similar to: Regression query ...

Dear R-users, There are tons of methods out there for fitting independant variables to a dependent variable. All stats books tell you about the assumptions behind OLS (ordinary least squares) and warn against abusive use of the method (which many of us do disregard by lack of a better knowledge). Most introductory text books stop there and don't tell you what the next best option might be. I

Dear All, Is it possible to perform OLS using C code? I am trying to optimize a n-period "moving window" OLS on a huge dataset hence was wondering if such a thing is possible. Ideally the solution that I am looking for would involve a C-code accepting two float arrays and returning back computed parameters such as t-stat, coefficient etc. I have glanced thru the FAQ's and tried

Hi, I am trying to construct a logistic regression model from my data (104 patients and 25 events). I build a full model consisting of five predictors with the use of penalization by rms package (lrm, pentrace etc) because of events per variable issue. Then, I tried to approximate the full model by step-down technique predicting L from all of the componet variables using ordinary least squares

Dear R experts: I just tried some simple test that told me that hand computing the OLS coefficients is about 3-10 times as fast as using the built-in lm() function. (code included below.) Most of the time, I do not care, because I like the convenience, and I presume some of the time goes into saving a lot of stuff that I may or may not need. But when I do want to learn the properties of an

I have a puzzle as to how R is computing Cook's distance in weighted linear regression. In this case cook's distance should be given not as in OLS case by h_ii*r_i^2/(1-hii)^2 divided by k*s^2 (1) (where r is plain unadjusted residual, k is number of parameters in model, etc. ) but rather by w_ii*h_ii*r_i^2/(1-hii)^2 divided by k*s^2,

Dear list, I have encountered a special case for searching a linear regression where I'm not satisfied with the results obtained using the traditional least squares method (sometimes called OLS) for estimating/optimizing the residues to the regression line (see code below). Basically, a group of my x-y data are a bit off the diagonal line (in my case the diagonal represents the ideal or

Hi I have some panel data for the 50 US states over about 25 years, and I would like to test a simple model via OLS, using this data. I know how to run OLS in R, and I think I can see how to create Panel Corrected Standard Errors using http://jackman.stanford.edu/classes/350C/pcse.r What I can't figure out is how to correct for autocorrelation over time. I have found a lot of R stuff on

Please forgive a statistics question. I know that a simple bivariate linear regression, y=f(x) or in R parlance lm(y~x) can be solved using the variance-covariance matrix: beta(x)=covariance(x,y)/variance(x). I also know that a linear regression with multiple independent variables, for example y=f(x,z) can also be solved using the variance-covariance matrix, but I don't know how to do this.

Thanks to Gabor for setting me right. My code is as follows. I found it useful for learning optim(), and you might find it similarly useful. I will be most grateful if you can guide me on how to do this better. Should one be using optim() or stats4::mle? set.seed(101) # For replicability # Setup problem X <- cbind(1, runif(100)) theta.true <- c(2,3,1) y <- X

I wrote a simple log likelihood (for the ordinary least squares (OLS) model), in two ways. The first works out the likelihood. The second merely calls the first, but after transforming the variance parameter, so as to allow an unconstrained maximisation. So the second suffers a slight cost for one exp() and then it pays the cost of calling the first. I did performance measurement. One would

Dear Mac users, Hi, as you might have probably read the thread of "[R] Rsquared in summary(lm)" on May 10, it seems that Mac version of lm() seem to be working incorrectly. I enclose the script to produce the result both for lm() and manual calculation for a simple regression. Could you run the script and report with the version of R, so I don't have to go through every builds

Hello, I have a general question about combining imputations as well as a question specific to the rms and Hmisc packages. The situation is multiple regression on a data set where multiple imputation has been used to give M imputed data sets. I know how to get the combined estimate of the covariance matrix of the estimated coefficients (average the M covariance matrices from the individual

Hi folks! Does anybody know how to use the OLS function in fMultivar to do dynamic regression? I've tried specifying lags in OLS using a data series created in fSeries and it doesn't seem to work. I've done dynamic regression using dyn$lm and I was wondering how to accomplish the same thing using the OLS function from fMultivar. Thanks! John [[alternative HTML version

I wrote this: # Setup problem x <- runif(100) y <- 2 + 3*x + rnorm(100) X <- cbind(1, x) # True OLS -- lm(y ~ x) # OLS likelihood function -- ols.lf <- function(theta, K, y, X) { beta <- theta[1:K] sigma <- exp(theta[K+1]) e <- (y - X%*%beta)/sigma logl <- sum(log(dnorm(e))) return(logl) } optim(c(2,3,0), ols.lf, gr=NULL, method="BFGS",

Hi, I am trying to estimate a quantile regression using panel data. I am trying to use the model that is described in Dr. Koenker's article. So I use the code the that is posted in the following link: http://www.econ.uiuc.edu/~roger/research/panel/rq.fit.panel.R How to estimate the panel data quantile regression if the regression contains no constant term? I tried to change the code of

Consider the following plot that shows separate regression lines ~ age for each subject in the Pothoff-Roy Orthodont data, with separate panels by Sex: library(nlme) #plot(Orthodont) xyplot(distance ~ age|Sex, data=Orthodont, type='r', groups=Subject, col=gray(.50), main="Individual linear regressions ~ age") I'd like to also show in each panel the pooled OLS

Hello R helpers, I am trying to do a linear OLS regression of y on two variables x1 and x2. I want to constrain the coefficients of x1 and x2 to sum up to 1. and therefore run a constrained OLS. Can anybody help with this? (I have seen some answers to similar questions but it was not clear to me what I need to do) - I have tried the lm function with offset but I must not have used it properly.

Hello R-user I want to compute a multiple regression but I would to include a check for collinearity of the variables. Therefore I would like to use a ridge regression. I tried lm.ridge() but I don't know yet how to get p-values (single Pr() and p of the whole model) out of this model. Can anybody tell me how to get a similar output like the summary(lm(...)) output? Or if there is

Is it possible to use some form of robust regression with the breakpoints routine so that it is less sensitive to outliers? --Rich Richard Kittler Advanced Micro Devices, Inc. Sunnyvale, CA

Hi, I would like to know if anyone has any idea of how to run an OLS with constraints? I need to contraint a coefficient estimate in the model equal to 1, and I am not sure how to include it into the OLS estimation... I was hoping to find something like "cnsreg" in STATA.. thank you Soyoko ______________________________________ Ms. Soyoko Umeno Graduate Research Assitant for the