Yes, you need to have the intercept term when you predict model-based
response.
This is what you need:
ridge.test=lm.ridge(tey_values~tedata, lambda)
yest <- drop(cbind(1, tedata) %*% coef(ridge.test))
Hope this helps,
Ravi.
----------------------------------------------------------------------------
-------
Ravi Varadhan, Ph.D.
Assistant Professor, The Center on Aging and Health
Division of Geriatric Medicine and Gerontology
Johns Hopkins University
Ph: (410) 502-2619
Fax: (410) 614-9625
Email: rvarad...@jhmi.edu
Webpage:
http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Varadhan.h
tml
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From: Eleni Christodoulou [mailto:elenic...@gmail.com]
Sent: Friday, January 08, 2010 11:18 AM
To: Ravi Varadhan
Cc: David Winsemius; r-help@r-project.org
Subject: Re: [R] Ridge regression
I am sorry, I just pressed the "send" button by accident before completing
my e-mail. The yest are the estimated values according to the ridge model.
Is the way that I calculate them correct? Or should I cut the
+coef(ridge.test)[1] term?
Thanks a lot!
Eleni
On Fri, Jan 8, 2010 at 6:16 PM, Eleni Christodoulou <elenic...@gmail.com>
wrote:
Hello again and Happy 2010!
I was looking back at this email because I need to do some additional
processing now. I was thinking that if I take the coef(ans) I get n+1
coefficients. I guess that the coef(ans)[1] is the constant term... Do I
need to add it when I calculate the estimated value for the outcome?
For example, lets say that I have divided my data into training data and
test data and I have the corresponding observed try_values and tey_values
(the real values for the samples that belong to the training set and the
test set respectively)
Here is my code:
library(MASS)
ridge.test=lm.ridge(tey_values~tedata,lambda)
est<-list()
yest<-numeric()
for(i in 1:length(tey_values)){
est[[i]]=coef(ridge.test)[-1]*tedata[i,]
yest[i]=sum(est[[i]])+coef(ridge.test)[1]
}
On Wed, Dec 2, 2009 at 8:22 PM, Ravi Varadhan <rvarad...@jhmi.edu> wrote:
The help page clearly states that ans$coef is "not on the original scale and
are for use by the coef method". You also see that ans$scales gives you the
scales used in the computation of ans$coef.
So, to get coefficients on the original scale, you can either use coef(ans)
or you can divide ans$coef by ans$scales.
X1 <- runif(20)
X2 <- runif(20)
Y <- 2 * X1 - 2 * X2 + rnorm(20, sd=0.1)
lam <- 10
ans1 <- lm.ridge(Y ~ X1 + X2, lambda = lam)
all.equal(ans1$coef / ans1$scales, coef(ans1)[2:3] )
Hope this helps,
Ravi.
----------------------------------------------------------------------------
-------
Ravi Varadhan, Ph.D.
Assistant Professor, The Center on Aging and Health
Division of Geriatric Medicine and Gerontology
Johns Hopkins University
Ph: (410) 502-2619
Fax: (410) 614-9625
Email: rvarad...@jhmi.edu
Webpage:
http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Varadhan.h
<http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Varadhan.
h%0Atml>
tml
----------------------------------------------------------------------------
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-----Original Message-----
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Ravi Varadhan
Sent: Wednesday, December 02, 2009 12:25 PM
To: 'David Winsemius'; 'Eleni Christodoulou'
Cc: r-help@r-project.org
Subject: Re: [R] Ridge regression
You are right that the ans$coef and coef(ans) are different in ridge
regression, where `ans' is the object from lm.ridge. It is the coef(ans)
that yields the coefficients on the original scale. ans$coef is the
coefficient of "X-scaled" and "Y-centered" version.
Here is an example that illustrates the workings of ridge regression.
First let us create some data:
X1 <- runif(20)
X2 <- runif(20)
Y <- 2 * X1 - 2 * X2 + rnorm(20, sd=0.1)
lam <- 10
ans1 <- lm.ridge(Y ~ X1 + X2, lambda = lam)
ans1$coef
coef(ans1)
# Note that these two are different
# Now Let us scale the variables X1 and X2 and center Y
#
cY <- scale(Y, scale=FALSE)
n <- length(Y)
sX1 <- scale(X1) * sqrt(n/(n-1))
sX2 <- scale(X2) * sqrt(n/(n-1))
require(MASS)
lam <- 10
ans2 <- lm.ridge(cY ~ sX1 + sX2, lambda = lam)
ans2$coef
coef(ans2)
# Now, see that the coefficients of sX1 and sX2 are the same
# This is the connection!
# Armed with this insight, we now compare the ans1$coef with scaled
coefficients
#
ans1$coef
c(coef(ans1)[2] * sd(X1), coef(ans1)[3] * sd(X2)) * sqrt((n-1)/n)
# Now they are the same!
I hope this is clear.
Best,
Ravi.
----------------------------------------------------------------------------
-------
Ravi Varadhan, Ph.D.
Assistant Professor, The Center on Aging and Health
Division of Geriatric Medicine and Gerontology
Johns Hopkins University
Ph: (410) 502-2619
Fax: (410) 614-9625
Email: rvarad...@jhmi.edu
Webpage:
http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Varadhan.h
<http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Varadhan.
h%0Atml>
tml
----------------------------------------------------------------------------
--------
-----Original Message-----
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of David Winsemius
Sent: Wednesday, December 02, 2009 11:04 AM
To: Eleni Christodoulou
Cc: r-help@r-project.org
Subject: Re: [R] Ridge regression
On Dec 2, 2009, at 10:42 AM, Eleni Christodoulou wrote:
> Dear list,
>
> I have a couple of questions concerning ridge regression. I am using
> the
> lm.ridge(...) function in order to fit a model to my microarray data.
> Thus *model=lm.ridge(...)*
> I retrieve some coefficients and some scales for each gene. First of
> all, I
> would like to ask: the real coefficients of the model are not
> included in
> the first argument of the output but in the result of coef(model),
> am I
> right?
Not exactly. coef(model) extracts the coefficients from the model but
the coefficients do in the example instance I created following the
help page happen to be in the first element of the model.
eg:
> long.rr$coef
GNP Unemployed Armed.Forces Population Year
Employed
25.3615288 3.3009416 0.7520553 -11.6992718 -6.5403380
0.7864825
> long.rr[[1]]
GNP Unemployed Armed.Forces Population Year
Employed
25.3615288 3.3009416 0.7520553 -11.6992718 -6.5403380
0.7864825
> Moreover, what does the scale argument represent? Which is its
> connection with the coefficients? The R help file os not very
> informative
> for me...
A plausible response to such a question might be that the help page is
a sketchy substitute for the MASS book. However, I cannot find ridge
regression in the table of contents or in the index of my copy, but I
only have ed. 2 and the current edition is the 4th. So we will both
need to wait for more knowledgeable (or with more recent editions of
MASS) persons to answer that question.
(And "scales" is not an argument, rather it's a returned value.)
>
> Thank you very much in advance,
> Eleni Christodoulou
>
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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