repGBprior.plan: Determine the design of a censored repetitive sampling plan...

In ULL-STAT/RepetPlan: Repetitive Design of Failure-Censored Sampling Plans with Prior Distribution

Description

This function computes the design of the censored repetitive sampling plan using a model prior of p and expected sampling risks given the requirements of the producer and consumer about maximum allowable risks and quality levels

Usage

repGBprior.plan(risks, p, asvar, beta.pars, pg_bar = TRUE)

Arguments

risks	Vector of producer and consumer maximum sampling risks
p	Vector of acceptance and rejection quality levels
asvar	List with the asymptotical variance-covariance matrix of MLE estimators of location and scale lifetime distribution parameters. See asympt.var() for more details.
beta.pars	Data frame with the GB parameters according to the available knowledge about p. See beta.params() for more details.

Value

A data.frame with the following variables of the censored repetitive design of the sampling plans.

• "q": Censoring degree

• "n": Sample size

• "n_low": Lower bound of sample size

• "n_up": Upper bound of sample size

• "ka": Acceptance constant

• "kr": Rejection constant

• "termcd": Termination code of Newton-Raphson optimization

• "message": Final message of Newton-Raphson optimization

• "p_alpha": Acceptable quality level (AQL)

• "p_beta": Rejectable quality level (RQL)

• "a": Parameter a

• "b": Parameter b

• "l": Lower limit of p

• "u": Upper limit of p

• "mean_p": Mean of p

• "var_p": Variance of p

• "dist": Distribution name

• "alpha": Maximum producer's risk

• "beta": Maximum consumer's risk

• "asn_alpha": ASN at AQL

• "asn_beta": ASN at RQL

• "asn_avg": Average of ASN at AQL and ASN at RQL

• "easn": Expected ASN

• "p_asn_max": Quality level in which ASN maximizes

• "asn_max": Maximum ASN over the quality level

Examples

risks<-c(0.05,0.10)
p<-c(0.00654, 0.0426)
q<- 0.1
asvar<-asympt.var(q,"normal")
l<- p[1]/5
u<- p[2]+(p[1]-l)

# GB parameters for a knowledge of mean and variance of p distribution
know_p<-list(mean_p=p[1],var_p=((p[2]-p[1])/4)^2)
beta.parms<-beta.params(p,l,u, know_p)

designs<-repGBprior.plan(risks,p,asvar, beta.parms)

optimal.design<-designs %>% group_by(q,dist,p_alpha,p_beta) %>%
                filter( (abs(alpha-risks[1])<1e-05) & (abs(risks[2]-beta)<1e-05) & (termcd==1)) %>%
                group_by(q,p_alpha,p_beta,a,b,l,u,dist) %>%
                mutate(easn_min=min(easn)) %>%
                slice(which.min(easn)) %>% as.data.frame()

ULL-STAT/RepetPlan documentation built on Dec. 18, 2021, 5:15 p.m.