mfmi.mleprobplot: Title

In Auburngrads/SMRD: Statistical Methods For Reliability Data

Description

Title

Usage

mfmi.mleprobplot(
  data.ld,
  distribution,
  distribution.vec = rep(distribution, length = length(data.mfld)),
  the.failure.modes = names(data.mfld),
  ylab = GetSMRDDefault("SMRD.LabelOnYaxis"),
  conf.level = GetSMRDDefault("SMRD.ConfLevel")/100,
  xlab = get.time.units(data.ld),
  xlim = c(NA, NA),
  ylim = c(NA, NA),
  time.range = c(NA, NA),
  dump = 1,
  grids = F,
  cex = 1.2,
  linear.axes = F,
  pch = (1:(length(the.failure.modes) + 1))[-2],
  lty = rep(1, length(the.failure.modes)),
  ci.list = NULL,
  lwd = rep(1, length(the.failure.modes)),
  slope.axis = F,
  plot.censored.ticks = F,
  trunc.correct = T,
  col.fhat.vec = (1:(length(the.failure.modes) + length(col.ci)))[-col.ci],
  col.ci = 4,
  shape = NULL,
  do.legend = NULL,
  ...
)

Arguments

data.ld
distribution
distribution.vec
the.failure.modes
ylab
conf.level
xlab
xlim
ylim
time.range
dump
grids
cex
linear.axes
pch
lty
ci.list
lwd
slope.axis
plot.censored.ticks
trunc.correct
col.fhat.vec
col.ci
shape
do.legend
...

Examples

## Not run: 

DeviceG.ld <- frame.to.ld(deviceg,
                          response.column = 1, 
                          failure.mode.column =  2)
                          
# Plots the individual failure modes separately

par(mfrow = c(1,2))
mfmi.mleprobplot(DeviceG.ld, distribution = "weibull") 
mfmi.mleprobplot(DeviceG.ld, distribution = "lognormal") 
par(mfrow = c(1,1))

# Plots both modes in one plot

mfmc.mleprobplot(DeviceG.ld, 
                 distribution = "Weibull") 

mfmc.mleprobplot(DeviceG.ld, 
                 distribution = "Weibull", 
                 band.type = "none")
                 
# different distributions for the different failure modes

mfmc.mleprobplot(DeviceG.ld, 
                 distribution = "Weibull", 
                 distribution.vec = c("Weibull","Lognormal"))

## End(Not run)

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