Hi All,
I have a parameter that is bimodal, and I want to get some sort of linear model
done with it
results = some.linear.function(bimodal.param ~ factor1 + some other stuff,
mydata)
I want to see if factor 1 matters (it has 3 levels, of of which can be taken as
baseline), i.e:
summary(results)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.108522 0.936666 -2.251 0.0272
factor1-1 0.769314 0.273368 2.814 0.0062
factor1-2 0.149841 0.198976 0.753 0.4537
[the numbers are made up]
I am not clear which function, if any, could handle such situation. Any
suggestions? I tried glm with family = quasi binomial, but it's obviously wrong
if no other reason that it does not accept the bimodal parameter (it wants it
to be 0s and 1s).
Cheers
Federico
--
Federico C. F. Calboli
Neuroepidemiology and Ageing Research
Imperial College, St. Mary's Campus
Norfolk Place, London W2 1PG
Tel +44 (0)20 75941602 Fax +44 (0)20 75943193
f.calboli [.a.t] imperial.ac.uk
f.calboli [.a.t] gmail.com
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