2 variable that is X1 without the extreme values (or X1 winsorized) by industry and year. #My reproducible example: firm<-sort(rep(1:1000,10),decreasing=F) year<-rep(1998:2007,1000) industry<-rep(c(rep(1,10),rep(2,10),rep(3,10),rep(4,10),rep(5,10),rep(6,10),rep(7,10),rep(8,10),rep(9,10), rep(10,10)),1000) X1<-rnorm(10000)

dear R experts: I have an academic question that borders on asking for consulting help, so I hope I am not too imposing. If I am, please ignore me. My data set has 100MB data set of daily stock returns. I want to compute rolling (recursive?) betas---either bivariate or multivariate---with respect to some other data time series. Many of these regressions are "take away the first

Dear List, Wonder if you have some thoughts on the following question using lsoda in desolve: I have the following data and function: require(deSolve) times <- c(0:24) tin  <- 0.5 D <- 400 V    <- 26.3 k <-0.056 k12  <- 0.197118 k21  <- 0.022665 yini <- c(dy1 = 0,dy2 = 0)  events <- data.frame(var = "dy1",time = c(10,15),value = c(200,100),method =

Hello everybody, after searching around for quite some time, I haven't been able to find a package that provides a function to compute the Windorized mean and variance. Also I haven't found a function that computes the trimmed variance. Is there any such package around? thanks, Roberto