Try this:
sapply(by(x, x$basestudy, lm, formula = ef ~ quartile), coef)
On Tue, Oct 26, 2010 at 1:08 PM, Small Sandy (NHS Greater Glasgow & Clyde) <
sandy.sm...@nhs.net> wrote:
> Hi
>
> I need some help getting results from multiple linear models into a
> dataframe.
> Let me explain the problem.
>
> I have a dataframe with ejection fraction results measured over a number of
> quartiles and grouped by base_study.
> My dataframe (800 different base_studies) looks like
>
> > afvtprelvefs
> basestudy quartile ef ef_std entropy
> CBP0908020 1 21.6 0.53 3.27
> CBP0908020 2 32.5 0.61 3.27
> CBP0908020 3 30.8 0.63 3.27
> CBP0908020 4 33.6 0.37 3.27
> CBP0908022 1 42.4 0.52 1.80
> CBP0908021 1 29.4 0.70 2.63
> CBP0908021 2 29.2 0.42 2.63
> CBP0908021 3 29.7 0.89 2.63
> CBP0908021 4 29.3 0.50 2.63
> CBP0908022 2 45.7 1.30 1.80
> ...
>
> What I want to do is apply a weighted linear fit to the results from each
> base study and get the gradient out of it. I then want to plot the gradient
> against the entropy (which is constant for each base study).
>
> I can get apply a linear fit with
>
> > fits <- by(afvtprelvefs, afvtprelvefs$basestudy, function (x) lm (ef ~
> quartile, data=x, weights=1/ef_std))
>
> but how do I get the results from that into a dataframe which I can use?
>
> I thought I might get somewhere with
> > sapply(fits, "[[", "coefficients")
>
> But that doesn't give me the basestudy separately so that I can match up
> the results with the entropy results.
>
> I am sure this must have been answered somewhere before but I have been
> unable to find a solution.
> Many thanks for your help
>
> Sandy Small
> NHS Greater Glasgow and Clyde
>
>
>
> ********************************************************************************************************************
>
> This message may contain confidential information. If ...{{dropped:20}}
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