Thanks for the answers Uwe!
So this is a common problem in biology - few number of cases and many,
many variables (genes, proteins, metabolites, etc etc)!
Under these conditions, is discriminant function analysis not an ideal
method to use then? Are there alternatives?
1) First problem, I
michael watson (IAH-C) wrote:
Thanks for the answers Uwe!
So this is a common problem in biology - few number of cases and many,
many variables (genes, proteins, metabolites, etc etc)!
Under these conditions, is discriminant function analysis not an ideal
method to use then? Are there
Dear All
This is more of a statistics question than a question about help for R,
so forgive me.
I am using lda from the MASS package to perform linear discriminant
function analysis. I have 14 cases belonging to two groups and have
measured each of 37 variables. I want to find those variables
michael watson (IAH-C) wrote:
Dear All
This is more of a statistics question than a question about help for R,
so forgive me.
I am using lda from the MASS package to perform linear discriminant
function analysis. I have 14 cases belonging to two groups and have
measured each of 37
In message [EMAIL PROTECTED], r-help-
[EMAIL PROTECTED] writes
Dear R Users,
I'm very very interested in learning how to use R to carry out a
classification of data using discriminant function analysis. I've
found the MASS package and the lda function, but the examples in the
help system are
Dear R Users,
I'm very very interested in learning how to use R to carry out a
classification of data using discriminant function analysis. I've
found the MASS package and the lda function, but the examples in the
help system are a bit over my head. I'm not exactly sure how to
interpret the
