od.1.111 {odr} | R Documentation |
Optimal sample allocation calculation for single-level randomized controlled trials (RCTs) investigating mediation effects (1-1-1)
Description
The optimal design of single-level RCTs
probing mediation effects is to identify the optimal sample
allocation that use the minimum budget to achieve a fixed level of
statistical power. The optimal design parameter is the proportion of
individuals/units to be assigned to the experimental condition.
This function identifies the optimal p
.
Usage
od.1.111(
a = NULL,
b = NULL,
c1 = NULL,
c1t = NULL,
m = NULL,
r.yx = 0,
r.mx = 0,
r.mw = 0,
q.a = 0,
q.b = 0,
test = "joint",
p = NULL,
n = NULL,
tol = 1e-11,
power = 0.8,
d.p = c(0.1, 0.5),
sig.level = 0.05,
two.tailed = TRUE,
plim = c(0.01, 0.99),
varlim = c(0, 0.001),
plab = NULL,
varlab = NULL,
vartitle = NULL,
nlim = c(6, 1e+06),
verbose = TRUE,
max.value = Inf,
max.iter = 300,
e = 1e-10,
n.of.ants = 10,
n.of.archive = 20,
q = 1e-04,
xi = 0.5
)
Arguments
a |
The treatment effect on the mediator. |
b |
The within-treatment correlation between the outcome and the mediator. |
c1 |
The cost of sampling an individual in the control group. |
c1t |
The cost of sampling an individual in the treated group. |
m |
Total budget. |
r.yx |
The within-treatment correlation between the outcome and the covariate(s) in the outcome model. |
r.mx |
The within-treatment correlation between the mediator and the covariate(s) in the outcome model. |
r.mw |
The within-treatment correlation between the mediator and the covariate(s) in the mediator model. |
q.a |
The number of covariates at the mediator model (except the treatment indicator), the default value is zero. |
q.b |
The number of covariates in the outcome model (except the treatment indicator and the mediator), the default value is zero. |
test |
The type of test will be used to detect mediation effects. The default is the joint significance test (i.e., test = "joint", "Joint","JOINT"). Another choice is the Sobel test by specifying the argument as test = "sobel", "Sobel", or "SOBEL". |
p |
The proportion of level-4 clusters/units to be assigned to treatment. |
n |
Total number of individuals in the experimental study, the default value is NULL. |
tol |
convergence tolerance. |
power |
Statistical power specified, default is .80. |
d.p |
The initial sampling domains for p. Default is c(0.10, 0.50). |
sig.level |
Significance level or type I error rate, default value is 0.05. |
two.tailed |
Two tailed test, the default value is TRUE. |
plim |
The plot range for p, default value is c(0, 1). |
varlim |
The plot range for variance, default value is c(0, 0.05). |
plab |
The plot label for |
varlab |
The plot label for variance, default value is "Variance". |
vartitle |
The title of variance plot, default value is NULL. |
nlim |
The interval/range used to numerically solve for n, the default values are c(6, 1e7). |
verbose |
Print out evaluation process if TRUE, default is TRUE. |
max.value |
Maximal value of optimization when used as the stopping criterion. Default is infinite. |
max.iter |
Maximal number of function evaluations when used as the stopping criterion. Default is 200. |
e |
Maximum error value used when solution quality used as the stopping criterion, default is 1e-10. |
n.of.ants |
Number of ants used in each iteration after the initialization stage, the default value is 10. |
n.of.archive |
Size of the solution archive, default is 20. |
q |
Locality of the search (0,1), default is 0.0001. |
xi |
Convergence pressure (0, Inf), suggested: (0, 1), default is 0.5. |
Value
Unconstrained or constrained optimal sample allocation p
).
The function also returns statistical power,
function name, design type,
and parameters used in the calculation.
Examples
myod <- od.1.111(a = .3, b = .5, c1 = 10, c1t = 100)
myod