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I am looking for an elegant solution to the following problem. I have one that works, but it is ugly. In a questionnaire, each of 80 subjects answered 8 questions about each of 30 different behaviors. My main method of analysis is within-subject regression, in which I predict the answer to one of the 8 questions from answers to some of the other questions - different subsets for different

Hi everyone, my apologies in advance if I'm overlooking something simple in this question. I am trying to use R's survey package to make a direct method age-adjustment to some complex survey data. I have played with postStratify, calibrate, rake, and simply multiplying the base weights by the correct proportions - nothing seems to hit the published numbers on the nose. I am trying to

Hi, I need to compare two matrices with each other. If you can get one of them out of the other one by resorting the rows and/or the columns, then both of them are equal, otherwise they're not. A matrix could look like this: [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] 0 1 1 1 0 1 1 0 [2,] 1 1 0 0 0 1 0 1 [3,] 1 0 1 0 0

Version 1.1-0 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and summary - Functions

SAS Enterprise Miner recommendeds to standardize using X / STDEV(X) versus [X ? mean(X)] / STDEV(X) Any thoughts on this? Pros Cons Philip

Version 1.1-0 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and summary - Functions