similar to: Multivariate BLUP

*Hello, I'm new to R and trying to do Split Split Plot Design analysis with aov function in R. Sharing any worked example and suggestion will be highly appreciated. Thanks Regards! * -- * Muhammad Yaseen * [[alternative HTML version deleted]]

hello guys, I need to do a BLUP in the simplest model y = Xm + Zg + e however I have missing data in the analysis which I can?t consider as 0(zero). So I need to generate the matrix X'Z, Z'X and Z'Z step by step; I can?t use crossprod(x) #neither X'X <- t(x)%*%x because I should skip the elements with missing data in the matrix I?ll try to be more clear, supposing a matrix x

Dear All, I'm using betadisper {vegan} and I'm interested not only in the dispersion within the group but also the distances between the groups. With betadisper I get distances to group centroids but is it possible to get distances to other groups centroids? It might be possible to do it by hand by the formula given in the description of the betadisper (below) but I'm a bit confused

Dear all I have a model that looks like this: m1 <- lmer(Difference ~ 1+ (1|Examiner) + (1|Item), data=englisho.data) I know it is not possible to estimate random effects but one can obtain BLUPs of the conditional modes with re1 <- ranef(m1, postVar=T) And then dotplot(re1) for the examiner and item levels gives me a nice prediction interval. But I would like to have the prediction

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)

Dear All, Is there any function in R for computing "multivariate chi-square distribution"? How about "multivariate gamma distribution"? I appreciate any comment on this subject. Thank you, Amin Zollanvari PhD student Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX

Dear?All, ? a few weeks ago I have posted a question on the R help listserv that?some of you have responded to with a great solution, would like to thank you for that? again.?I thought I would reach out to you with the issue I am trying to solve now. I have posted the question a few days ago, but probably it was not?clear enough, so I thought i try it again.?At times I have a multivariate example

R-help Digest wrote: > > Date: Tue, 28 Nov 2000 08:20:43 +0100 > From: "Muhammad Rashid Ahmed" <rahmed at julian.uwo.ca> > Subject: [R] Fitting of Garch Model in R [forwarded] > > This accidentally (;-) didn't go to the R-help mailing list .. > > - ---- > -- start of forwarded message ------- > > To: <maechler at stat.math.ethz.ch> >

I'm reading a old statistics book "MARVIN J. Karson (1982), Multivariate Statistical Methods, The IOWA State University Press, Iowa". In the chapter XI i can find some information about discrete multivariate analysis. This chapter is restricted to an introduction to log-linear models for analysis of multidimensional contingency tables. For example, in the log-linear model for

Hey guys, I'd really appreciate any help. I have a multivariate analysis done, the output of which is: > GraphData <-read.table("eigen.coa") > GraphData V1 V2 V3 V4 1 1 0.371970 0.8552 0.8552 2 2 0.061785 0.1420 0.9972 3 3 0.001211 0.0028 1.0000 4 4 0.000000 0.0000 1.0000 > summary(GraphData) V1 V2 V3

-----Original Message----- From: Vayssi?res, Marc Sent: Tuesday, June 20, 2006 9:35 AM To: 'r-help-bounces at stat.math.ethz.ch' Subject: RE: [R] multivariate splits Glen De'ath's package for R is on cran! It is called mvpart, see: http://cran.cnr.berkeley.edu/doc/packages/mvpart.pdf Cheers, Marc Vayssi?res -----Original Message----- From: r-help-bounces at stat.math.ethz.ch

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 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

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

20 subjects were measured in 5 conditions (thus repeated measures) and for each subject in each condition there are 4 response measures (thus multivariate as it is a combined score that needs to be compared across the conditions). So, using a multivariate approach to repeated measures this is a doubly multivariate analysis. I would appreciate any suggestions as to the best way to do such a

Dear All,   thanks to Berend, my question posted yesturday was solved succesfully here: http://r.789695.n4.nabble.com/hep-on-arithmetic-covariance-conversion-to-log-covariance-td4646068.html . I posted the question with the assumption of using the results with rlnorm.rplus() from compositions. Unfortunatelly, I am not getting reasonable enough outcome. Am I applying the results wrongfully? The

Hi r-help, I have been using your RHmm package for some time and have recently had to try using the package for a new dataset. Basically I have a dataset with a number of discrete observation variables that change over time, and I would love to try modeling them using a HMM. Basically I was wondering if RHmm can be used to model a multivariate discrete HMM, i.e., the observations are a vector

Hello, I am switching from SPSS-Windows to R-Linux. My university is very SPSS-oriented so maybe that's the cause of my problems. I am a beginner in R and my assignments are SPSS-oriented, so I hope I don't annoy anyone with my questions... Right now I've got 2 problems: -I have to evaluate discriminant functions I have calculated with lda(MASS). I can't find a measure that

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

Dear R-users I've simulated a longitudinal multivariate normal data set from which I've simulated missing-patterns such as MCAR MAR and a simple kind of non-MAR. I've imputated the values so I now have 'complete' data sets. I'm trying to perform a T2-test as done in the multivariate case under th enormal assumption. Is there something I've to think about when performing