[R] singular information matrix in lrm.fit

[Gad Abraham]

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. However, the rank of the matrix is 10:
 > qr(x)$rank
[1] 10

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: gabraham at csse.unimelb.edu.au
web: http://www.csse.unimelb.edu.au/~gabraham