similar to: Plotting and quantiles

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)

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

Dear all i use R only a few days and don't understand the difference between fivenum(x) und summary(x). > x [1] 20.77 22.56 22.71 22.99 26.39 27.08 27.32 27.33 27.57 27.81 28.69 29.36 [13] 30.25 31.89 32.88 33.23 33.28 33.40 33.52 33.83 33.95 34.82 > fivenum(x) [1] 20.770 27.080 29.025 33.280 34.820 > summary(x) Min. 1st Qu. Median Mean 3rd Qu. Max. 20.77 27.14

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

Can anyone please tell me if there is a function to calculate confidence intervals for the results of the quantile function. Some of my data is normally distributed but some is also a squewed distribution or a capped normal distribution. Some of the data sets contain about 700 values whereas others are smaller with about 100-150 values, so I would like to see how the confidence intervals change

Hi, I guess that my problem has an obvious answer, but I have not been able to find it. Suppose I create a custom function, consisting of two beta-distributions: myfunction <- function(x) { dbeta(x,2,6) + dbeta(x,6,2) } How can I calculate the quantiles of myfunction? I have not seen any continous function treated in the docs, and applying the "quantile function" gives me an

bradleyd wrote > Excuse the request from an R novice! I have a data frame (DATA) that has > two numeric columns (YEAR and DAY) and 4000 rows. For each YEAR I need to > determine the 10% and 90% quantiles of DAY. I'm sure this is easy enough, > but I am a new to this. > >> quantile(DATA$DAY,c(0.1,0.9)) > 10% 90% > 12 29 > > But this is for the entire

Hi, I'm attempting to calculate the 0.25 and 0.97 quantiles for tree height (0-50 meters) against tree age (0-300 years) and I am running into some difficulty with the plotted grafic. I've run the examples in the quantreg help and can get those to work properly and by plugging in my data I can also get the lines plotted on my dataset. Unfortunately I'm running into a problem with the

Dear List, quantile(x) function allows to obtain specified quantiles of a vector of observations x. Is there an analogous function to compute quantiles in the case one have the vector of the observations x and the correspondent vector f of relative frequencies ? Thank you Paolo Radaelli [[alternative HTML version deleted]]

Hi, I'm working on a data set that contains a couple of factors and a number of dependent variables. From all of these dependent variables I would like to calculate mean, standard deviation and quantiles. With the function FUN I get all the means and stdev that I want but quantiles are only calculated for the first of the dependent variables (column 8 in the summarize command). What do I

I'm trying to work out the simplest way to remove the upper and lower quantiles, in this case upper and lower 25% from an array. I can do it in two steps but when I try it in one, it fails. Is there something simple missing from my syntax or are there other simple elegant way to accomplish this? Thanks J > i <-1:20 > i2 <- i[i<quantile(i,.75)] > i3 <-

Dear all, I am searching for a way to compute a test comparable to Chuang et al. ("Causality in Quantiles and Dynamic Stock Return-Volume Relations"). The aim of this test is to check wheter the coefficient of a quantile regression granger-causes Y in a quantile range. I have nearly computed everything but I am searching for an estimator of the density of the distribution at several

When all else fails, read the help page... ?fivenum says to look at ?boxplot.stats, and the "Details" section of ?boxplot.stats has, well, details. Tukey had reasons to call those hinges rather than quartiles. Andy > -----Original Message----- > From: Knut M. Wittkowski [mailto:kmw at rockefeller.edu] > Sent: Thursday, May 29, 2003 5:58 PM > To: Matthias Kirschner >

I have a set of values and their corresponding weights. I can use the function weighted.mean to calculate the weighted mean, I would like to be able to similarly calculate the weighted median and quantiles? Is there a function in R that can do this? thanks, Spencer [[alternative HTML version deleted]]

Hi All, I am looking at applications of percentiles to time sequenced data. I had just been using the quantile function to get percentiles over various periods, but am more interested in if there is an accepted (and/or R-implemented) method to apply weighting to the data so as to weigh recent data more heavily. I wrote the following function, but it seems quite inefficient, and not really very

Guys: 1).When I using the 'quantile' function, I get really confused. Here is what I met: > x<-zoo(rnorm(500,0,1)) > quantile(x,0.8) 400 1.060258 > c=rnorm(500,0,1) > quantile(c,0.8) 80% 0.9986075 why do the results display different? Is that because of the different type of the class? 2).And I want to use the 'rollapply' function to compute a

Motivated by Deepayan's recent inquiries about the efficiency of the R 'quantile' function: http://tolstoy.newcastle.edu.au/R/devel/05/11/3305.html http://tolstoy.newcastle.edu.au/R/devel/06/03/4358.html I decided to try to revive an old project to implement a version of the Floyd and Rivest (1975) algorithm for finding quantiles with O(n) comparisons. I used

Dear all, I would like to aggregate a data frame (consisting of 2 columns - one for the bins, say factors, and one for the values) along bins and quantiles within the bins. I have tried aggregate(data.frame$values, list(bin = data.frame $bin,Quantile=cut2(data.frame$bin,g=10)),sum) but then the quantiles apply to the population as a whole and not the individual bins. Upon this

I have 20 variables: 5,9,6,1,5,9,7,4,5,6,3,2,4,8,9,6,1,8,4,8 How do I calculate the corresponding quantiles from a normal distribution with the same mean and variance as the sample? Also, how do I draw a QQ plot of the data? Thanks for any help! -- View this message in context: http://www.nabble.com/Quantiles-and-QQ-plots-tf4925742.html#a14097909 Sent from the R help mailing list archive at

I apologise for asking this question here but I am hoping that someone can either give me direct guidance and/or point me to a better group for this type of disucssion. I have what I feel should be a fairly simple problem but which my limited stats knowledge can't answer. I have two overlapping distributions (both normal) and I want to answer the question how do I calculate the cut-off value