| Type: | Package |
| Title: | Power Analyses for Common Designs (Power to the People) |
| Version: | 0.5.0 |
| Author: | Chris Aberson |
| Maintainer: | Chris Aberson <chris.aberson@gmail.com> |
| Description: | Statistical power analysis for designs including t-tests, correlations, multiple regression, ANOVA, mediation, and logistic regression. Functions accompany Aberson (2019) <doi:10.4324/9781315171500>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.1.2 |
| Imports: | car (≥ 3.0-0), MASS (≥ 7.3-51), dplyr (≥ 0.8.0), tidyr (≥ 0.8.0), ez (≥ 0.4.3), nlme (≥ 3.1-139), phia (≥ 0.2-0), afex (≥ 0.22-1), MBESS (≥ 4.5.0), lavaan (≥ 0.6-2), stats (≥ 3.5.0), semTools (≥ 0.5), quantreg (≥ 5.50), broom (≥ 0.7.00), lmtest (≥ 0.9-30), lmPerm, pls |
| NeedsCompilation: | no |
| Packaged: | 2022-08-28 09:08:16 UTC; cla18 |
| Repository: | CRAN |
| Date/Publication: | 2022-09-06 11:40:05 UTC |
Compute power for Multiple Regression with Violated assumptions (Beta)
Description
Compute power for Multiple Regression with Violated assumptions (Beta)
Usage
Assumptions(
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 = NULL,
nruns = 500
)
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) |
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 (none, sqrt, log, inv, robust, boot, quantile, hc0, hc1, hc2, hc3) |
nruns |
number of runs, default is 500 |
Value
Power for Resampled Multiple Regression with Non Normal Variables
Examples
Assumptions(ry1=.0,ry2=.3,r12=.3,sy=1,s1=2,s2=2,ky=1,k1=1,k2=1,n=100,nruns=20,test="sqrt")
Compute power for Multiple Regression with Violated assumptions using Resamples
Description
Compute power for Multiple Regression with Violated assumptions using Resamples
Usage
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
)
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) |
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. |
Value
Power for Multiple Regression with Non Normal Variables via resample
Examples
Assumptions_resample(ry1=.0,ry2=.3,r12=.3,sy=1,s1=2,s2=2,ky=1,k1=1,k2=1,n=100)
Compute power for an Chi Square 2x3 Takes proportions for each group. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for an Chi Square 2x3 Takes proportions for each group. Alpha is .05 by default, alternative values may be entered by user
Usage
Chi2X3(r1c1, r1c2, r1c3, r2c1, r2c2, r2c3, n, alpha = 0.05)
Arguments
r1c1 |
Proportion of overall scores in Row 1, Column 1 |
r1c2 |
Proportion of overall scores in Row 1, Column 2 |
r1c3 |
Proportion of overall scores in Row 1, Column 3 |
r2c1 |
Proportion of overall scores in Row 2, Column 1 |
r2c2 |
Proportion of overall scores in Row 2, Column 2 |
r2c3 |
Proportion of overall scores in Row 2, Column 3 |
n |
Total sample size |
alpha |
Type I error (default is .05) |
Value
Power for 2x3 Chi Square
Examples
Chi2X3(r1c1=.25,r1c2=.25,r1c3=.10, r2c1=.10,r2c2=.25,r2c3=.05,n=200)
Compute power for an Chi Square 2x2 Takes proportions for each group. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for an Chi Square 2x2 Takes proportions for each group. Alpha is .05 by default, alternative values may be entered by user
Usage
Chi2x2(r1c1, r1c2, r2c1, r2c2, n, alpha = 0.05)
Arguments
r1c1 |
Proportion of overall scores in Row 1, Column 1 |
r1c2 |
Proportion of overall scores in Row 1, Column 2 |
r2c1 |
Proportion of overall scores in Row 2, Column 1 |
r2c2 |
Proportion of overall scores in Row 2, Column 2 |
n |
Total sample size |
alpha |
Type I error (default is .05) |
Value
Power for 2x2 Chi Square
Examples
Chi2x2(r1c1=.28,r1c2=.22,r2c1=.38,r2c2=.12,n=100)
Compute power for Chi Square Based on Effect Size Takes phi, degrees of freedom, and a range of sample sizes. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for Chi Square Based on Effect Size Takes phi, degrees of freedom, and a range of sample sizes. Alpha is .05 by default, alternative values may be entered by user
Usage
ChiES(phi, df, nlow, nhigh, by = 1, alpha = 0.05)
Arguments
phi |
phi coefficient (effect size for 2x2) |
df |
degrees of freedom |
nlow |
starting sample size |
nhigh |
ending sample size |
by |
Incremental increase in sample (e.g. nlow = 10, nhigh = 24, by = 2, produces estimates of 10, 12, and 14) |
alpha |
Type I error (default is .05) |
Value
Power for Chi Square Based on Effect Size
Examples
ChiES(phi=.3,df=1,nlow=10,nhigh=200,by=10, alpha = .01)
Compute power for an Chi Square Goodness of Fit Takes proportions for up to six group. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for an Chi Square Goodness of Fit Takes proportions for up to six group. Alpha is .05 by default, alternative values may be entered by user
Usage
ChiGOF(
groups,
po1,
po2,
po3 = NULL,
po4 = NULL,
po5 = NULL,
po6 = NULL,
n,
alpha = 0.05
)
Arguments
groups |
Number of groups |
po1 |
Proportion observed Group 1 |
po2 |
Proportion observed Group 2 |
po3 |
Proportion observed Group 3 |
po4 |
Proportion observed Group 4 |
po5 |
Proportion observed Group 5 |
po6 |
Proportion observed Group 6 |
n |
Total sample size |
alpha |
Type I error (default is .05) |
Value
Power for Chi Square Goodness of Fit
Examples
ChiGOF(po1=.25, po2=.20, po3=.20, po4=.35, groups=4,n=100)
Compute Power for Logistic Regression with a Single Categorical Predictor
Description
Compute Power for Logistic Regression with a Single Categorical Predictor
Usage
LRcat(p0 = NULL, p1 = NULL, prop = 0.5, alpha = 0.05, power, R2 = 0)
Arguments
p0 |
Probability of a Desirable Outcome in the Control Condition |
p1 |
Probability of a Desirable Outcome in the Treatment Condition |
prop |
Proportion in the Treatment Condition |
alpha |
Type I error (default is .05) |
power |
Desired Power |
R2 |
How Well Predictor of Interest is Explained by Other Predictors (default is 0) |
Value
Power for Logistic Regression with a Single Categorical Predictor
Examples
LRcat(p0=.137,p1=.611,prop =.689,power=.95)
Compute Power for Logistic Regression with Continuous Predictors
Description
Compute Power for Logistic Regression with Continuous Predictors
Usage
LRcont(OR = NA, r = NA, ER = NULL, alpha = 0.05, power = NULL, R2 = 0)
Arguments
OR |
Odds Ratio for Predictor of Interest |
r |
Correlation for Predictor of Interest |
ER |
Event Ratio Probability of a Desirable Outcome Overall |
alpha |
Type I error (default is .05) |
power |
Desired Power |
R2 |
How Well Predictor of Interest is Explained by Other Predictors (default is 0) |
Value
Power for Logistic Regression with Continuous Predictors
Examples
LRcont(OR = 4.05, ER = .463,power=.95)
Compute power for a One Factor MANOVA with up to two levels and up to four measures. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for a One Factor MANOVA with up to two levels and up to four measures. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Usage
MANOVA1f(
m1.1,
m2.1,
m3.1 = NA,
m4.1 = NA,
m1.2,
m2.2,
m3.2 = NA,
m4.2 = NA,
s1.1 = NA,
s2.1 = NA,
s3.1 = NA,
s4.1 = NA,
s1.2 = NA,
s2.2 = NA,
s3.2 = NA,
s4.2 = NA,
r1.2_1 = NULL,
r1.3_1 = NULL,
r1.4_1 = NULL,
r2.3_1 = NULL,
r2.4_1 = NULL,
r3.4_1 = NULL,
r1.2_2 = NULL,
r1.3_2 = NULL,
r1.4_2 = NULL,
r2.3_2 = NULL,
r2.4_2 = NULL,
r3.4_2 = NULL,
r = NULL,
s = NULL,
n,
alpha = 0.05
)
Arguments
m1.1 |
Mean of first DV, 1st level Between Factor |
m2.1 |
Mean of second DV, 1st level Between Factor |
m3.1 |
Mean of third DV, 1st level Between Factor |
m4.1 |
Mean of fourth DV, 1st level Between Factor |
m1.2 |
Mean of first DV, 2nd level Between Factor |
m2.2 |
Mean of second DV, 2nd level Between Factor |
m3.2 |
Mean of third DV, 2nd level Between Factor |
m4.2 |
Mean of fourth DV, 2nd level Between Factor |
s1.1 |
Standard deviation of first DV, 1st level Between Factor |
s2.1 |
Standard deviation of second DV, 1st level Between Factor |
s3.1 |
Standard deviation of third DV, 1st level Between Factor |
s4.1 |
Standard deviation of forth DV, 1st level Between Factor |
s1.2 |
Standard deviation of first DV, 2nd level Between Factor |
s2.2 |
Standard deviation of second DV, 2nd level Between Factor |
s3.2 |
Standard deviation of third DV, 2nd level Between Factor |
s4.2 |
Standard deviation of forth DV, 2nd level Between Factor |
r1.2_1 |
correlation DV 1 and DV 2, 1st level Between |
r1.3_1 |
correlation DV 1 and DV 3, 1st level Between |
r1.4_1 |
correlation DV 1 and DV 4, 1st level Between |
r2.3_1 |
correlation DV 1 and DV 3, 1st level Between |
r2.4_1 |
correlation DV 1 and DV 4, 1st level Between |
r3.4_1 |
correlation DV 1 and DV 4, 1st level Between |
r1.2_2 |
correlation DV 1 and DV 2, 2nd level Between |
r1.3_2 |
correlation DV 1 and DV 3, 2nd level Between |
r1.4_2 |
correlation DV 1 and DV 4, 2nd level Between |
r2.3_2 |
correlation DV 1 and DV 3, 2nd level Between |
r2.4_2 |
correlation DV 1 and DV 4, 2nd level Between |
r3.4_2 |
correlation DV 1 and DV 4, 2nd level Between |
r |
sets same correlations between DVs on all factor levels (seriously, just use this) |
s |
sets same standard deviation for factor levels (see comment above) |
n |
Sample size for first group |
alpha |
Type I error (default is .05) |
Value
Power for the One Factor Within Subjects and One Factor Between ANOVA
Examples
MANOVA1f(n=40,m1.1=0,m2.1=1,m3.1=2.4,m4.1=-0.7,
m1.2=-0.25,m2.2=-2,m3.2=2,m4.2=-1,
s1.1=.4,s2.1=5,s3.1=1.6,s4.1=1.2,
s1.2=.4,s2.2=5,s3.2=1.6,s4.2=1.2,
r1.2_1=.1,r1.3_1=.1,r1.4_1=.1,
r2.3_1=.35,r2.4_1=.45,r3.4_1=.40,
r1.2_2=.1,r1.3_2=.1,r1.4_2=.1,
r2.3_2=.35,r2.4_2=.45,r3.4_2=.40,alpha=.05)
MANOVA1f(n=40,m1.1=0,m2.1=1,m3.1=2.4,m4.1=-0.7,
m1.2=-0.25,m2.2=-2,m3.2=2,m4.2=-1,
s=.4,r=.5,alpha=.05)
Compute power for Multiple Regression with up to Five Predictors Example code below for three predictors. Expand as needed for four or five
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
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
MRC(ry1=.40,ry2=.40, r12=-.15,n=30)
MRC(ry1=.40,ry2=.40,ry3=-.40, r12=-.15, r13=-.60,r23=.25,n=24)
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)
Compute Multiple Regression shortcuts with three predictors for Ind Coefficients 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 for Ind Coefficients Requires correlations between all variables as sample size. Means and sds are option. Also computes Power(All)
