similar to: Compute quantiles with values and correspondent frequencies

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]]

I'm looking for a way to improve code that's proven to be inefficient. Suppose that a data source generates the following table every minute: Index Count ------------ 0 234 1 120 7 11 30 1 I save the tables in the following CSV format: time,index,count 0,0:1:7:30,234:120:11:1 1,0:2:3:19,199:110:87:9 That is, each line represents a table, and I

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

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 R-users, I don't know if there is a problem in wtd.quantile (from library "Hmisc"): -------------------------------- x <- c(1,2,3,4,5) w <- c(0.5,0.4,0.3,0.2,0.1) wtd.quantile(x,weights=w) ------------------------------- The output is: 0% 25% 50% 75% 100% 3.00 3.25 3.50 3.75 4.00 The version of R I am using is: 2.1.0 Best,Jing

Dear All, I am economist and working on poverty / income inequality. I need descriptive statitics like the ratio of education expentitures between different income quintiles where each household has a different weight. After a bit of google search I found 'Hmisc' and 'quantreg' libraries for weighted quantiles. The problem is that these packages give me only weighted quintiles;

Dear all, I'm trying to manage with user defined split function in rpart (file rpart\tests\usersplits.R in http://cran.r-project.org/src/contrib/rpart_3.1-34.tar.gz - see bottom of the email). Suppose to have the following data.frame (note that x's values are already sorted) > D y x 1 7 0.428 2 3 0.876 3 1 1.467 4 6 1.492 5 3 1.703 6 4 2.406 7 8 2.628 8 6 2.879 9 5 3.025 10 3 3.494

Hi, I have data in the following form: index count -7 32 1 9382 2 2192 7 190 11 201 I'd like to get quantiles from the data. I thought about something like this: index <- c(-7, 1, 2, 7, 11) count <- c(32, 9382, 2192, 190, 201) quantile(rep(index, count)) It answers correctly, but I feel it's wasteful especially when count is

Hi everyone, now I am trying to finish writing the code (I had asked for assistance on subtracting arrays) This is what I what I am running in R: > source("/home/ie/Documents/TTU/GA_Research/GLUE/R-Project/R_GLUE_Example/NSEr.R") NSEr <- function (obs, sim) { {jjh <- (as.vector(obs) - sim)^2 Xjjhs <- apply(Xjjh, 2, sum) Yii <- (obs - mean(obs))^2 Yiis <- apply(Yii, 2,

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 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

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

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'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 <-

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

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

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

Hi Duncan I tried Ecdf and/or wtd.quantile from Hmisc and it is working (probably). Ecdf(x, q=.5) Ecdf(x, weights=xw,col=2, add=T, q=.5) wtd.quantile(x) 0% 25% 50% 75% 100% 10 10 10 100 300 wtd.quantile(x, weights=xw, type="i/n") 0% 25% 50% 75% 100% 10.0000 138.8667 192.5778 246.2889 300.0000 But could you please be more specific in this? >

Dear R users, I'm working with the rpart package and want to evaluate the performance of user defined split functions. I have some problems in understanding the meaning of the xval argument in the two functions rpart.control and xpred.rpart. In the former it is defined as the number of cross-validations while in the latter it is defined as the number of cross-validation groups. If I am

I need to make weighted boxplots. I found that ggplot makes them. I would however like to label them with the boxplot statistics (the median, q1 and q3). In the boxplot function in r-base, I could output boxplot statistics and then write a text on the plot to place the labels. How would one do it with ggplot? Vikas