Collineariy Diagnostics

Hi, and thanks for your help

in order to do collinearity analysis I downloaded the perturb package. I run
a lm (regression) and on that the ??calldiag?? commad to get condition numbers
but i get the following message: the variable XY with modus ??numeric?? was
not found (it does the same with all predictors despite all variables are
numeric and exists).

Can anyone tell me how can I go arround this problem? Is there another way
to have ??condition numbers??? What about VIF?

Please return message to: antoine at ruetter.ch

-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch 
[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Sundar 
Dorai-Raj
Sent: Tuesday, September 13, 2005 5:16 AM
To: Antoine de Bary
Cc: r-help at stat.math.ethz.ch
Subject: Re: [R] Collineariy Diagnostics



Antoine de Bary wrote:

Hi, and thanks for your help

in order to do collinearity analysis I downloaded the 

perturb package. 

I run a lm (regression) and on that the ??calldiag?? commad to get 
condition numbers but i get the following message: the variable XY 
with modus ??numeric?? was not found (it does the same with all 
predictors despite all variables are numeric and exists).

Can anyone tell me how can I go arround this problem? Is 

there another 

way to have ??condition numbers??? What about VIF?

Please return message to: antoine at ruetter.ch

I cannot comment on the "perturb" package. However for 
condition numbers see ?kappa.lm, and for variance inflation 
factors see ?vif. The latter is in the Design package.

set.seed(1)
x1 <- rnorm(100)
x2 <- x1 + 0.1 * rnorm(100)
y  <- rnorm(100)
f  <- lm(y ~ x1 + x2)

vif(f)
kappa(f)

HTH,

--sundar

Hi,

I am using nls() with the form: nls(~my.fcn(...)) because I have to 
iteratively compute the expected y values. The function my.fcn() returns 
y.obs-y.pred

However, I want to fix some of the parameters in my.fcn at various values 
and compute the parameter estimates. In Splus there is such a thing as a 
parameterized dataframe. I don't think this exists in R so does anyone know 
how to set one or more of the parameters as constants in the model? Thank 
you.

Jeff Breiwick