Usage
MRC_short2(
ry1_1,
ry2_1,
ry3_1 = NULL,
r12_1,
r13_1 = NULL,
r23_1 = NULL,
n1,
ry1_2,
ry2_2,
ry3_2 = NULL,
r12_2,
r13_2 = NULL,
r23_2 = NULL,
n2,
alpha = 0.05,
my_1 = 0,
m1_1 = 0,
m2_1 = 0,
m3_1 = 0,
s1_1 = 1,
s2_1 = 1,
s3_1 = 1,
sy_1 = 1,
my_2 = 0,
m1_2 = 0,
m2_2 = 0,
m3_2 = 0,
s1_2 = 1,
s2_2 = 1,
s3_2 = 1,
sy_2 = 1
)
Arguments
ry1_1 |
Correlation between DV (y) and first predictor (1), first group |
ry2_1 |
Correlation between DV (y) and second predictor (2), first group |
ry3_1 |
Correlation between DV (y) and third predictor (3), first group |
r12_1 |
Correlation between first (1) and second predictor (2), first group |
r13_1 |
Correlation between first (1) and third predictor (3), first group |
r23_1 |
Correlation between second (2) and third predictor (3), first group |
n1 |
Sample size, first group |
ry1_2 |
Correlation between DV (y) and first predictor (1), second group |
ry2_2 |
Correlation between DV (y) and second predictor (2), second group |
ry3_2 |
Correlation between DV (y) and third predictor (3), second group |
r12_2 |
Correlation between first (1) and second predictor (2), second group |
r13_2 |
Correlation between first (1) and third predictor (3), second group |
r23_2 |
Correlation between second (2) and third predictor (3), second group |
n2 |
Sample size, second group |
alpha |
Type I error (default is .05) |
my_1 |
Mean of DV (default is 0), first group |
m1_1 |
Mean of first predictor (default is 0), first group |
m2_1 |
Mean of second predictor (default is 0), first group |
m3_1 |
Mean of third predictor (default is 0), first group |
s1_1 |
Standard deviation of first predictor (default is 1), first group |
s2_1 |
Standard deviation of second predictor (default is 1), first group |
s3_1 |
Standard deviation of third predictor (default is 1), first group |
sy_1 |
Standard deviation of DV (default is 1), first group |
my_2 |
Mean of DV (default is 0), second group |
m1_2 |
Mean of first predictor (default is 0), second group |
m2_2 |
Mean of second predictor (default is 0), second group |
m3_2 |
Mean of third predictor (default is 0), second group |
s1_2 |
Standard deviation of first predictor (default is 1), second group |
s2_2 |
Standard deviation of second predictor (default is 1), second group |
s3_2 |
Standard deviation of third predictor (default is 1), second group |
sy_2 |
Standard deviation of DV (default is 1), second group |
Value
Multiple Regression shortcuts with three predictors for Ind Coefficients
Examples
MRC_short2(ry1_1=.40, ry2_1=.40, ry3_1 =-.40, r12_1=-.15,r13_1=-.60, r23_1=.25,
ry1_2=.40, ry2_2=.10, ry3_2 =-.40, r12_2=-.15,r13_2=-.60, r23_2=.25,
n1=50,n2=50,alpha=.05,my_1=1,m1_1=1,m2_1=1,m3_1=1,
sy_1=7,s1_1=1,s2_1=1,s3_1=2,
my_2=1,m1_2=1,m2_2=1,m3_2=1,sy_2=7,s1_2=1,s2_2=1,s3_2=2)
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)
Compute Precision Analyses for R-Squared This approach simply loops a function from MBESS
Description
Compute Precision Analyses for R-Squared This approach simply loops a function from MBESS
Usage
R2_prec(R2, nlow, nhigh, pred, ci = 0.95, by = 1)
Arguments
R2 |
R-squared |
nlow |
starting sample size |
nhigh |
ending sample size |
pred |
Number of Predictors |
ci |
Type of Confidence Interval (e.g., .95) |
by |
Incremental increase in sample (e.g. nlow = 10, nhigh = 24, by = 2, produces estimates of 10, 12, and 14) |
Value
Precision Analyses for R-Squared
Examples
R2_prec(R2=.467, nlow=24, nhigh=100, pred=3, by=4)
Compute power for R2 change in Multiple Regression (up to three predictors) Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All) Example code below for three predictors. Expand as needed for four or five
Description
Compute power for R2 change in Multiple Regression (up to three predictors) Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All) Example code below for three predictors. Expand as needed for four or five
Usage
R2ch(
ry1 = NULL,
ry2 = NULL,
ry3 = NULL,
r12 = NULL,
r13 = NULL,
r23 = NULL,
n = NULL,
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
Power for R2 change in Multiple Regression (up to three predictors)
Examples
R2ch(ry1=.40,ry2=.40,ry3=-.40, r12=-.15, r13=-.60,r23=.25,n=24)
Compute Power for One or Two Factor ANCOVA with a single covariate Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user Factor A can have up to four levels, Factor B, if used, can only be two
Description
Compute Power for One or Two Factor ANCOVA with a single covariate Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user Factor A can have up to four levels, Factor B, if used, can only be two
Usage
anc(
m1.1,
m2.1,
m1.2,
m2.2,
m3.1 = NULL,
m3.2 = NULL,
m4.1 = NULL,
m4.2 = NULL,
s1.1 = NULL,
s2.1 = NULL,
s1.2 = NULL,
s2.2 = NULL,
s3.1 = NULL,
s3.2 = NULL,
s4.1 = NULL,
s4.2 = NULL,
r,
s = NULL,
alpha = 0.05,
factors,
levelsA = NULL,
n
)
Arguments
m1.1 |
Cell mean for First level of Factor A, First level of Factor B |
m2.1 |
Cell mean for Second level of Factor A, First level of Factor B |
m1.2 |
Cell mean for First level of Factor A, Second level of Factor B |
m2.2 |
Cell mean for Second level of Factor A, Second level of Factor B |
m3.1 |
Cell mean for Third level of Factor A, First level of Factor B |
m3.2 |
Cell mean for Third level of Factor A, Second level of Factor B |
m4.1 |
Cell mean for Fourth level of Factor A, First level of Factor B |
m4.2 |
Cell mean for Fourth level of Factor A, Second level of Factor B |
s1.1 |
Cell standard deviation for First level of Factor A, First level of Factor B |
s2.1 |
Cell standard deviation for Second level of Factor A, First level of Factor B |
s1.2 |
Cell standard deviation for First level of Factor A, Second level of Factor B |
s2.2 |
Cell standard deviation for Second level of Factor A, Second level of Factor B |
s3.1 |
Cell standard deviation for Third level of Factor A, First level of Factor B |
s3.2 |
Cell standard deviation for Third level of Factor A, Second level of Factor B |
s4.1 |
Cell standard deviation for Fourth level of Factor A, First level of Factor B |
s4.2 |
Cell standard deviation for Fourth level of Factor A, Second level of Factor B |
r |
Correlation between covariate and dependent variable. |
s |
Overall standard deviation. Sets all cell sds equal |
alpha |
Type I error (default is .05) |
factors |
Number of factors (1 or 2) |
levelsA |
levels for factor A (up to four) |
n |
Sample Size per cell |
Value
Power for One or Two Factor ANCOVA with a single covariate
Examples
anc(m1.1=.85,m2.1=2.5, s1.1 = 1.7, s2.1=1,
m1.2=0.85, m2.2= 2.5, s1.2 = 1.7, s2.2=1,
m3.1=0.0,m3.2=2.5, s3.1 = 1.7, s3.2=1,
m4.1=0.6, m4.2 = 2.5, s4.1 = 1.7, s4.2=1, r= 0.4,
n=251, factors =2,levelsA = 4)
Compute power for a One Factor ANOVA with three levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for a One Factor ANOVA with three levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Usage
anova1f_3(
m1 = NULL,
m2 = NULL,
m3 = NULL,
s1 = NULL,
s2 = NULL,
s3 = NULL,
n1 = NULL,
n2 = NULL,
n3 = NULL,
alpha = 0.05
)
Arguments
m1 |
Mean of first group |
m2 |
Mean of second group |
m3 |
Mean of third group |
s1 |
Standard deviation of first group |
s2 |
Standard deviation of second group |
s3 |
Standard deviation of third group |
n1 |
Sample size for first group |
n2 |
Sample size for second group |
n3 |
Sample size for third group |
alpha |
Type I error (default is .05) |
Value
Power for the One Factor ANOVA
Examples
anova1f_3(m1=80, m2=82, m3=82, s1=10, s2=10, s3=10, n1=60, n2=60, n3=60)
Compute power for a One Factor ANOVA with three levels and contrasts. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for a One Factor ANOVA with three levels and contrasts. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Usage
anova1f_3c(
m1 = NULL,
m2 = NULL,
m3 = NULL,
s1 = NULL,
s2 = NULL,
s3 = NULL,
n1 = NULL,
n2 = NULL,
n3 = NULL,
alpha = 0.05,
c1 = 0,
c2 = 0,
c3 = 0
)
Arguments
m1 |
Mean of first group |
m2 |
Mean of second group |
m3 |
Mean of third group |
s1 |
Standard deviation of first group |
s2 |
Standard deviation of second group |
s3 |
Standard deviation of third group |
n1 |
Sample size for first group |
n2 |
Sample size for second group |
n3 |
Sample size for third group |
alpha |
Type I error (default is .05) |
c1 |
Weight for Contrast 1 (default is 0) |
c2 |
Weight for Contrast 2 (default is 0) |
c3 |
Weight for Contrast 3 (default is 0) |
Value
Power for the One Factor ANOVA
Examples
anova1f_3c(m1=80, m2=82, m3=82, s1=10, s2=10, s3=10,
n1=60, n2=60, n3=60, c1=2, c2=-1, c3=-1, alpha=.05)
Compute power for a One Factor Between Subjects ANOVA with four levels Takes means, sds, and sample sizes for each group
Description
Compute power for a One Factor Between Subjects ANOVA with four levels Takes means, sds, and sample sizes for each group
Usage
anova1f_4(
m1 = NULL,
m2 = NULL,
m3 = NULL,
m4 = NULL,
s1 = NULL,
s2 = NULL,
s3 = NULL,
s4 = NULL,
n1 = NULL,
n2 = NULL,
n3 = NULL,
n4 = NULL,
alpha = 0.05
)
Arguments
m1 |
Mean of first group |
m2 |
Mean of second group |
m3 |
Mean of third group |
m4 |
Mean of fourth group |
s1 |
Standard deviation of first group |
s2 |
Standard deviation of second group |
s3 |
Standard deviation of third group |
s4 |
Standard deviation of forth group |
n1 |
Sample size for first group |
n2 |
Sample size for second group |
n3 |
Sample size for third group |
n4 |
Sample size for fourth group |
alpha |
Type I error (default is .05) |
Value
Power for the One Factor Between Subjects ANOVA
Examples
anova1f_4(m1=80, m2=82, m3=82, m4=86, s1=10, s2=10, s3=10,
s4=10, n1=60, n2=60, n3=60, n4=60)
Compute power for a One Factor ANOVA with four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for a One Factor ANOVA with four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Usage
anova1f_4c(
m1 = NULL,
m2 = NULL,
m3 = NULL,
m4 = NULL,
s1 = NULL,
s2 = NULL,
s3 = NULL,
s4 = NULL,
n1 = NULL,
n2 = NULL,
n3 = NULL,
n4 = NULL,
alpha = 0.05,
c1 = 0,
c2 = 0,
c3 = 0,
c4 = 0
)
Arguments
