rep.plan: Determine the designs of conventional censored repetitive...

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

Description

This function computes the designs of censored repetitive sampling plans using conventional sampling risks given the requirements of the producer and consumer about maximum allowable risks and quality levels

Usage

## S3 method for class 'plan'
rep(risks, p, asvar, 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.

Value

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

• "q": Censoring degree

• "n": 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)

• "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

• "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")
designs<-rep.plan(risks,p,asvar)

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)) %>%
                slice(which.min(asn_avg)) %>% arrange(q,p_alpha,p_beta) %>% as.data.frame()

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