similar to: orthogonal and nested model

...as promised. This describes the mdct used in my d.m.l patch. I think it is the same as the Lee fast-dct. I typed it in a kind of pseudo-TeX, 'cause the ascii art would kill me. Hope you can read TeX source; if not, ask someone who can to make a .ps/.gif/.whatever of the TeX output, and put it on a webpage or something. I'm to lazy to do it (and besides, I don't have access to TeX,

Hello, I want to do this model: Y_ml=Mu+M+L(M)+Error M is a fixed factor with two levels, 1 and 2 L is a random factor and nested in M, with 9 levels (9 places inside each level of M) amarc is the dependent variable (animal abundance) datos is the matrix I used the next orders in R, but there is a problem with the Df calculation: > m [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Hi I have developed the code below. I am worried that the parameters I want to be estimated are "not being found" when I ran my code. Is there a way I can code them so that R recognize that they should be estimated. This is the error I am getting. > out1=optim(llik,par=start.par) Error in pnorm(au_j, mean = b_j * R_m, sd = sigma_j) : object 'au_j' not found #Yet

Hi I have a code that calculate maximisation using optimx and it is working just fine. I want to extend the code to run several colomns of R_j where j runs from 1 to 200. If I am to run the code in its current state, it means I will have to run it 200 times manually. May you help me adjust it to accomodate several rows of R_j and print the 200 results. ***Please do not get intimidated by the

Hi May you help me correct my loop function. I want optim to estimates al_j; au_j; sigma_j; b_j by looking at 0 to 20, 21 to 40, 41 to 60 data points. The final result should have 4 columns of each of the estimates AND 4 rows of each of 0 to 20, 21 to 40, 41 to 60. ###MY code is n=20 runs=4 out=matrix(0,nrow=runs) llik = function(x) { al_j=x[1]; au_j=x[2]; sigma_j=x[3]; b_j=x[4]

Buenas tardes, tengo 2 dfs: Dieta de (108x11) y Abund de (591x108). Necesito multiplicar cada columna de la 1ª (108 elementos) por cada fila de la 2ª (108 elementos) y crear una nueva df con las sumas de esas multiplicaciones. He hecho esto, pero no sale y creo que está lejos de estar bien: Res <- matrix(nrow=nrow(Abund),ncol=ncol(Dieta)) Res <- as.data.frame(Res) for(i in

Hi I need help figure out how to fix my code. When I call into R >optimize(llik,init.params=F) I get this error message ####Error in optimize(llik, init.params = F) : element 1 is empty; the part of the args list of 'min' being evaluated was: (interval)#### My data and my code looks like below. R_j R_m 0.002 0.026567296 0.01 0.003194435 . . . . . . . . 0.0006

Dear R users, I am having problems trying to fit quasipoisson and negative binomials glm. My data set contains abundance (counts) of a species under different management regimens. First, I tried to fit a poisson glm: > summary(model.p<-glm(abund~mgmtcat,poisson)) Call: glm(formula = abund ~ mgmtcat, family = poisson) . . . (Dispersion parameter

Hello, the more general thing I'd like to learn here is how to compute Function of Data on the basis of grouping determiend by n variables. In terms of the reason why I am interested in this, I need to compute the average of my data based on the value of the month and day across years. I have come up withy the code below which, as far as I can see, does what I need but getting either a more

Hi all, When upgrading to 2.11.1 recently I noticed different results being produced by my code. After much digging I have finally narrowed it to a call to nlm(). This can be replicated by: FixedRemovals<-1836180125888 AbStageInitial<-2223033830403 Rates<- 0.3102445 nlm(function(rootM,Abund,Loss,OtherM) {(Loss-(rootM/(rootM+OtherM)* (1-exp(-(rootM+OtherM)))*

Hello all, I'm a beginner in R working on a script that will produce a set of models (linear, polynomial and logistic) for each location in a dataset. However, the self-starting logistic model often fails - if this happens I would like to just skip to the next iteration of the loop using tryCatch. I've looked at a few examples and read the help file, but didn't understand tryCatch

This very likely falls in the category of an unexpected result due to user ignorance. I generated the following data: time <- 0:10 set.seed(4302007) y <- 268 + -9*time + .4*(time^2) + rnorm(11, 0, .1) I then fit models using both orthogonal and raw polynomials: fit1 <- lm(y ~ poly(time, 2)) fit2 <- lm(y ~ poly(time, degree=2, raw=TRUE)) > predict(fit1, data.frame(time =

Hey, all Will you please tell me how to generate multiple square orthogonal matrices for data transformation usage? Thanks. Fred

Hi, I'm a pgmm (plm) user and would like to know if a orthogonal transformation is available, as in Stata xtabond2. Can someone help me? Thanks! Kinds regards, Eva [[alternative HTML version deleted]]

Hey, R-listers, I am now going to use Bayesian mathod to estimate a matrix parameter C. It is assumed that C is an orthogonal matrix already. We know, if C is an arbitrary column vector, we may use multivariate Gaussian prior on it. However, now it is a matrix, so what can I do to define a proper prior probability density function for C? Thanks for your point. Have a nice day! Fred

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

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

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

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

[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