m1 |
Mean of first group |
m2 |
Mean of second group |
m3 |
Mean of third group |
m4 |
Mean of fourth group |
s1 |
Standard deviation of first group |
s2 |
Standard deviation of second group |
s3 |
Standard deviation of third group |
s4 |
Standard deviation of forth group |
n1 |
Sample size for first group |
n2 |
Sample size for second group |
n3 |
Sample size for third group |
n4 |
Sample size for fourth group |
alpha |
Type I error (default is .05) |
c1 |
Weight for Contrast 1 (default is 0) |
c2 |
Weight for Contrast 2 (default is 0) |
c3 |
Weight for Contrast 3 (default is 0) |
c4 |
Weight for Contrast 4 (default is 0) |
Examples
anova1f_4c(m1=80, m2=82, m3=82, m4=86, s1=10, s2=10,
s3=10, s4=10, n1=60, n2=60, n3=60, n4=60,
c1=1, c2=1, c3=-1, c4=-1, alpha=.05)
anova1f_4c(m1=80, m2=82, m3=82, m4=86, s1=10, s2=10,
s3=10, s4=10, n1=60, n2=60, n3=60, n4=60,
c1=1, c2=-1, c3=-0, c4=0, alpha=.05)
anova1f_4c(m1=80, m2=82, m3=82, m4=86, s1=10, s2=10,
s3=10, s4=10, n1=60, n2=60, n3=60, n4=60,
c1=0, c2=0, c3=1, c4=-1, alpha=.05)
#'@return Power for the One Factor ANOVA
Compute power for a Two by Two Between Subjects ANOVA. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for a Two by Two Between Subjects ANOVA. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Usage
anova2x2(
m1.1 = NULL,
m1.2 = NULL,
m2.1 = NULL,
m2.2 = NULL,
s1.1 = NULL,
s1.2 = NULL,
s2.1 = NULL,
s2.2 = NULL,
n1.1 = NULL,
n1.2 = NULL,
n2.1 = NULL,
n2.2 = NULL,
alpha = 0.05,
all = "OFF"
)
Arguments
m1.1 |
Cell mean for First level of Factor A, First level of Factor B |
m1.2 |
Cell mean for First level of Factor A, Second level of Factor B |
m2.1 |
Cell mean for Second level of Factor A, First level of Factor B |
m2.2 |
Cell mean for Second level of Factor A, Second level of Factor B |
s1.1 |
Cell standard deviation for First level of Factor A, First level of Factor B |
s1.2 |
Cell standard deviation for First level of Factor A, Second level of Factor B |
s2.1 |
Cell standard deviation for Second level of Factor A, First level of Factor B |
s2.2 |
Cell standard deviation for Second level of Factor A, Second level of Factor B |
n1.1 |
Cell sample size for First level of Factor A, First level of Factor B |
n1.2 |
Cell sample size for First level of Factor A, Second level of Factor B |
n2.1 |
Cell sample size for Second level of Factor A, First level of Factor B |
n2.2 |
Cell sample size for Second level of Factor A, Second level of Factor B |
alpha |
Type I error (default is .05) |
all |
Power(ALL) - Power for detecting all predictors in the model at once (default is "OFF") |
Value
Power for the Two Factor ANOVA
Examples
anova2x2(m1.1=0.85, m1.2=0.85, m2.1=0.00, m2.2=0.60,
s1.1=1.7, s1.2=1.7, s2.1=1.7, s2.2=1.7,
n1.1=100, n1.2=100, n2.1=100, n2.2=100, alpha=.05)
anova2x2(m1.1=0.85, m1.2=0.85, m2.1=0.00, m2.2=0.60,
s1.1=1.7, s1.2=1.7, s2.1=1.7, s2.2=1.7,
n1.1=100, n1.2=100, n2.1=100, n2.2=100,
alpha=.05, all="ON")
Compute power for Simple Effects in a Two by Two Between Subjects ANOVA with two levels for each factor. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for Simple Effects in a Two by Two Between Subjects ANOVA with two levels for each factor. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Usage
anova2x2_se(
m1.1 = NULL,
m1.2 = NULL,
m2.1 = NULL,
m2.2 = NULL,
s1.1 = NULL,
s1.2 = NULL,
s2.1 = NULL,
s2.2 = NULL,
n1.1 = NULL,
n1.2 = NULL,
n2.1 = NULL,
n2.2 = NULL,
alpha = 0.05
)
Arguments
m1.1 |
Cell mean for First level of Factor A, First level of Factor B |
m1.2 |
Cell mean for First level of Factor A, Second level of Factor B |
m2.1 |
Cell mean for Second level of Factor A, First level of Factor B |
m2.2 |
Cell mean for Second level of Factor A, Second level of Factor B |
s1.1 |
Cell standard deviation for First level of Factor A, First level of Factor B |
s1.2 |
Cell standard deviation for First level of Factor A, Second level of Factor B |
s2.1 |
Cell standard deviation for Second level of Factor A, First level of Factor B |
s2.2 |
Cell standard deviation for Second level of Factor A, Second level of Factor B |
n1.1 |
Cell sample size for First level of Factor A, First level of Factor B |
n1.2 |
Cell sample size for First level of Factor A, Second level of Factor B |
n2.1 |
Cell sample size for Second level of Factor A, First level of Factor B |
n2.2 |
Cell sample size for Second level of Factor A, Second level of Factor B |
alpha |
Type I error (default is .05) examples anova2x2_se(m1.1=0.85, m1.2=0.85, m2.1=0.00, m2.2=0.60, s1.1=1.7, s1.2=1.7, s2.1=1.7, s2.2=1.7, n1.1=250, n1.2=250, n2.1=250, n2.2=250, alpha=.05) |
Value
Power for Simple Effects Tests in a Two By Two ANOVA
Compute power for Pearson's Correlation Takes correlation and range of values
Description
Compute power for Pearson's Correlation Takes correlation and range of values
Usage
corr(r, nlow, nhigh, alpha = 0.05, tails = 2, by = 1)
Arguments
r |
Correlation |
nlow |
Starting sample size |
nhigh |
Ending sample size |
alpha |
Type I error (default is .05) |
tails |
one or two-tailed tests (default is 2) |
by |
Incremental increase in sample size from low to high |
Value
Power for Pearson's Correlation
Examples
corr(r=.30, nlow=60, nhigh=100,by=2)
Compute Precision Analyses for Standardized Mean Differences
Description
Compute Precision Analyses for Standardized Mean Differences
Usage
d_prec(d, nlow, nhigh, propn1 = 0.5, ci = 0.95, tails = 2, by = 1)
Arguments
d |
Standardized means difference between groups |
nlow |
starting total sample size |
nhigh |
ending total sample size |
propn1 |
Proportion in First Group |
ci |
Type of Confidence Interval (e.g., .95) |
tails |
number of tails for test (default is 2) |
by |
Incremental increase in sample (e.g. nlow = 10, nhigh = 24, by = 2, produces estimates of 10, 12, and 14) |
Value
Precision Analyses for Standardized Mean Differences
Examples
d_prec(d=.4,nlow=100, nhigh=2000, propn1=.5, ci=.95, by=100)
Power for Comparing Dependent Coefficients in Multiple Regression with Two or Three Predictors Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All)
Description
Power for Comparing Dependent Coefficients in Multiple Regression with Two or Three Predictors Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All)
Usage
depb(ry1, ry2, ry3 = NULL, r12, r13 = NULL, r23 = 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) |
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 |
Total Sample size |
alpha |
Type I error (default is .05) |
Value
Power for Comparing Dependent Coefficients in Multiple Regression with Two or Three Predictors
Examples
depb(ry1=.40, ry2=.40, ry3=-.40, r12=-.15, r13=-.60, r23=.25,n=110, alpha=.05)
Compute Power for Comparing Two Dependent Correlations, No Variables in Common Takes correlations and range of values. First variable in each pair is termed predictor, second is DV
Description
Compute Power for Comparing Two Dependent Correlations, No Variables in Common Takes correlations and range of values. First variable in each pair is termed predictor, second is DV
Usage
depcorr0(
r12,
rxy,
r1x,
r1y,
r2x,
r2y,
nlow,
nhigh,
alpha = 0.05,
tails = 2,
by = 1
)
Arguments
r12 |
Correlation between the predictor and DV (first set of measures) |
rxy |
Correlation between the predictor and DV (second set of measures) |
r1x |
Correlation between the predictor (first measure) and the predictor variable (first measure) |
r1y |
Correlation between the predictor (first measure) and the dependent variable (second measure) |
r2x |
Correlation between the DV (first measure) and the predictor variable (first measure) |
r2y |
Correlation between the DV (first measure) and the dependent variable (second measure) |
nlow |
Starting sample size |
nhigh |
Ending sample size |
alpha |
Type I error (default is .05) |
tails |
one or two-tailed tests (default is 2) |
by |
Incremental increase in sample size from low to high |
Value
Power for Comparing Two Dependent Correlations, No Variables in Common
Examples
depcorr0(r12=.4,rxy=.7,r1x=.3,r1y=.1,r2x=.45,r2y=.35,nlow=20,nhigh=200,by=10, tails=2)
Compute Power for Comparing Two Dependent Correlations, One Variable in Common Takes correlations and range of values
Description
Compute Power for Comparing Two Dependent Correlations, One Variable in Common Takes correlations and range of values
Usage
depcorr1(r1y, r2y, r12, nlow, nhigh, alpha = 0.05, tails = 2, by = 1)
Arguments
r1y |
Correlation between the first predictor and the dependent variable |
r2y |
Correlation between the second predictor and the dependent variable |
r12 |
Correlation between the first predictor and the second predictor |
nlow |
Starting sample size |
nhigh |
Ending sample size |
alpha |
Type I error (default is .05) |
tails |
one or two-tailed tests (default is 2) |
by |
Incremental increase in sample size from low to high |
Value
Power for Comparing Dependent Correlations, One Variable in Common
Examples
depcorr1(r1y=.3,r2y=.04,r12 = .2, nlow=100,nhigh=300,by=10, tails=2)
Power for Comparing Independent R2 in Multiple Regression with Two or Three Predictors Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All)
Description
Power for Comparing Independent R2 in Multiple Regression with Two or Three Predictors Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All)
Usage
indR2(
ry1_1,
ry2_1,
ry3_1 = NULL,
r12_1,
r13_1 = NULL,
r23_1 = NULL,
n1,
ry1_2,
ry2_2,
ry3_2 = NULL,
r12_2,
r13_2 = NULL,
r23_2 = NULL,
n2,
alpha = 0.05,
tails = 2
)
Arguments
ry1_1 |
Correlation between DV (y) and first predictor (1), first test |
ry2_1 |
Correlation between DV (y) and second predictor (2), first test |
ry3_1 |
Correlation between DV (y) and third predictor (3), first test |
r12_1 |
Correlation between first (1) and second predictor (2), first test |
r13_1 |
Correlation between first (1) and third predictor (3), first test |
r23_1 |
Correlation between second (2) and third predictor (3), first test |
n1 |
Sample size first test |
ry1_2 |
Correlation between DV (y) and first predictor (1), second test |
ry2_2 |
Correlation between DV (y) and second predictor (2), second test |
