We simulate normally distributed data as follows:
require(weco);
## Loading required package: weco
set.seed(10000);
n.sim <- 50000;
sdx <- 2;
simu.data <- rnorm(n.sim, sd = sdx);
quants <- c(0.25, 0.5, 0.75);
xmax <- 3000;
ymax <- 0.015;
1 point \(>L\) standard deviations from center line
l <- 3;
rst.1 <- weco.rule(rule=1, x=simu.data, l=l, sdx=sdx, mux=0);
simu.arl <- weco.rl(rst.1);
hist(simu.arl, breaks = 100, freq = F,
xlim=c(0, xmax), ylim=c(0,ymax),
xlab="Running Length", ylab="Probability", main="Rule 1");
\(K\) points in a row on the same side of the center line
k <- 9;
rst.2 <- weco.rule(rule=2, x=simu.data, k=k, sdx=sdx, mux=0);
simu.arl <- weco.rl(rst.2);
hist(simu.arl, breaks = 100, freq = F,
xlim=c(0, xmax), ylim=c(0,ymax),
xlab="Running Length", ylab="Probability", main="Rule 2");
\(K\) points in a row, all increasing or decreasing
k <- 6;
rst.3 <- weco.rule(rule=3, x=simu.data, k=k, sdx=sdx, mux=0);
simu.arl <- weco.rl(rst.3);
hist(simu.arl, breaks = 100, freq = F,
xlim=c(0, xmax), ylim=c(0,ymax),
xlab="Running Length", ylab="Probability", main="Rule 3");
\(K\) points in a row, alternating up and down
k <- 14;
rst.4 <- weco.rule(rule=4, x=simu.data, k=k, sdx=sdx, mux=0);
simu.arl <- weco.rl(rst.4);
hist(simu.arl, breaks = 100, freq = F,
xlim=c(0, xmax), ylim=c(0,ymax),
xlab="Running Length", ylab="Probability", main="Rule 3");
\(K\) out \(K+1\) points out of 2 standard deviations from center line
k <- 2;
rst.5 <- weco.rule(rule=5, x=simu.data, k=k, l=2, sdx=sdx, mux=0);
simu.arl <- weco.rl(rst.5);
hist(simu.arl, breaks = 100, freq = F,
xlim=c(0, xmax), ylim=c(0,ymax),
xlab="Running Length", ylab="Probability", main="Rule 5");
\(K\) out \(K+1\) points out of 1 standard deviations from center line
k <- 4;
rst.6 <- weco.rule(rule=6, x=simu.data, k=k, l=1, sdx=sdx, mux=0);
simu.arl <- weco.rl(rst.6);
hist(simu.arl, breaks = 100, freq = F,
xlim=c(0, xmax), ylim=c(0,ymax),
xlab="Running Length", ylab="Probability", main="Rule 6");
\(K\) points in a row within 1 standard deviations from center line (either side)
k <- 15;
rst.7 <- weco.rule(rule=7, x=simu.data, k=k, l=1, sdx=sdx, mux=0);
simu.arl <- weco.rl(rst.7);
hist(simu.arl, breaks = 100, freq = F,
xlim=c(0, xmax), ylim=c(0,ymax),
xlab="Running Length", ylab="Probability", main="Rule 7");
\(K\) points in a row > 1 standard deviations from center line (either side)
k <- 8;
rst.8 <- weco.rule(rule=8, x=simu.data, k=k, l=1, sdx=sdx, mux=0);
simu.arl <- weco.rl(rst.8);
hist(simu.arl, breaks = 100, freq = F,
xlim=c(0, xmax), ylim=c(0,ymax),
xlab="Running Length", ylab="Probability", main="Rule 8");
Multiple rules can be combined as a list.
##rules 1 and 2
lst.rules <- list(list(1, l=3),
list(2, k=9));
rst.m <- weco.combine(simu.data, lst.rules=lst.rules);
simu.arl <- weco.rl(rst.m);
hist(simu.arl, breaks = 100, freq = F,
xlim=c(0, xmax), ylim=c(0,ymax),
xlab="Running Length", ylab="Probability", main="Multiple Rules");
A trace plot may be plotted as follows
plot(rst.m, start=1000, end=1500);
The package provides a graphical user interface based on Shiny, which can be brought up by
run.weco();