Martin Kellner wrote:
I have a set of data that is not normally distributed and for which I
need to build a model. So, I tried the lrm function from the
design-package. The first run went well, and I got the following
results:
Wald Statistics Response: RVCL2PROC.mott
Factor Chi-Square d.f. P
TTV.mott (Factor+Higher Order Factors) 69.01 4 <.0001
All Interactions 12.13 3 0.0069
BEHANDLING (Factor+Higher Order Factors) 14.94 6 0.0208
All Interactions 12.13 3 0.0069
TTV.mott * BEHANDLING (Factor+Higher Order Factors) 12.13 3
0.0069
TOTAL 69.76 7 <.0001
Now, how is it to be interpreted? Does it mean that the p-value for the
interaction (TTV.mott*BEHANDLING) is 0.0069? And what does the "TOTAL"
p-value signify?
To understand all that, first make sure you understand all the
parameters in the model - as printed when you print the fit object.
Then get print.anova.Design to annotate the output to display exactly
which parameters are being tested in each line:
a <- anova(fit)
print(a, which='subscripts') # or 'names' or 'dots'
Then, I ran exactly the same script on another dataset and got the
following error message:
singular information matrix in lrm.fit (rank= 0 ). Offending
variable(s): Error in est[z$pivot[nvi:(irank + 1)] - kint] :
only 0's may be mixed with negative subscripts
Please follow the posting guide and submit a small example that
reproduces this problem.
Frank
Does anyone know? I suspect that the first question is probably rather
easy for you clever guys but I'm a statistics noob so... looking forward
to your help.
Martin Kellner
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--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