ry3_2 |
Correlation between DV (y) and third predictor (3), second test |
r12_2 |
Correlation between first (1) and second predictor (2), second test |
r13_2 |
Correlation between first (1) and third predictor (3), second test |
r23_2 |
Correlation between second (2) and third predictor (3), second test |
n2 |
Sample size second test |
alpha |
Type I error (default is .05) |
tails |
number of tails for test (default is 2) |
Value
Power for Comparing R2 Coefficients in Multiple Regression
Examples
indR2(ry1_1=.40, ry2_1=.40, ry3_1 =-.40, r12_1=-.15,r13_1=-.60, r23_1=.25,
ry1_2=.40, ry2_2=.10, ry3_2 =-.40, r12_2=-.15,r13_2=-.60, r23_2=.25,
n1=115,n2=115, alpha=.05)
Power for Comparing Independent Coefficients in Multiple Regression with Two or Three Predictors Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All)
Description
Power for Comparing Independent Coefficients in Multiple Regression with Two or Three Predictors Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All)
Usage
indb(
ry1_1,
ry2_1,
ry3_1 = NULL,
r12_1,
r13_1 = NULL,
r23_1 = NULL,
n1,
ry1_2,
ry2_2,
ry3_2 = NULL,
r12_2,
r13_2 = NULL,
r23_2 = NULL,
n2,
alpha = 0.05
)
Arguments
ry1_1 |
Correlation between DV (y) and first predictor (1), first test |
ry2_1 |
Correlation between DV (y) and second predictor (2), first test |
ry3_1 |
Correlation between DV (y) and third predictor (3), first test |
r12_1 |
Correlation between first (1) and second predictor (2), first test |
r13_1 |
Correlation between first (1) and third predictor (3), first test |
r23_1 |
Correlation between second (2) and third predictor (3), first test |
n1 |
Sample size first test |
ry1_2 |
Correlation between DV (y) and first predictor (1), second test |
ry2_2 |
Correlation between DV (y) and second predictor (2), second test |
ry3_2 |
Correlation between DV (y) and third predictor (3), second test |
r12_2 |
Correlation between first (1) and second predictor (2), second test |
r13_2 |
Correlation between first (1) and third predictor (3), second test |
r23_2 |
Correlation between second (2) and third predictor (3), second test |
n2 |
Sample size second test |
alpha |
Type I error (default is .05) |
Value
Power for Comparing Independent Coefficients in Multiple Regression
Examples
indb(ry1_1=.40, ry2_1=.40, ry3_1 =-.40, r12_1=-.15,r13_1=-.60, r23_1=.25,
ry1_2=.40, ry2_2=.10, ry3_2 =-.40, r12_2=-.15,r13_2=-.60, r23_2=.25,
n1=50,n2=50, alpha=.05)
Compute Power for Comparing Two Independent Correlations Takes correlations and range of values
Description
Compute Power for Comparing Two Independent Correlations Takes correlations and range of values
Usage
indcorr(r1, r2, nlow, nhigh, propn1 = 0.5, alpha = 0.05, tails = 2, by = 1)
Arguments
r1 |
Correlation for Group 1 |
r2 |
Correlation for Group 2 |
nlow |
Starting sample size |
nhigh |
Ending sample size |
propn1 |
Proportion of sample in first group (default is .50 for equally size groups) |
alpha |
Type I error (default is .05) |
tails |
one or two-tailed tests (default is 2) |
by |
Incremental increase in sample size from low to high |
Value
Power for Comparing Two Independent Correlations
Examples
indcorr(r1=.3,r2=.1,nlow=200,nhigh=800,by=50, tails=1)
Compute power for an Independent Samples t-test Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for an Independent Samples t-test Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Usage
indt(
m1 = NULL,
m2 = NULL,
s1 = NULL,
s2 = NULL,
n1 = NULL,
n2 = NULL,
alpha = 0.05
)
Arguments
m1 |
Mean of first group |
m2 |
Mean of second group |
s1 |
Standard deviation of first group |
s2 |
Standard deviation of second group |
n1 |
Sample size for first group |
n2 |
Sample size for second group |
alpha |
Type I error (default is .05) |
Value
Power for Independent Samples t-test
Examples
indt(m1=22,m2=20,s1=5,s2=5,n1=99,n2=99)
indt(m1=1.3, m2=0, s1=4,s2=1,n1=78,n2=234)
Compute power for a One Factor Within Subjects Linear Mixed Model with up to four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for a One Factor Within Subjects Linear Mixed Model with up to four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Usage
lmm1F(
m1,
m2,
m3 = NA,
m4 = NA,
s1,
s2,
s3 = NULL,
s4 = NULL,
r12,
r13 = NULL,
r14 = NULL,
r23 = NULL,
r24 = NULL,
r34 = NULL,
n,
alpha = 0.05
)
Arguments
m1 |
Mean of first time point |
m2 |
Mean of second time point |
m3 |
Mean of third time point |
m4 |
Mean of fourth time point |
s1 |
Standard deviation of first time point |
s2 |
Standard deviation of second time point |
s3 |
Standard deviation of third time point |
s4 |
Standard deviation of forth time point |
r12 |
correlation Time 1 and Time 2 |
r13 |
correlation Time 1 and Time 3 |
r14 |
correlation Time 1 and Time 4 |
r23 |
correlation Time 2 and Time 3 |
r24 |
correlation Time 2 and Time 4 |
r34 |
correlation Time 3 and Time 4 |
n |
Sample size for first group |
alpha |
Type I error (default is .05) |
Value
Power for the One Factor Within Subjects Linear Mixed Model
Examples
lmm1F(m1=-.25,m2=.00,m3=.10,m4=.15,s1=.4,s2=.5,s3=.6,s4=.7,
r12=.50, r13=.30, r14=.15, r23=.5, r24=.30, r34=.50, n=25)
lmm1F(m1=-.25,m2=.00,m3=.10,m4=.15,s1=.4,s2=.5,s3=2.5,s4=2.0,
r12=.50, r13=.30, r14=.10, r23=.5, r24=.30, r34=.40, n=100)
Compute power for a One Factor Within Subjects LMM Trends with up to four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for a One Factor Within Subjects LMM Trends with up to four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Usage
lmm1Ftrends(
m1,
m2,
m3 = NA,
m4 = NA,
s1,
s2,
s3 = NULL,
s4 = NULL,
r12,
r13 = NULL,
r14 = NULL,
r23 = NULL,
r24 = NULL,
r34 = NULL,
n,
alpha = 0.05
)
Arguments
m1 |
Mean of first time point |
m2 |
Mean of second time point |
m3 |
Mean of third time point |
m4 |
Mean of fourth time point |
s1 |
Standard deviation of first time point |
s2 |
Standard deviation of second time point |
s3 |
Standard deviation of third time point |
s4 |
Standard deviation of forth time point |
r12 |
correlation Time 1 and Time 2 |
r13 |
correlation Time 1 and Time 3 |
r14 |
correlation Time 1 and Time 4 |
r23 |
correlation Time 2 and Time 3 |
r24 |
correlation Time 2 and Time 4 |
r34 |
correlation Time 3 and Time 4 |
n |
Sample size for first group |
alpha |
Type I error (default is .05) |
Value
Power for the One Factor Within Subjects LMM Trends
Examples
lmm1Ftrends(m1=-.25,m2=-.15,m3=-.05,m4=.05,s1=.4,s2=.5,s3=.6,s4=.7,
r12=.50, r13=.30, r14=.15, r23=.5, r24=.30, r34=.50, n=25)
Compute power for a One Factor Within Subjects and One Factor Between LMM with up to two by four levels (within). Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for a One Factor Within Subjects and One Factor Between LMM with up to two by four levels (within). Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Usage
lmm1w1b(
m1.1,
m2.1,
m3.1 = NA,
m4.1 = NA,
m1.2,
m2.2,
m3.2 = NA,
m4.2 = NA,
s1.1 = NA,
s2.1 = NA,
s3.1 = NA,
s4.1 = NA,
s1.2 = NA,
s2.2 = NA,
s3.2 = NA,
s4.2 = NA,
r1.2_1 = NULL,
r1.3_1 = NULL,
r1.4_1 = NULL,
r2.3_1 = NULL,
r2.4_1 = NULL,
r3.4_1 = NULL,
r1.2_2 = NULL,
r1.3_2 = NULL,
r1.4_2 = NULL,
r2.3_2 = NULL,
r2.4_2 = NULL,
r3.4_2 = NULL,
r = NULL,
s = NULL,
n,
alpha = 0.05
)
Arguments
m1.1 |
Mean of first level Within Factor, 1st level Between Factor |
m2.1 |
Mean of second level Within Factor, 1st level Between Factor |
m3.1 |
Mean of third level Within Factor, 1st level Between Factor |
m4.1 |
Mean of fourth level Within Factor, 1st level Between Factor |
m1.2 |
Mean of first level Within Factor, 2nd level Between Factor |
m2.2 |
Mean of second level Within Factor, 2nd level Between Factor |
m3.2 |
Mean of third level Within Factor, 2nd level Between Factor |
m4.2 |
Mean of fourth level Within Factor, 2nd level Between Factor |
s1.1 |
Standard deviation of first level Within Factor, 1st level Between Factor |
s2.1 |
Standard deviation of second level Within Factor, 1st level Between Factor |
s3.1 |
Standard deviation of third level Within Factor, 1st level Between Factor |
s4.1 |
Standard deviation of forth level Within Factor, 1st level Between Factor |
s1.2 |
Standard deviation of first level Within Factor, 2nd level Between Factor |
s2.2 |
Standard deviation of second level Within Factor, 2nd level Between Factor |
s3.2 |
Standard deviation of third level Within Factor, 2nd level Between Factor |
s4.2 |
Standard deviation of forth level Within Factor, 2nd level Between Factor |
r1.2_1 |
correlation Within Factor Level 1 and Within Factor, Level 2, 1st level Between |
r1.3_1 |
correlation Within Factor Level 1 and Within Factor, Level 3, 1st level Between |
r1.4_1 |
correlation Within Factor Level 1 and Within Factor, Level 4, 1st level Between |
r2.3_1 |
correlation Within Factor Level 1 and Within Factor, Level 3, 1st level Between |
r2.4_1 |
correlation Within Factor Level 1 and Within Factor, Level 4, 1st level Between |
r3.4_1 |
correlation Within Factor Level 1 and Within Factor, Level 4, 1st level Between |
r1.2_2 |
correlation Within Factor Level 1 and Within Factor, Level 2, 2nd level Between |
r1.3_2 |
correlation Within Factor Level 1 and Within Factor, Level 3, 2nd level Between |
r1.4_2 |
correlation Within Factor Level 1 and Within Factor, Level 4, 2nd level Between |
r2.3_2 |
correlation Within Factor Level 1 and Within Factor, Level 3, 2nd level Between |
r2.4_2 |
correlation Within Factor Level 1 and Within Factor, Level 4, 2nd level Between |
r3.4_2 |
correlation Within Factor Level 1 and Within Factor, Level 4, 2nd level Between |
r |
sets same correlations between DVs on all factor levels (seriously, just use this) |
s |
sets same standard deviation for factor levels (see comment above) |
n |
n for each between group level |
alpha |
Type I error (default is .05) |
Value
Power for the One Factor Within Subjects and One Factor Between LMM
Examples
lmm1w1b(m1.1 = -.25, m2.1=0, m3.1=0.10, m4.1=.15,
m1.2=-.25,m2.2=-.25,m3.2=-.25, m4.2=-.25,
s1.1 = .4, s2.1=.5, s3.1=0.6, s4.1=.7,
s1.2=.4,s2.2=.5,s3.2=.6, s4.2=.7,n = 50,
r1.2_1=.5,r1.3_1=.3,r1.4_1=.15,r2.3_1=.5,r2.4_1=.3,r3.4_1=.5,
r1.2_2=.5,r1.3_2=.3,r1.4_2=.15, r2.3_2=.5,r2.4_2=.3,r3.4_2=.5)
