search for: orthogonality

Hey, R-listers, I have a question about determining the orthogonal basis vectors. In the d-dimensinonal space, if I already know the first r orthogonal basis vectors, should I be able to determine the remaining d-r orthognal basis vectors automatically? Or the answer is not unique? Thanks for your attention. Fred

A common point made in discussion of contrasts, type I, II, III SS etc is that for sensible comparisons one should use contrasts that are 'orthogonal in the row-basis of the model matrix' (to quote from http://finzi.psych.upenn.edu/R/Rhelp02/archive/111550.html) Question: How would one check, in R, that this is so for a particular fitted linear model object? Steve Ellison

We have just put a package GPArotation on CRAN. The functions in this package perform an number of different orthogonal and oblique rotations for factor analysis, using the gradient projection algorithm described in Coen A. Bernaards and Robert I. Jennrich (2005), "Gradient Projection Algorithms and Software for Arbitrary Rotation Criteria in Factor Analysis, ", Educational and

We have just put a package GPArotation on CRAN. The functions in this package perform an number of different orthogonal and oblique rotations for factor analysis, using the gradient projection algorithm described in Coen A. Bernaards and Robert I. Jennrich (2005), "Gradient Projection Algorithms and Software for Arbitrary Rotation Criteria in Factor Analysis, ", Educational and

...nd do it from scripts. Having -1 and -2 would make life a lot easier, if only for testing purposes. Even a "-o [option]" with scp would be nice. A lot of these different products only interoperate in certain modes so I have to try a lot of things. I would also like to argue for more orthogonality between sch, ssh and sshd. Why do you turn on verbosity and debugging with "-v" in scp and ssh, but use "-d" with sshd? Why do you select a port with scp and sshd with "-P port" but with "-p port" on sshd? My poor little brain has enough to remember wi...

Hello [R]-help I am trying to find > a package where you can do ANOVA based trend analysis on grouped data > using orthogonal polynomial contrasts coefficients, for unequally > spaced factor levels. The closest hit I've had is from this web site: >(http://webcache.googleusercontent.com/search?q=cache:xN4K_KGuYGcJ:www.datavis.ca/sasmac/orpoly.html+Orthogonal+polynomial >l but I

Dear all, Can any one tell me how can i perform Orthogonal Polynomial Regression parameter estimation in R? -------------------------------------------- Here is an "Orthogonal Polynomial" Regression problem collected from Draper, Smith(1981), page 269. Note that only value of alpha0 (intercept term) and signs of each estimate match with the result obtained from coef(orth.fit). What

Hi all, The package crossdes could contruct a complete sets of mutually orthogonal latin squares. The construction works for prime powers only. I hope to know whether there is a way to construct a mutually orthogonal Lation square for 10 or other numbers that could not be prime powers. Thanks for any suggestions. Best wishes, Jinsong Zhao

Dear All, I have found in the poly help this sentence: The orthogonal polynomial is summarized by the coefficients, which can be used to evaluate it via the three-term recursion given in Kennedy & Gentle (1980, pp. 343–4), and used in the predict part of the code. My question: which type of orthogonal polynomials are used by this function? Hrmite, legendre.. TIA Giovanni [[alternative HTML

Hello, How I can compute in R the orthogonal complement of one matrix? If A (n x m ) matrix of full column rank (n>m), its orthogonal complement is denoted by A_ . A_ is n X (n-m) matrix of full column rank and such that A'A_=0. I need to compute A_. How I can compute A_ in R? Best Regards, /Florin -- Florin G. Maican Ph.D. candidate, Department of Economics School of

Looking to the wonderful statistical advice that this group can offer. In behavioral science applications of stats, we are often introduced to coefficients for orthogonal polynomials that are nice integers. For instance, Kirk's experimental design book presents the following coefficients for p=4: Linear -3 -1 1 3 Quadratic 1 -1 -1 1 Cubic -1 3 -3 1 In R orthogonal

Hi, I have eight vectors that I would like to orthogonalize preferably using R. The vectors are of considerable length, however due to their nature I know they satisfy the conditions needed to apply the Gram-Schmidt algorithm. Before I embark on some R coding, I wanted to check that there is no facility / function already around that computes the orthogonalized set of vectors? I have performed

Hi, I work with environmental data and want to determine correlations between variables that either have no "dependent/independent" relationship or the relationship is unknown. Therefore I prefer to use orthogonal distance regression (orthogonal linear regression, perpendicular sum of squares, etc.). I am trying to get set up to do this in R, but the various terminologies are

I'm working with a orthogonal and nested model (mixed). I have four factors, A,B,C,D; A and B are fixed and orthogonal C is nested in AB interaction and finally, D is nested in C. I would like to model the following Y_ijklm=Mu+A_i+B_j+AB_ij+C_k(ij)+D_l(k(ij))+Error_m(...) I used the next command >summary(aov(abund~A*B + C % in % A:B + D % in % C % in % A:B ,datos)) Is it the correct

Dear all using the orthogonal polymial on a set of data, I get an approximate density which basically is in the form: exp(-polynomial), as you know, the parameters are the converged coeeficients. obviously, It is hard, if not impossible, to use the inverse CDF method to get a sample and then plot density. then how can I plot the approximated density in order to have a graphical comparision

Could someone please help me by giving me a reference to how one computes standard errors for the coefficients in an orthogonal linear regression, or perhaps someone has some R code? (I would accept a derivation or formula, but as a former teacher, I know how that can rankle.) I tried to imitate what's done in the code for lm() but went astray somewhere and got nonsense. (This type of

I'm trying to fit a simple linear regression of just Y ~ X, but both X and Y are noisy. Thus instead of fitting a standard linear model minimizing vertical residuals, I would like to minimize orthogonal/perpendicular residuals. I have tried searching the R-packages, but have not found anything that seems suitable. I'm not sure what these types of residuals are typically called

Dear R-Users, is there any statement to fit a orthogonal regression in R environment? Many thanks in advance. Best regards, Vito Diventare costruttori di soluzioni Became solutions' constructors "The business of the statistician is to catalyze the scientific learning process." George E. P. Box "Statistical thinking will one day be as necessary for efficient

[Originally sent this to r-help at lists.R-projects.org, but in case that's the wrong list I'm re-posting. Apologies if this becomes a re-post] -------------- next part -------------- An embedded message was scrubbed... From: Jonathon Kopecky <jkopecky at umich.edu> Subject: Need to fit a regression line using orthogonal residuals Date: Mon, 29 Jan 2007 14:52:24 -0500 Size: 1138

Hello I seem to find only two types of rotation for the factanal function in R, the Varimax and Promax, but is it possible to run a orthogonal and oblique rotations in R? Thanks in advance Rosario