MRC: Compute power for Multiple Regression with up to Five... In pwr2ppl: Power Analyses for Common Designs (Power to the People)

Description

Compute power for Multiple Regression with up to Five Predictors Example code below for three predictors. Expand as needed for four or five

Usage

 1 2 3 4 MRC(ry1 = NULL, ry2 = NULL, ry3 = NULL, ry4 = NULL, ry5 = NULL, r12 = NULL, r13 = NULL, r14 = NULL, r15 = NULL, r23 = NULL, r24 = NULL, r25 = NULL, r34 = NULL, r35 = NULL, r45 = NULL, n = NULL, alpha = 0.05)

Arguments

 ry1 Correlation between DV (y) and first predictor (1) ry2 Correlation between DV (y) and second predictor (2) ry3 Correlation between DV (y) and third predictor (3) ry4 Correlation between DV (y) and fourth predictor (4) ry5 Correlation between DV (y) and fifth predictor (5) r12 Correlation between first (1) and second predictor (2) r13 Correlation between first (1) and third predictor (3) r14 Correlation between first (1) and fourth predictor (4) r15 Correlation between first (1) and fifth predictor (5) r23 Correlation between second (2) and third predictor (3) r24 Correlation between second (2) and fourth predictor (4) r25 Correlation between second (2) and fifth predictor (5) r34 Correlation between third (3) and fourth predictor (4) r35 Correlation between third (3) and fifth predictor (5) r45 Correlation between fourth (4) and fifth predictor (5) n Sample size alpha Type I error (default is .05)

Value

Power for Multiple Regression with Two to Five Predictors

Examples

 1 2 MRC(ry1=.40,ry2=.40, r12=-.15,n=30) MRC(ry1=.40,ry2=.40,ry3=-.40, r12=-.15, r13=-.60,r23=.25,n=24)

pwr2ppl documentation built on June 12, 2019, 5:03 p.m.