lmm1w1b(m1.1 = -.25, m2.1=0, m3.1=0.10, m4.1=.15,
m1.2=-.25,m2.2=-.25,m3.2=-.25, m4.2=-.25, s=.4, r = .5, n=100)
Compute power for a Two Factor Within Subjects Using Linear Mixed Models with up to two by four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for a Two Factor Within Subjects Using Linear Mixed Models with up to two by four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Usage
lmm2F(
m1.1,
m2.1,
m3.1 = NA,
m4.1 = NA,
m1.2,
m2.2,
m3.2 = NA,
m4.2 = NA,
s1.1 = NA,
s2.1 = NA,
s3.1 = NA,
s4.1 = NA,
s1.2 = NA,
s2.2 = NA,
s3.2 = NA,
s4.2 = NA,
r12 = NULL,
r13 = NULL,
r14 = NULL,
r15 = NULL,
r16 = NULL,
r17 = NULL,
r18 = NULL,
r23 = NULL,
r24 = NULL,
r25 = NULL,
r26 = NULL,
r27 = NULL,
r28 = NULL,
r34 = NULL,
r35 = NULL,
r36 = NULL,
r37 = NULL,
r38 = NULL,
r45 = NULL,
r46 = NULL,
r47 = NULL,
r48 = NULL,
r56 = NULL,
r57 = NULL,
r58 = NULL,
r67 = NULL,
r68 = NULL,
r78 = NULL,
r = NULL,
s = NULL,
n,
alpha = 0.05
)
Arguments
m1.1 |
Mean of first level factor 1, 1st level factor two |
m2.1 |
Mean of second level factor 1, 1st level factor two |
m3.1 |
Mean of third level factor 1, 1st level factor two |
m4.1 |
Mean of fourth level factor 1, 1st level factor two |
m1.2 |
Mean of first level factor 1, 2nd level factor two |
m2.2 |
Mean of second level factor 1, 2nd level factor two |
m3.2 |
Mean of third level factor 1, 2nd level factor two |
m4.2 |
Mean of fourth level factor 1, 2nd level factor two |
s1.1 |
Standard deviation of first level factor 1, 1st level factor two |
s2.1 |
Standard deviation of second level factor 1, 1st level factor two |
s3.1 |
Standard deviation of third level factor 1, 1st level factor two |
s4.1 |
Standard deviation of forth level factor 1, 1st level factor two |
s1.2 |
Standard deviation of first level factor 1, 2nd level factor two |
s2.2 |
Standard deviation of second level factor 1, 2nd level factor two |
s3.2 |
Standard deviation of third level factor 1, 2nd level factor two |
s4.2 |
Standard deviation of forth level factor 1, 2nd level factor two |
r12 |
correlation Factor 1, Level 1 and Factor 1, Level 2 |
r13 |
correlation Factor 1, Level 1 and Factor 1, Level 3 |
r14 |
correlation Factor 1, Level 1 and Factor 1, Level 4 |
r15 |
correlation Factor 1, Level 1 and Factor 2, Level 1 |
r16 |
correlation Factor 1, Level 1 and Factor 2, Level 2 |
r17 |
correlation Factor 1, Level 1 and Factor 2, Level 3 |
r18 |
correlation Factor 1, Level 1 and Factor 2, Level 4 |
r23 |
correlation Factor 1, Level 2 and Factor 1, Level 3 |
r24 |
correlation Factor 1, Level 2 and Factor 1, Level 4 |
r25 |
correlation Factor 1, Level 2 and Factor 2, Level 1 |
r26 |
correlation Factor 1, Level 2 and Factor 2, Level 2 |
r27 |
correlation Factor 1, Level 2 and Factor 2, Level 3 |
r28 |
correlation Factor 1, Level 2 and Factor 2, Level 4 |
r34 |
correlation Factor 1, Level 3 and Factor 1, Level 4 |
r35 |
correlation Factor 1, Level 3 and Factor 2, Level 1 |
r36 |
correlation Factor 1, Level 3 and Factor 2, Level 2 |
r37 |
correlation Factor 1, Level 3 and Factor 2, Level 3 |
r38 |
correlation Factor 1, Level 3 and Factor 2, Level 4 |
r45 |
correlation Factor 1, Level 4 and Factor 2, Level 1 |
r46 |
correlation Factor 1, Level 4 and Factor 2, Level 2 |
r47 |
correlation Factor 1, Level 4 and Factor 2, Level 3 |
r48 |
correlation Factor 1, Level 4 and Factor 2, Level 4 |
r56 |
correlation Factor 2, Level 1 and Factor 2, Level 2 |
r57 |
correlation Factor 2, Level 1 and Factor 2, Level 3 |
r58 |
correlation Factor 2, Level 1 and Factor 2, Level 4 |
r67 |
correlation Factor 2, Level 2 and Factor 2, Level 3 |
r68 |
correlation Factor 2, Level 2 and Factor 2, Level 4 |
r78 |
correlation Factor 2, Level 3 and Factor 2, Level 4 |
r |
sets same correlations between DVs on all factor levels (seriously, just use this) |
s |
sets same standard deviation for factor levels (see comment above) |
n |
Sample size for first group |
alpha |
Type I error (default is .05) |
Value
Power for the Two Factor Within Subjects LMM
Examples
lmm2F(m1.1=-.25,m2.1=0,m1.2=-.25,m2.2=.10,s1.1=.4,s2.1=.5,s1.2=.4,s2.2=.5,r=.5,n=200)
Compute power for a Two Factor Within Subjects Using Linear Mixed Models with up to two by four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for a Two Factor Within Subjects Using Linear Mixed Models with up to two by four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Usage
lmm2Fse(
m1.1,
m2.1,
m3.1 = NA,
m4.1 = NA,
m1.2,
m2.2,
m3.2 = NA,
m4.2 = NA,
s1.1 = NA,
s2.1 = NA,
s3.1 = NA,
s4.1 = NA,
s1.2 = NA,
s2.2 = NA,
s3.2 = NA,
s4.2 = NA,
r12 = NULL,
r13 = NULL,
r14 = NULL,
r15 = NULL,
r16 = NULL,
r17 = NULL,
r18 = NULL,
r23 = NULL,
r24 = NULL,
r25 = NULL,
r26 = NULL,
r27 = NULL,
r28 = NULL,
r34 = NULL,
r35 = NULL,
r36 = NULL,
r37 = NULL,
r38 = NULL,
r45 = NULL,
r46 = NULL,
r47 = NULL,
r48 = NULL,
r56 = NULL,
r57 = NULL,
r58 = NULL,
r67 = NULL,
r68 = NULL,
r78 = NULL,
r = NULL,
s = NULL,
n,
alpha = 0.05
)
Arguments
m1.1 |
Mean of first level factor 1, 1st level factor two |
m2.1 |
Mean of second level factor 1, 1st level factor two |
m3.1 |
Mean of third level factor 1, 1st level factor two |
m4.1 |
Mean of fourth level factor 1, 1st level factor two |
m1.2 |
Mean of first level factor 1, 2nd level factor two |
m2.2 |
Mean of second level factor 1, 2nd level factor two |
m3.2 |
Mean of third level factor 1, 2nd level factor two |
m4.2 |
Mean of fourth level factor 1, 2nd level factor two |
s1.1 |
Standard deviation of first level factor 1, 1st level factor two |
s2.1 |
Standard deviation of second level factor 1, 1st level factor two |
s3.1 |
Standard deviation of third level factor 1, 1st level factor two |
s4.1 |
Standard deviation of forth level factor 1, 1st level factor two |
s1.2 |
Standard deviation of first level factor 1, 2nd level factor two |
s2.2 |
Standard deviation of second level factor 1, 2nd level factor two |
s3.2 |
Standard deviation of third level factor 1, 2nd level factor two |
s4.2 |
Standard deviation of forth level factor 1, 2nd level factor two |
r12 |
correlation Factor 1, Level 1 and Factor 1, Level 2 |
r13 |
correlation Factor 1, Level 1 and Factor 1, Level 3 |
r14 |
correlation Factor 1, Level 1 and Factor 1, Level 4 |
r15 |
correlation Factor 1, Level 1 and Factor 2, Level 1 |
r16 |
correlation Factor 1, Level 1 and Factor 2, Level 2 |
r17 |
correlation Factor 1, Level 1 and Factor 2, Level 3 |
r18 |
correlation Factor 1, Level 1 and Factor 2, Level 4 |
r23 |
correlation Factor 1, Level 2 and Factor 1, Level 3 |
r24 |
correlation Factor 1, Level 2 and Factor 1, Level 4 |
r25 |
correlation Factor 1, Level 2 and Factor 2, Level 1 |
r26 |
correlation Factor 1, Level 2 and Factor 2, Level 2 |
r27 |
correlation Factor 1, Level 2 and Factor 2, Level 3 |
r28 |
correlation Factor 1, Level 2 and Factor 2, Level 4 |
r34 |
correlation Factor 1, Level 3 and Factor 1, Level 4 |
r35 |
correlation Factor 1, Level 3 and Factor 2, Level 1 |
r36 |
correlation Factor 1, Level 3 and Factor 2, Level 2 |
r37 |
correlation Factor 1, Level 3 and Factor 2, Level 3 |
r38 |
correlation Factor 1, Level 3 and Factor 2, Level 4 |
r45 |
correlation Factor 1, Level 4 and Factor 2, Level 1 |
r46 |
correlation Factor 1, Level 4 and Factor 2, Level 2 |
r47 |
correlation Factor 1, Level 4 and Factor 2, Level 3 |
r48 |
correlation Factor 1, Level 4 and Factor 2, Level 4 |
r56 |
correlation Factor 2, Level 1 and Factor 2, Level 2 |
r57 |
correlation Factor 2, Level 1 and Factor 2, Level 3 |
r58 |
correlation Factor 2, Level 1 and Factor 2, Level 4 |
r67 |
correlation Factor 2, Level 2 and Factor 2, Level 3 |
r68 |
correlation Factor 2, Level 2 and Factor 2, Level 4 |
r78 |
correlation Factor 2, Level 3 and Factor 2, Level 4 |
r |
sets same correlations between DVs on all factor levels (seriously, just use this) |
s |
sets same standard deviation for factor levels (see comment above) |
n |
Sample size for first group |
alpha |
Type I error (default is .05) |
Value
Power for Simple Effects in Two Factor Within Subjects LMM
Examples
lmm2Fse(m1.1=-.25,m2.1=0,m3.1=.10,m4.1=.15,m1.2=-.25,m2.2=.10,m3.2=.30,m4.2=.35,
s1.1=.4,s2.1=.5,s3.1=2.5,s4.1=2.0,s1.2=.4,s2.2=.5,s3.2=2.5,s4.2=2.0,r=.5,n=220)
Compute Precision Analyses for Mean Differences
Description
Compute Precision Analyses for Mean Differences
Usage
md_prec(m1, m2, s1, s2, nlow, nhigh, propn1 = 0.5, ci = 0.95, by = 1)
Arguments
m1 |
Mean of first group |
m2 |
Mean of second group |
s1 |
Standard deviation of first group |
s2 |
Standard deviation of second group |
nlow |
starting sample size |
nhigh |
ending sample size |
propn1 |
Proportion in First Group |
ci |
Type of Confidence Interval (e.g., .95) |
by |
Incremental increase in sample (e.g. nlow = 10, nhigh = 24, by = 2, produces estimates of 10, 12, and 14) |
Value
Precision Analyses for Mean Differences
Examples
md_prec(m1=2,m2 =0, s1=5, s2=5,nlow=100, nhigh =1600, propn1=.5, ci=.95, by=100)
md_prec(m1=0,m2 =0, s1=5, s2=5,nlow=100, nhigh =40000, propn1=.5, ci=.95, by=1000)
Compute Power for Mediated (Indirect) Effects Requires correlations between all variables as sample size. This approach calculates power for the Sobel test. The medjs function calculates power based on joint significance (recommended)
Description
Compute Power for Mediated (Indirect) Effects Requires correlations between all variables as sample size. This approach calculates power for the Sobel test. The medjs function calculates power based on joint significance (recommended)
Usage
med(
rxm1,
rxm2 = 0,
rxm3 = 0,
rxm4 = 0,
rxy,
rym1,
rym2 = 0,
rym3 = 0,
rym4 = 0,
rm1m2 = 0,
rm1m3 = 0,
rm1m4 = 0,
rm2m3 = 0,
rm2m4 = 0,
rm3m4 = 0,
alpha = 0.05,
mvars,
n
)
Arguments
rxm1 |
Correlation between predictor (x) and first mediator (m1) |
rxm2 |
Correlation between predictor (x) and second mediator (m2) |
rxm3 |
Correlation between predictor (x) and third mediator (m3) |
rxm4 |
Correlation between predictor (x) and fourth mediator (m4) |
rxy |
Correlation between DV (y) and predictor (x) |
rym1 |
Correlation between DV (y) and first mediator (m1) |
rym2 |
