R多条线的控制图

Question

我正在与一位项目的医生合作，监测抗生素使用剂量的合规情况。来跟踪是不符合事件的比例，医生喜欢用P charts

我想以产生P-图表与3限制线（对应于1，2，和3个SDS）上方和下方的中线。我还没有找到办法做到这一点。我还希望情节有几次休息，将数据分成几个时间段，我可以在qicharts包中进行，但不能在其他包中进行。

R有几个包用于生成P图表。我最喜欢的是qicharts。来自qicharts的标准P-Chart以及我见过的所有其他软件包都会生成一条中心线，上限控制限和下限控制限，分别位于中心线+3和-3 SD处。

我想弄清楚如何在同一图上生成额外的+1，+2和-1，-2 SD控制线。一些选项，如

LimitLines = c(1, 2, 3) where the default is LimitlLines = 3 

下面是代码，从r-projects修改，生成数据，创建图表，包括两次破发：

# Setup parameters 
m.beds  <- 300 
m.stay  <- 4 
m.days  <- m.beds * 7 
m.discharges <- m.days/m.stay 
p.pu   <- 0.08 

# Simulate data 
discharges <- rpois(24, lambda = m.discharges) 
patientdays <- round(rnorm(24, mean = m.days, sd = 100)) 
n.pu  <- rpois(24, lambda = m.discharges * p.pu * 1.5) 
n.pat.pu <- rbinom(24, size = discharges, prob = p.pu) 
week  <- seq(as.Date('2014-1-1'), 
       length.out = 24, 
       by   = 'week') 

# Combine data into a data frame 
d <- data.frame(week, discharges, patientdays,n.pu, n.pat.pu) 

# Create a P-chart to measure the number of patients with pressure ulcers (n.pat.pu) each week (week) as a proportion of all discharges (discharges) with breaks one third (8) and two thirds (16) of the way through the data 

qic(n.pat.pu, 
n  = discharges, 
x  = week, 
data  = d, 
chart = 'p', 
multiply = 100, 
breaks = c(8,16), 
main  = 'Hospital acquired pressure ulcers (P chart)', 
ylab  = 'Percent patients', 
xlab  = 'Week') 

Answer 1

如果你只需要提交的数据，很容易自己创建图表。随意修改功能以满足您的需求，让您更轻松。

数据：

Groups <- c(120, 110, 150, 110, 140, 160, 100, 150, 100, 130, 130, 100, 120, 110, 130, 110, 150, 110, 110) 
Errors <- c(4, 3, 3, 3, 0, 6, 2, 2, 1, 5, 1, 5, 1, 1, 0, 1, 4, 0, 0) 
Week <- length(Groups) #optional: input vector of week numbers 
PchartData <- data.frame(Week,Groups,Errors) 

功能：

Shewhart.P.Chart <- function(Groups, Errors, Week) 
{ 
## Create from scratch 
# p value 
p <- Errors/Groups 
# pbar 
pbar <- mean(p) 
# calculate control limits 
UCL3 <- pbar+3*sqrt((pbar * (1 - pbar))/Groups) 
UCL2 <- pbar+2*sqrt((pbar * (1 - pbar))/Groups) 
UCL1 <- pbar+1*sqrt((pbar * (1 - pbar))/Groups) 
LCL1 <- pbar-1*sqrt((pbar * (1 - pbar))/Groups) 
LCL2 <- pbar-2*sqrt((pbar * (1 - pbar))/Groups) 
LCL3 <- pbar-3*sqrt((pbar * (1 - pbar))/Groups) 
## adjust the minimal value of the LCL to 0 
LCL3[LCL3 < 0] <- 0 
LCL2[LCL2 < 0] <- 0 
LCL1[LCL1 < 0] <- 0 
# plot pvalues 
plot(c(1:length(Groups)),p, ylim = c(min(LCL3,p),max(UCL3,p)), 
    main = "p Chart \n for Prescription Errors", xlab = "weeks", 
    ylab = 'Proportion nonconforming', col = "green", pch = 20, 
    lty = 1, type = "b") 
# add centerline reference 
abline(h = pbar, col = "red") 
# plot control limits at ±1s, 2s, and 3s 
lines(c(1:length(Groups)),UCL1, col = "blue", lty = 2) 
lines(c(1:length(Groups)),UCL2, col = "blue", lty = 2) 
lines(c(1:length(Groups)),UCL3, col = "blue", lty = 2) 
lines(c(1:length(Groups)),LCL3, col = "blue", lty = 2) 
lines(c(1:length(Groups)),LCL2, col = "blue", lty = 2) 
lines(c(1:length(Groups)),LCL1, col = "blue", lty = 2) 
} 

符可以很容易地加入到前述，你只需要在相应的隔离数据。但应该记住，如果您在所使用的过程中没有变化，则不应更改限制的计算方法，而且您的过程可能会超出统计控制范围，并且需要标准化。