similar to: hotelling and confidence region

Hello everyone. In my last post I did not explained my problem quite well. I made a principal component analysis and took the 2 first principal components. I made ​​a chart of my points based on the score of the 2 PC. I would like to add on this graph a 95% confidence region. To do this I used the ellipse function as follows: pcsref=PC$score[data[,1]==ref,1:2] #matrix containing the scores

Dear R users; Is there any function to plot the confidence ellipse (Hotelling's T^2) in an score plot from a PCA? I have the scores off course , say scores for PC1 and PC2, and the value of the Hotelling's T^2 statistic. Thanks for any hint PM

By accident, I left out the lines defining n1 and n2. Here it is as a function. Peter B. hotelling <- function(d1,d2){ k <- ncol(d1) n1 <- nrow(d1) n2 <- nrow(d2) xbar1 <- apply(d1,2,mean) xbar2 <- apply(d2,2,mean) dbar <- xbar2-xbar1 v <- ((n1-1)*var(d1)+(n2-1)*var(d2))/(n1+n2-2) t2 <- n1*n2*dbar%*%solve(v)%*%dbar/(n1+n2) f <-

Hi all, I've got the following matrix : ? mat <- matrix(rnorm(700), ncol=5, dimnames=list( paste("f", c(1:140), sep="_"), c("A", "B", "C", "D", "E"))) I can see that currently most of the multivariate Hotelling T2 tests are limited for application on two groups/samples. I wud appreciate if someone can provide me a

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

This is a function I coded a few years ago to calculate a confidence interval for a coefficient of variation. The code is based on a paper by Mark Vangel in The American Statistician. I have not used the function much, but it could be useful for comparing cv's from different groups. Kevin Wright confint.cv <- function(x,alpha=.05, method="modmckay"){ # Calculate the

Hi I spent hours looking over my formula. Somehow I cant find the reason why it gives me different answer. help appreciated. x = as.matrix(read.table("http://www.niehs.nih.gov/research/atniehs/core/microarrays/docs/heinloth.txt",1)) x = t(x) #now rows are subjects, cols are genes x = x[order(rownames(x)),] #order by treatment group oxygen, ultra-violet, gamma radiation y =

Hi I have a couple of functions that work on the object created by another R command and then print out or summarise the results of this work. The main function is defined as: hotelling.t <- function(obj) { #internal commands } I then have print.hotelling.t() that takes the list returned by hotelling.t and prints it with some extra significance calculations, formatting, etc. I want to

Hi, I want to do a 2 sample hotelling's test but i can't figure out how. When i type T2.test it says there is no such test and when i tried library(rrcov) it says there is no such program. Cheers. -- View this message in context: http://r.789695.n4.nabble.com/How-to-do-Hotelling-s-t2-test-tp4128748p4128748.html Sent from the R help mailing list archive at Nabble.com.

Dear R-Users, I am resending this message just to reminder my question regarding the calculation of a bootstrap confidence intervals for a GAM plot. I am trying to apply a bootstrap to a GAM in order to calculate the 95% confidence intervals for a smooth curve obtained by the ?plot.gam? function of the mgcv package. Nonetheless, I am getting some difficulties in transposing the results for

Dear R-Users, I am trying to apply a bootstrap to a GAM in order to calculate the 95% confidence intervals for a smooth curve obtained by the ?plot.gam? function of the mgcv package. Nonetheless, I am getting some difficulties in transposing the results for the graphs. I used the following commands in R, ?mgcv? and ?boot? packages: *> attach(bbvc_11Jul08)* *>

I am curious if there is code developed to plot confidence regions in 3D. The scatterplot3d function generates the plot I want, but would like an 3D equivalent to the data.ellipse function. Any help in this direction would be appreciated, be it theoretical, graphical, or otherwise. Melanie Edwards Senior Statistician Exponent 15375 SE 30th PL, Suite 250 Bellevue, WA 98007 Tel: (425) 519-8714

Respected R Users, I looking for help with generating theoretical confidence regions for any of non-symmetric bivariate statistical distributions (bivariate Chi-squared distribution<Wishart distribution>, bivariate F-distribution, or any of the others). I want to to used it as a benchmark to compare a few strategies constructing confidence regions for non-symmetric bivariate data. There is

Hello, I am trying to complete a PCA on a set of standardized ring widths from 8 different sites (T10, T9, T8, T7, T6, T5, T3, and T2). The following is a small portion of my data: T10 T9 T8 T7 T6 T5 T3 T2 1.33738 0.92669 0.91146 0.98922 0.9308 0.88201 0.92287 0.91775 0.82181 1.05319 0.92908 0.97971 0.95165 0.98029 1.14048 0.77803 0.88294 0.96413 0.90893 0.87957 0.9961 0.74926 0.71394 0.70877

I have two questions related to plotting predicted values for a linear mixed model using xyplot: 1: With a groups= argument, I can't seem to get the key to appear inside the xyplot. (I have the Lattice book, but don't find an example that actually does this.) 2: With lme(), how can I generate confidence bands or prediction intervals around the fitted values? Once I get them, I'd

John Fox wrote: >The confidence ellipse for a pair of coefficients is just (with an >adjustment for size) the perpendicular shadow of the joint ellipsoid, in >the same sense that the confidence intervals for individual coefficients >are shadows of the joint ellipsoid or ellipse. The usual confidence ellipse >for 2 of q coefficients uses qf(.95, 2, df.error) to scale the ellipse; if

Dear all, I have read various descriptions of employing resampling techniques, such as the bootstrap, to estimate the uncertainties of the eigenvectors computed by PCA. When I try

Hello R-users, I've been looking for a function performing one and two sample Hotelling test for testing equality of mean vectors. Has anyone implemented such a function in R? thanks a lot, Bill ============== Bill Donner Statistician

Full_Name: Yves Rosseel Version: 2.2.1 OS: Submission from: (NULL) (157.193.116.152) The code below sketches a possible implementation of a function 'mlh.mlm' which I think would be a good complement to the 'anova.mlm' function in the stats package. It tests a single linear hypothesis of the form H_0: LBT'= 0 where B is the matrix of regression coefficients; L is a matrix

Hi, I am a graduate student at Stanford University and I have a general statistics question. What exactly is the difference between doing a two-factor repeated measures ANOVA and a Hotelling T-squared test for a paired comparison of mean vectors? Given: Anova: repeated measures on both factors, 1st factor = two different treatments, 2nd factor = 4 time points, where you are measuring the blood