Correlation between DV (y) and second mediator (m2) |
rym3 |
Correlation DV (y) and third mediator (m3) |
rym4 |
Correlation DV (y) and fourth mediator (m4) |
rm1m2 |
Correlation first mediator (m1) and second mediator (m2) |
rm1m3 |
Correlation first mediator (m1) and third mediator (m3) |
rm1m4 |
Correlation first mediator (m1) and fourth mediator (m4) |
rm2m3 |
Correlation second mediator (m2) and third mediator (m3) |
rm2m4 |
Correlation second mediator (m2) and fourth mediator (m4) |
rm3m4 |
Correlation third mediator (m3) and fourth mediator (m4) |
alpha |
Type I error (default is .05) |
mvars |
Number of Mediators |
n |
Sample size |
Value
Power for Mediated (Indirect) Effects
Examples
med(rxm1=.25, rxy=-.35, rym1=-.5,mvars=1, n=150)
med(rxm1=.3, rxm2=.3, rxm3=.25, rxy=-.35, rym1=-.5,rym2=-.5, rym3 = -.5,
rm1m2=.7, rm1m3=.4,rm2m3=.4, mvars=3, n=150)
Compute Power for Mediated (Indirect) Effects Using Joint Significance Requires correlations between all variables as sample size. This is the recommended approach for determining power
Description
Compute Power for Mediated (Indirect) Effects Using Joint Significance Requires correlations between all variables as sample size. This is the recommended approach for determining power
Usage
medjs(
rx1x2 = NULL,
rx1m1,
rx1m2 = NULL,
rx1m3 = NULL,
rx1m4 = NULL,
rx1y,
rx2m1 = NULL,
rx2m2 = NULL,
rx2m3 = NULL,
rx2m4 = NULL,
rx2y,
rym1,
rym2 = NULL,
rym3 = NULL,
rym4 = NULL,
rm1m2 = NULL,
rm1m3 = NULL,
rm1m4 = NULL,
rm2m3 = NULL,
rm2m4 = NULL,
rm3m4 = NULL,
n,
alpha = 0.05,
mvars,
rep = 1000,
pred = 1
)
Arguments
rx1x2 |
Correlation between first predictor (x1) and second predictor (x2) |
rx1m1 |
Correlation between first predictor (x1) and first mediator (m1) |
rx1m2 |
Correlation between first predictor (x1) and second mediator (m2) |
rx1m3 |
Correlation between first predictor (x1) and third mediator (m3) |
rx1m4 |
Correlation between first predictor (x1) and fourth mediator (m4) |
rx1y |
Correlation between DV (y) and first predictor (x1) |
rx2m1 |
Correlation between second predictor (x2) and first mediator (m1) |
rx2m2 |
Correlation between second predictor (x2) and second mediator (m2) |
rx2m3 |
Correlation between second predictor (x2) and third mediator (m3) |
rx2m4 |
Correlation between second predictor (x2) and fourth mediator (m4) |
rx2y |
Correlation between DV (y) and second predictor (x2) |
rym1 |
Correlation between DV (y) and first mediator (m1) |
rym2 |
Correlation between DV (y) and second mediator (m2) |
rym3 |
Correlation DV (y) and third mediator (m3) |
rym4 |
Correlation DV (y) and fourth mediator (m4) |
rm1m2 |
Correlation first mediator (m1) and second mediator (m2) |
rm1m3 |
Correlation first mediator (m1) and third mediator (m3) |
rm1m4 |
Correlation first mediator (m1) and fourth mediator (m4) |
rm2m3 |
Correlation second mediator (m2) and third mediator (m3) |
rm2m4 |
Correlation second mediator (m2) and fourth mediator (m4) |
rm3m4 |
Correlation third mediator (m3) and fourth mediator (m4) |
n |
Sample size |
alpha |
Type I error (default is .05) |
mvars |
Number of Mediators |
rep |
number of repetitions (1000 is default) |
pred |
number of predictors (default is one) |
Value
Power for Mediated (Indirect) Effects
Examples
medjs(rx1m1=.3, rx1m2=.3, rx1m3=.25, rx1y=-.35, rym1=-.5,rym2=-.5, rym3 = -.5,
rm1m2=.7, rm1m3=.4,rm2m3=.4, mvars=3, n=150)
Compute Power for Mediated (Indirect) Effects Using Joint Significance Requires paths for all effects (and if 2 mediators, correlation) Standard deviations/variances set to 1.0 so paths are technically standardized
Description
Compute Power for Mediated (Indirect) Effects Using Joint Significance Requires paths for all effects (and if 2 mediators, correlation) Standard deviations/variances set to 1.0 so paths are technically standardized
Usage
medjs_paths(
a1,
a2 = NULL,
b1,
b2 = NULL,
rm1m2 = NULL,
cprime,
n,
alpha = 0.05,
mvars,
rep = 1000
)
Arguments
a1 |
path between predictor and first mediator |
a2 |
path between predictor and first mediator |
b1 |
Path between first mediator and dependent variable |
b2 |
Path between first mediator and dependent variable |
rm1m2 |
Correlation first mediator (m1) and second mediator (m2) |
cprime |
Path between predictor and dependent variable |
n |
Sample size |
alpha |
Type I error (default is .05) |
mvars |
Number of Mediators |
rep |
number of repetitions (1000 is default) |
Value
Power for Mediated (Indirect) Effects using Paths Coefficients
Examples
medjs_paths(a1=.25, b1=-.5,cprime=.2,mvars=1, n=150)
medjs_paths(a1=.25, a2=.1, b1=-.5,b2=-.2,cprime=.2,mvars=1, n=150)
Compute Power for Serial Mediation Effects Requires correlations between all variables as sample size. This approach calculates power for the serial mediation using joint significance (recommended)
Description
Compute Power for Serial Mediation Effects Requires correlations between all variables as sample size. This approach calculates power for the serial mediation using joint significance (recommended)
Usage
medserial(rxm1, rxm2, rxy, rm1m2, rym1, rym2, n, alpha = 0.05, rep = 1000)
Arguments
rxm1 |
Correlation between predictor (x) and first mediator (m1) |
rxm2 |
Correlation between predictor (x) and second mediator (m2) |
rxy |
Correlation between DV (y) and predictor (x) |
rm1m2 |
Correlation first mediator (m1) and second mediator (m2) |
rym1 |
Correlation between DV (y) and first mediator (m1) |
rym2 |
Correlation between DV (y) and second mediator (m2) |
n |
sample size |
alpha |
Type I error (default is .05) |
rep |
number of repetitions (1000 is default) |
Value
Power for Serial Mediated (Indirect) Effects
Examples
medserial(rxm1=.3, rxm2=.3, rxy=-.35,
rym1=-.5,rym2=-.5, rm1m2=.7,n=150)
Compute Power for Serial Mediation Effects Requires correlations between all variables as sample size. This approach calculates power for the serial mediation using joint significance (recommended) and path coefficients
Description
Compute Power for Serial Mediation Effects Requires correlations between all variables as sample size. This approach calculates power for the serial mediation using joint significance (recommended) and path coefficients
Usage
medserial_paths(a1, a2, b1, b2, d, cprime, n, alpha = 0.05, reps = 1000)
Arguments
a1 |
path between predictor and first mediator |
a2 |
path between predictor and first mediator |
b1 |
Path between first mediator and dependent variable |
b2 |
Path between first mediator and dependent variable |
d |
Path first mediator (m1) and second mediator (m2) |
cprime |
Path between predictor and dependent variable |
n |
Sample size |
alpha |
Type I error (default is .05) |
reps |
number of repetitions (1000 is default) |
Value
Power for Serial Mediated (Indirect) Effects
Examples
medserial_paths(a1=.3, a2=.3, b1=.35,
b2=.3,d=.2,cprime=.1,n=150)
Compute Power for Conditional Process Model 14 Joint Significance Requires correlations between all variables as sample size. This is the recommended approach for determining power
Description
Compute Power for Conditional Process Model 14 Joint Significance Requires correlations between all variables as sample size. This is the recommended approach for determining power
Usage
modmed14(
rxw,
rxm,
rxxw = 0,
rxy,
rwm = 0,
rxww = 0,
rwy,
rxwm = 0,
rxwy,
rmy,
n,
alpha = 0.05,
rep = 5000
)
Arguments
rxw |
Correlation between predictor (x) and moderator (w) |
rxm |
Correlation between predictor (x) and mediator (m) |
rxxw |
Correlation between predictor (x) and xweraction term (xw) - defaults to 0 |
rxy |
Correlation between DV (y) and predictor (x) |
rwm |
Correlation between moderator (w) and mediator (m) |
rxww |
Correlation between moderator (w) and xweraction (xw) - defaults to 0 |
rwy |
Correlation between DV (y) and moderator (w) |
rxwm |
Correlation between mediator (m) and xweraction (xw) - Key value |
rxwy |
Correlation between DV (y) and xweraction (xw) - defaults to 0 |
rmy |
Correlation between DV (y) and mediator (m) |
n |
Sample size |
alpha |
Type I error (default is .05) |
rep |
Number of samples drawn (defaults to 5000) |
Value
Power for Model 14 Conditional Processes
Examples
modmed14(rxw=.2, rxm=.2, rxy=.31,rwy=.35, rxwy=.2,
rmy=.32, n=200, rep=1000,alpha=.05)
Compute Power for Model 7 Conditional Processes Using Joint Significance Requires correlations between all variables as sample size Several values default to zero if no value provided This is the recommended approach for determining power
Description
Compute Power for Model 7 Conditional Processes Using Joint Significance Requires correlations between all variables as sample size Several values default to zero if no value provided This is the recommended approach for determining power
Usage
modmed7(
rxm,
rxw,
rxxw = 0,
rxy,
rwm,
rwxw = 0,
rwy = 0,
rmxw,
rmy,
rxwy = 0,
alpha = 0.05,
rep = 1000,
n = NULL
)
Arguments
rxm |
Correlation between predictor (x) and mediator (m) |
rxw |
Correlation between predictor (x) and moderator (w) |
rxxw |
Correlation between predictor (x) and interaction term (xw) - defaults to 0 |
rxy |
Correlation between DV (y) and predictor (x) |
rwm |
Correlation between moderator (w) and mediator (m) |
rwxw |
Correlation between moderator (w) and interaction (xw) - defaults to 0 |
rwy |
Correlation between DV (y) and moderator (w) |
rmxw |
Correlation between mediator (m) and interaction (xw) - Key value |
rmy |
Correlation between DV (y) and mediator (m) |
rxwy |
Correlation between DV (y) and interaction (xw) - defaults to 0 |
alpha |
Type I error (default is .05) |
rep |
Number of samples drawn (defaults to 5000) |
n |
Sample size |
Value
Power for Model 7 Conditional Processes
Examples
modmed7(rxm=.4, rxw=.2, rxy=.3, rwm=.2, rmxw=.1, rmy=.3,n=200)
Compute power for a Paired t-test Takes means, sd, and sample sizes. Alpha is .05 by default, alternative values may be entered by user. correlation (r) defaults to .50.
Description
Compute power for a Paired t-test Takes means, sd, and sample sizes. Alpha is .05 by default, alternative values may be entered by user. correlation (r) defaults to .50.
