View source: R/Assumptions_resample.R
Assumptions_resample | R Documentation |
Compute power for Multiple Regression with Violated assumptions using Resamples
Assumptions_resample( 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, sy = NULL, s1 = NULL, s2 = NULL, s3 = NULL, s4 = NULL, s5 = NULL, ky = NULL, k1 = NULL, k2 = NULL, k3 = NULL, k4 = NULL, k5 = NULL, n = NULL, alpha = 0.05, test = "boot", reps = 200, boots = 500 )
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) |
sy |
Skew of outcome variable |
s1 |
Skew of first predictor |
s2 |
Skew of second predictor |
s3 |
Skew of third predictor |
s4 |
Skew of fourth predictor |
s5 |
Skew of fifth predictor |
ky |
Kurtosis of outcome variable |
k1 |
Kurtosis of first predictor |
k2 |
Kurtosis of second predictor |
k3 |
Kurtosis of third predictor |
k4 |
Kurtosis of fourth predictor |
k5 |
Kurtosis of fifth predictor |
n |
Sample size |
alpha |
Type I error (default is .05) |
test |
type of test ("boot","jack","perm") |
reps |
number of replications, default is 200 - use larger for final analyses |
boots |
number of bootstrap samples. Default is 500. Use larger for final. |
Power for Multiple Regression with Non Normal Variables via resample
Assumptions_resample(ry1=.0,ry2=.3,r12=.3,sy=1,s1=2,s2=2,ky=1,k1=1,k2=1,n=100)
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