Prof Brian Ripley wrote:
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.
Thanks, centering and scaling did the trick (after increasing maxit):
> lrm(y ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9 + X10,
data=data.frame(scale(x)), maxit=50)
Logistic Regression Model
lrm(formula = y ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9 +
X10, data = data.frame(scale(x)), maxit = 50)
Frequencies of Responses
0 1
14 14
Obs Max Deriv Model L.R. d.f. P C
Dxy
28 5e-04 38.81 10 0 1
1
Gamma Tau-a R2 Brier
1 0.519 1 0
Coef S.E. Wald Z P
Intercept 42.48 125.56 0.34 0.7351
X1 147.43 379.96 0.39 0.6980
X2 43.93 119.86 0.37 0.7140
X3 -24.21 102.98 -0.24 0.8141
X4 -34.26 111.18 -0.31 0.7580
X5 -14.16 44.01 -0.32 0.7476
X6 102.23 315.00 0.32 0.7455
X7 32.31 88.59 0.36 0.7153
X8 -123.62 322.01 -0.38 0.7011
X9 174.07 464.86 0.37 0.7081
X10 -36.59 99.23 -0.37 0.7123
--
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
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