similar to: Questions on Mahalanobis Distance

Hey, I am confused about how to generate the sample data from a mixture of Multivariate Gaussian ditribution. For example, there are 2 component Gaussian with prior probability of 0.4 and 0.6, the means and variances are u1=[1 1]', Cov1=[1 0;0 1] and u2=[-1 -1]', Cov2=[1 0;0 1] repectively. So how can I generate a sample of 500 data from the above mixture distribution? Thanks. Fred

First, here is your message as it appears on R-help. On 10/14/2012 05:00 AM, r-help-request@r-project.org wrote: > I?m trying to set up proportional hazard model that is stratified with > respect to covariate 1 and has an interaction between covariate 1 and > another variable, covariate 2. Both variables are categorical. In the > following, I try to illustrate the two problems that

I can use stepAIC on an lme object in 1.6.2, but I get the following error if I try to do the same in 1.7.0: Error in lme(fixed = resp ~ cov1 + cov2, data = a, random = structure(list( : unused argument(s) (formula ...) Does anybody know why? Here's an example: library(nlme) library(MASS) a <- data.frame( resp=rnorm(250), cov1=rnorm(250), cov2=rnorm(250),

the study design of the data I have to analyse is simple. There is 1 control group (CTRL) and 2 different treatment groups (TREAT_1 and TREAT_2). The data also includes 2 covariates COV1 and COV2. I have been asked to check if there is a linear or quadratic treatment effect in the data. I created a dummy data set to explain my situation: df1 <- data.frame( Observation =

I have a fairly large model: > length(Y) [1] 3051 > dim(covariates) [1] 3051 211 All of these 211 covariates need to be nested hierarchically within a grouping "class", of which there are 8. I have an accessory vector, " cov2class" that specifies the mapping between covariates and the 8 classes. Now, I understand I can break all this information up into individual

Hi, i would to know, if someone have ever write the code to estimate the parameter (mixing proportion, mean, a var/cov matrix) of a mixture of two multivariate normal distribution. I wrote it and it works (it could find mean and mixing proportion, if I fix the var/cov matrix), while if I fix anything, it doesn't work. My suspect is that when the algorithm iterates the var/cov matrix, something

Hi, i would to know, if someone have ever write the code to estimate the parameter (mixing proportion, mean, a var/cov matrix) of a mixture of two multivariate normal distribution. I wrote it and it works (it could find mean and mixing proportion, if I fix the var/cov matrix), while if I fix anything, it doesn't work. My suspect is that when the algorithm iterates the var/cov matrix, something

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))

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,

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

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

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

I?m trying to set up proportional hazard model that is stratified with respect to covariate 1 and has an interaction between covariate 1 and another variable, covariate 2. Both variables are categorical. In the following, I try to illustrate the two problems that I?ve encountered, using the lung dataset. The first problem is the warning: To me, it seems that there are too many dummies

On 10/4/2024 5:13 PM, Steven Yen wrote: > Pardon me!!! > > What makes you think this is a homework question? You are not > obligated to respond if the question is not intelligent enough for you. > > I did the following: two ways to calculate a covariance matrix but > wonder how I might replicate the results with "apply". I am not too > comfortable with the

Hello, I have a question regarding competing risk regression using cmprsk package (function crr()). I am using R2.9.1. How can I do to assess the effect of qualitative predictor (gg) with more than two categories (a,b,c) categorie c is the reference category. See above results, gg is considered like a ordered predictor ! Thank you for your help Jan > # simulated data to test > set.seed(10)

Is there a function that calculate the mahalanobis distance in R . The dist function calculates "euclidean"', '"maximum"', '"manhattan"', '"canberra"', '"binary"' or '"minkowski"'. Thanks ../Murli

Pardon me!!! What makes you think this is a homework question? You are not obligated to respond if the question is not intelligent enough for you. I did the following: two ways to calculate a covariance matrix but wonder how I might replicate the results with "apply". I am not too comfortable with the online do of apply. > set.seed(122345671) > n<-3 > x<-rnorm(n); x

Dear r-helpers, I have a question about multiple testing. Here an example that puzzles me: All matrixes and contrast vectors are presented in treatment contrasts. 1. example: library(multcomp) n<-60; sigma<-20 # n = sample size per group # sigma standard deviation of the residuals cov1 <- matrix(c(3/4,-1/2,-1/2,-1/2,1,0,-1/2,0,1), nrow = 3, ncol=3, byrow=TRUE, dimnames =

How can I perform cluster analysis using the mahalanobis distance instead of the euclidean distance? thank you Naama Wolf -- View this message in context: http://n4.nabble.com/cluster-with-mahalanobis-distance-tp1577038p1577038.html Sent from the R help mailing list archive at Nabble.com.

Even if this is not a homework question, it smells like one. If you read the Posting Guide it warns you that homework is off-topic, so when you impose an arbitrary constraint like "must use specific unrelated function" we feel like you are either cheating or wasting our time, and it is up to you to explain why we should follow you down this rabbit hole, keeping in mind that statistics