`St.Rd`

Performs DBM or OR significance testing for the dataset.

```
St(
dataset,
FOM,
method = "OR",
covEstMethod = "jackknife",
analysisOption = "RRRC",
alpha = 0.05,
FPFValue = 0.2,
nBoots = 200,
seed = NULL,
details = 0
)
```

- dataset
The dataset to be analyzed, see

`RJafroc-package`

. The dataset design can be "FCTRL" or "FCTRL-X-MOD".- FOM
The figure of merit, see

`UtilFigureOfMerit`

- method
The significance testing method to be used:

`"DBM"`

for the Dorfman-Berbaum-Metz method or`"OR"`

for the Obuchowski-Rockette method (default).- covEstMethod
The covariance matrix estimation method in

`ORH`

analysis (for`method = "DBM"`

the jackknife is always used).`"Jackknife"`

(default),`"Bootstrap"`

, in which case`nBoots`

is relevant, default 200,`"DeLong"`

; requires`FOM = "Wilcoxon" or "ROI" or "HrAuc"`

.

- analysisOption
Determines which factors are regarded as random and which are fixed:

`"RRRC"`

= random-reader random case (default),`"FRRC"`

= fixed-reader random case,`"RRFC"`

= random-reader fixed case,

- alpha
The significance level (alpha) of the test of the null hypothesis that all modality effects are zero (default: alpha = 0.05).

- FPFValue
Only needed for

`LROC`

data**and**FOM = "PCL" or "ALROC"; where to evaluate a partial curve based figure of merit. (default: FPFValue = 0.2).- nBoots
The number of bootstraps (defaults to 200), only needed if

`covEstMethod = "bootstrap"`

and`method = "OR"`

- seed
For bootstraps the seed of the RNG (default: seed =

`NULL`

), only needed if`method = "OR"`

and`covEstMethod = "bootstrap"`

.- details
Amount of explanations in output, default is 0 for no explanations and 1 for explanations.

A list containing the results of the analysis.

`details`

= 0 should suffice for factorial dataset analysis since
the names of the output lists are self-explanatory. For cross-modality
analysis `details`

= 1 is suggested to better understand the output.

Dorfman DD, Berbaum KS, Metz CE (1992) ROC characteristic rating analysis: Generalization to the Population of Readers and Patients with the Jackknife method, Invest. Radiol. 27, 723-731.

Obuchowski NA, Rockette HE (1995) Hypothesis Testing of the Diagnostic Accuracy for Multiple Diagnostic Tests: An ANOVA Approach with Dependent Observations, Communications in Statistics: Simulation and Computation 24, 285-308.

Hillis SL (2014) A marginal-mean ANOVA approach for analyzing multireader multicase radiological imaging data, Statistics in medicine 33, 330-360.

Thompson JD, Chakraborty DP, Szczepura K, et al. (2016) Effect of reconstruction methods and x-ray tube current-time product on nodule detection in an anthropomorphic thorax phantom: a crossed-modality JAFROC observer study. Medical Physics. 43(3):1265-1274.

Chakraborty DP (2017) *Observer Performance Methods for Diagnostic Imaging - Foundations,
Modeling, and Applications with R-Based Examples*, CRC Press, Boca Raton, FL.
https://www.routledge.com/Observer-Performance-Methods-for-Diagnostic-Imaging-Foundations-Modeling/Chakraborty/p/book/9781482214840

```
result <- St(dataset02,FOM = "Wilcoxon", method = "DBM")
result <- St(dataset02,FOM = "Wilcoxon", method = "OR")
result <- St(datasetX, FOM = "wAFROC", method = "OR", analysisOption = "RRRC")
# \donttest{
result <- St(dataset05, FOM = "wAFROC")
result <- St(dataset05, FOM = "HrAuc", method = "DBM")
# }
```