## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 7, fig.height = 5 ) library(AccSamplingDesign) ## ----------------------------------------------------------------------------- # Create an attribute plan with binomial assumption plan_attr <- optPlan( PRQ = 0.01, # Acceptable quality level (1%) CRQ = 0.05, # Rejectable quality level (5%) alpha = 0.02, # Producer's risk beta = 0.15, # Consumer's risk distribution = "binomial" ) # Summary of the plan summary(plan_attr) # Probability of accepting 3% defective accProb(plan_attr, 0.03) # Plot the OC curve plot(plan_attr) ## ----------------------------------------------------------------------------- # Create a variable plan assuming known sigma plan_var <- optPlan( PRQ = 0.025, CRQ = 0.1, alpha = 0.05, beta = 0.10, distribution = "normal", sigma_type = "known" ) # Summary summary(plan_var) # Plot OC curve plot(plan_var) ## ----------------------------------------------------------------------------- # Create a variable plan assuming known sigma plan_var2 <- optPlan( PRQ = 0.025, CRQ = 0.1, alpha = 0.05, beta = 0.10, distribution = "normal", sigma_type = "unknown" ) # Summary summary(plan_var2) ## ----------------------------------------------------------------------------- # Create a variable plan using Beta distribution plan_beta <- optPlan( PRQ = 0.05, CRQ = 0.2, alpha = 0.05, beta = 0.10, distribution = "beta", theta = 44000000, theta_type = "known", LSL = 0.00001 # Lower Specification Limit ) # Summary summary(plan_beta) # Plot OC curve plot(plan_beta) # Plot OC curve be the process mean plot(plan_beta, by = "mean") ## ----------------------------------------------------------------------------- # Create a variable plan using Beta distribution plan_beta2 <- optPlan( PRQ = 0.05, CRQ = 0.2, alpha = 0.05, beta = 0.10, distribution = "beta", theta = 44000000, theta_type = "unknown", LSL = 0.00001 ) # Summary summary(plan_beta2) ## ----------------------------------------------------------------------------- # Define range of defect rates pd <- seq(0, 0.15, by = 0.001) # Generate OC data from optimal plan oc_opt <- OCdata(plan = plan_attr, pd = pd) # Compare with manual plans mplan1 <- manualPlan(n = plan_attr$n, c = plan_attr$c - 1, distribution = "binomial") oc_alt1 <- OCdata(plan = mplan1, pd = pd) # Plot comparison plot(pd, oc_opt$paccept, type = "l", col = "blue", lwd = 2, xlab = "Proportion Defective", ylab = "Probability of Acceptance", main = "OC Curves Comparison for Attributes Sampling Plan") lines(pd, oc_alt1$paccept, col = "red", lwd = 2, lty = 2) legend("topright", legend = c("Optimal Plan", "Manual Plan c - 1"), col = c("blue", "red"), lty = c(1, 2), lwd = 2)