Description Usage Arguments Value Examples
For four-level randomized block designs (treatment at level 1, random effects across level 2, 3 and 4), use mdes.bira4()
to calculate the minimum detectable effect size, power.bira4()
to calculate the statistical power, and mrss.bira4r1()
to calculate the minimum required sample size.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | mdes.bira4(power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, rho4, esv2=NULL, esv3=NULL, esv4=NULL,
omega2=esv2/rho2, omega3=esv3/rho3, omega4=esv4/rho4,
p=.50, r21=0, r2t2=0, r2t3=0, r2t4=0, g4=0,
n, J, K, L)
power.bira4(es=.25, alpha=.05, two.tailed=TRUE,
rho2, rho3, rho4, esv2=NULL, esv3=NULL, esv4=NULL,
omega2=esv2/rho2, omega3=esv3/rho3, omega4=esv4/rho4,
p=.50, r21=0, r2t2=0, r2t3=0, r2t4=0, g4=0,
n, J, K, L)
mrss.bira4(es=.25, power=.80, alpha=.05, two.tailed=TRUE,
n, J, K, L0=10, tol=.10,
rho2, rho3, rho4, esv2=NULL, esv3=NULL, esv4=NULL,
omega2=esv2/rho2, omega3=esv3/rho3, omega4=esv4/rho4,
p=.50, r21=0, r2t2=0, r2t3=0, r2t4=0, g4=0)
|
power |
statistical power (1-β). |
es |
effect size. |
alpha |
probability of type I error. |
two.tailed |
logical; |
rho2 |
proportion of variance in the outcome between level 2 units (unconditional ICC2). |
rho3 |
proportion of variance in the outcome between level 3 units (unconditional ICC3). |
rho4 |
proportion of variance in the outcome between level 4 units (unconditional ICC4). |
esv2 |
effect size variability as the ratio of the treatment effect variance between level 2 units to the total variance in the outcome (level 1 + level 2 + level 3 + level 4). Ignored when |
esv3 |
effect size variability as the ratio of the treatment effect variance between level 3 units to the total variance in the outcome (level 1 + level 2 + level 3 + level 4). Ignored when |
esv4 |
effect size variability as the ratio of the treatment effect variance between level 4 units to the total variance in the outcome (level 1 + level 2 + level 3 + level 4). Ignored when |
omega2 |
treatment effect heterogeneity as ratio of treatment effect variance among level 2 units to the residual variance at level 2. |
omega3 |
treatment effect heterogeneity as ratio of treatment effect variance among level 3 units to the residual variance at level 3. |
omega4 |
treatment effect heterogeneity as ratio of treatment effect variance among level 4 units to the residual variance at level 4. |
p |
average proportion of level 1 units randomly assigned to treatment within level 2 units. |
g4 |
number of covariates at level 4. |
r21 |
proportion of level 1 variance in the outcome explained by level 1 covariates. |
r2t2 |
proportion of treatment effect variance among level 2 units explained by level 2 covariates. |
r2t3 |
proportion of treatment effect variance among level 3 units explained by level 3 covariates. |
r2t4 |
proportion of treatment effect variance among level 4 units explained by level 4 covariates. |
n |
harmonic mean of level 1 units across level 2 units (or simple average). |
J |
harmonic mean of level 2 units across level 3 units (or simple average). |
K |
harmonic mean of level 3 units across level 4 units (or simple average). |
L |
number of level 4 units. |
L0 |
starting value for |
tol |
tolerance to end iterative process for finding |
fun |
function name. |
parms |
list of parameters used in power calculation. |
df |
degrees of freedom. |
ncp |
noncentrality parameter. |
power |
statistical power (1-β). |
mdes |
minimum detectable effect size. |
L |
number of level 4 units. |
1 2 3 4 5 6 7 8 9 10 | # cross-checks
mdes.bira4(rho4=.05, rho3=.15, rho2=.15,
omega4=.50, omega3=.50, omega2=.50,
n=10, J=4, K=4, L=27)
power.bira4(es = 0.142, rho4=.05, rho3=.15, rho2=.15,
omega4=.50, omega3=.50, omega2=.50,
n=10, J=4, K=4, L=27)
mrss.bira4(es = 0.142, rho4=.05, rho3=.15, rho2=.15,
omega4=.50, omega3=.50, omega2=.50,
n=10, J=4, K=4)
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