On 4 Aug 2009, at 18:27, David Winsemius wrote:
Your first posting made me think that you were complaining that the
fitted values were less than the raw values. Your second posting makes
me think that you may be conflating the English word "less" with the
word English "fewer". Many native speakers make the same error, but in
this context it may be a critical problem for communicating what you
are seeing (or not seeing).
ok, so what I meant is
length(mod$fitted) < length(observations)
Perhaps you could be more expansive about what you see and what you
expect with explicit attention to the numbers involved? Even better
would be small *reproducible* example.
I'll have to cook that up, the data is more or less confidential. Not
very much but enough no to go on google ;)
F
--
David
On Aug 4, 2009, at 12:51 PM, Federico Calboli wrote:
Actually, I tried doing
data2 = unique(data)
mod = lm(y ~ x1 + ... + xn, data2)
fitted(mod)
and I still get les fitted values than observations.
Federico
On 4 Aug 2009, at 12:18, Federico Calboli wrote:
Hi All,
I have some data where the dependent variable is a score, low (1:3)
or
high (8:9), and the independent variables are 21 genotypic markers.
I'm fitting a logistic regression on the whole dataset after
transforming the score to 0/1 and normal linear regression on the
high
and low subsets.
I all cases I have a numer of cases of data 'duplications', i.e.
different individuals with the same score and the same genotype at
the
21 markers.
When I do:
mod$fitted.values I get a number of fitted values corresponding to
the
umber of unique lines in the dataset. Is there a way to have the
fitted values match the observation, even though some are
duplicated
and so have the same fitted value? I could do it by hand but it's
laborious and I'd venture there is a better way.
Best,
Federico
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
--
Federico C. F. Calboli
Department of Epidemiology and Public Health
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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