similar to: reshape with function(x,y)?

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

The function wtd.var(x,w) in Hmisc calculates the weighted variance of x where w are the weights. It appears to me that wtd.var(x,w) = var(x) if all of the weights are equal, but this does not appear to be the case. Can someone point out to me where I am going wrong here? Thanks. Tom La Bone [[alternative HTML version deleted]]

Hello, Using this data: http://n4.nabble.com/file/n1676330/US_Final_Values.txt US_Final_Values.txt and the following code i got the image at the end of this message: US.final.values<-read.table("c:/tmp/US_Final_Values.txt",header=T,sep=" ") US.nls.1<-nls(US.final.values$ECe~a*US.final.values$WTD^b+c,data=US.final.values,start=list(a=2.75,b=-0.95,c=0.731),trace=TRUE)

Hi, I have a data frame with two columns: dt and tf. The dt column is datetime and the tf column is a temperature. dt tf 1 2009-06-20 00:53:00 73 2 2009-06-20 01:08:00 73 3 2009-06-20 01:44:00 72 4 2009-06-20 01:53:00 71 5 2009-06-20 02:07:00 72 ... I need a subset of the rows where the minutes are 53. The hour is immaterial. I can not find a wildcard

Hello, I probably have a syntax error in trying to generate an expected survival curve from a weighted cox model, but I can't see it. I used the help sample code to generate a weighted model, with the addition of a "weights=albumin" argument (I only chose albumin because it had no missing values, not because of any real relevance). Below are my code with the resulting error

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

Hello, all. I'm new to R and just beginning to learn to write functions. I know I'm out of my depth posting here, and I'm sure my issue is mundane. But here goes. I'm analyzing the American National Election Study (nes), looking at mean values of a numeric dep_var (environ.therm) across values of a factor (partyid3). I use ddply from plyr and wtd.mean from Hmisc. The nes requires a

Hello everyone, I have tried several ways of doing this and searched the documentation and help lists and I have been unable to find an answer or even whether it is possible to do it. I am pasting together a formula and I need to insert double quotes around the strings. Here's an example: location <- c("AL", "AK", "MA", "PA") v=2 test <-

Hi Holger, > Note that I did specify "-g", but not any "-Ox" switches. That > made the size of all *.o files together being 143 MB. LLVM represents debug info as explicit calls to intrinsics. This approach has many advantages, but a possible disadvantage is that it can significantly increase the size of the bitcode. I don't know if that explains your observations.

Hello, I was unable to find clues to my problem in ?optim. Using the data and code below, I get an error ("system is exactly singular") when a particular line of code is left in, but have found that 'optim' works when I comment it out. The line of code in question is after the closeAllConnections() line of code and contains a call to "na.approx" from the zoo package.

Hi, I am doing double for loops to calculate SDs with some weights and wondering if I can get rid of the outer for loop as well. I made a simple examples which is essentially what I am doing. Thanks for your help! -Young #------------------------------------------------------ # wtd.var is Hmisc package # you can replace the 3 lines inside for loop as # sdx[i,] =

I' m trying to compute weighted mean on different groups but it only returns NA. If I use the following data.frame truc: x y w 1 1 1 1 2 2 1 3 1 1 4 2 0 2 1 0 3 2 0 4 1 0 5 1 where x is a factor, and then use the command : tapply(truc$y,list(truc$x),wtd.mean, weights=truc$w) I just get NA. What's the problem ? What can I do ?

Hi, I have a financial (zoo) time series with prices and volumes (although I can get the coredata as a matrix). Due to the data-source some indices have multiple observations. I want to aggregate these according to a weighted average. 11:00:01 34 1000 11:00:01 35 500 11:00:01 35 1000 11:00:02 34 500 11:00:02 35 500 should become 11:00:01 34.6 2500 11:00:02 34.5 1000 I currently do this

I've noticed that R has a number of very useful functions for obtaining descriptive statistics on groups of variables, including summary {stats}, describe {Hmisc}, and describe {psych}, but none that I have found is able to provided weighted descriptives of subsets of a data set (ex. descriptives for both males and females for age, where accurate results require use of sampling

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;

I want to share a little LLVM experiment. I tried LLVM on one of my bigger Qt 3.x based projects. I used llvm from SVN trunk (r39999) and SVN llvm-gcc-4.01 (r370) and did compile every file with /usr/src/llvm/dist/bin/g++ -c -pipe -g \ -Wall -Wextra -Wno-sign-compare \ ... lots of -Dxxxx ... \ --emit-llvm -I/usr/share/qt3/mkspecs/default \ -I. -I.. -I../../../include/qt3 -I.obj/ \

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? >

I needed to compute a complicated cross tabulation to show weighted means and standard deviations and the only method I could get that worked uses a series of nested for next loops. I know that there must be a better way to do so, but could use some assistance pointing the way. Here is my working, but inefficient script: library(Hmisc) rm(list=ls()) load('NHTS.Rdata') day.wt <-

Hi Petr, I think that Duncan suggests something like this: x<- c(rep(10,20), rep(300,5), rep(100, 10)) tx <- table(x) prop.x <- tx / sum(tx) vx <- as.integer(names(tx)) prop.wx <- tx * vx / sum(tx * vx) plot(ecdf(x)) plot(vx, cumsum(prop.x), ylim = 0:1) plot(vx, cumsum(prop.wx), ylim = 0:1) Best regards, Thierry ir. Thierry Onkelinx Statisticus / Statistician Vlaamse

Hi. I am using R 2.3.1 on Windows XP. I had installed a library package into my first session and wanted the same package in my second session, so I went out to the CRAN mirror and tried to install the package, and got the following message: ********************************************************************* >utils:::menuInstallPkgs() trying URL