Use mdes.bira2r1()
to calculate the minimum detectable effect size, power.bira2r1()
to calculate the statistical power, and mrss.bira2r1()
to calculate the minimum required sample size.
mdes.bira2r1(power=.80, alpha=.05, two.tailed=TRUE,
rho2, omega2, p=.50, g2=0, r21=0, r2t2=0,
n, J)power.bira2r1(es=.25, alpha=.05, two.tailed=TRUE,
rho2, omega2, g2=0, p=.50, r21=0, r2t2=0,
n, J)
mrss.bira2r1(es=.25, power=.80, alpha=.05, two.tailed=TRUE,
n, J0=10, tol=.10,
rho2, omega2, g2=0, p=.50, r21=0, r2t2=0)
statistical power
effect size.
probability of type I error.
logical; TRUE
for two-tailed hypothesis testing, FALSE
for one-tailed hypothesis testing.
proportion of variance in the outcome between level 2 units (unconditional ICC2).
treatment effect heterogeneity as ratio of treatment effect variance among level 2 units to the residual variance at level 2.
average proportion of level 1 units randomly assigned to treatment within level 2 units.
number of covariates at level 2.
proportion of level 1 variance in the outcome explained by level 1 covariates.
proportion of treatment effect variance among level 2 units explained by level 2 covariates.
harmonic mean of level 1 units across level 2 units (or simple average).
level 2 sample size.
starting value for J
.
tolerance to end iterative process for finding J
.
function name.
list of parameters used in power calculation.
degrees of freedom.
noncentrality parameter.
statistical power
minimum detectable effect size.
number of level 2 units.
# NOT RUN {
# cross-checks
mdes.bira2r1(rho2=.17, omega2=.50, n=15, J=20)
power.bira2r1(es=.366, rho2=.17, omega2=.50, n=15, J=20)
mrss.bira2r1(es=.366, rho2=.17, omega2=.50, n=15)
# }
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