View source: R/SsSampleSizeKGivenJ.R
SsSampleSizeKGivenJ | R Documentation |
Number of cases to achieve the desired power, for specified number of readers J, and specified DBM or ORH analysis method
SsSampleSizeKGivenJ( dataset, ..., J, FOM, effectSize = NULL, method = "OR", alpha = 0.05, desiredPower = 0.8, analysisOption = "RRRC", LegacyCode = FALSE )
dataset |
The pilot dataset. If set to NULL then variance components must be supplied. |
... |
Optional variance components, VarTR, VarTC and VarErr. These are needed if dataset is not supplied. |
J |
The number of readers in the pivotal study. |
FOM |
The figure of merit. Not needed if variance components are supplied. |
effectSize |
The effect size to be used in the pivotal study. Default is NULL. Must be supplied if dataset is set to NULL and variance components are supplied. |
method |
"OR" (default) or "DBM". |
alpha |
The significance level of the study, default is 0.05. |
desiredPower |
The desired statistical power, default is 0.8. |
analysisOption |
Desired generalization, "RRRC", "FRRC", "RRFC" or "ALL" (the default). |
LegacyCode |
Logical, default is |
effectSize
= NULL uses the observed effect size in
the pilot study. A numeric value over-rides the default value. This
argument must be supplied if dataset = NULL and variance compenents
(the optional ... arguments) are supplied.
A list of two elements:
K |
The minimum number of cases K in the pivotal study
to just achieve the desired statistical power, calculated
for each value of |
power |
The predicted statistical power. |
The procedure is valid for ROC studies only; for FROC studies see Vignettes 19.
## the following two should give identical results SsSampleSizeKGivenJ(dataset02, FOM = "Wilcoxon", effectSize = 0.05, J = 6, method = "DBM") a <- UtilVarComponentsDBM(dataset02, FOM = "Wilcoxon")$VarCom SsSampleSizeKGivenJ(dataset = NULL, J = 6, effectSize = 0.05, method = "DBM", LegacyCode = TRUE, list(VarTR = a["VarTR",1], VarTC = a["VarTC",1], VarErr = a["VarErr",1])) ## the following two should give identical results SsSampleSizeKGivenJ(dataset02, FOM = "Wilcoxon", effectSize = 0.05, J = 6, method = "OR") a <- UtilORVarComponentsFactorial(dataset02, FOM = "Wilcoxon")$VarCom KStar <- length(dataset02$ratings$NL[1,1,,1]) SsSampleSizeKGivenJ(dataset = NULL, J = 6, effectSize = 0.05, method = "OR", list(KStar = KStar, VarTR = a["VarTR",1], Cov1 = a["Cov1",1], Cov2 = a["Cov2",1], Cov3 = a["Cov3",1], Var = a["Var",1])) for (J in 6:10) { ret <- SsSampleSizeKGivenJ(dataset02, FOM = "Wilcoxon", J = J, analysisOption = "RRRC") message("# of readers = ", J, " estimated # of cases = ", ret$K, ", predicted power = ", signif(ret$powerRRRC,3), "\n") }
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