similar to: Weighted SD

Hi, I would like to make a box and whisker plot but use a sample weight for each observation. I've searched around a bit and have not found a method of doing this. Anyone have any advice? Thanks much, Sean Parks -- View this message in context: http://www.nabble.com/sample-weight-for-box-plot--tp26104205p26104205.html Sent from the R help mailing list archive at Nabble.com.

Dear all, how to calculate values of t-distribution for given values of d.f. using R functions? Anton

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]]

Hi I'm new to R and most things I want to do I can do but I'm stuck on how to weight a sample. I have had a look through the post but I can't find anything that addresses my specific problem. I am wanting to scale up a sample which has been taken based on a single variable (perf) which has 4 attributes H,I, J and K. The make up of the sample is shown below:- Perf Factored

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 ?

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 have a several questions about R graphic properties. I use a "barplot2" function for creating plots with error bars. My data include species' names in first column, and I need make plots for each species. I know how to select species for each plot: D<-read.table("FD_R.txt", h=T) Dens<-D[D[,1]=="Sit.eur",] but I want to make a cycle which will

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

On 24/11/2017 6:27 AM, PIKAL Petr wrote: > Dear all > > Strictly speaking it is not R question but as you are the most capable persons I know I give it a try. > > I am strugling with recalculation of number weighted to volume weighted distribution. > > Suppose I have objects (cubes) with size > > x<- c(rep(10,20), rep(100, 10), rep(300,5)) > I can get >

Dear all, is there any functions which allow to calculate Student t-criterion using means, their SE and sample size? I've seek for, but bulit-in t-criterion works only with initial sample... Best regards, A.Morkovin

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? >

Hello! I have data frame with 'weights' in one of the columns. I need to compute weighted mean on another column other factor variable and i am trying to: res<-tapply(data$k,list(data$model),weighted.mean,w=data$w,na.rm=T) and i get: Warning messages: 1: longer object length is not a multiple of shorter object length in: x * w 2: longer object length is not a multiple of shorter

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

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

Dear all Strictly speaking it is not R question but as you are the most capable persons I know I give it a try. I am strugling with recalculation of number weighted to volume weighted distribution. Suppose I have objects (cubes) with size x<- c(rep(10,20), rep(100, 10), rep(300,5)) I can get plot(ecdf(x)) or the number weighted average mean(x) [1] 77.14286 or volume weighted average

Pat Burns makes a good point. -Peter -------- Original Message -------- Subject: Re: [R] Using seq_len() vs 1:n Date: Fri, 12 Feb 2010 09:01:20 +0000 From: Patrick Burns <pburns at pburns.seanet.com> To: Peter Ehlers <ehlers at ucalgary.ca> References: <4B746AEF.10900 at ucalgary.ca> If you want your code to be compatible with S+, then 'seq_len' isn't going to work.

Hi list, One of my co-workers found this problem with 'cor' in his code and I confirm it too (see below). He's using R 2.2.1 under Win 2K and I'm using R 2.3.0 under Win XP. =========================================== > R.Version() $platform [1] "i386-pc-mingw32" $arch [1] "i386" $os [1] "mingw32" $system [1] "i386, mingw32" $status

I need to do some fairly deep tables, and ftable() offers most of what I need, except for the weighting. With smaller samples, I've just used replicate to let me have a weighted data set, but with this data set, I'm afraid replicate is going to make my data set too big for R to handle comfortably. That being said, is there some way to weight my table (similar to wtd.table) but offer the

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, I am a new R user and am trying to construct a palaeoenvironmental transfer function (weighted averaging method) using the package rioja. I've managed to insert the two matrices (the species abundance and the environmental data) and have assigned them to the y and x values respectively. When I try and enter the 'WA' function though, I get an 'Error in FUN' message (see