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] 0.118 0.0113 1.694 1.3366 0.0128 ...
#> $ 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] 1 0.9843 1 1 0.0697 ...
#> $ P : num 0.907
#> $ betaGen : num [1:1000] 1.38e-02 4.14e-03 1.40e-01 1.39e-01 7.23e-05 ...
#> $ 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] 6.25e-11 5.90e-12 8.98e-10 7.08e-10 4.74e-13 ...
#> $ 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)