In Auburngrads/SMRD: Statistical Methods For Reliability Data
SMRD:::vinny()
library(SMRD)
In this echapter...
Specify prior distribution characteristics for the bearing cage data using a diffuse quantile and a diffuse sigma
BearingCage.ld <- frame.to.ld(bearingcage,
response.column = 1,
censor.column = 2,
case.weight.column = 3)
DT::datatable(BearingCage.ld)
bcage.prior.weibull.spec1 <-
specify.simple.prior(p = .01,
qdist = "loguniform",
qlower = 100,
qupper = 5000,
sigma.dist = "lognormal",
sigma.lower = 0.2,
sigma.upper = 0.5,
distribution = "Weibull")
Specify prior distribution characteristics for the bearing cage data using a diffuse quantile and an informative sigma
bcage.prior.weibull.spec2 <-
specify.simple.prior(p = .01,
qdist = "loguniform",
qlower = 1000,
qupper = 1400,
sigma.dist = "lognormal",
sigma.lower = 1.5,
sigma.upper = 2.5,
distribution = "Weibull")
Specify prior distribution characteristics for the bearing cage data using an informative quantile and an informative sigma
bcage.prior.weibull.spec3 <-
specify.simple.prior(p = .01,
qdist = "lognormal",
qlower = 1000,
qupper = 1400,
sigma.dist = "lognormal",
sigma.lower = 1.5,
sigma.upper = 2.5,
distribution = "Weibull")
Lognormal distribution
Specify prior distribution characteristics for the bearing cage data using a diffuse quantile and a diffuse sigma
bcage.prior.lognormal.spec1 <-
specify.simple.prior( p = .04,
qdist = "loguniform",
qlower = 100,
qupper = 5000,
sigma.dist = "lognormal",
sigma.lower = 0.2,
sigma.upper = 5,
distribution = "Lognormal")
Specify prior distribution characteristics for the bearing cage data using a diffuse quantile and a informative sigma
bcage.prior.lognormal.spec2 <-
specify.simple.prior(p = .01,
qdist = "loguniform",
qlower = 1000,
qupper = 1400,
sigma.dist = "lognormal",
sigma.lower = 1,
sigma.upper = 1.5,
distribution = "Lognormal")
Specify prior distribution characteristics for the bearing cage data using a informative quantile and a informative sigma
bcage.prior.lognormal.spec3 <-
specify.simple.prior(p = .01,
qdist = "loguniform",
qlower = 1000,
qupper = 1400,
sigma.dist = "lognormal",
sigma.lower = 1.,
sigma.upper = 1.5,
distribution = "Lognormal")
Create the prior distributions
prior2.bcage <-
make.prior(spec = bcage.prior.lognormal.spec1,
number.in.prior = 3000)
prior.and.post2.bcage <-
get.big.posterior(bcage.prior.lognormal.spec1,
BearingCage.ld)
prior.and.post2.bcage$post[1:10,]
prior.and.post3.bcage <-
make.small.posterior.object(prior.and.post2.bcage)
Marginal distribution of the parameters
summarize.posterior.or.prior(prior.and.post2.bcage,
post.or.prior = "post",
task = "Marginals only",
marginal.on.sigma = T,
marginal.on.pos = F,
type.position = "Parameter",
newdata = "mu",
include.likelihood = T)
#quantle marginal
summarize.posterior.or.prior(prior.and.post2.bcage,
post.or.prior = "post",
task = "Marginals only",
marginal.on.sigma = F,
marginal.on.pos = T,
type.position = "Quantile",
newdata = .1,
include.likelihood = T)
#sigma marginal
summarize.posterior.or.prior(prior.and.post2.bcage,
post.or.prior = "post",
task = "Marginals only",
marginal.on.sigma = T,
marginal.on.pos = F,
type.position = "Quantile",
newdata = .1,
include.likelihood = T)
#prob
summarize.posterior.or.prior(prior.and.post2.bcage,
post.or.prior = "post",
task = "Marginals only",
marginal.on.sigma = F,
marginal.on.pos = T,
type.position = "Failure probability",
newdata = 1000,
include.likelihood = T)
#Joint only axes.range.default.post = T
summarize.posterior.or.prior(prior.and.post2.bcage,
post.or.prior = "post",
task = "Joint only",
axes.range.default.post = T,
marginal.on.sigma = F,
marginal.on.pos = F,
type.position = "Failure probability",
newdata = 1000,
include.likelihood = T)
#Joint only
summarize.posterior.or.prior(prior.and.post2.bcage,
post.or.prior = "prior",
task = "Joint only",