Usage
pairt(m1 = NULL, m2 = NULL, s = NULL, n = NULL, r = NULL, alpha = 0.05)
Arguments
m1 |
Mean for Pre Test |
m2 |
Mean for Post Test |
s |
Standard deviation |
n |
Sample size |
r |
Correlation pre-post measures (default is .50) |
alpha |
Type I error (default is .05) |
Value
Power for the Paired t-test
Examples
pairt(m1=25,m2=20, s = 5, n = 25, r = .5)
Compute power for a single sample proportion test Takes phi, degrees of freedom, and a range of sample sizes. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for a single sample proportion test Takes phi, degrees of freedom, and a range of sample sizes. Alpha is .05 by default, alternative values may be entered by user
Usage
prop1(p1, p0, nlow, nhigh, alpha = 0.05, tails = 2, by = 1)
Arguments
p1 |
expected proportion (a.k.a. alternative proportion) |
p0 |
null proportion |
nlow |
starting sample size |
nhigh |
ending sample size |
alpha |
Type I error (default is .05) |
tails |
number of tails for test (default is 2) |
by |
Incremental increase in sample (e.g. nlow = 10, nhigh = 24, by = 2, produces estimates of 10, 12, and 14) |
Value
Power for Tests of Single Proportion
Examples
prop1(p1=.60, p0=.42,nlow=20,nhigh=100, tails=1, by=10)
Compute power for Tests of Two Independent Proportions Takes phi, degrees of freedom, and a range of sample sizes. Alpha is .05 by default, alternative values may be entered by user This test uses what is sometimes called the chi-square test for comparing proportions
Description
Compute power for Tests of Two Independent Proportions Takes phi, degrees of freedom, and a range of sample sizes. Alpha is .05 by default, alternative values may be entered by user This test uses what is sometimes called the chi-square test for comparing proportions
Usage
propind(p1, p2, nlow, nhigh, nratio = 0.5, alpha = 0.05, tails = 2, by = 1)
Arguments
p1 |
expected proportion Group 1 |
p2 |
expected proportion Group 2 |
nlow |
starting sample size |
nhigh |
ending sample size |
nratio |
ratio of sample size of first group to second (default is .5 for equally sized groups) |
alpha |
Type I error (default is .05) |
tails |
number of tails for test (default is 2) |
by |
Incremental increase in sample (e.g. nlow = 10, nhigh = 24, by = 2, produces estimates of 10, 12, and 14) |
Value
Power for Tests of Two Independent Proportions
Examples
propind(p1=.62, p2=.55,nlow=200,nhigh=2500, by=100,nratio=.2)
Compute Precision Analyses for Correlations This approach simply loops a function from MBESS
Description
Compute Precision Analyses for Correlations This approach simply loops a function from MBESS
Usage
r_prec(r, nlow, nhigh, ci = 0.95, by = 1)
Arguments
r |
Correlation |
nlow |
starting sample size |
nhigh |
ending sample size |
ci |
Type of Confidence Interval (e.g., .95) |
by |
Incremental increase in sample (e.g. nlow = 10, nhigh = 24, by = 2, produces estimates of 10, 12, and 14) |
Value
Precision Analyses for Correlations
Examples
r_prec(r=.3, nlow=80, nhigh=400, by=20, ci=.95)
Compute Power for Regression Interaction (Correlation/Coefficient Approach)
Description
Compute Power for Regression Interaction (Correlation/Coefficient Approach)
Usage
regint(
Group1,
Group2,
sx1 = 1,
sx2 = 1,
sy1 = 1,
sy2 = 1,
nlow,
nhigh,
alpha = 0.05,
Prop_n1 = 0.5,
by = 2,
Estimates = 1
)
Arguments
Group1 |
Estimates (r or b) for Group 1 |
Group2 |
Estimates (r or b) for Group 2 |
sx1 |
Standard deviation of predictor, group 1 (defaults to 1) |
sx2 |
Standard deviation of predictor, group 2 (defaults to 1) |
sy1 |
Standard deviation of outcome, group 1 (defaults to 1) |
sy2 |
Standard deviation of outcome, group 2 (defaults to 1) |
nlow |
starting sample size |
nhigh |
ending sample size |
alpha |
Type I error (default is .05) |
Prop_n1 |
Proportion of Sample in First Group (defaults to equal sample sizes) |
by |
incremental increase in sample (e.g. nlow = 10, nhigh = 24, by = 2, produces estimates of 10, 12, and 14) |
Estimates |
1 for Correlations (default), 2 for coefficients |
Value
Power for Regression Interaction (Correlation/Coefficient Approach)
Examples
regint(Group1=-.26,Group2=.25, alpha=.05,Prop_n1=0.5,nlow=110, nhigh=140,by=2,Estimates=1)
Compute Power for Regression Interaction (R2 Change Approach)
Description
Compute Power for Regression Interaction (R2 Change Approach)
Usage
regintR2(R2Mod, R2Ch, mod_pred, ch_pred, nlow, nhigh, by = 1, alpha = 0.05)
Arguments
R2Mod |
Full Model R2 |
R2Ch |
Change in R2 Added by Interaction |
mod_pred |
Full Model Number of Predictors |
ch_pred |
Change Model Number of Predictors |
nlow |
starting sample size |
nhigh |
ending sample size |
by |
incremental increase in sample (e.g. nlow = 10, nhigh = 24, by = 2, produces estimates of 10, 12, and 14) |
alpha |
Type I error (default is .05) |
Value
Power for Regression Interaction (R2 Change Approach)
Examples
regintR2(R2Mod=.092,R2Ch=.032,mod_pred=3, ch_pred=1,nlow=100,nhigh=400,by=20)
Compute power for a t test using d statistic Takes d, sample size range, type of test, and tails.
Description
Compute power for a t test using d statistic Takes d, sample size range, type of test, and tails.
Usage
tfromd(d, nlow, nhigh, alpha = 0.05, test = "I", tails = 2, by = 2)
Arguments
d |
standardize mean difference (Cohen's d) |
nlow |
Starting total sample size |
nhigh |
Ending total sample size |
alpha |
Type I error (default is .05) |
test |
"I" for independent, "P" for paired |
tails |
one or two-tailed tests (default is 2) |
by |
Incremental increase in sample size from low to high |
Value
Power for the t-test from d statistic
Examples
tfromd(d=.2,nlow=10,nhigh=200,by=10, test="P")
tfromd(d=.2,nlow=10,nhigh=200,by=10, test="I")
Compute power for a One Factor Within Subjects ANOVA with up to four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for a One Factor Within Subjects ANOVA with up to four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Usage
win1F(
m1,
m2,
m3 = NA,
m4 = NA,
s1,
s2,
s3 = NULL,
s4 = NULL,
r12,
r13 = NULL,
r14 = NULL,
r23 = NULL,
r24 = NULL,
r34 = NULL,
n,
alpha = 0.05
)
Arguments
m1 |
Mean of first time point |
m2 |
Mean of second time point |
m3 |
Mean of third time point |
m4 |
Mean of fourth time point |
s1 |
Standard deviation of first time point |
s2 |
Standard deviation of second time point |
s3 |
Standard deviation of third time point |
s4 |
Standard deviation of forth time point |
r12 |
correlation Time 1 and Time 2 |
r13 |
correlation Time 1 and Time 3 |
r14 |
correlation Time 1 and Time 4 |
r23 |
correlation Time 2 and Time 3 |
r24 |
correlation Time 2 and Time 4 |
r34 |
correlation Time 3 and Time 4 |
n |
Total sample size |
alpha |
Type I error (default is .05) |
Value
Power for the One Factor Within Subjects ANOVA
Examples
win1F(m1=-.25,m2=.00,m3=.10,m4=.15,s1=.4,s2=.5,s3=.6,s4=.7,
r12=.50, r13=.30, r14=.15, r23=.5, r24=.30, r34=.50, n=25)
win1F(m1=-.25,m2=.00,m3=.10,m4=.15,s1=.4,s2=.5,s3=2.5,s4=2.0,
r12=.50, r13=.30, r14=.10, r23=.5, r24=.30, r34=.40, n=100)
Compute power for a One Factor Within Subjects Trends with up to four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for a One Factor Within Subjects Trends with up to four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Usage
win1Ftrends(
m1,
m2,
m3 = NA,
m4 = NA,
s1,
s2,
s3 = NULL,
s4 = NULL,
r12,
r13 = NULL,
r14 = NULL,
r23 = NULL,
r24 = NULL,
r34 = NULL,
n,
alpha = 0.05
)
Arguments
m1 |
Mean of first time point |
m2 |
Mean of second time point |
m3 |
Mean of third time point |
m4 |
Mean of fourth time point |
s1 |
Standard deviation of first time point |
s2 |
Standard deviation of second time point |
s3 |
Standard deviation of third time point |
s4 |
Standard deviation of forth time point |
r12 |
correlation Time 1 and Time 2 |
r13 |
correlation Time 1 and Time 3 |
r14 |
correlation Time 1 and Time 4 |
r23 |
correlation Time 2 and Time 3 |
r24 |
correlation Time 2 and Time 4 |
r34 |
correlation Time 3 and Time 4 |
n |
Sample size for first group |
alpha |
Type I error (default is .05) |
Value
Power for the One Factor Within Subjects Trends
Examples
win1Ftrends(m1=-.25,m2=-.15,m3=-.05,m4=.05,s1=.4,s2=.5,s3=.6,s4=.7,
r12=.50, r13=.30, r14=.15, r23=.5, r24=.30, r34=.50, n=25)
Compute power for a One Factor Within Subjects and One Factor Between ANOVA with up to two by four levels (within). Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for a One Factor Within Subjects and One Factor Between ANOVA with up to two by four levels (within). Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Usage
win1bg1(
m1.1,
m2.1,
m3.1 = NA,
m4.1 = NA,
m1.2,
m2.2,
m3.2 = NA,
m4.2 = NA,
s1.1 = NA,
s2.1 = NA,
s3.1 = NA,
s4.1 = NA,
s1.2 = NA,
s2.2 = NA,
s3.2 = NA,
s4.2 = NA,
r1.2_1 = NULL,
r1.3_1 = NULL,
r1.4_1 = NULL,
r2.3_1 = NULL,
r2.4_1 = NULL,
r3.4_1 = NULL,
r1.2_2 = NULL,
r1.3_2 = NULL,
r1.4_2 = NULL,
r2.3_2 = NULL,
r2.4_2 = NULL,
r3.4_2 = NULL,
r = NULL,
s = NULL,
n,
alpha = 0.05
)
Arguments
m1.1 |
Mean of first level Within Factor, 1st level Between Factor |
m2.1 |
Mean of second level Within Factor, 1st level Between Factor |
m3.1 |
Mean of third level Within Factor, 1st level Between Factor |
m4.1 |
Mean of fourth level Within Factor, 1st level Between Factor |
m1.2 |
Mean of first level Within Factor, 2nd level Between Factor |
m2.2 |
Mean of second level Within Factor, 2nd level Between Factor |
m3.2 |
Mean of third level Within Factor, 2nd level Between Factor |
m4.2 |
Mean of fourth level Within Factor, 2nd level Between Factor |
s1.1 |
Standard deviation of first level Within Factor, 1st level Between Factor |
s2.1 |
Standard deviation of second level Within Factor, 1st level Between Factor |
s3.1 |
Standard deviation of third level Within Factor, 1st level Between Factor |
s4.1 |
Standard deviation of forth level Within Factor, 1st level Between Factor |
s1.2 |
Standard deviation of first level Within Factor, 2nd level Between Factor |
s2.2 |
Standard deviation of second level Within Factor, 2nd level Between Factor |
s3.2 |
Standard deviation of third level Within Factor, 2nd level Between Factor |
s4.2 |
Standard deviation of forth level Within Factor, 2nd level Between Factor |
