The statistical quality control tool aims to address measurementerror effects when constructing exponentially weighted moving average pcontrol charts. The method primarily focuses on binary random variables,but it can be applied to any continuous random variables by using signstatistic to transform them to discrete ones. With the correction ofmeasurement error effects, we can obtain the corrected control limits ofexponentially weighted moving average p control chart and reasonablyadjusted exponentially weighted moving average p control charts.
You can install the released version of EATME fromgithub
install.packages('EATME')library(EATME)This is an example which shows how find the control limits for twosided EWMA-p chart:
library(EATME)#0.2 is the in-control probability#0.05 is the smoothing parameter for EWMA chart#5 is sample size#1 is the correctness of true and false#200 is ARL0#10000 is the number of simulation for Monte Carlo method#1 is the maximum error what we can allow between hat_ARL0 and ARL0EWMA_p_two(0.2,0.05,5,1,1,200,10000,1)#> $L1#> [1] 2.319918#>#> $L2#> [1] 2.223654#>#> $hat_ARL0#> [1] 199.339#>#> $hat_MRL#> [1] 131#>#> $hat_SDRL#> [1] 210.7766#>#> $UCL#> [1] 0.2664531#>#> $LCL#> [1] 0.1363043This is an example which shows how plot the two sided adjusted EWMA-pchart :
set.seed(6)#We generate the discrete IC data with measurement error.IC=ME_data_generate(0.4,50,50,0.9,0.9)#We generate the discrete IC data with measurement error.OC=ME_data_generate(0.5,50,50,0.9,0.9)#The real EWMA-p chartEWMA_p_chart_two(IC$real_data,OC$real_data,0.05,50,1,1)
#The unadjusted EWMA-p chartEWMA_p_chart_two(IC$obs_data,OC$obs_data,0.05,50,1,1)
#The adjusted EWMA-p chartEWMA_p_chart_two(IC$obs_data,OC$obs_data,0.05,50,0.9,0.9)
