PowerUpR (version 0.2.3)

cra2r2: Two-Level Cluster-level Random Assignment Design, Treatment at Level 2

Description

mdes.cra2r2 calculates minimum detectable effect size, power.cra2r2 calculates statistical power, mrss.cra2r2 calculates minimum required sample size.

Usage

mdes.cra2r2(power=.80, alpha=.05, two.tailed=TRUE,
            rho2, p=.50, g2=0, r21=0, r22=0,
            n, J, ...)
power.cra2r2(es=.25, alpha=.05, two.tailed=TRUE,
             rho2, g2=0, p=.50, r21=0, r22=0,
             n, J, ...)
mrss.cra2r2(es=.25, power=.80, alpha=.05, two.tailed=TRUE,
            n, J0=10, tol=.10,
            rho2, g2=0, p=.50, r21=0, r22=0, ...)

Arguments

power

statistical power \((1-\beta)\).

es

effect size.

alpha

probability of type I error.

two.tailed

logical; TRUE for two-tailed hypothesis testing, FALSE for one-tailed hypothesis testing.

rho2

proportion of variance in the outcome explained by level 2 units.

p

proportion of level 2 units randomly assigned to treatment.

g2

number of covariates at level 2.

r21

proportion of level 1 variance in the outcome explained by level 1 covariates.

r22

proportion of level 2 variance in the outcome explained by level 2 covariates.

n

harmonic mean of level 1 units across level 2 units (or simple average).

J

level 2 sample size.

J0

starting value for J.

tol

tolerance to end iterative process for finding J.

...

to handle depreciated or defunct arguments.

Value

fun

function name.

parms

list of parameters used in power calculation.

df

degrees of freedom.

ncp

noncentrality parameter.

power

statistical power \((1-\beta)\).

mdes

minimum detectable effect size.

J

number of level 2 units.

See Also

cosa.crd2r2

Examples

# NOT RUN {
# cross-checks
mdes.cra2r2(rho2=.17, n=15, J=20)

power.cra2r2(es=.629, rho2=.17, n=15, J=20)

mrss.cra2r2(es=.629, rho2=.17, n=15)
# }