SAS Macro Programs: rpower

The rpower macro ( get rpower.sas)

Retrospective power analysis for univariate GLMs

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 specified alpha and power values, the required sample size to achieve that power is also determined.

Caution

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.

Method

Usage

proc glm data=  outstat=STATS;                                
	class  classvars;                                          
	model  depvars = independents / SS3;  * use SSn option;
	contrast 'name' effect {coefficients};                     

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

   %rpower;
   %rpower(data=STATS, ..., )

Parameters

DATA=_LAST_

OUTSTAT= data set from GLM. If not specified, the most recently created dataset is used.

ALPHA=.05

Desired alpha level(s) of tests A separate computation is performed for each value specified. You may specify a single value, a list of values separated by commas, a range of the form x TO y BY z, or a combination of these.

POWER=.90

The desired power value(s). A separate computation is performed for each value specified. You may specify a single value, a list of values separated by commas, a range of the form x TO y BY z, or a combination of these. However, you must surround the POWER= value with %STR() if any commas appear in it. For example,

power=.70 to .9 by .1
power=%str(.7, .8, .85)
power=%str(.5, .70 to .9 by .1)

SIGMA=1

alternate estimate of root MSE. If not specified, the macro uses the MSE from the OUTSTAT= dataset.

OUT=_power_

Name of the output dataset containing power statistics

Example

data learning;
	do grade = 5,12;
	do words = 'low ', 'high';
	do feedback = 'Control', 'Pos', 'Neg';
		input mean @@;
		output;
		end; end; end;
lines;
8.8 8.0 7.6
8.0 4.4 3.8
9.0 8.4 8.0
7.8 7.4 7.2
;

%stat2dat(data=learning, class=grade words feedback,
	out=raw, mean=mean, n=5, MSE=1.75, depvar=recall);

proc glm data=raw outstat=stats;
	class feedback words grade;
	model recall = feedback|words|grade / ss3;
	contrast 'Cont:+,-' feedback  -2 1 1;
	freq freq;

%include macros(rpower);        *-- or include in an autocall library;
%rpower(data=stats);

                                             F Nominal Adj.     Non-
SOURCE               _NAME_ _TYPE_   DF  Value  power  power Centrality

FEEDBACK             RECALL SS3       2   9.61  0.975  0.950    19.22
WORDS                RECALL SS3       1  29.87  1.000  0.999    29.87
FEEDBACK*WORDS       RECALL SS3       2   1.61  0.324  0.134     3.22
GRADE                RECALL SS3       1  12.34  0.931  0.897    12.34
FEEDBACK*GRADE       RECALL SS3       2   3.11  0.573  0.390     6.23
WORDS*GRADE          RECALL SS3       1   6.44  0.701  0.606     6.44
FEEDBACK*WORDS*GRADE RECALL SS3       2   2.47  0.472  0.280     4.93
Cont:+,-             RECALL CONTRAST  1  18.30  0.987  0.979    18.30

                             Adj_Non-   Req'd  Total  Req'd
SOURCE               _NAME_ Centrality    N      N    power  ALPHA

FEEDBACK             RECALL    16.42      2.2    7.5   0.9    0.05
WORDS                RECALL    27.62      1.9    3.6   0.9    0.05
FEEDBACK*WORDS       RECALL     1.08     12.7  447.4   0.9    0.05
GRADE                RECALL    10.83      2.4    7.0   0.9    0.05
FEEDBACK*GRADE       RECALL     3.97      4.4   44.3   0.9    0.05
WORDS*GRADE          RECALL     5.17      3.4   15.8   0.9    0.05
FEEDBACK*WORDS*GRADE RECALL     2.73      5.8   82.8   0.9    0.05
Cont:+,-             RECALL    16.54      2.2    5.0   0.9    0.05

See also