PowerUpR (version 0.1.3)

mrss.ira1r1: Model 1.0: MRSS Calculator for Individual Random Assignment Designs, Completely Randomized Controlled Trials

Description

mrss.ira1r1 calculates minimum required sample size (MRSS) for completely randomized controlled trials where individuals are randomly assigned to treatment and control groups.

Usage

mrss.ira1r1(mdes=.25, power=.80, alpha=.05, two.tail=TRUE,
              gm=10, ncase=10, constrain="power", n=NULL, n0=10, tol=.10,
              P=.50, g1=0, R12=0)

Arguments

mdes

minimum detectable effect size.

power

statistical power (1 - type II error).

alpha

probability of type I error.

two.tail

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

gm

grid multiplier to increase the range of sample size search.

ncase

number of cases to show in the output.

constrain

parameter to constrain; "cost", "power", or "mdes".

n

included for consistency, it should remain NULL.

n0

starting value for n

tol

tolerance to stop the search algorithm.

P

proportion of units randomly assigned to treatment.

g1

number of covariates.

R12

proportion of variance in the outcome explained by covariates.

Value

fun

function name.

par

list of parameters used in MRSS calculation.

round.mrss

solution after rounding.

integer.mrss

best integer solutions around round.mrss solution.

Details

Sample size n) is calculated using an iterative procedure described in Dong & Maynard (2013) due to degrees of freedom dependency on n. Further definition of design parameters can be found in Dong & Maynard (2013).

References

Dong, N., & Maynard, R. A. (2013). PowerUp!: A Tool for Calculating Minum Detectable Effect Sizes and Minimum Required Sample Sizes for Experimental and Quasi-Experimental Design Studies,Journal of Research on Educational Effectiveness, 6(1), 24-6.

See Also

mdes.ira1r1, power.ira1r1, optimal.ira1r1

Examples

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#      mrss.ira1r1(n=83)
# 
#   
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