On Tue, 2009-08-04 at 18:37 +0100, Federico Calboli wrote:
> 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
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 speak
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 speak
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 contex
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:
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 su
6 matches
Mail list logo
