calculate_ATS {DySS} | R Documentation |
Calculate ATS
Description
The function calculate_ATS
calculates the average time to signals (ATS) given
a control chart matrix and a specified control limit (CL). ATS is defined as the average time from
the start of process monitoring to signal times.
Usage
calculate_ATS(
chart_matrix,
time_matrix,
nobs,
starttime,
endtime,
design_interval,
n_time_units,
time_unit,
CL,
no_signal_action = "omit"
)
Arguments
chart_matrix |
charting statistic values arranged as a numeric matrix. |
time_matrix |
observation times arranged as a numeric matrix. |
nobs |
number of observations arranged as an integer vector. |
starttime |
a numeric vector that gives the start times. |
endtime |
a numeric vector that gives the end times. |
design_interval |
a numeric vector of length two that
gives the left- and right- limits of the design interval.
By default, |
n_time_units |
an integer value that gives the number of basic time units
in the design time interval. |
time_unit |
an optional numeric value of basic time unit. Only used when |
CL |
a numeric value specifying the control limit. |
no_signal_action |
a character specifying the method to use when a signal is not given to a process.
If |
Details
Calculate ATS
Value
a numeric value, the ATS given the charting statistics and the control limit.
References
Qiu, P. and Xiang, D. (2014). Univariate dynamic screening system: an approach for identifying individuals with irregular longitudinal behavior. Technometrics, 56:248-260.
Qiu, P., Xia, Z., and You, L. (2020). Process monitoring roc curve for evaluating dynamic screening methods. Technometrics, 62(2).
Examples
data("data_example_long_1d")
result_pattern<-estimate_pattern_long_1d(
data_matrix=data_example_long_1d$data_matrix_IC,
time_matrix=data_example_long_1d$time_matrix_IC,
nobs=data_example_long_1d$nobs_IC,
design_interval=data_example_long_1d$design_interval,
n_time_units=data_example_long_1d$n_time_units,
estimation_method="meanvar",
smoothing_method="local linear",
bw_mean=0.1,
bw_var=0.1)
result_monitoring<-monitor_long_1d(
data_matrix_new=data_example_long_1d$data_matrix_OC,
time_matrix_new=data_example_long_1d$time_matrix_OC,
nobs_new=data_example_long_1d$nobs_OC,
pattern=result_pattern,
side="upward",
chart="CUSUM",
method="standard",
parameter=0.5)
result_ATS<-calculate_ATS(
chart_matrix=result_monitoring$chart,
time_matrix=data_example_long_1d$time_matrix_OC,
nobs=data_example_long_1d$nobs_OC,
starttime=rep(0,nrow(data_example_long_1d$time_matrix_OC)),
endtime=rep(1,nrow(data_example_long_1d$time_matrix_OC)),
design_interval=data_example_long_1d$design_interval,
n_time_units=data_example_long_1d$n_time_units,
CL=2.0)