rbmacc {rbcc} | R Documentation |
Risk-based Moving Average Control Charts
Description
Calculate Risk-based Moving Average univarate Control Charts
Usage
rbmacc (X, UC, C, n=1, w=2, K=0)
Arguments
X |
vector of variable (numeric vector). Either can be simulated using data_gen or defined by using available data set. |
UC |
vector of measuerement error (numeric vector).Either can be simulated using data_gen or defined by using available previous information. |
C |
vector of decision costs (default value is vector of 1). |
n |
the sample size for grouping. For individual obervations use n=1). |
w |
moving average spam. The defualt value is 2. |
K |
a correction component(default value is 0). |
Value
cost0 |
Total cost of a monitoring process |
cost1 |
Total cost of correct acceptance related to a process monitoring |
cost2 |
Total cost of decision error type 1 related to a process monitoring |
cost3 |
Total cost of decision error type 2 related to a process monitoring |
cost4 |
Total cost of correct reject related to a process monitoring |
LCLx |
Lower control limit of MA chart for a given data |
UCLx |
Upper control limit of MA control chart for a given data |
LCLy |
Lower control limit of MA chart for for a given data with measurement uncertainity |
UCLy |
Upper control limit of MA control chart for a given data with measurement uncertainity |
real |
Real values of MA statistic for a given data |
Observed |
Observed values of MA statistic with measurement errors for a given data |
Author(s)
Aamir Saghir, Attila I. Katona, Zsolt T. Kosztyan*
e-mail: kzst@gtk.uni-pannon.hu
References
KosztyƔn, Z. T., and Katona, A. I. (2016). Risk-based multivariate control chart. Expert Systems with Applications, 62, 250-262.
See Also
data_gen
, rbcc
, rbcc_opt
, rbewmacc
, rbewmacc_opt
, rbmacc_opt
, rbmcc
, rbmcc_opt
, plot.rbcc
, summary.rbcc
.
Examples
# Data generation for vector X
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.
obs <- 200 # Total number of observations of a process.
X <- data_gen (obs, mu_X, va_X, sk_X, ku_X)
# Data generation for measurement error vector UC
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.
UC <- data_gen(obs,mu_UC, va_UC, sk_UC, ku_UC)
C <- c(1,1,1,1) # Define a vector of decision costs.
H <- rbmacc(X, UC, C, w=2, n=1) # for subgroups of size 1
summary(H) # summarize the reults
plot(H) # plot RBMACC
# with vector of proportional decision costs
C <- c(1, 5, 60, 5) # vector of decision costs
H <- rbmacc(X, UC, C, w=2, n=2) # for subgroups of size 1
summary(H) # summarize the reults
plot(H) # plot RBMACC