Hello,
I want to make a linear discriminant analysis for the dataset olive, and I
get always this error:#
Warning message:
variables are collinear in: lda.default(x, grouping, ...)
## Loading Data
library(MASS)
olive - url(
Thanks for explaining...
Im just sitting at the homework for 6 hours after taking for one week
antibiotica, because i had an amygdalitis...
I just wanted some tipps for solving this homework, but thanks, I will try
to get help on another way :)
I think i solved it, but I still get this Error :(
So what about asking your teacher (who seems to be Peter Filzmoser) and
try to find out your homework yourself?
You might want to think about some assumptions that must hold for LDA
and look at the class of your explaining variables ...
Uwe Ligges
Soare Marcian-Alin wrote:
Hello,
I want
Sent: Wed 06/06/2007 4:45 PM
To: Uwe Ligges; R-help@stat.math.ethz.ch
Subject: Re: [R] Linear Discriminant Analysis
Thanks for explaining...
Im just sitting at the homework for 6 hours after taking for one week
antibiotica, because i had an amygdalitis...
I just wanted some tipps for solving
Dear Prof. Ripley
I'm sorry about the confusion; this reply will simply avoid any humor
attempts (good or bad).
About S
I'm sorry, as a user I was not aware of any S still existing outside
of s-plus or R. So your right, the procedure I was referring to was
conducted on s-plus. I used the GUI
On Mon, 20 Feb 2006, Alain Paquette wrote:
Hello R people
I now know how to run my discriminant analysis with the lda function in
MASS:
lda.alain=lda(Groupes ~ Ht.D0 + Lc.Dc + Ram + IDF, gr, CV = FALSE)
and it works fine.
CV=FALSE is the default and so not needed.
But I am missing a test
Hello R people
I now know how to run my discriminant analysis with the lda function in
MASS:
lda.alain=lda(Groupes ~ Ht.D0 + Lc.Dc + Ram + IDF, gr, CV = FALSE)
and it works fine.
But I am missing a test and cannot find any help on how to get it, if it
exist.
The S equivalent:
Dear R
I'm trying to plot the lda means onto a 2 D plot of discriminant scores.
Preferably I'd like these to be in a larger font compared to the
discriminant scores.
I tried
skull.mean.pred - predict(skulls.lda, as.data.frame(skulls.lda$means),
dimen=2)
from which I got
skull.mean.pred
Hi all,
When I use lda for discriminant analysis, should I
normalized my data (variables) to mean 0, variance 1
before running lda if my variables might not be
exactly on the same scale? I have this question
because in principle component analysis, this is
indeed an issue where we can choose
