Introduction to Power and Sample Size Analysis
Example 77.8 of Chapter 77: The POWER Procedure, demonstrates various ways you can modify and enhance plots created in the GLMPOWER or POWER procedures:
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assigning analysis parameters to axes
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fine-tuning a sample size axis
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adding reference lines
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linking plot features to analysis parameters
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choosing key (legend) styles
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modifying symbol locations
For example, replace the default PLOT statement with the following statement to modify the graphical results in Figure 18.2 to lower the minimum sample size to 60, show a reference line at power=0.9 with corresponding sample size values, distinguish
standard deviation by color instead of panel, and swap the roles of 
and mean difference:
plot
min=60
yopts=(ref=0.9 crossref=yes)
vary(color by stddev, linestyle by meandiff, symbol by alpha);
Figure 18.3 shows the results. The plot reveals that only the scenarios with the largest mean difference and smallest standard deviation achieve a power of at least 0.9 for this sample size range.
