DySS (version 1.0)

calculate_signal_times: Calculate Signal Times

Description

The function calculate_signal_times calculates the time to signals given a control chart matrix and a specified control limit (CL).

Usage

calculate_signal_times(
  chart_matrix,
  time_matrix,
  nobs,
  starttime,
  endtime,
  design_interval,
  n_time_units,
  time_unit,
  CL
)

Value

A list of two vectors:

$signal_times

times to signals, a numeric vector.

$signals

whether the subject received signals, a logical vector.

Arguments

chart_matrix

a matrix of charting statistic values.
chart_matrix[i,j] is the jth charting statistic of the ith subject.

time_matrix

a matrix of observation times.
time_matrix[i,j] is the jth observation time of the ith subject, corresponding to the time the charting statistic chart_matrix[i,j] is computed.

nobs

number of observations arranged as an integer vector.
nobs[i] is the number of observations for the ith subject.

starttime

a vector of times from the start of monitoring.
starttime[i] is the time that the ith subject starts to be monitored.

endtime

a vector of times from the start of monitoring.
endtime[i] is the time that the ith subject is lost to be monitored.

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)

CL

a numeric value specifying the control limit.
CL is the control limit, signals will be given if charting statistics are greater than the control limit.

Details

Calculate Signal Times

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$nobs_OC,
  pattern=result_pattern,
  side="upward",
  chart="CUSUM",
  method="standard",
  parameter=0.5)
result_signal_times<-calculate_signal_times(
  chart_matrix=result_monitoring$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)