similar to: doubly multivariate analysis in R

Dear R: I am trying to fit a doubly multivariate LME (DM) where I have two response variables measured on two occasions per person. Specifically, reading and math scores measured at the beginning and ending of a school year. The response variables have a correlation of r = .85. The response variables in the data matrix are stacked in a vector with a dummy code flagging each outcome and with

Dear Professor I have read your questions in the website on Doubly non-central F-distribution. I am looking for source code for evaluating this function now. I have matlab code but it's only accurate up to the 4th decimal point. Dataplot is more accurate, but it is not user friendly as you only can evaluate one function at a time. So I would like to know do you have found any R code for

Hello All, Has anyone written a function for the distribution function of a *doubly* non-central F-distribution? I looked through the archives, but didn't find anything. Thanks! Wolfgang

Hi - if I have a doubly patched kernel (2.4.5) (1) Apply gkernel/CVS ext3 patches (2) Apply Neil Brown/Reiserfs team knfsd and reiserfs patches Will ext3 be exportable by knfsd (i.e. inherit automagically the nfsd_operations thingies from ext2) or does it need further patching? -- Chan Shih-Ping (Richard) <cshihpin@dso.org.sg> DSO National Laboratories 20 Science Park Drive

Barrett & Ling, JASA, 1992, v.87(417), pp184-191 define general classes of influence measures for multivariate regression models, including analogs of Cook's D, Andrews & Pregibon COVRATIO, etc. As in univariate response models, these are based on leverage and residuals based on omitting one (or more) observations at a time and refitting, although, in the univariate case, the

Dear subscribers, I'm trying to make the switch from M$ Windows to Linux (KDE) and found the R-cran project for statistical analysis. I'm not a genius in statistics so the command line interface is a bit hard for me. I need an analogue way to do the SPSS General Linear Model->Repeated Measures (multivariate) but I don't have a clue how to perform this in R-cran. Can maybe

I've just released the latest R-Forge versions of heplots 0.9-3 and candisc 0.5-18 to CRAN. They should appear there within a day or two. == heplots The heplots package provides functions for visualizing hypothesis tests in multivariate linear models (MANOVA, multivariate multiple regression, MANCOVA, etc.). They represent sums-of-squares-and-products matrices for linear hypotheses and for

Dear all, Edmond Ng (http://multilevel.ioe.ac.uk/softrev/reviewsplus.pdf) provides an example to fit the mixed effects meta-analysis in Splus 6.2. The syntax is: lme(fixed=d~wks, data=meta, random=~1|study, weights=varFixed(~Vofd), control=lmeControl(sigma=1)) where d is the effect size, study is the study number, Vofd is the variance of the effect size and meta is the data frame.

Full_Name: Ludo Pagie Version: 2.8.1 OS: linux Submission from: (NULL) (194.171.7.39) p.adjust in stats seems to have a bug in handling n>length(p) for (at least) the methods 'holm' and 'hochberg'. For method 'holm' the relevant code: i <- 1:n o <- order(p) ro <- order(o) pmin(1, cummax((n - i + 1) * p[o]))[ro] where p is the

Hi, there. I have two questions about the diagnostics in multivarite statistics. 1. Is there any diagnostics tool to check if a multivariate sample is from multivariate normal distribution? If there is one, is there any function doing it in R? 2. Is there any function of testing if two multivariate distribution are same, i.e. the multivariate extension of Kolomogrov-Smirnov test? Thanks for

Hi, How can I generate data from multivariate gamma distribution & multivariate beta distribution? I only found command for multivariate normal only. Many thanks in advance :) Regards Rofizah [[alternative HTML version deleted]]

Dear Community, For my masters thesis I need to perform a multivariate granger causality test. I have found a code for bivariate testing on this page (http://www.econ.uiuc.edu/~econ472/granger.R.txt), which I think would not be useful for the multivariate case. Does anybody know a code for a multivariate granger causality test. Thank you in advance. Best Regards -- View this message in context:

dear R users, I need some help for multivariate analysis. I have 2 anaesthetic treatment groups (20 patients/group) where I register heart frequency and pressure for 60 min (repeated measures every 5 minutes). I would like to perform a test to check if treatments are different in controlling freq and pressures during the anaesthesia, but i would like to have also an overall measure and not

Greetings ~ I need some assistance determining an appropriate approach to analyzing multivariate binary response data, and how to do it in R. The setting: Data from an assay, wherein 6-hours-post-fertilization zebrafish embryos (n=20, say) are exposed in a vial to a chemical (replicated 3 times, say), and 5 days later observed for the presence/absence (1/0) of defects in several organ systems

Dear R Helpers, I have searched for any R package or code for simulating multivariate fractional Brownian motion (mFBM) or multivariate fractional Gaussian noise (mFGN) when a covariance matrix are given. Unfortunately, I could not find such a package or code. Can you suggest any solution for multivariate FBM and FGN simulation? Thank you for your help. Best Regards, Ryan ----- Wonsang You

Hello, I have a question about multivariate Shapiro-Wilks test. I tried to analyze if the data I have are multivariate normal, or how far they are from being multivariate normal. However, any time I did >mshapiro.test(mydata) I get the message: Error in solve.default(R %*% t(R), tol = 1e-18) : system is computationally singular: reciprocal condition number = 5.38814e-021 I tried

Dear Metafor users, I'm conducting a metaanalysis of prevalence of a particular behaviour based on someone elses' code. I've been labouring under the impression that this: summary(rma.1<-rma(yi,vi,mods=cbind(approxmeanage,interviewmethodcode),data=mal,method="DL",knha=F,weighted=F,intercept=T)) is doing the multivariate analysis that i want, but have read that

Hi all, rmvnorm()can be used to generate the random numbers from a multivariate normal distribution with specified means and covariance matrix, but i want to specify the correlation matrix instead of covariance matrix for the multivariate normal distribution. Does anybody know how to generate the random numbers from a multivariate normal distribution with specified correlation matrix? What about

Hello folks, many multivariate anayses (e.g., structural equation modeling) require multivariate normal distributions. Real data, however, most often significantly depart from the multinormal distribution. Some researchers (e.g., Yuan et al., 2000) have proposed a multivariate transformation of the variables. Can you tell me, if and how such a transformation can be handeled in R? Thanks in

Dear R Users, I'm working on a problem where I have a multivariate response vector of counts and a continuous predictor. I've thought about doing this the same way you would do a Multvariate regression model with normally distributed data, but since these data are counts, they are probably better modeled with a Poisson distribution. For example y1<-rpois(100,3.5) y2<-rpois(100,1.5)