I believe lrm has a criterion appropriate to single-precision calculations
(as S-PLUS used to use). Try reducing 'tol' from its default of 1e-7.
But your design matrix *is* near singular
kappa(cbind(1,x))
[1] 557390.5
so try centring/scaling your variables.
On Sun, 12 Oct 2008, Gad Abraham wrote:
Hi,
I'm trying to do binary logistic regression on 10 covariables, comparing glm
to lrm from Harrell's Design package. They don't seem to agree on whether the
data is collinear:
library(Design)
load(url("http://www.csse.unimelb.edu.au/~gabraham/data.Rdata";))
lrm(y ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9 + X10, data=x)
singular information matrix in lrm.fit (rank= 10 ). Offending variable(s):
X10
Error in j:(j + params[i] - 1) : NA/NaN argument
If I understand correctly, lrm is complaining about collinearity in the data.
Not quite: it is complaining about singularity in a weighted covariance
matrix of the inputs.
However, the rank of the matrix is 10:
qr(x)$rank
[1] 10
You have forgotten about the intercept.
glm doesn't seem to care about the supposed collinearity, but does say that
the data are perfectly separable:
glm(y ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9 + X10, data=x,
+ family=binomial(), control=glm.control(maxit=50))
Call: glm(formula = y ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9 + X10,
family = binomial(), data = x, control = glm.control(maxit = 50))
Coefficients:
(Intercept) X1 X2 X3 X4 X5
-6.921e+03 7.185e-02 4.344e-02 -3.980e-02 -5.362e-02 -6.387e-03
X6 X7 X8 X9 X10
2.455e-01 2.753e-02 -1.848e-01 1.903e-01 -3.187e-02
Degrees of Freedom: 27 Total (i.e. Null); 17 Residual
Null Deviance: 38.82
Residual Deviance: 4.266e-10 AIC: 22
Warning message:
In glm.fit(x = X, y = Y, weights = weights, start = start, etastart =
etastart, :
fitted probabilities numerically 0 or 1 occurred
What's the reason for this discrepancy?
Thanks,
Gad
--
Gad Abraham
Dept. CSSE and NICTA
The University of Melbourne
Parkville 3010, Victoria, Australia
email: [EMAIL PROTECTED]
web: http://www.csse.unimelb.edu.au/~gabraham
--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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