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Display the operating characteristics results using an oc object

Source: R/plotOc.R
plotOc.Rd

Plots results from simulated results of :

  • [ocPostprob()]

  • [ocPostprobDist()]

  • [ocPostpred()]

  • [ocPostpredDist()]

  • [ocRctPostprobDist()]

  • [ocRctPredprobDist()]

Usage 

plotOc(decision, all_sizes, all_looks, wiggle_status)

Arguments

decision

(numeric):
Go, Stop or Gray Zone decisions of all looks simulated length(sim) times.

all_sizes

(numeric):
sample sizes of all looks simulated length(sim) times if dist applied.

all_looks

(numeric):
original looks before adjustment by wiggle = TRUE, if applied. Different to all_sizes which is after the adjustment, if made.

wiggle_status

(flag):
from wiggle flag in object.

Value

ggplot object

Examples 

# Plot result of `ocPostprob()`:
res1 <- ocPostprob(
  nnE = c(10, 20, 30),
  truep = 0.40,
  p0 = 0.20,
  p1 = 0.30,
  tL = 0.60,
  tU = 0.80,
  parE = c(1, 1),
  sim = 100,
  wiggle = FALSE,
  nnF = c(10, 20, 30)
)
#> Warning: Advise to use sim >= 50000 to achieve convergence
plotOc(
  decision = res1$Decision,
  all_sizes = res1$SampleSize,
  all_looks = res1$Looks,
  wiggle_status = res1$params$wiggle
)


# Plot result of `ocPostprob()` when `wiggle = TRUE`:
res4 <- ocPostprob(
  nnE = c(10, 20, 30),
  truep = 0.40,
  p0 = 0.20,
  p1 = 0.30,
  tL = 0.60,
  tU = 0.80,
  parE = c(1, 1),
  sim = 100,
  wiggle = TRUE,
  nnF = 30
)
#> Warning: Advise to use sim >= 50000 to achieve convergence
plotOc(
  decision = res4$Decision,
  all_sizes = res4$SampleSize,
  all_looks = res4$Looks,
  wiggle_status = res4$params$wiggle
)


# Plot result of `cPredProbDist()` when `wiggle = TRUE`:
res7 <- ocPredprobDist(
  nnE = c(10, 20, 30),
  truep = 0.40,
  deltaE = 0.10,
  deltaF = 0.10,
  relativeDelta = FALSE,
  tT = 0.6,
  phiU = 0.80,
  phiL = 0.20,
  parE = c(1, 1),
  parS = c(5, 25),
  weights = 1,
  weightsS = 1,
  sim = 50,
  wiggle = TRUE,
  decision1 = TRUE
)
#> Warning: Advise to use sim >= 50000 to achieve convergence
plotOc(
  decision = res7$Decision,
  all_sizes = res7$SampleSize,
  all_looks = res7$Looks,
  wiggle_status = res7$params$wiggle
)


# Plot result of `ocRctPostProbDist()` :
res9 <- ocRctPostprobDist(
  nnE = c(10, 20, 30),
  pE = 0.4,
  pS = 0.3,
  deltaE = 0.15,
  deltaF = 0.05,
  relativeDelta = FALSE,
  tL = 0.2,
  tU = 0.8,
  parE = c(a = 1, b = 1),
  parS = c(a = 1, b = 1),
  randRatio = 1,
  sim = 50,
  wiggle = TRUE,
  nnF = c(10, 20, 30)
)
#> Warning: Advise to use sim >= 50000 to achieve convergence
plotOc(
  decision = res9$Decision,
  all_sizes = res9$SampleSize,
  all_looks = res9$Looks,
  wiggle_status = res9$params$wiggle
)


# Plot result of `ocRctPostProbDist()` when `relativeDelta = FALSE`
res10 <- ocRctPostprobDist(
  nnE = c(10, 20, 30),
  pE = 0.4,
  pS = 0.3,
  deltaE = 0.15,
  deltaF = 0.05,
  relativeDelta = FALSE,
  tL = 0.2,
  tU = 0.8,
  parE = c(a = 1, b = 1),
  parS = c(a = 1, b = 1),
  randRatio = 1,
  sim = 50,
  wiggle = FALSE,
  nnF = c(10, 20, 30)
)
#> Warning: Advise to use sim >= 50000 to achieve convergence
plotOc(
  decision = res10$Decision,
  all_sizes = res10$SampleSize,
  all_looks = res10$Looks,
  wiggle_status = res10$params$wiggle
)


# Plot result of `ocRctPredProbDist()` when `relativeDelta = TRUE`:
res12 <- ocRctPredprobDist(
  nnE = c(10, 20, 30),
  pE = 0.3,
  pS = 0.3,
  deltaE = 0.2,
  deltaF = 0.1,
  phiU = 0.8,
  phiFu = 0.2,
  relativeDelta = TRUE,
  tT = 0.6,
  tF = 0.4,
  parE = c(a = 1, b = 1),
  parS = c(a = 1, b = 1),
  weights = 1,
  weightsS = 1,
  randRatio = 1,
  sim = 50,
  wiggle = FALSE,
  nnF = c(10, 20, 30),
  decision1 = TRUE
)
#> Warning: Advise to use sim >= 50000 to achieve convergence
plotOc(
  decision = res12$Decision,
  all_sizes = res12$SampleSize,
  all_looks = res12$Looks,
  wiggle_status = res12$params$wiggle
)