sampSize | R Documentation |
Sample size calculations
sampSize(
graph,
esf,
effSize,
powerReqFunc,
target,
corr.sim,
alpha,
corr.test = NULL,
type = c("quasirandom", "pseudorandom"),
upscale = FALSE,
n.sim = 10000,
verbose = FALSE,
...
)
graph |
A graph of class |
esf |
... |
effSize |
... |
powerReqFunc |
One power requirement function or a list of these.
If one is interested in the power to reject hypotheses 1 and 3
one could specify: |
target |
Target power that should be at least achieved. Either a numeric scalar between 0 and 1 or if parameter |
corr.sim |
Covariance matrix under the alternative. |
alpha |
... |
corr.test |
Correlation matrix that should be used for the parametric test.
If |
type |
What type of random numbers to use. |
upscale |
Logical. If |
n.sim |
... |
verbose |
Logical, whether verbose output should be printed. |
... |
... |
test |
In the parametric case there is more than one way to handle
subgraphs with less than the full alpha. If the parameter |
...
## Not run:
graph <- BonferroniHolm(4)
powerReqFunc <- function(x) { (x[1] && x[2]) || x[3] }
#TODO Still causing errors / loops.
#sampSize(graph, alpha=0.05, powerReqFunc, target=0.8, mean=c(6,4,2) )
#sampSize(graph, alpha=0.05, powerReqFunc, target=0.8, mean=c(-1,-1,-1), nsim=100)
sampSize(graph, esf=c(1,1,1,1), effSize=c(1,1,1,1),
corr.sim=diag(4), powerReqFunc=powerReqFunc, target=0.8, alpha=0.05)
powerReqFunc=list('all(x[c(1,2)])'=function(x) {all(x[c(1,2)])},
'any(x[c(0,1)])'=function(x) {any(x[c(0,1)])})
sampSize(graph=graph,
effSize=list("Scenario 1"=c(2, 0.2, 0.2, 0.2),
"Scenario 2"=c(0.2, 4, 0.2, 0.2)),
esf=c(0.5, 0.7071067811865476, 0.5, 0.7071067811865476),
powerReqFunc=powerReqFunc,
corr.sim=diag(4), target=c(0.8, 0.8), alpha=0.025)
## End(Not run)
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