marginal.on.sigma = F,
marginal.on.pos = F,
type.position = "Parameter",
newdata = "mu",
include.likelihood = T)
#Joint only
summarize.posterior.or.prior(prior.and.post2.bcage,
post.or.prior = "prior",
task = "Joint only",
marginal.on.sigma = F,
marginal.on.pos = F,
type.position = "Quantile",
newdata = .1,
include.likelihood = T)
#Joint only axes.range.default.post = F
summarize.posterior.or.prior(prior.and.post2.bcage,
post.or.prior = "post",
task = "Joint only",
axes.range.default.post = F,
marginal.on.sigma = F,
marginal.on.pos = F,
type.position = "Failure probability",
newdata = 1000,
include.likelihood = F)
#Joint only axes.range.default.post = F
summarize.posterior.or.prior(prior.and.post2.bcage,
post.or.prior = "prior",
task = "Joint only",
axes.range.default.post = F,
marginal.on.sigma = F,
marginal.on.pos = F,
type.position = "Failure probability",
newdata = 1000,
include.likelihood = F)
summarize.posterior.or.prior(prior.and.post2.bcage,
post.or.prior = "prior",
task = "Joint and Marginal",
marginal.on.sigma = F,
marginal.on.pos = F,
type.position = "Parameter",
newdata = "mu")
summarize.posterior.or.prior(prior.and.post2.bcage,
post.or.prior = "post",
task = "Joint only",
marginal.on.sigma = F,
marginal.on.pos = F,
type.position = "Parameter",
newdata = "mu",
include.likelihood = T)
summarize.posterior.or.prior(prior.and.post2.bcage,
post.or.prior = "prior",
task = "Joint only",
marginal.on.sigma = F,
marginal.on.pos = F,
type.position = "Parameter",
newdata = "mu",
include.likelihood = T)
summarize.posterior.or.prior(prior.and.post2.bcage,
post.or.prior = "prior",
task = "Joint and Marginal",
marginal.on.sigma = F,
marginal.on.pos = F,
type.position = "Quantile",
newdata = .1)
summarize.posterior.or.prior(prior.and.post2.bcage,
post.or.prior = "prior",
task = "Joint and Marginal",
marginal.on.sigma = F,
marginal.on.pos = F,
type.position = "Failure probability",
newdata = 1000)
summarize.posterior.or.prior(prior.and.post2.bcage,
post.or.prior = "prior",
task = "Joint and Marginal",
marginal.on.sigma = F,
marginal.on.pos = F,
type.position = "Failure probability",
newdata = 6000)
summarize.posterior.or.prior(prior.and.post2.bcage,
post.or.prior = "post",
task = "Joint only",
marginal.on.sigma = F,
marginal.on.pos = F,
type.position = "Parameter",
newdata = "mu",
include.likelihood = T)
summarize.posterior.or.prior(prior.and.post2.bcage,
post.or.prior = "post",
task = "Joint and Marginal",
marginal.on.sigma = F,
marginal.on.pos = T,
type.position = "Parameter",
newdata = "mu")
summarize.posterior.or.prior(prior.and.post2.bcage,
post.or.prior = "post",
task = "Joint and Marginal",
marginal.on.sigma = F,
marginal.on.pos = F,
type.position = "Quantile",
newdata = .1)
summarize.posterior.or.prior(prior.and.post2.bcage,post.or.prior = "post",
task = "Joint and Marginal",
marginal.on.sigma = F,
marginal.on.pos = F,
type.position = "Failure probability",
newdata = 1000)
summarize.posterior.or.prior(prior.and.post2.bcage,
post.or.prior = "prior",
task = "Joint and Marginal",
marginal.on.sigma = F,
marginal.on.pos = F,
type.position = "Failure probability",
newdata = 1000)
summarize.posterior.or.prior(prior.and.post2.bcage,
post.or.prior = "post",
task = "Joint and Marginal",
marginal.on.sigma = F,
marginal.on.pos = F,
type.position = "Failure probability",
newdata = 6000)
prior.and.post3.bcage <- make.small.posterior.object(prior.and.post2.bcage)
SMRD:::plot.prediction(prior.and.post2.bcage,
time.range = log(c(500,20000000)),
xlab = "Hours")
The plotting functions below can take a long time
SMRD:::plot.prediction.order(x = 1,
nsamsize = 3,
prior.and.post2.bcage,
time.range = log(c(50,200000)),
xlab = "Hours")
SMRD:::plot.prediction.order(x = 1,
nsamsize = 50,
prior.and.post2.bcage,
time.range = log(c(10,100000)),
xlab = "Hours")
Auburngrads/SMRD documentation built on Sept. 14, 2020, 2:21 a.m.
