Multiple-test procedures and smile plots

multproc carries out multiple-test procedures, taking as input a list of p-values and an uncorrected critical p-value, and calculating a corrected overall critical p-value for rejection of null hypotheses. These procedures define a confidence region for a set-valued parameter, namely the set of null hypotheses that are true. They aim to control either the family-wise error rate (FWER) or the false discovery rate (FDR) at a level no greater than the uncorrected critical p-value. smileplot calls multproc and then creates a smile plot, with data points corresponding to estimated parameters, the p-values (on a reverse log scale) on the y-axis, and the parameter estimates (or another variable) on the x-axis. There are y-axis reference lines at the uncorrected and corrected overall critical p-values. The reference line for the corrected overall critical p-value, known as the parapet line, is an informal “upper confidence limit” for the set of null hypotheses that are true and defines a boundary between data mining and data dredging. A smile plot summarizes a set of multiple analyses just as a Cochrane forest plot summarizes a meta-analysis.


Issue Date:
2003
Publication Type:
Journal Article
DOI and Other Identifiers:
st0035 (Other)
Record Identifier:
http://ageconsearch.umn.edu/record/116061
PURL Identifier:
http://purl.umn.edu/116061
Published in:
Stata Journal, Volume 03, Number 2
Page range:
109-132
Total Pages:
24

Record appears in:



 Record created 2017-04-01, last modified 2018-01-22

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