MRC_all: Compute power for Multiple Regression with Up to Five...

In pwr2ppl: Power Analyses for Common Designs (Power to the People)

Compute power for Multiple Regression with Up to Five Predictors Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All)

Description

Compute power for Multiple Regression with Up to Five Predictors Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All)

Usage

MRC_all(
  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,
  rep = 10000
)

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)
rep	number of replications (default is 10000)

Value

Power for Multiple Regression (ALL)

Examples

MRC_all(ry1=.50,ry2=.50,ry3=.50, r12=.2, r13=.3,r23=.4,n=82, rep=10000)

pwr2ppl documentation built on Sept. 6, 2022, 5:06 p.m.