control values for estimateSnSp

The values supplied in the function–call replace the defaults and a list with all possible arguments is returned. The returned list is used as the control argument to the function estimateSnSp.

Usage

estimateSnSpControl(
  seed = NULL,
  Sn.distn = NULL,
  Sn.spread = NULL,
  Sp.distn = NULL,
  Sp.spread = NULL,
  prev.distn = NULL,
  prev.spread = NULL,
  tolerance = 0.001,
  alpha = 0.05,
  step.size = 1e-06,
  parm = NULL,
  rep.iter = TRUE,
  iter.n = 50
)

Arguments

seed

The seed used in the random generation of the distributions of sensitivity and specificity for all reference tests and prevalence of each population. See also set.seed.

Sn.distn

vector A named vector with length equal to the number of reference tests. Determines which disibution should be used for sampling sensitivity of each reference test. Inputs are "beta" or "triangular". Defaults to "beta" for each reference test.

Sn.spread

vector A named vector with length equal to the number of reference tests. Describes the width of the distribution for the sensitivity of each reference test. Inputs are "wide", "medium", or "narrow". Defaults to "wide" for each reference test.

Sp.distn

vector A named vector with length equal to the number of reference tests. Determines which disibution should be used for sampling specificity of each reference test. Inputs are "beta" or "triangular". Defaults to "beta" for each reference test.

Sp.spread

vector A named vector with length equal to the number of reference tests. Describes the width of the distribution for the specificity of each reference test. Inputs are "wide", "medium", or "narrow". Defaults to "wide" for each reference test.

prev.distn

vector A named vector with length equal to the number of populations. Determines which disibution should be used for sampling the prevalence of each population. Inputs are "beta" or "triangular". Defaults to "beta".

prev.spread

vector A named vector with length equal to the number of populations. Describes the width of the distribution for the prevalence of each population. Inputs are "wide", "medium", or "narrow". Defaults to "wide" for each population.

tolerance

Setting a limit on the pgtol used in the optim function with the "L-BFGS-B" method. See also optim. Defaults to 1E-03.

alpha

Significance levels. Defaults to 0.05.

step.size

Provides the level of resolution in values simulated from a triangular distribution. Defaults to 1E-06.

parm

vector Starting values for the optimization of the parameters of the experimental test. If the experimental test has 2 states, this vector is of length two with elements corresponding to sensitivity and specificity, respectively. If the experimental test has 3 states, this vector is of length 4 with elements corresponding to sensitivity (\(\pi\)), the proportion of 1-Sn corresponding to the suspect region for disease positive samples (\(\delta\)), specificity (\(\theta\)), and the proportion of 1-Sp corresponding to the suspect region for disease negative samples (\(\gamma\)). All values are between 0 and 1, inclusive.

rep.iter

logical (TRUE/FALSE) Indicates if updates should be printed regarding the number of iterations completed. Defaluts to TRUE.

iter.n

integer indicating the frequency of updates for the number of iterations completed. Defaluts to 50.

Value

A list with the following elements (as defined above): seed, Sn.disn, Sn.spread, Sp.distn, Sp.spread, prev.distn, prev.spread, tolerance, step.size, parm.

Author

DiagTestKit-package

Examples

estimateSnSpControl()
#> $seed
#> [1] 34924
#> 
#> $Sn.distn
#> NULL
#> 
#> $Sn.spread
#> NULL
#> 
#> $Sp.distn
#> NULL
#> 
#> $prev.distn
#> NULL
#> 
#> $prev.spread
#> NULL
#> 
#> $tolerance
#> [1] 0.001
#> 
#> $step.size
#> [1] 1e-06
#> 
#> $parm
#> NULL
#> 
#> $alpha
#> [1] 0.05
#> 
#> $rep.iter
#> [1] TRUE
#> 
#> $iter.n
#> [1] 50
#> 
estimateSnSpControl(seed = 64725)
#> $seed
#> [1] 64725
#> 
#> $Sn.distn
#> NULL
#> 
#> $Sn.spread
#> NULL
#> 
#> $Sp.distn
#> NULL
#> 
#> $prev.distn
#> NULL
#> 
#> $prev.spread
#> NULL
#> 
#> $tolerance
#> [1] 0.001
#> 
#> $step.size
#> [1] 1e-06
#> 
#> $parm
#> NULL
#> 
#> $alpha
#> [1] 0.05
#> 
#> $rep.iter
#> [1] TRUE
#> 
#> $iter.n
#> [1] 50
#>