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...ession model, and the error terms in each model can be correlated. My understanding is that although lm() handles vector Y's on the left-hand side of the model formula, it really just fits m separate lm models. What should I use to do a full multivariate analysis (as in section 7.7 of Johnson/Wichern)? Thanks. Iain Pardoe <ipardoe at lcbmail.uoregon.edu> University of Oregon

...e. I was wondering what packages in R would help me analyze the data? I was also curious if anyone could recomend me some textbooks that would help with the analysis of such data? I have the 5th edition of "Applied Multivariate Statistical Analysis" by Richard A. Johnson and Dean W. Wichern already, but that doesn't seem to cover the material I'm looking for. Thanks, -Max

Dear helpeRs, the following data set comes from Johnson/Wichern: Applied Multivariate Statistical Analysis, 6th ed, pp. 304-306. /X <- structure(c(9, 6, 9, 3, 2, 7), .Dim = as.integer(c(3, 2))) Y <- structure(c(0, 2, 4, 0), .Dim = as.integer(c(2, 2))) Z <- structure(c(3, 1, 2, 8, 9, 7), .Dim = as.integer(c(3, 2)))/ I would like to compute Wilk's...

...ayout and the other with boxplot(t1.5$wind) at (1,2). pairs function help file has a similar example with hist function but I am stuck with producing boxplot version. Thanks for your time and reply in advance. The below is t1.5 data from Applied Multivariate Statistical Analysis 5th, Johnson and Wichern, p40 Table 1.5. wind radiation co no no2 o3 hc 8 98 7 2 12 8 2 7 107 4 3 9 5 3 7 103 4 3 5 6 3 10 88 5 2 8 15 4 6 91 4 2 8 10 3 8 90 5 2 12 12 4 9 84 7 4 12 15 5 5 72 6 4 21 14 4 7 82 5 1 11 11 3 8 64 5 2 13 9 4 6 7...

Hi, we''re playing around with XEN (kernel 2.6.32) and REMUS. For us, sees this combination of very interesting and we would like to use the productive. However, the configuration is a great effort and often leads to problems. Until the first test position ran, it took several tries. As the further development of REMUS is planned? I''ve read that it will soon be over

...ues from a multivariate regression fit. For example, suppose I have m=2 responses (y1,y2) and a single set of predictor variables (z1,z2). Each response is assumed to follow its own regression model, and the error terms in each model can be correlated (as in example 7.10 of section 7.7 of Johnson/Wichern): > ex7.10 <- + data.frame(y1 = c(141.5, 168.9, 154.8, 146.5, 172.8, 160.1, 108.5), + y2 = c(301.8, 396.1, 328.2, 307.4, 362.4, 369.5, 229.1), + z1 = c(123.5, 146.1, 133.9, 128.5, 151.5, 136.2, 92), + z2 = c(2.108, 9.213, 1.905, .815, 1.061, 8.603, 1....

Hello there, I have two data sets with 14 variables each, and wish to do the test for equality of their covariance matrices and mean vectors. Normally these tests should be done by chi square test (box provided) and Hotelling's T square test respectively. Which R functions could do this kind of test? I just find some functions could do for one dimension, but no for multidimension. Some one

I would like to test the hypothesis that the difference between pairs, for several variables, is zero. This is easily done separately for each variable with: lm(Y ~ rep(0, nrow(Y))) where Y is a matrix whose columns are the differences for each variable between pair members. However, I would like to get an overall probability across all variables from a Wilks or Pillai-Bartlett statistic as in

I hope this question is sufficiently different from the other requests for book recommendations that it's not repetitious. If not, I apologize in advance. I'm curious what standard reference books working statisticians, or biostatisticians, have within easy reach of their desk. I'm a computer systems administrator, and have a two-foot bookshelf directory under my monitor that contains

Buenas tardes para tod en s (de nuevo) Tengo el siguiente dataframe: