search for: winsorized

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 then return it to its origina...

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

Dear all, someone can find what I doing wrong with the following function. It is for winsorisation mean. At my eyes it is ok, but for reason I sometimes it is changing the results when I change the k value. wmean <- function (x, na.rm = FALSE, k = 1) { if (any(i.na <- is.na(x))) { if (na.rm) x <- x[!i.na] else return(NA) } n

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) data<-data.frame(firm, in...

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 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

(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 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 =>

.../((1-2*alpha)*n1)*(sum(mat1[e1:f1]) + r1*(mat1[k1]+mat1[n1-k1+1])) trim2=1/((1-2*alpha)*n2)*(sum(mat2[e2:f2]) + r2*(mat2[k2]+mat2[n2-k2+1])) trim3=1/((1-2*alpha)*n3)*(sum(mat3[e3:f3]) + r3*(mat3[k3]+mat3[n3-k3+1])) trim4=1/((1-2*alpha)*n4)*(sum(mat4[e4:f4]) + r4*(mat4[k4]+mat4[n4-k4+1])) ## sample winsorized mean x1=(1-r1)*mat1[k1+1]+r1*mat1[k1] x2=(1-r2)*mat2[k2+1]+r2*mat2[k2] x3=(1-r3)*mat3[k3+1]+r3*mat3[k3] x4=(1-r4)*mat4[k4+1]+r4*mat4[k4] u1=(1-r1)*mat1[n1-k1]+r1*mat1[n1-k1+1] u2=(1-r2)*mat2[n2-k2]+r2*mat2[n2-k2+1] u3=(1-r3)*mat3[n3-k3]+r3*mat3[n3-k3+1] u4=(1-r4)*mat4[n4-k4]+r4*mat4[n4-k4+1] win1...

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

...n2)*(sum(__mat2[e2:f2]) + > r2*(mat2[k2]+mat2[n2-k2+1])) > trim3=1/((1-2*alpha)*n3)*(sum(__mat3[e3:f3]) + > r3*(mat3[k3]+mat3[n3-k3+1])) > trim4=1/((1-2*alpha)*n4)*(sum(__mat4[e4:f4]) + > r4*(mat4[k4]+mat4[n4-k4+1])) > > ## sample winsorized mean > x1=(1-r1)*mat1[k1+1]+r1*mat1[__k1] > x2=(1-r2)*mat2[k2+1]+r2*mat2[__k2] > x3=(1-r3)*mat3[k3+1]+r3*mat3[__k3] > x4=(1-r4)*mat4[k4+1]+r4*mat4[__k4] > > u1=(1-r1)*mat1[n1-k1]+r1*mat1[__n1-k1+1] > u2=(1-r2)*mat2[n2-k2]+r2*mat2[...

Hello, I'm seeking ideas on how to remove outliers from a non-normal distribution predictor variable. We wish to reset points deemed outliers to a truncated value that is less extreme. (I've seen many posts requesting outlier removal systems. It seems like most of the replies center around "why do you want to remove them", "you shouldn't remove them", "it

Hello everyone, I want to calculate type I error rate for modified F statistic for one way robust anova. I need to find the j group trimmed mean and winsorized sum of squared deviations. Here I attached my code for j=2 to make it simple. Originally I have j=4. Hope you can help. I need to run it for 1000 times My problem is: i) the value of F-test obtain from my simulation below is in negative value..There might be something wrong in my coding ii) after...

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

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 R users, This is most likely very basic question but I am new to R and would really appreciate some tips on those two problems. 1) I need to plot variables from a data frame. Because of some few high numbers my graph is really strange looking. How could I plot a fraction of the samples (like 0.1 (10%), 0.2 up to for example 0.6) on x axis and values 'boundaries' (like any value