similar to: Weighted median

Dear R-users, I intend to do a spatial analysis on the genetic structuring within a population. For this I had thought to prepare a kernel density estimate map showing the spatial distribution of individuals, while incorporating the genetic distances among individuals. I have a dataset of locations of N unique individuals (XY-coordinates) and an NxN matrix with the genetic distances given as a

I am trying to calculate the weighted mean for a of 10 deciles and I get an error: > decile <- tapply(X=mat$trt1m, INDEX=mat$Rank, FUN=weighted.mean, w=mat$mcap) Error in FUN(X[[1]], ...) : 'x' and 'w' must have the same length All three of my inputs have the same length, as shown below, and the weighted.mean calculation works by itself, just not in tapply() >

Sorry for starting another topic, this is actually a reply to Segher's post on Sun Oct 22 on the 'mdct question' topic. I wasn't subscribed properly and so I didn't get email confirmation and thus can't add to that thread. So Segher, if the equation is indeed what you say it is, then replacing mdct_backward with this version should work, but it doesn't. Am I applying

Hello, I was wondering if someone knows the formula used by the function lm to compute the t-values. I am trying to implement a linear regression myself. Assuming that I have K variables, and N observations, the formula I am using is: For the k-th variable, t-value= b_k/sigma_k With b_k is the coefficient for the k-th variable, and sigma_k =(t(x) x )^(-1) _kk is its standard deviation.

How do I go about generating a WEIGHTED mean (and standard error) of a variable (e.g., expenditures) for each level of a categorical variable (e.g., geographic region)? I'm looking for something comparable to PROC MEANS in SAS with both a class and weight statement. Thanks. Marc [[alternative HTML version deleted]]

Greetings, I'm having trouble finding a simple way to calculate a weighted histogram where there may be zero raw counts in a given interval. Given equal-length vectors of data 'data' and weights 'w', and breaks (intervals) for the histogram, I calculate a weighted histogram as follows (see MASS's 'truehist' for an unweighted histogram): bin <- cut(data,

Hi I Have a very large dataset that I would like to conduct ANOVA tests on. Im not a very strong programmer so any help would be appreciated. the format is Identifier             A1       A2        B1      B2       C1   C2      Norm1         Norm2 1234                  1        1            NA     NA      4       3        NA               NA 4567                  2        2              4     

Hi, Can someone tell me which MDCT and invMDCT equation uses? I implemented the invMDCT one given in eusipco.corrected.ps file (handed out by Monty way back) and it produces different time domain samples. I tried both the FFT method and the slow way directly from the equation and couldn't reproduce the results from the original code. This leads me to believe that the forward MDCT used in

Dear list: since it took me a little while to figure out how to make use of naresid, I thought that the below R code might be useful as an example on the help page. Regards, Markus # generate some data x1 <- runif(20) y <- 10 + 5*x1 + rnorm(20) summary(lm.0 <- lm(y ~ x1)) # append some NA's to y y <- c(y, rep(NA, 5)) # generate some further x1s x1 <- c(x1, runif(5)) #

Dear list: since it took me a little while to figure out how to make use of naresid, I thought that the below R code might be useful as an example on the help page. Regards, Markus # generate some data x1 <- runif(20) y <- 10 + 5*x1 + rnorm(20) summary(lm.0 <- lm(y ~ x1)) # append some NA's to y y <- c(y, rep(NA, 5)) # generate some further x1s x1 <- c(x1, runif(5)) #

I am having some trouble getting reftex, in particular the bibtex related features, to work properly in xemacs when editing text in Sweave files. I have added (defun Rnw-mode () (noweb-mode) (if (fboundp 'R-mode) (setq noweb-default-code-mode 'R-mode))) (add-to-list 'auto-mode-alist '("\\.Rnw\\'" . Rnw-mode)) (add-to-list 'auto-mode-alist

Dear all, I've only just joined this list, but I can't find any mention of this idea anywhere else, so I thought I'd just post here before getting too deep into programming and possibly reinventing the wheel. Here at Aber, we have around 30 unix and linux servers doing core services. Each one is maintaining its own logfiles and, for various reasons, we want to keep these on the

This smells like a bug to me. The error is triggered by the line: variables <- eval(predvars, data, env) inside model.frame.default(). At that point, na.action has not been applied, so poly() ended being called on data that still contains missing values. The qr() that issued the error is for generating the orthogonal basis when evaluating poly(), not for fitting the linear model itself.

This smells like a bug to me. The error is triggered by the line: variables <- eval(predvars, data, env) inside model.frame.default(). At that point, na.action has not been applied, so poly() ended being called on data that still contains missing values. The qr() that issued the error is for generating the orthogonal basis when evaluating poly(), not for fitting the linear model itself.

I am doing work on changes in establishment sizes and came across behavior that is quite understandable and easily worked around but a little surprising. On R 1.5.1 on Debian unstable (see below for R.version output): > cut.off <- c(-Inf, 0, Inf) > x <- c(-Inf, -10, 0, 10, Inf) > is.numeric(x) [1] TRUE > is.double(x) [1] TRUE > # but > cut(x, cut.off, include.lowest=T) [1]

Dear readers, Is it possible to specify a model y=X %*% beta + Z %*% b ; b=(b_1,..,b_k) and b_i~N(0,v^2) for i=1,..,k that is, a model where the random slopes for different covariates are i.i.d., in lmer() and how? In lme() one needs a constant grouping factor (e.g.: all=rep(1,n)) and would then specify: lme(fixed= y~X, random= list(all=pdIdent(~Z-1)) ) , that?s how it's done in the

Hello, Say one starts with creating an archive rsync work -> archive and periodically (below, i = 1 to N) does rsync --backup-dir=a_<i> work -> archive and rsync --compare-dest=archive work -> b_<i> Then suppose one wants to recover the work directory as it was at time k. Using the b_<i> directories, one would merely merge

There is a bug in the command-line handling of 0.65.0 under Unix that may affect some users of R-inferior-mode under ESS, as that sets --no-readline as the first argument, and any arguments after that are ignored. The fix is simple: delete line 448 of src/unix/sys-common.c (`break;') and re-compile. The most used arguments are (I'm told) --vsize and --nsize. I find it more convenient to

> For those who *are* desperate, I've left a copy in > ftp://blueberry.kubism.ku.dk/pub/R-devel/R-0.64.0.tgz > (Be gentle, that's my desktop PC!) Well, *now* it's there anyway... -- O__ ---- Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918

> For those who *are* desperate, I've left a copy in > ftp://blueberry.kubism.ku.dk/pub/R-devel/R-0.64.0.tgz > (Be gentle, that's my desktop PC!) Well, *now* it's there anyway... -- O__ ---- Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918