View source: R/power_law_intensity.R
power_law_intensity | R Documentation |
power_law_intensity
implements the intensity function
(i.e. failure rate, or rate of occurrence of failures (ROCOF))
for a Nonhomogeneous Poisson Process (NHPP) with
Power Law Intensity Function (Crow-AMSAA model) given lambda
(scale) and beta (shape) parameters/parameter estimates.
power_law_intensity(t, lambda, beta)
t |
A vector or list of failure times. As with |
lambda |
the scale parameter or parameter estimate for a
Power Law NHPP. Can be calculated using |
beta |
the shape parameter or parameter estimate for a
Power Law NHPP. Can be calculated using |
The output will be a data.frame
containing,
the ordered failure times ("t") and corresponding Power Law
intensity values ("power_intensity").
power_law_process
, mcf
, rocof
,
power_law_mcf
, trend_test
,
ttt
, common_beta
data(amsaa)
data(cbPalette)
# Three systems all time truncated at 200 hours
# fit a NHPP Power Law (AMSAA-Crow) Model
(m <- power_law_process(
t = split(amsaa$Time, amsaa$System),
T = list(200,200,200),
alpha = 0.05,
fail.trunc = FALSE,
iter = 10))
# Get the nonparametric mcf estimates
# and change the name of "t" to "Time"
# so it matches with the name in the
# amsaa data set
df_mcf <- mcf(
t = split(amsaa$Time, amsaa$System),
T = list(200,200,200))
names(df_mcf)[1] <- "Time"
# Merge the nonparametric mcf estimates
# with amsaa into a new data.frame amsaa1
amsaa1 <- merge(amsaa, df_mcf, by = "Time")
head(amsaa1)
# Either one of the following works
power_law_intensity(t = amsaa$Time, m$est[1], m$est[2])
power_law_intensity(t = split(amsaa$Time, amsaa$System), m$est[1], m$est[2])
rm(list = c("amsaa", "cbPalette", "df_mcf", "amsaa1"))
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