I am running lrm() with a single factor. I then run anova() on the fitted
model to obtain a p-value associated with having that factor in the model.
I am noticing that the Model L.R. in the lrm results is almost the same
as the Chi-Square in the anova results, but not quite; the latter value
is
Quoting Frank E Harrell Jr [EMAIL PROTECTED]:
anova (anova.Design) computes Wald statistics. When the log-likelihood
is very quadratic, these statistics will be very close to log-likelihood
ratio chi-square statistics. In general LR chi-square tests are better;
we use Wald tests for speed.
[EMAIL PROTECTED] wrote:
Quoting Frank E Harrell Jr [EMAIL PROTECTED]:
anova (anova.Design) computes Wald statistics. When the log-likelihood
is very quadratic, these statistics will be very close to log-likelihood
ratio chi-square statistics. In general LR chi-square tests are better;
we
I am running lrm() with a single factor. I then run anova() on the fitted
model to obtain a p-value associated with having that factor in the model.
I am noticing that the Model L.R. in the lrm results is almost the same
as the Chi-Square in the anova results, but not quite; the latter value
is
[EMAIL PROTECTED] wrote:
I am running lrm() with a single factor. I then run anova() on the fitted
model to obtain a p-value associated with having that factor in the model.
I am noticing that the Model L.R. in the lrm results is almost the same
as the Chi-Square in the anova results, but
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