summary.rbmcc: Summary function of Risk-based Multivariate Control Charts

In rbcc: Risk-Based Control Charts

Summary function of Risk-based Multivariate Control Charts

Description

Print summary of Risk-based multivariate control charts

Usage

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

Arguments

object	an object of class 'rbmcc'.
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 for matrix X
mu_X <- c(0,1,2)               # vector of means.
va_X  <- c(1,2, 0.5)           # vector of standard deviation.
sk_X <- c(0,0.5, 0.8)          # vector of skewness.
ku_X <- c(3,3.5, 4)            # vector of kurtosis.
obs <- 200                     # Total number of observations of a process.

X <- data_gen (obs, mu_X, va_X, sk_X, ku_X) # generate data pints

# Data generation for measurement error matrix UC.
mu_UC <- c(0,0,0)        # vector of means of measurement errors.
va_UC <- c(1,2, 0.5)     # vector of standard deviation of measurement errors.
sk_UC <- c(0,0,0)        # Vector of skewness of measurement errors.
ku_UC <- c(3,3,3)        # Vector of kurtosis of measurement errors.

# Example for generation of measurement error matrix of 3 variables.
UC <- data_gen(obs,mu_UC, va_UC, sk_UC, ku_UC)

# with default vector of decision costs
C <- c(1,1,1,1)                # vector of decision costs
H <- rbmcc(X, UC, C)           # for subgroups of size 1
summary(H)                     # summarize the results
H_opt <- rbmcc_opt(X, UC, C)# optimal risk-based multivariate control chart
summary(H_opt)

# with vector of proportional decision costs
C <- c(1, 5, 60, 5)        # vector of decision costs
H <- rbmcc(X, UC, C)           # for subgroups of size 1
H_opt <- rbmcc_opt(X, UC, C)   # optimal risk-based multivariate control chart
summary(H_opt)

# with vector of proportional decision costs and sugbroup size 3
C <- c(1, 5, 60, 5)           # vector of decision costs
H <- rbmcc(X, UC, C, 3)        # for subgroups of size 3
H_opt <- rbmcc_opt(X, UC, C, 3)# optimal risk-based multivariate control chart
summary(H_opt)                  # summarize the results

# Example of considering the real sample

data("t2uc")                # load the dataset

X <- as.matrix(t2uc[,1:2])  # get optical measurements ar "real" values
UC <- as.matrix(t2uc[,5:6]) # get measurement errors
C <- c(1,20,160,5) # define cost structure

# Fit optimized RBT2 control chart
R <- rbmcc_opt(X, UC, C, 1,confidence_level = 0.99)
summary(R) # summarize the results

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