# Plot function for Risk-based Control Charts Plot function for Risk-based Univariate (shewhart, exponentially weighted moving average(EWMA) and moving average (MA))Control Charts ```r ## S3 method for class 'rbcc' plot(x, ...) ``` ## Arguments - `x`: an object of class 'rbcc'. - `...`: other graphical parameters. ## Returns No return value, called for side effects ## 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. ## Author(s) Aamir Saghir, Attila I. Katona, Zsolt T. Kosztyan* e-mail: kzst@gtk.uni-pannon.hu ## See Also `data_gen`, `rbcc`, `rbcc_opt`, `rbcusumcc`, `rbcusumcc_opt`, `rbewmacc`, `rbewmacc_opt`, `rbmacc`, `rbmacc_opt`, `rbmcc`, `rbmcc_opt`, `summary.rbcc`. ## Examples ```r # Data Generation and Xbar 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. # Simulation of 200 obervations of 1 variable. X <- data_gen (obs, mu_X, va_X, sk_X, ku_X) # 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 Xbar chart with default vector of decision costs C <- c(1,1,1,1) # vector of decision costs H <- rbcc(X, UC, C, n=3, type="xbar") # for subgroups of size 3 # optimal risk-based xbar control chart H_opt <- rbcc_opt(X, UC, C, n=3, type="xbar") plot(H_opt) ```

plot.rbcc function