Evaluate Control Charts (in a single dataset)
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.
chart_matrix[i,j] is the jth charting statistic of the ith subject.
time_matrix: observation times arranged as a numeric matrix.
time_matrix[i,j] is the jth observation time of the ith subject.
chart_matrix[i,j] is the charting statistic of the ith subject at time_matrix[i,j].
nobs: number of observations arranged as an integer vector.
nobs[i] is the number of observations for the ith subject.
starttime: a numeric vector. starttime[i] is the time when monitoring starts for ith subject.
endtime: a numeric vector, times when monitoring end. endtime[i] is the time when monitoring ends for ith subject.
status: a logical vector. status[i]=FALSE if the ith subject is IC, while status[i]=TRUE indicates the the ith subject is OC.
design_interval: a numeric vector of length two that gives the left- and right- limits of the design interval. By default, design_interval=range(time_matrix,na.rm=TRUE).
n_time_units: an integer value that gives the number of basic time units in the design time interval.
The design interval will be discretized to seq(design_interval[1],design_interval[2],length.out=n_time_units)
time_unit: an optional numeric value of basic time unit. Only used when n_time_units is missing.
The design interval will be discretized to seq(design_interval[1],design_interval[2],by=time_unit)
no_signal_action: a character value specifying how to set signal times when processes with no signals.
If no_signal_action=="omit", the signal time is set to be missing.
If no_signal_action=="maxtime", the signal time is set to be the time from start time to the end of the design interval.
If no_signal_action=="endtime", the signal time is set to be the time from start time to the end time.
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.
$ATS1: A numeric vector. Out-of-control ATS.
Evaluate Control Charts
result_pattern<-estimate_pattern_long_surv( data_array=data_example_long_surv$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")
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).
Useful links