summaryRiskLot generic function to print the risk summary statistics at lot level

Print summary MC risk results per lot

Usage

summaryRiskLot.qraLm(x, ...)

Arguments

x

qraLm object. See Lot2LotGen()

...

optional plot parameters passed to the plot function

Author

Vasco Cadavez

Examples

dat <- Lot2LotGen(
                  nLots = 500,
                  sizeLot = 500,
                  unitSize = 500,
                  betaAlpha = 0.5112,
                  betaBeta = 9.959,
                  C0MeanLog = 1.023,
                  C0SdLog = 0.3267,
                  propVarInter = 0.7
                  )
DRmodel = "JEMRA"
population = 2
res <- DRForModel(dat, 
                  model=DRmodel,
                  population = population)
str(res)
#> List of 15
#>  $ Lot2LotGenParameters:List of 9
#>   ..$ nLots       : num 500
#>   ..$ sizeLot     : num 500
#>   ..$ unitSize    : num 500
#>   ..$ betaAlpha   : num 0.511
#>   ..$ betaBeta    : num 9.96
#>   ..$ C0MeanLog   : num 1.02
#>   ..$ C0SdLog     : num 0.327
#>   ..$ propVarInter: num 0.7
#>   ..$ Poisson     : logi FALSE
#>  $ lotMeans            : num [1:500] 0.0102 0.1726 0.0862 0.7233 0.0857 ...
#>  $ unitsCounts         : num [1:250000] 0 0 0 0 0 ...
#>  $ N                   : num [1:500, 1:500] 0 0 0 0 0 ...
#>  $ ProbUnitPos         : num [1:500] 0.000725 0.996398 0.994545 1 0.999794 ...
#>  $ P                   : num 0.868
#>  $ betaGen             : num [1:500] 1.45e-06 1.12e-02 1.04e-02 5.60e-02 1.68e-02 ...
#>  $ nLots               : num 500
#>  $ sizeLot             : num 500
#>  $ unitSize            : num 500
#>  $ Risk                : num [1:500, 1:500] 0 0 0 0 0 ...
#>  $ lotMeanRisk         : num [1:500] 3.93e-15 9.11e-11 4.54e-11 3.83e-10 4.54e-11 ...
#>  $ servingRisk         : num [1:500, 1:500] 0 0 0 0 0 ...
#>  $ Model               : chr "JEMRA"
#>  $ Population          : num 2
#>  - attr(*, "class")= chr "qraLm"
summaryRiskLot.qraLm(res)