similar to: Re: Hotelling corrected

# Peter B. Mandeville kindly offered to send me code for Hotelling's T^2 # Test. Unfortunately there seems to be no route to his machine. # So i'm trying to reach him via the Mailing List. ------------------------------------------------------------------------ Sir, this morning i recieved your message about the availability of the code for Hotelling's Test. I hurried to find out

I am having a problem understanding the lda package. I have a dataset here: [,1] [,2] [,3] [1,] 2.95 6.63 0 [2,] 2.53 7.79 0 [3,] 3.57 5.65 0 [4,] 3.16 5.47 0 [5,] 2.58 4.46 1 [6,] 2.16 6.22 1 [7,] 3.27 3.52 1 If I do the following; "names(d)<-c("y","x1","x2") d$x1 = d$x1 * 100 d$x2 = d$x2 * 100 g<-lda( y ~ x1 + x2, data=d) v2

Hi, I run this code to get the power of the test for modified bartlett's test..but I'm not really sure that my coding is right.. #normal distribution unequal variance asim<-5000 pv<-rep(NA,asim) for(i in 1:asim) {print(i) set.seed(i) n1<-20 n2<-20 n3<-20 mu<-0 sd1<-sqrt(25) sd2<-sqrt(50) sd3<-sqrt(100) g1<-rnorm(n1,mu,sd1) g2<-rnorm(n2,mu,sd2)

Thanks David, Yes, I am talking about the MASS package.Thank you for pointing out that these scale the same. My question is, how do I get from the V1 data: V1 1 164.4283 2 166.2492 3 170.5232 4 156.5622 5 127.7540 6 136.7704 7 136.3436 to the other set of data: + 1 -2.3769280 + 2 -2.7049437 + 3 -3.4748309 + 4 -0.9599825 + 5 4.2293774 + 6 2.6052193 + 7 2.6820884 On Mon, Sep 28, 2009

Dear R/S users; I'm using S+ version 4 Release 3. and I am trying to do a simple calculation like the following : > X1<-X[1:5] > W1<-W[1:5,1:5] > X1 [1] 1.250000 1.292308 1.176471 0.937500 1.538462 > W1 VAR00003 VAR00004 VAR00005 VAR00006 VAR00007 1 0 1 0 1 0 2 1 0 0 0 1 3 0 0 0

Hi All, I am trying to reproduce this using R instead. [image: Full-size image (38 K)] I tried using the following code *SChla <- read.csv("SM_Chla_data.csv")* *Atlantis <- SChla[16:66,]* *head(Atlantis)* * * Seamount Station Depth Pico Nano Micro Total_Ch dbar Latitude Longitud 16 Atlantis 1217 Surface 0.0639 0.1560 0.0398 0.2597 2.082 -32.71450 57.29733

In one particular situation predict.coxph gives an error message. Namely: stratified data, predict='expected', new data, se=TRUE. I think I found the error but I'll leave that to you to decide. Thanks, Chris ######## CODE library(survival) set.seed(20121221) nn <- 10 # sample size in each group lambda0 <- 0.1 # event rate in group 0 lambda1 <- 0.2 # event rate in group 1

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

Good morning I've made an PCA and I'd like to plot a confidence region based on Hotelling T2? Does anyone know how to compute it? Thank you -- View this message in context: http://r.789695.n4.nabble.com/hotelling-and-confidence-region-tp3524204p3524204.html Sent from the R help mailing list archive at Nabble.com.

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

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-list, I am sorry for my shortage of stat knowlege. I want know how to conduct a hypothesis test : Ho:|E(X)|=|E(Y)|<->H1:otherwise. Actually, in my study, X and Y is two observations of bias, where bias=u^hat-u, u is a parameter I concerned. Given X=(u^hat_xi - u) and Y=(u^hat_yi - u), I want to know which bias is smaller, or the absolute expection of which is smaller. Due to limit

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

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

This is a proposal for either adding a new, or updating an existing command line option such that a diagnostic can optionally be produced for unused -D macros. Long-lived large projects with thousands of files and many contributors have a tendency to accumulate build options over time. As time passes some build options like macros become replaced, obsolete, or simply no longer used. At the same

Hi I can do this: formula = as.factor(outcome) ~ . in glm and other model building functions. I think there is a way to get the product of the determinants (that is d1 * d2, d1 * d3, etc) and also another way to get all the polynomials (that is like poly(d1,2) would produce for a single determinant). Can anyone tell me how you write them? Stephen [[alternative HTML version deleted]]

R-help, I have the following file I want to import to R (some lines removed) Calibrated CTD data for station:00280001 Calibrated:23/8 2001, Salinity Unsmoothed, Fluorescence Uncalibrated Maximum observed depth: 36 m QUAL has one digit for each of pressure, temp., sal. and fluor. QUAL=1:Uncal., QUAL=2:OK, QUAL=6:Interp., QUAL=9:No data

Dear All, I am running a simulation to obtain coverage probability of Wald type confidence intervals for my parameter d in a function of two parameters (mu,d). I am optimizing it using "optim" method "L-BFGS-B" to obtain MLE. As, I want to invert the Hessian matrix to get Standard errors of the two parameter estimates. However, my Hessian matrix at times becomes

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 =

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