summarize.simultation.results: Title

In Auburngrads/SMRD: Statistical Methods For Reliability Data

Description

Title

Usage

summarize.simultation.results(
  results.object,
  task,
  marginal.on.fq1 = T,
  marginal.on.fq2 = T,
  focus.quantity1,
  focus.quantity.detail1,
  x.of.interest1 = NA,
  focus.quantity2,
  focus.quantity.detail2,
  x.of.interest2 = NA,
  plot.type = "histogram",
  number.points.plot = 500,
  xlim = c(NA, NA),
  ylim = c(NA, NA),
  my.title = NULL,
  xlab = NULL,
  ylab = NULL,
  filter.bad = TRUE,
  ...
)

Arguments

results.object
task
marginal.on.fq1
marginal.on.fq2
focus.quantity1
focus.quantity.detail1
x.of.interest1
focus.quantity2
focus.quantity.detail2
x.of.interest2
plot.type
number.points.plot
xlim
ylim
my.title
xlab
ylab
filter.bad
...

Examples

## Not run: 

NelsonInsulation.Weibull.altpv  <- 
  get.alt.plan.values.from.slope.and.point(distribution = "Weibull",
                                           slope= c(-12.28,-1.296),
                                           relationship = c("log","log"),
                                           accelvar.units = c("vpm","cm"),
                                           time.units = "Hours", 
                                           censor.time = 1000, probs = c(1.8e-6),
                                           accelvar = c(80,.266), 
                                           beta = 1/.6734, 
                                           use.conditions = c(80,.266))

print(NelsonInsulation.Weibull.altpv)

##	define two-variable ALT test plans

NelsonInsulation.altplan <- 
  get.alt.test.plan.direct(accel.variable.levels = cbind(c(120,120,120,150,150,150,175,175,175,200,200,200),
                                                         c(.163,.266,.355,.163,.266,.355,.163,.266,.355,.163,.266,.355)),
                           number.of.units = c(11,18,11,8,14,8,8,14,8,11,18,11),
                           censor.times = rep(1000,12),
                           accelvar.names = c("Volts per mm","Thick"),
                           describe.string = "NelsonInsulation Factorial Plan")

print(NelsonInsulation.altplan)
print(NelsonInsulation.Weibull.altpv)

##	compute the fisher and vcv matrices

ALT.vcv(NelsonInsulation.altplan,
        NelsonInsulation.Weibull.altpv)

##	compute the large-sample approximate precision (R) factors

evaluate(NelsonInsulation.altplan, 
         NelsonInsulation.Weibull.altpv,
         quantile.of.interest = c(.1,.5))

evaluate(NelsonInsulation.altplan, 
         NelsonInsulation.Weibull.altpv,
         use.conditions = c(175,.163),
         quantile.of.interest = c(.1,.5))

evaluate(NelsonInsulation.altplan, 
         NelsonInsulation.Weibull.altpv,
         use.conditions = c(100,.1),
         quantile.of.interest = c(.1,.5))

##	sample size needed for a given value of R

plot.alt.sample.size(NelsonInsulation.altplan,
                     NelsonInsulation.Weibull.altpv)


NelsonInsulation.sim.out <- ALTsim(NelsonInsulation.altplan, 
                                   NelsonInsulation.Weibull.altpv, number.sim = 400,
                                   show.detail.on = 1)

ALT.plot.time.v.x(NelsonInsulation.sim.out)

ALT.plot.time.v.x(NelsonInsulation.sim.out,
                  x.of.interest = c(100,.1),
                  xlim = c(100,200))

ALT.plot.FracFail.v.Time(NelsonInsulation.sim.out)

ALT.plot.FracFail.v.Time(NelsonInsulation.sim.out,x.of.interest = c(25,10), 
                         xlim = c(100,10000))

summarize.simultation.results(NelsonInsulation.sim.out, 
                              "Joint and Marginal", 
                              focus.quantity1 = "quantile",
                              focus.quantity.detail1 = 0.1,
                              x.of.interest1 = "100;.1",
                              focus.quantity2 = "parameter",
                              focus.quantity.detail2 = 3,
                              x.of.interest2 = NA,
                              plot.type = "density")



## End(Not run)

Auburngrads/SMRD documentation built on Sept. 14, 2020, 2:21 a.m.