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
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