r1.2_1 |
correlation Within Factor Level 1 and Within Factor, Level 2, 1st level Between |
r1.3_1 |
correlation Within Factor Level 1 and Within Factor, Level 3, 1st level Between |
r1.4_1 |
correlation Within Factor Level 1 and Within Factor, Level 4, 1st level Between |
r2.3_1 |
correlation Within Factor Level 1 and Within Factor, Level 3, 1st level Between |
r2.4_1 |
correlation Within Factor Level 1 and Within Factor, Level 4, 1st level Between |
r3.4_1 |
correlation Within Factor Level 1 and Within Factor, Level 4, 1st level Between |
r1.2_2 |
correlation Within Factor Level 1 and Within Factor, Level 2, 2nd level Between |
r1.3_2 |
correlation Within Factor Level 1 and Within Factor, Level 3, 2nd level Between |
r1.4_2 |
correlation Within Factor Level 1 and Within Factor, Level 4, 2nd level Between |
r2.3_2 |
correlation Within Factor Level 1 and Within Factor, Level 3, 2nd level Between |
r2.4_2 |
correlation Within Factor Level 1 and Within Factor, Level 4, 2nd level Between |
r3.4_2 |
correlation Within Factor Level 1 and Within Factor, Level 4, 2nd level Between |
r |
sets same correlations between DVs on all factor levels (seriously, just use this) |
s |
sets same standard deviation for factor levels (see comment above) |
n |
for each between group level |
alpha |
Type I error (default is .05) |
Value
Power for the One Factor Within Subjects and One Factor Between ANOVA
Examples
win1bg1(m1.1 = -.25, m2.1=0, m3.1=0.10, m4.1=.15,
m1.2=-.25,m2.2=-.25,m3.2=-.25, m4.2=-.25,
s1.1 = .4, s2.1=.5, s3.1=0.6, s4.1=.7,
s1.2=.4,s2.2=.5,s3.2=.6, s4.2=.7,n = 50,
r1.2_1=.5,r1.3_1=.3,r1.4_1=.15,r2.3_1=.5,r2.4_1=.3,r3.4_1=.5,
r1.2_2=.5,r1.3_2=.3,r1.4_2=.15, r2.3_2=.5,r2.4_2=.3,r3.4_2=.5)
win1bg1(m1.1 = -.25, m2.1=0, m3.1=0.10, m4.1=.15,
m1.2=-.25,m2.2=-.25,m3.2=-.25, m4.2=-.25, s=.4, r = .5, n = 100)
Compute power for a Two Factor Within Subjects ANOVA with up to two by four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for a Two Factor Within Subjects ANOVA with up to two by four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Usage
win2F(
m1.1,
m2.1,
m3.1 = NA,
m4.1 = NA,
m1.2,
m2.2,
m3.2 = NA,
m4.2 = NA,
s1.1 = NA,
s2.1 = NA,
s3.1 = NA,
s4.1 = NA,
s1.2 = NA,
s2.2 = NA,
s3.2 = NA,
s4.2 = NA,
r12 = NULL,
r13 = NULL,
r14 = NULL,
r15 = NULL,
r16 = NULL,
r17 = NULL,
r18 = NULL,
r23 = NULL,
r24 = NULL,
r25 = NULL,
r26 = NULL,
r27 = NULL,
r28 = NULL,
r34 = NULL,
r35 = NULL,
r36 = NULL,
r37 = NULL,
r38 = NULL,
r45 = NULL,
r46 = NULL,
r47 = NULL,
r48 = NULL,
r56 = NULL,
r57 = NULL,
r58 = NULL,
r67 = NULL,
r68 = NULL,
r78 = NULL,
r = NULL,
s = NULL,
n,
alpha = 0.05
)
Arguments
m1.1 |
Mean of first level factor 1, 1st level factor two |
m2.1 |
Mean of second level factor 1, 1st level factor two |
m3.1 |
Mean of third level factor 1, 1st level factor two |
m4.1 |
Mean of fourth level factor 1, 1st level factor two |
m1.2 |
Mean of first level factor 1, 2nd level factor two |
m2.2 |
Mean of second level factor 1, 2nd level factor two |
m3.2 |
Mean of third level factor 1, 2nd level factor two |
m4.2 |
Mean of fourth level factor 1, 2nd level factor two |
s1.1 |
Standard deviation of first level factor 1, 1st level factor two |
s2.1 |
Standard deviation of second level factor 1, 1st level factor two |
s3.1 |
Standard deviation of third level factor 1, 1st level factor two |
s4.1 |
Standard deviation of forth level factor 1, 1st level factor two |
s1.2 |
Standard deviation of first level factor 1, 2nd level factor two |
s2.2 |
Standard deviation of second level factor 1, 2nd level factor two |
s3.2 |
Standard deviation of third level factor 1, 2nd level factor two |
s4.2 |
Standard deviation of forth level factor 1, 2nd level factor two |
r12 |
correlation Factor 1, Level 1 and Factor 1, Level 2 |
r13 |
correlation Factor 1, Level 1 and Factor 1, Level 3 |
r14 |
correlation Factor 1, Level 1 and Factor 1, Level 4 |
r15 |
correlation Factor 1, Level 1 and Factor 2, Level 1 |
r16 |
correlation Factor 1, Level 1 and Factor 2, Level 2 |
r17 |
correlation Factor 1, Level 1 and Factor 2, Level 3 |
r18 |
correlation Factor 1, Level 1 and Factor 2, Level 4 |
r23 |
correlation Factor 1, Level 2 and Factor 1, Level 3 |
r24 |
correlation Factor 1, Level 2 and Factor 1, Level 4 |
r25 |
correlation Factor 1, Level 2 and Factor 2, Level 1 |
r26 |
correlation Factor 1, Level 2 and Factor 2, Level 2 |
r27 |
correlation Factor 1, Level 2 and Factor 2, Level 3 |
r28 |
correlation Factor 1, Level 2 and Factor 2, Level 4 |
r34 |
correlation Factor 1, Level 3 and Factor 1, Level 4 |
r35 |
correlation Factor 1, Level 3 and Factor 2, Level 1 |
r36 |
correlation Factor 1, Level 3 and Factor 2, Level 2 |
r37 |
correlation Factor 1, Level 3 and Factor 2, Level 3 |
r38 |
correlation Factor 1, Level 3 and Factor 2, Level 4 |
r45 |
correlation Factor 1, Level 4 and Factor 2, Level 1 |
r46 |
correlation Factor 1, Level 4 and Factor 2, Level 2 |
r47 |
correlation Factor 1, Level 4 and Factor 2, Level 3 |
r48 |
correlation Factor 1, Level 4 and Factor 2, Level 4 |
r56 |
correlation Factor 2, Level 1 and Factor 2, Level 2 |
r57 |
correlation Factor 2, Level 1 and Factor 2, Level 3 |
r58 |
correlation Factor 2, Level 1 and Factor 2, Level 4 |
r67 |
correlation Factor 2, Level 2 and Factor 2, Level 3 |
r68 |
correlation Factor 2, Level 2 and Factor 2, Level 4 |
r78 |
correlation Factor 2, Level 3 and Factor 2, Level 4 |
r |
sets same correlations between DVs on all factor levels (seriously, just use this) |
s |
sets same standard deviation for factor levels (see comment above) |
n |
Sample size for first group |
alpha |
Type I error (default is .05) |
Value
Power for the Two Factor Within Subjects ANOVA
Examples
win2F(m1.1=-.25,m2.1=0,m3.1=.10,m4.1=.15,m1.2=-.25,m2.2=.10,m3.2=.30,m4.2=.35,
s1.1=.4,s2.1=.5,s3.1=2.5,s4.1=2.0,s1.2=.4,s2.2=.5,s3.2=2.5,s4.2=2.0,r=.5,n=80)
win2F(m1.1=-.25,m2.1=0,m1.2=-.25,m2.2=.10,s1.1=.4,s2.1=.5,,s1.2=.4,s2.2=.5,
r12=.5,r13=.4,r14=.55,r23=.4,r24=.5,r34=.45,n=200)
Compute power for Simple Effects in Two Factor Within Subjects ANOVA with up to two by four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Description
Compute power for Simple Effects in Two Factor Within Subjects ANOVA with up to two by four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Usage
win2Fse(
m1.1,
m2.1,
m3.1 = NA,
m4.1 = NA,
m1.2,
m2.2,
m3.2 = NA,
m4.2 = NA,
s1.1 = NA,
s2.1 = NA,
s3.1 = NA,
s4.1 = NA,
s1.2 = NA,
s2.2 = NA,
s3.2 = NA,
s4.2 = NA,
r12 = NULL,
r13 = NULL,
r14 = NULL,
r15 = NULL,
r16 = NULL,
r17 = NULL,
r18 = NULL,
r23 = NULL,
r24 = NULL,
r25 = NULL,
r26 = NULL,
r27 = NULL,
r28 = NULL,
r34 = NULL,
r35 = NULL,
r36 = NULL,
r37 = NULL,
r38 = NULL,
r45 = NULL,
r46 = NULL,
r47 = NULL,
r48 = NULL,
r56 = NULL,
r57 = NULL,
r58 = NULL,
r67 = NULL,
r68 = NULL,
r78 = NULL,
r = NULL,
s = NULL,
n,
alpha = 0.05
)
Arguments
m1.1 |
Mean of first level factor 1, 1st level factor two |
m2.1 |
Mean of second level factor 1, 1st level factor two |
m3.1 |
Mean of third level factor 1, 1st level factor two |
m4.1 |
Mean of fourth level factor 1, 1st level factor two |
m1.2 |
Mean of first level factor 1, 2nd level factor two |
m2.2 |
Mean of second level factor 1, 2nd level factor two |
m3.2 |
Mean of third level factor 1, 2nd level factor two |
m4.2 |
Mean of fourth level factor 1, 2nd level factor two |
s1.1 |
Standard deviation of first level factor 1, 1st level factor two |
s2.1 |
Standard deviation of second level factor 1, 1st level factor two |
s3.1 |
Standard deviation of third level factor 1, 1st level factor two |
s4.1 |
Standard deviation of forth level factor 1, 1st level factor two |
s1.2 |
Standard deviation of first level factor 1, 2nd level factor two |
s2.2 |
Standard deviation of second level factor 1, 2nd level factor two |
s3.2 |
Standard deviation of third level factor 1, 2nd level factor two |
s4.2 |
Standard deviation of forth level factor 1, 2nd level factor two |
r12 |
correlation Factor 1, Level 1 and Factor 1, Level 2 |
r13 |
correlation Factor 1, Level 1 and Factor 1, Level 3 |
r14 |
correlation Factor 1, Level 1 and Factor 1, Level 4 |
r15 |
correlation Factor 1, Level 1 and Factor 2, Level 1 |
r16 |
correlation Factor 1, Level 1 and Factor 2, Level 2 |
r17 |
correlation Factor 1, Level 1 and Factor 2, Level 3 |
r18 |
correlation Factor 1, Level 1 and Factor 2, Level 4 |
r23 |
correlation Factor 1, Level 2 and Factor 1, Level 3 |
r24 |
correlation Factor 1, Level 2 and Factor 1, Level 4 |
r25 |
correlation Factor 1, Level 2 and Factor 2, Level 1 |
r26 |
correlation Factor 1, Level 2 and Factor 2, Level 2 |
r27 |
correlation Factor 1, Level 2 and Factor 2, Level 3 |
r28 |
correlation Factor 1, Level 2 and Factor 2, Level 4 |
r34 |
correlation Factor 1, Level 3 and Factor 1, Level 4 |
r35 |
correlation Factor 1, Level 3 and Factor 2, Level 1 |
r36 |
correlation Factor 1, Level 3 and Factor 2, Level 2 |
r37 |
correlation Factor 1, Level 3 and Factor 2, Level 3 |
r38 |
correlation Factor 1, Level 3 and Factor 2, Level 4 |
r45 |
correlation Factor 1, Level 4 and Factor 2, Level 1 |
r46 |
correlation Factor 1, Level 4 and Factor 2, Level 2 |
r47 |
correlation Factor 1, Level 4 and Factor 2, Level 3 |
r48 |
correlation Factor 1, Level 4 and Factor 2, Level 4 |
r56 |
correlation Factor 2, Level 1 and Factor 2, Level 2 |
r57 |
correlation Factor 2, Level 1 and Factor 2, Level 3 |
r58 |
correlation Factor 2, Level 1 and Factor 2, Level 4 |
r67 |
correlation Factor 2, Level 2 and Factor 2, Level 3 |
r68 |
correlation Factor 2, Level 2 and Factor 2, Level 4 |
r78 |
correlation Factor 2, Level 3 and Factor 2, Level 4 |
r |
sets same correlations between DVs on all factor levels (seriously, just use this) |
s |
sets same standard deviation for factor levels (see comment above) |
n |
Sample size for first group |
alpha |
Type I error (default is .05) |
Value
Power for Simple Effects for Two Factor Within Subjects ANOVA
Examples
win2Fse(m1.1=-.25,m2.1=0,m3.1=.10,m4.1=.15,m1.2=-.25,m2.2=.10,m3.2=.30,m4.2=.35,
s1.1=.4,s2.1=.5,s3.1=2.5,s4.1=2.0,s1.2=.4,s2.2=.5,s3.2=2.5,s4.2=2.0,r=.5,n=220)