MRC_shortcuts: Compute Multiple Regression shortcuts with three predictors...

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

Compute Multiple Regression shortcuts with three predictors (will expand to handle two to five) Requires correlations between all variables as sample size. Means and sds are option. Also computes Power(All)

Description

Compute Multiple Regression shortcuts with three predictors (will expand to handle two to five) Requires correlations between all variables as sample size. Means and sds are option. Also computes Power(All)

Usage

MRC_shortcuts(
  ry1 = NULL,
  ry2 = NULL,
  ry3 = NULL,
  r12 = NULL,
  r13 = NULL,
  r23 = NULL,
  n = 100,
  alpha = 0.05,
  my = 0,
  m1 = 0,
  m2 = 0,
  m3 = 0,
  s1 = 1,
  s2 = 1,
  s3 = 1,
  sy = 1
)

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)
r12	Correlation between first (1) and second predictor (2)
r13	Correlation between first (1) and third predictor (3)
r23	Correlation between second (2) and third predictor (3)
n	Sample size
alpha	Type I error (default is .05)
my	Mean of DV (default is 0)
m1	Mean of first predictor (default is 0)
m2	Mean of second predictor (default is 0)
m3	Mean of third predictor (default is 0)
s1	Standard deviation of first predictor (default is 1)
s2	Standard deviation of second predictor (default is 1)
s3	Standard deviation of third predictor (default is 1)
sy	Standard deviation of DV (default is 1)

Value

Multiple Regression shortcuts with three predictors

Examples

MRC_shortcuts(ry1=.40,ry2=.40,ry3=-.40, r12=-.15, r13=-.60,r23=.25,
n=110, my=1,m1=1,m2=1,m3=1,sy=7,s1=1,s2=1,s3=2)

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