search for: reestimated

Dear List: I am working to construct a matrix of a particular form. For the most part, developing the matrix is simple and is built as follows: vl.mat<-matrix(c(0,0,0,0,0,64,0,0,0,0,64,0,0,0,0,64),nc=4) Now to expand this matrix to be block-diagonal, I do the following: sample.size <- 100 # number of individual students I<- diag(sample.size) bd.mat<-kronecker(I,vl.mat) This

Hi, What I'm trying to achieve is very fast algorithm for fitting logistic regression model. I have to estimate regression coeficients using about 10k observations. Once I have coefficients estimated, new 100 rows of data becomes available.... Now I need to reestimate coeficients using 100 newly arrived observations and removing 100 oldest observations. So, my question is would it be

Dear List: I am estimating a gls model and am having to make some rather unconventional modifications to handle a particular problem I have identified. My aim is to fit a GLS with an AR1 structure, obtain the variance-covariance matrix (V), modify it as needed given my research problem, and then reestimate the GLS by brute force using matrix operations. All seems to be working almost perfectly,

I appear to have hit one of the "drop" issues raised in some discussions a couple of years ago by Frank Harrell. They don't seem to have been fixed, and I'm under some pressure to get a quick solution for a forecasting task I'm doing. I have been modelling some retail sales data, and the days just after Thanksgiving (US version!) are important. So I created some dummy

I used to consider using R and "Optim" to replace my commercial packages: Gauss and Matlab. But it turns out that "Optim" does not converge completely. The same data for Gauss and Matlab are converged very well. I see that there are too many packages based on "optim" and really doubt if they can be trusted! -- View this message in context:

...d dispatch etc.) as possible. I do want to have the choice if I'm carrying (possibly loads of) data with me in the fields of my object or if I compute/get them based on some function whenever I actually need it. For example, I'm thinking about parameter estimates that could automatically be reestimated based on rules that take into account a constantly changing data structure (new observations come it quite frequently or something like that). What do you think of the way I've implemented this? Does this make sense to you or is something like this done in another (and probably more elegant ;...

...ethod dispatch etc.) as possible. I do want to have the choice if I?m carrying (possibly loads of) data with me in the fields of my object or if I compute/get them based on some function whenever I actually need it. For example, I'm thinking about parameter estimates that could automatically be reestimated based on rules that take into account a constantly changing data structure (new observations come it quite frequently or something like that). What do you think of the way I?ve implemented this? Does this make sense to you or is something like this done in another (and probably more elegant ;-))...

Dear all, I am planning to use nlme library for analysis of experiments in semiconductor industry. Currently I am using "lm" but plan to move to "lme" to handle within wafer / wafer-to-wafer and lot-to-lot variation correctly. So far everything is working well, but I have a fundamentel question: NLME offers "maximum likelihood" and "restricted maximum