similar to: ML estimators of bivariate cauchy

I have question (curiosity) regarding returned values of R's qcauchy () function, for nonexceedance probability (F). It seems the ideal returned range of cauchy distribution should be [-Inf,Inf]. For F=0 > qcauchy(0) [1] -Inf but for F=1 > qcauchy(1) [1] 8.16562e+15 It seems to me that the proper return value should be Inf??? For default (location=0,scale=1) quantile function of

Hi, I am trying to recreate the right graph on page 524 of Gelman's 2006 paper "Prior distributions for variance parameters in hierarchical models" in Bayesian Analysis, 3, 515-533. I am only interested, however, in recreating the portion of the graph for the overlain prior density for the half-Cauchy with scale 25 and not the posterior distribution. However, when I try:

Hi I'm using R(2.9.0) and gstat package under Windows to plot sample variograms. When I want to fit them, I don't have the possibility to choose Cauchy functions. Under Mathematica for example, we have it and it seems to be the best one to fit the variograms I have I tested the others functions, they can fit but I would,if possible, use the Cauchy one. If anybody knows something about

Dear R-experts, Searching the help archives I found a recommendation to do multivariate outlier identification by mahalanobis distances based on a robustly estimated covariance matrix and compare the resulting distances to a chi^2-distribution with p (number of your variables) degrees of freedom. I understand that compared to euclidean distances this has the advantage of being scale-invariant.

http://www.nabble.com/file/p24569511/mahalanobis.txt mahalanobis.txt http://www.nabble.com/file/p24569511/concentrations.txt concentrations.txt Dear Forum members, I have a problem calculating mahalanobis distances. My data file mahalanobis.txt and categories file concentrations.txt are attached. I do the following steps: x <- as.matrix(read.table("mahalanobis.txt", header=TRUE))

Here is my dataframe with 1000 rows: employee_id weigth p-value 100 150 101 200 102 300 103 180 ..... My question: how can I calculate the p-value in R for each employee? the distribution of the weigth will be established from the above 1000 samples. -- View this message in context:

Hi listers, I have a simple doubt... I need to manipulate the data base as: var1 var2 7 0.1 7 0.1 8 0.12 10 0.15 12 0.18 20 0.31 I would like to aggregate the equal cases as var1 making the sum of var2... I would get... var1 var2 7 0.2 8 0.12 10 0.15 12 0.18 20 0.31 Thanks in advance, Marcio -- View this message in context:

Hi listers, I have the following code... data<-as.matrix(c(1,2,3,4,5,6,7,8,9,10),nrow=10,ncol=1,byrow=TRUE) n<-nrow(data) m<-n-1 boot<-data[sample(m,replace=T),] So, I need to count the number of times each observation was selected at the sample with replacement... Suppose I sampled... 4 5 1 3 7 5 1 4 7 So, I would count... x count 1 2 2 0 3 1 4 2 5 2 6 0 7 2 8 0 9 0 10 0

Hello R helpers, I'm trying to use Mahalanobis distance to calculate distance of two time series, to make some comparations with euclidean distance, DTW, etc, but I'm having some dificults. I have, for example, two objects: s.1 <- c( 5.6324702, 1.3994353, -3.2572327, -3.8311846, -1.2248719, 0.9894694, -2.2835332, -5.1969285, -5.2823988, -3.1499400, -1.7307950, 2.8221209,

Dear all Why isn'it possible to calculate Mahalanobis distances with R for a matrix with 1 row (observations) more than the number of columns (variables)? > mydata <- matrix(runif(12,-5,5), 4, 3) > mahalanobis(x=mydata, center=apply(mydata,2,mean), cov=var(mydata)) [1] 2.25 2.25 2.25 2.25 > mydata <- matrix(runif(420,-5,5), 21, 20) > mahalanobis(x=mydata,

Dear R-friends, I have to simple questions. First I would like to exclude some columns from a dataframe and after I need select rows that satisfy some conditions. My data frame looks like Region Species Bodysize Weigth Age Africa Sp1 10.2 20 2 Africa Sp2 12.2 12 2 Africa Sp3 15.3 18 3 Africa Sp4 11.5 40 4 Brazil Sp1 10.2 40 3 Brazil Sp2 22.2 32 2 Brazil Sp3 12.3

Hello, I know this must be a very simple problem, but I can't work it out from the documentation that is available. I've got a list of data I would like to plot (the weights of a single neuron that was trained using the neural package). The problem I'm encountering is that this set of weights, are in the form of a list. > network$weigth[1] [[1]] [,1] [1,]

I want to calculate the probability that a group will include a particular point using the squared Mahalanobis distance to the centroid. I understand that the squared Mahalanobis distance is distributed as chi-squared but that for a small number of random samples from a multivariate normal population the Hotellings T2 (T squared) distribution should be used. I cannot find a function for

Full_Name: Jörg Polzehl Version: 1.5.1 OS: Windows 2000 Submission from: (NULL) (193.175.148.198) When calculating MLE's in a variance component model using constrained optimization, i.e. optim(...,method="L-BFGS-B",...) I observed an inproper behaviour in cases where the likelihood function was evalueted at the constraint. Parameters and value of the function at the constraint

I am a new R user. i have a question about Mahalanobis distance.actually i have 300 rows and 7 columns. columns are different measurements, 300 rows are genes. since genes can classify into 4 categories. i used dist() with euclidean distance and cmdscale to do MDS plot. but find out Mahalanobis distance may be better. how do i use Mahalanobis() to generate similar dist object which i can use

Hello all, I am a 2 month newbie to R and am stumped. I have a data set that I've run multivariate stats on using the manova function (I included the data set). Now it comes time for a table of effect sizes with significance. The univariate tests are easy. Where I run into trouble filling in the table of effect sizes is the Mahalanobis D as an effect size. I've included the table so

Hi, help on 'mahalanobis' (in the stats package in Rv2.2.0) now says: "Description: Returns the Mahalanobis distance of all rows in 'x' and the vector mu='center' with respect to Sigma='cov'. This is (for vector 'x') defined as D^2 = (x - mu)' Sigma^{-1} (x - mu)" It does return D^2 as written. However,

Dear R ExpeRts, I'm having memory difficulties using mahalanobis distance to trying to cluster in R. I was wondering if anyone has done it with a matrix of 6525x17 (or something similar to that size). I have a matrix of 6525 genes and 17 samples. I have my R memory increased to the max and am still getting "cannot allocate vector of size" errors. My matrix "x" is

Hello all, I am a 2 month newbie to R and am stumped. I have a data set that I've run multivariate stats on using the manova function (I included the data set). Now it comes time for a table of effect sizes with significance. The univariate tests are easy. Where I run into trouble filling in the table of effect sizes is the Mahalanobis D as an effect size. I've included the table so

Hi listers, I have a data frame with 5 variables... And I want reorder the data frame according a specific variable X... I checked the sort and order functions... In my case I think that the ORDER function is applicable... But I did not understand at all... The idea is simple... Data order by X... I know that this is very simple... Anyways... How I would do that... Thanks in advance, Marcio --