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SMRD:::vinny() library(SMRD)
In this echapter...
Specify prior distribution characteristics for the bearing cage data using a diffuse quantile and a diffuse sigma
BearingCage.ld <- frame.to.ld(bearingcage, response.column = 1, censor.column = 2, case.weight.column = 3) DT::datatable(BearingCage.ld)
bcage.prior.weibull.spec1 <- specify.simple.prior(p = .01, qdist = "loguniform", qlower = 100, qupper = 5000, sigma.dist = "lognormal", sigma.lower = 0.2, sigma.upper = 0.5, distribution = "Weibull")
Specify prior distribution characteristics for the bearing cage data using a diffuse quantile and an informative sigma
bcage.prior.weibull.spec2 <- specify.simple.prior(p = .01, qdist = "loguniform", qlower = 1000, qupper = 1400, sigma.dist = "lognormal", sigma.lower = 1.5, sigma.upper = 2.5, distribution = "Weibull")
Specify prior distribution characteristics for the bearing cage data using an informative quantile and an informative sigma
bcage.prior.weibull.spec3 <- specify.simple.prior(p = .01, qdist = "lognormal", qlower = 1000, qupper = 1400, sigma.dist = "lognormal", sigma.lower = 1.5, sigma.upper = 2.5, distribution = "Weibull")
Lognormal distribution
Specify prior distribution characteristics for the bearing cage data using a diffuse quantile and a diffuse sigma
bcage.prior.lognormal.spec1 <- specify.simple.prior( p = .04, qdist = "loguniform", qlower = 100, qupper = 5000, sigma.dist = "lognormal", sigma.lower = 0.2, sigma.upper = 5, distribution = "Lognormal")
Specify prior distribution characteristics for the bearing cage data using a diffuse quantile and a informative sigma
bcage.prior.lognormal.spec2 <- specify.simple.prior(p = .01, qdist = "loguniform", qlower = 1000, qupper = 1400, sigma.dist = "lognormal", sigma.lower = 1, sigma.upper = 1.5, distribution = "Lognormal")
Specify prior distribution characteristics for the bearing cage data using a informative quantile and a informative sigma
bcage.prior.lognormal.spec3 <- specify.simple.prior(p = .01, qdist = "loguniform", qlower = 1000, qupper = 1400, sigma.dist = "lognormal", sigma.lower = 1., sigma.upper = 1.5, distribution = "Lognormal")
Create the prior distributions
prior2.bcage <- make.prior(spec = bcage.prior.lognormal.spec1, number.in.prior = 3000) prior.and.post2.bcage <- get.big.posterior(bcage.prior.lognormal.spec1, BearingCage.ld) prior.and.post2.bcage$post[1:10,] prior.and.post3.bcage <- make.small.posterior.object(prior.and.post2.bcage)
Marginal distribution of the parameters
summarize.posterior.or.prior(prior.and.post2.bcage, post.or.prior = "post", task = "Marginals only", marginal.on.sigma = T, marginal.on.pos = F, type.position = "Parameter", newdata = "mu", include.likelihood = T) #quantle marginal summarize.posterior.or.prior(prior.and.post2.bcage, post.or.prior = "post", task = "Marginals only", marginal.on.sigma = F, marginal.on.pos = T, type.position = "Quantile", newdata = .1, include.likelihood = T) #sigma marginal summarize.posterior.or.prior(prior.and.post2.bcage, post.or.prior = "post", task = "Marginals only", marginal.on.sigma = T, marginal.on.pos = F, type.position = "Quantile", newdata = .1, include.likelihood = T) #prob summarize.posterior.or.prior(prior.and.post2.bcage, post.or.prior = "post", task = "Marginals only", marginal.on.sigma = F, marginal.on.pos = T, type.position = "Failure probability", newdata = 1000, include.likelihood = T) #Joint only axes.range.default.post = T summarize.posterior.or.prior(prior.and.post2.bcage, post.or.prior = "post", task = "Joint only", axes.range.default.post = T, marginal.on.sigma = F, marginal.on.pos = F, type.position = "Failure probability", newdata = 1000, include.likelihood = T) #Joint only summarize.posterior.or.prior(prior.and.post2.bcage, post.or.prior = "prior", task = "Joint only", marginal.on.sigma = F, marginal.on.pos = F, type.position = "Parameter", newdata = "mu", include.likelihood = T) #Joint only summarize.posterior.or.prior(prior.and.post2.bcage, post.or.prior = "prior", task = "Joint only", marginal.on.sigma = F, marginal.on.pos = F, type.position = "Quantile", newdata = .1, include.likelihood = T) #Joint only axes.range.default.post = F summarize.posterior.or.prior(prior.and.post2.bcage, post.or.prior = "post", task = "Joint only", axes.range.default.post = F, marginal.on.sigma = F, marginal.on.pos = F, type.position = "Failure probability", newdata = 1000, include.likelihood = F) #Joint only axes.range.default.post = F summarize.posterior.or.prior(prior.and.post2.bcage, post.or.prior = "prior", task = "Joint only", axes.range.default.post = F, marginal.on.sigma = F, marginal.on.pos = F, type.position = "Failure probability", newdata = 1000, include.likelihood = F) summarize.posterior.or.prior(prior.and.post2.bcage, post.or.prior = "prior", task = "Joint and Marginal", marginal.on.sigma = F, marginal.on.pos = F, type.position = "Parameter", newdata = "mu") summarize.posterior.or.prior(prior.and.post2.bcage, post.or.prior = "post", task = "Joint only", marginal.on.sigma = F, marginal.on.pos = F, type.position = "Parameter", newdata = "mu", include.likelihood = T) summarize.posterior.or.prior(prior.and.post2.bcage, post.or.prior = "prior", task = "Joint only", marginal.on.sigma = F, marginal.on.pos = F, type.position = "Parameter", newdata = "mu", include.likelihood = T) summarize.posterior.or.prior(prior.and.post2.bcage, post.or.prior = "prior", task = "Joint and Marginal", marginal.on.sigma = F, marginal.on.pos = F, type.position = "Quantile", newdata = .1) summarize.posterior.or.prior(prior.and.post2.bcage, post.or.prior = "prior", task = "Joint and Marginal", marginal.on.sigma = F, marginal.on.pos = F, type.position = "Failure probability", newdata = 1000) summarize.posterior.or.prior(prior.and.post2.bcage, post.or.prior = "prior", task = "Joint and Marginal", marginal.on.sigma = F, marginal.on.pos = F, type.position = "Failure probability", newdata = 6000) summarize.posterior.or.prior(prior.and.post2.bcage, post.or.prior = "post", task = "Joint only", marginal.on.sigma = F, marginal.on.pos = F, type.position = "Parameter", newdata = "mu", include.likelihood = T) summarize.posterior.or.prior(prior.and.post2.bcage, post.or.prior = "post", task = "Joint and Marginal", marginal.on.sigma = F, marginal.on.pos = T, type.position = "Parameter", newdata = "mu") summarize.posterior.or.prior(prior.and.post2.bcage, post.or.prior = "post", task = "Joint and Marginal", marginal.on.sigma = F, marginal.on.pos = F, type.position = "Quantile", newdata = .1) summarize.posterior.or.prior(prior.and.post2.bcage,post.or.prior = "post", task = "Joint and Marginal", marginal.on.sigma = F, marginal.on.pos = F, type.position = "Failure probability", newdata = 1000) summarize.posterior.or.prior(prior.and.post2.bcage, post.or.prior = "prior", task = "Joint and Marginal", marginal.on.sigma = F, marginal.on.pos = F, type.position = "Failure probability", newdata = 1000) summarize.posterior.or.prior(prior.and.post2.bcage, post.or.prior = "post", task = "Joint and Marginal", marginal.on.sigma = F, marginal.on.pos = F, type.position = "Failure probability", newdata = 6000) prior.and.post3.bcage <- make.small.posterior.object(prior.and.post2.bcage) SMRD:::plot.prediction(prior.and.post2.bcage, time.range = log(c(500,20000000)), xlab = "Hours")
The plotting functions below can take a long time
SMRD:::plot.prediction.order(x = 1, nsamsize = 3, prior.and.post2.bcage, time.range = log(c(50,200000)), xlab = "Hours")
SMRD:::plot.prediction.order(x = 1, nsamsize = 50, prior.and.post2.bcage, time.range = log(c(10,100000)), xlab = "Hours")
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