mpower | Retrospective power analysis for multivariate GLMs | mpower |

York University

The program reads the OUTSTAT= data set constructed in a PROC GLM step. For each effect tested, the program calculates the nominal power of the test, if the sample means were population values.

For proper power analysis and sample size planning, you should consider the size of differences among groups which are meaningful to detect in terms of the research questions.

Power is then calculated from the equivalent F-statistic.

proc glm data= outstat=STATS; class classvars; model depvars = independents / SS3 nouni; * use SSn option; contrast 'name' effect {coefficients}; manova h=effects;For every effect tested on the MODEL statement or in a CONTRAST statement, observations in the OUTSTAT= dataset will be produced containing the hypothesis SSCP matrix. If you do not specify an SSn option on the MODEL statement, GLM will produce both Type I (SS1) and Type III (SS3) sum of squares. The

Then invoke the `mpower` macro, supplying the OUTSTAT=
dataset as the data= parameter to mpower

%mpower; %mpower(data=STATS, ..., )You must supply a value for the YVAR= parameter to specify the dependent variables in the dataset. The arguments may be listed within parentheses in any order, separated by commas. For example:

%mpower(data=STATS, yvar=depvars) %mpower(data=STATS, yvar=depvars, alpha=.01, tests=WILKS ROY)

- YVAR=
- List of dependent varriables
- DATA=_LAST_
- OUTSTAT= data set from GLM. If not specified, the most recently created dataset is used.
- OUT=_DATA_
- The name of the output dataset. If not specified, the new dataset is named according to the DATAn convention.
- ALPHA=.05
- Error rate for each test.
- TESTS=WILKS PILLAI LAWLEY ROY
- Specify any one or more of these multivariate tests for which power is computed.

%include data(dogfood); title 'Retrospective Power Analysis: OneWay MANOVA Design'; proc glm order=data outstat=stats; class formula; model start amount = formula / ss3 nouni; contrast 'Ours vs. Theirs' formula 1 1 -1 -1; * contrast 'Old - New' formula 1 -1 0 0; * contrast 'Major vs. Alps' formula 0 0 1 -1; manova h=formula; run;Power analysis for the effect of FORMULA and the contrast 'Ours vs. Theirs' is carried out by the mpower macro:

%include macros(mpower); *-- or include in an autocall library; title2 'Multivariate power'; %mpower(data=stats, yvar=start amount);The results include:

Retrospective Power Analysis: OneWay MANOVA Design Multivariate power EFFECT ALPHA Power analysis for FORMULA SS3 0.05 Value F df1 df2 Eta##2 Non-Cent. Power Wilks' Lambda 0.319 2.827 6 22 0.4353 8.48 0.4352 Pillai's Trace 0.702 2.162 6 24 0.3509 6.4869 0.3414 Lawley Trace 2.071 3.452 6 20 0.5088 10.357 0.5135 Roy's max. Root 2.040 8.158 3 12 0.671 24.475 0.9529 EFFECT ALPHA Power analysis for Ours vs. Theirs CONTRAST 0.05 Value F df1 df2 Eta##2 Non-Cent. Power Wilks' Lambda 0.375 9.178 2 11 0.6253 9.1778 0.6509 Pillai's Trace 0.625 9.178 2 11 0.6253 9.1778 0.6509 Lawley Trace 1.669 9.178 2 11 0.6253 9.1778 0.6509 Roy's max. Root 1.669 9.178 2 11 0.6253 9.1778 0.6509

fpower Power computations for ANOVA designs

meanplot

rpower Retrospective power analysis for univariate GLMs