From: peter salzman <peter.salzmanuser_at_gmail.com>

Date: Thu, 24 Jan 2008 14:51:17 -0500

Date: Thu, 24 Jan 2008 14:51:17 -0500

thank you,

peter

On 1/24/08, Prof Brian Ripley <ripley_at_stats.ox.ac.uk> wrote:

*>
*

> On Thu, 24 Jan 2008, peter salzman wrote:

*>
**> > Dear list,
**> >
**> > i'm trying to test if a linear combination of coefficients of glm is
**> equal
**> > to 0. For example :
**> > class 'cl' has 3 levels (1,2,3) and 'y' is a response variable. We want
**> to
**> > test H0: mu1 + mu2 - mu3 =0 where mu1,mu2, and mu3 are the means for
**> each
**> > level.
**> >
**> > for me, the question is how to get the covariance matrix of the
**> estimated
**> > parameters from glm. but perhaps there is a direct solution in one of
**> the
**> > packages.
**>
**> See ?vcov .
**>
**> BTW, help.search("covariance matrix") finds it.
**>
**> >
**> > i know how to solve this particular problem (i wrote it below) but i'm
**> > curious about the covariance matrix of coefficient as it seems to be
**> > important.
**> >
**> > the R code example :
**> > ###
**> > nObs <- 10
**> > cl <- as.factor( sample(c(1,2,3),nObs,replace=TRUE) )
**> > y <- rnorm(nObs)
**> >
**> > model <- glm(y ~ cl)
**> > b <- model$coefficients
**> > H <- c(1,1,-1) # we want to test H0: Hb = 0
**> >
**> > ### the following code will NOT run unless we can compute covModelCoeffs
**> >
**> > #the mean of Hb is
**> > mu = H %*% model$coefficients
**> > #the variance is HB is
**> > var = H %*% covModelCoeffs %*% t(H)
**> >
**> > p.val <- 2 * pnorm( -abs(mu), mean=0, sd=sqrt(var),lower.tail = TRUE)
**> >
**> >
**> > how do i get the covariance matrix of the estimated parameters ?
**> >
**> > thanks,
**> > peter
**> >
**> > P.S. the simple solution for this particular problem:
**> >
**> > ## get the mean for each level
**> > muV <- by(y,cl,mean)
**> > ## get the variance for each level
**> > varV <- by(y,cl,var)
**> >
**> > ## the mean of Hb is
**> > muHb <- H %*% muV
**> > ## because of independence, the variance of Hb is
**> > varHb <- sum(varV)
**> >
**> > ## the probability of error, so-called p-value:
**> > p.val <- 2 * pnorm( -abs(muHb), mean=0, sd=sqrt(varHb),lower.tail =
**> TRUE)
**> >
**> > thanks again,
**> > peter
**> >
**> >
**> >
**>
**> --
**> Brian D. Ripley, ripley_at_stats.ox.ac.uk
**> Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
**> University of Oxford, Tel: +44 1865 272861 (self)
**> 1 South Parks Road, +44 1865 272866 (PA)
**> Oxford OX1 3TG, UK Fax: +44 1865 272595
**>
*

-- Peter Salzman, PhD Department of Biostatistics and Computational Biology University of Rochester [[alternative HTML version deleted]] ______________________________________________ R-help_at_r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.Received on Thu 24 Jan 2008 - 20:04:27 GMT

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