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

Description Usage Arguments Value Examples

View source: R/MRC_shortcuts.R

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

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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

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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 April 4, 2021, 9:06 a.m.