Plot risk distribution per contaminated lot
Arguments
- x
qraLm object see
Lot2LotGen()- ...
optional plot parameters passed to the plot function
Examples
prod <- Lot2LotGen(
nLots = 1000,
sizeLot = 1000,
unitSize = 500,
betaAlpha = 0.5112,
betaBeta = 9.959,
C0MeanLog = 1.023,
C0SdLog = 0.3267,
propVarInter = 0.7
)
DRmodel <- "JEMRA"
population <- 2
risk <- DRForModel(prod,
model = DRmodel,
population = population)
str(risk)
#> List of 15
#> $ Lot2LotGenParameters:List of 9
#> ..$ nLots : num 1000
#> ..$ sizeLot : num 1000
#> ..$ 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 [1:1000] 0.07909 0.0113 0.00927 0.10177 0.23561 ...
#> $ unitsCounts : num [1:1000000] 0 0 0 0 0 0 0 0 0 0 ...
#> $ N : num [1:1000, 1:1000] 0 0 0 0 0 0 0 0 0 0 ...
#> $ ProbUnitPos : num [1:1000] 0.9438 0.0068 0.2166 0.9991 1 ...
#> $ P : num 0.907
#> $ betaGen : num [1:1000] 2.87e-03 6.82e-06 2.44e-04 6.96e-03 2.03e-02 ...
#> $ nLots : num 1000
#> $ sizeLot : num 1000
#> $ unitSize : num 500
#> $ Risk : num [1:1000, 1:1000] 0 0 0 0 0 0 0 0 0 0 ...
#> $ lotMeanRisk : num [1:1000] 3.96e-11 4.07e-14 1.06e-12 5.39e-11 1.25e-10 ...
#> $ servingRisk : num [1:1000, 1:1000] 0 0 0 0 0 0 0 0 0 0 ...
#> $ Model : chr "JEMRA"
#> $ Population : num 2
#> - attr(*, "class")= chr "qraLm"
plotLotRisk.qraLm(risk)