summary.rbcusumcc: Summary function of Risk-based CUSUM Control Charts

In rbcc: Risk-Based Control Charts

Summary function of Risk-based CUSUM Control Charts

Description

Print summary of Risk-based Cumulative sum (CUSUM) control charts

Usage

## S3 method for class 'rbcusumcc'
summary(object, digits = getOption("digits"), ...)

Arguments

object	an object of class 'rbcusumcc'.
digits	the number of significant digits to use when add.stats = TRUE.
...	additional arguments affecting the summary produced.

Value

No return value, called for side effects

Author(s)

Aamir Saghir, Attila I. Katona, Zsolt T. Kosztyan*

e-mail: [e-mail]

References

Katona, A. I., Saghir, A., Hegedűs, C., & Kosztyán, Z. T. (2023). Design of Risk-Based Univariate Control Charts with Measurement Uncertainty. IEEE Access, 11, 97567-97573.

Kosztyán, Z. T., & Katona, A. I. (2016). Risk-based multivariate control chart. Expert Systems with Applications, 62, 250-262.

See Also

data_gen, rbcc, rbcc_opt, rbcusumcc, rbcusumcc_opt, rbewmacc, rbewmacc_opt, rbmacc, rbmacc_opt, rbmcc, rbmcc_opt, plot.rbcc.

Examples

# Data Generation and CUSUM chart.

## Example for generation of data vector X and measuremenet error vector UC.
obs <- 200                 # Total number of observations of a process.
mu_X <- c(0)               # Define data mean.
va_X  <- c(1)              # Define data standard deviation.
sk_X <- c(0)               # Define data skewness.
ku_X <- c(3)               # Define data kurtosis.
mu_UC <- c(0)              # Define mean of measurement errors.
va_UC <- c(1)              # Define standard deviation of measurement errors.
sk_UC <- c(0)              # Define skewness of measurement errors.
ku_UC <- c(3)              # Define kurtosis of measurement errors.

X <- data_gen (obs, mu_X, va_X, sk_X, ku_X)    # Simulation of 200 obervations of 1 variable.
# Simulation of 200 muasurement erros related to 1 variable.
UC <- data_gen(obs,mu_UC, va_UC, sk_UC, ku_UC)
# Construction of risk-based CUSUM chart with default vector of decision costs
C <- c(1,1,1,1)                                              # vector of decision costs
H <- rbcusumcc(X, UC, C, n=1,T=5, se.shift=1,  K=5)          # for subgroups of size 1
summary(H)                                        # Summarizing the results 
 
 # optimal risk-based xbar control chart
H_opt <- rbcusumcc_opt(X, UC, C, n=1, T=5, se.shift=1, K_init=0, LKL=0, UKL=6)
summary(H_opt)

rbcc documentation built on April 3, 2025, 9:21 p.m.