power.1 {odr} | R Documentation |
Budget and/or sample size, power, MDES calculation for single-level experiments detecting main effects
Description
This function can calculate required budget for desired power, power or minimum detectable effect size (MDES) under fixed budget for single-level experiments. It also can perform conventional power analyses (e.g., required sample size, power, and MDES calculation).
Usage
power.1(
cost.model = TRUE,
expr = NULL,
constraint = NULL,
sig.level = 0.05,
two.tailed = TRUE,
d = NULL,
power = NULL,
m = NULL,
n = NULL,
p = NULL,
r12 = NULL,
q = NULL,
c1 = NULL,
c1t = NULL,
dlim = NULL,
powerlim = NULL,
nlim = NULL,
mlim = NULL,
rounded = TRUE
)
Arguments
cost.model |
Logical; power analyses accommodating costs and budget (e.g., required budget for desired power, power/MDES under fixed budget) if TRUE, otherwise conventional power analyses (e.g., required sample size, power, or MDES calculation); default value is TRUE. |
expr |
Returned object from function |
constraint |
Specify the constrained value of
|
sig.level |
Significance level or type I error rate, default value is 0.05. |
two.tailed |
Logical; two-tailed tests if TRUE, otherwise one-tailed tests; default value is TRUE. |
d |
Effect size. |
power |
Statistical power. |
m |
Total budget. |
n |
The total sample size. |
p |
The proportion of individuals to be assigned to treatment. |
r12 |
The proportion of outcome variance explained by covariates. |
q |
The number of covariates. |
c1 |
The cost of sampling one unit in control condition. |
c1t |
The cost of sampling one unit in treatment condition. |
dlim |
The range for solving the root of effect size ( |
powerlim |
The range for solving the root of power ( |
nlim |
The range for searching the root of sample size ( |
mlim |
The range for searching the root of budget ( |
rounded |
Logical; round |
Value
Required budget (or required sample size), statistical power, or MDES depending on the specification of parameters. The function also returns the function name, design type, and parameters used in the calculation.
Examples
# Unconstrained optimal design
myod1 <- od.1(r12 = 0.5, c1 = 1, c1t = 5, varlim = c(0, 0.2))
myod1$out # p = 0.31
# ------- Power analyses by default considering costs and budget -------
# Required budget and sample size
mym.1 <- power.1(expr = myod1, d = 0.2, q = 1, power = 0.8)
mym.1$out # m = 1032 n = 461
# mym.1$par # parameters and their values used for the function
# Or, equivalently, specify every argument in the function
mym.1 <- power.1(d = 0.2, power = 0.8, c1 = 1, c1t = 5,
r12 = 0.5, p = 0.31, q = 1)
# Required budget and sample size with constrained p
mym.2 <- power.1(expr = myod1, d = 0.2, q = 1, power = 0.8,
constraint = list(p = 0.5))
mym.2$out # m = 1183, n = 394
# Power calculation
mypower <- power.1(expr = myod1, q = 1, d = 0.2, m = 1032)
mypower$out # power = 0.80
# Power calculation under constrained p (p = 0.5)
mypower.1 <- power.1(expr = myod1, q = 1, d = 0.2, m = 1032,
constraint = list(p = 0.5))
mypower.1$out # power = 0.74
# MDES calculation
mymdes <- power.1(expr = myod1, q = 1, power = 0.80, m = 1032)
mymdes$out # d = 0.20
# ------- Conventional power analyses with cost.model = FALSE-------
# Required sample size n
myn <- power.1(cost.model = FALSE, expr = myod1, d = 0.2, q = 1, power = 0.8)
myn$out # n = 461
# myn$par # parameters and their values used for the function
# Or, equivalently, specify every argument in the function
myn <- power.1(cost.model = FALSE, d = 0.2, power = 0.8,
r12 = 0.5, p = 0.31, q = 1)
# Power calculation
mypower1 <- power.1(cost.model = FALSE, expr = myod1, n = 461, d = 0.2, q = 1)
mypower1$out # power = 0.80
# MDES calculation
mymdes1 <- power.1(cost.model = FALSE, expr = myod1, n = 461, power = 0.8, q = 1)
mymdes1$out # d = 0.20