monitor_long_1d {DySS} | R Documentation |
Monitor Univariate Longitudinal Data
Description
Monitor Univariate Longitudinal Data
Usage
monitor_long_1d(
data_matrix_new,
time_matrix_new,
nobs_new,
pattern,
side = "upward",
chart = "CUSUM",
method = "standard",
parameter = 0.5,
CL = Inf
)
Arguments
data_matrix_new |
observed data arranged in a numeric matrix format. |
time_matrix_new |
observation times arranged in a numeric matrix format. |
nobs_new |
number of observations arranged as an integer vector. |
pattern |
the estimated regular longitudinal pattern |
side |
a character value specifying the sideness/direction of process monitoring |
chart |
a string specifying the control charts to use.
If |
method |
a string |
parameter |
a numeric value |
CL |
a numeric value speficying the control limit. |
Value
a list that stores the result.
$chart |
a numeric matrix, |
$standardized_values |
a numeric matrix, |
References
Qiu, P. and Xiang, D. (2014). Univariate dynamic screening system: an approach for identifying individuals with irregular longitudinal behavior. Technometrics, 56:248-260.
Li, J. and Qiu, P. (2016). Nonparametric dynamic screening system for monitoring correlated longitudinal data. IIE Transactions, 48(8):772-786.
You, L. and Qiu, P. (2019). Fast computing for dynamic screening systems when analyzing correlated data. Journal of Statistical Computation and Simulation, 89(3):379-394.
You, L., Qiu, A., Huang, B., and Qiu, P. (2020). Early detection of severe juvenile idiopathic arthritis by sequential monitoring of patients' health-related quality of life scores. Biometrical Journal, 62(5).
You, L. and Qiu, P. (2021). A robust dynamic screening system by estimation of the longitudinal data distribution. Journal of Quality Technology, 53(4).
Examples
data("data_example_long_1d")
result_pattern<-estimate_pattern_long_1d(
data_matrix=data_example_long_1d$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)