Thanks to Kit Baum, there is now a new version of the -smileplot- package
on SSC. To describe and install the package, type -ssc describe smileplot-
from Web-aware Stata.
The new version of the -smileplot- package represents a major overhaul. The
package now contains two programs, -multproc- and -smileplot- (which calls
-multproc-). -multproc- takes, as input, a variable containing multiple
P-values and a user-specified uncorrected P-value threshold, and calculates
a corrected P-value threshold using a choice of 11 multiple test procedure
methods. -smileplot- calls -multproc- and then creates a smile plot, with
data points corresponding to estimated parameters, parameter estimates on
the X-axis, and P-values on a reverse log scale on the Y-axis. (So, the
higher a data point is, the more statistically significant it is.) The
Y-axis has reference lines indicating the uncorrected and corrected overall
critical P-values. The line indicating the corrected critical P-value is
called the parapet line, and represents an "upper confidence limit" for the
set of null hypotheses that are true. The methods used to calculate the
corrected critical P-value may control the familywise error rate (eg the
Bonferroni, Holm and Hochberg procedures) or control the false discovery
rate (eg the Simes and Benjamini-Yekutieli procedures). If a method
controls the familywise error rate, and the uncorrected critical P-value is
alpha, then we are 100*(1-alpha) percent confident that all rejected null
hypotheses are false. If a method controls the false discovery rate, then
we are 100*(1-alpha) percent confident that, if there are any rejected null
hypotheses, then at least some of them are false. (In the limit, as the
number of multiple tests tends to infinity, it may be argued that, if the
FDR is controlled at alpha, then we are 100% confident that 100*(1-alpha)
percent of rejected null hypotheses are false.) False discovery rate (FDR)
is a fashionable idea in statistics at the moment (rightly or wrongly), and
new FDR-controlling procedures come out all the time. I have done some
preliminary certification of the package, testing its results against
results in the literature.
Best wishes
Roger
--
Roger Newson
Lecturer in Medical Statistics
Department of Public Health Sciences
King's College London
5th Floor, Capital House
42 Weston Street
London SE1 3QD
United Kingdom
Tel: 020 7848 6648 International +44 20 7848 6648
Fax: 020 7848 6620 International +44 20 7848 6620
or 020 7848 6605 International +44 20 7848 6605
Email: [email protected]
Opinions expressed are those of the author, not the institution.
