View source: R/evaluate_control_chart.R
evaluate_control_chart_one_group | R Documentation |
The function evaluate_control_chart_one_group
evaluates a control chart when
the in-control (IC) and out-of-control (OC) charting statistics are supplied together in one matrix chart_matrix
.
The logical vector status
indicates if the ith subject is IC or OC.
evaluate_control_chart_one_group( chart_matrix, time_matrix, nobs, starttime, endtime, status, design_interval, n_time_units, time_unit, no_signal_action = "omit" )
chart_matrix |
charting statistics 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.
|
endtime |
a numeric vector, times when monitoring end.
|
status |
a logical vector.
|
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 |
no_signal_action |
a character value specifying how to set signal times when processes with no signals. |
Evaluate Control Charts
an list that stores the evaluation measures.
$thres |
A numeric vector. Threshold values for control limits. |
$FPR |
A numeric vector. False positive rates. |
$TPR |
A numeric vector. True positive rates. |
$ATS0 |
A numeric vector. In-control ATS. |
$ATS1 |
A numeric vector. Out-of-control ATS. |
Qiu, P. and Xiang, D. (2014). Univariate dynamic screening system: an approach for identifying individuals with irregular longitudinal behavior. Technometrics, 56:248-260.
Qiu, P., Xia, Z., and You, L. (2020). Process monitoring ROC curve for evaluating dynamic screening methods. Technometrics, 62(2).
result_pattern<-estimate_pattern_long_surv( data_array=data_example_long_surv$data_array_IC, time_matrix=data_example_long_surv$time_matrix_IC, nobs=data_example_long_surv$nobs_IC, starttime=data_example_long_surv$starttime_IC, survtime=data_example_long_surv$survtime_IC, survevent=data_example_long_surv$survevent_IC, design_interval=data_example_long_surv$design_interval, n_time_units=data_example_long_surv$n_time_units, estimation_method="risk", smoothing_method="local linear", bw_beta=0.05, bw_mean=0.1, bw_var=0.1) result_monitoring<-monitor_long_surv( data_array_new=data_example_long_surv$data_array_IC, time_matrix_new=data_example_long_surv$time_matrix_IC, nobs_new=data_example_long_surv$nobs_IC, pattern=result_pattern, method="risk", parameter=0.5) output_evaluate<-evaluate_control_chart_one_group( chart_matrix=result_monitoring$chart[1:200,], time_matrix=data_example_long_surv$time_matrix_IC[1:200,], nobs=data_example_long_surv$nobs_IC[1:200], starttime=rep(0,200), endtime=rep(1,200), status=data_example_long_surv$survevent_IC[1:200], design_interval=data_example_long_surv$design_interval, n_time_units=data_example_long_surv$n_time_units, no_signal_action="maxtime")
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