```
# show plot using runquantile
k=31; n=200;
x = rnorm(n,sd=30) + abs(seq(n)-n/4)
y=runquantile(x, k, probs=c(0.05, 0.25, 0.5, 0.75, 0.95))
col = c("black", "red", "green", "blue", "magenta", "cyan")
plot(x, col=col[1], main = "Moving Window Quantiles")
lines(y[,1], col=col[2])
lines(y[,2], col=col[3])
lines(y[,3], col=col[4])
lines(y[,4], col=col[5])
lines(y[,5], col=col[6])
lab = c("data", "runquantile(.05)", "runquantile(.25)", "runquantile(0.5)",
"runquantile(.75)", "runquantile(.95)")
legend(0,230, lab, col=col, lty=1 )
# show plot using runquantile
k=15; n=200;
x = rnorm(n,sd=30) + abs(seq(n)-n/4)
y=runquantile(x, k, probs=c(0.05, 0.25, 0.5, 0.75, 0.95))
col = c("black", "red", "green", "blue", "magenta", "cyan")
plot(x, col=col[1], main = "Moving Window Quantiles (smoothed)")
lines(runmean(y[,1],k), col=col[2])
lines(runmean(y[,2],k), col=col[3])
lines(runmean(y[,3],k), col=col[4])
lines(runmean(y[,4],k), col=col[5])
lines(runmean(y[,5],k), col=col[6])
lab = c("data", "runquantile(.05)", "runquantile(.25)", "runquantile(0.5)",
"runquantile(.75)", "runquantile(.95)")
legend(0,230, lab, col=col, lty=1 )
# basic tests against runmin & runmax
y = runquantile(x, k, probs=c(0, 1))
a = runmin(x,k) # test only the inner part
stopifnot(all(a==y[,1], na.rm=TRUE));
a = runmax(x,k) # test only the inner part
stopifnot(all(a==y[,2], na.rm=TRUE));
# basic tests against runmed, including testing endrules
a = runquantile(x, k, probs=0.5, endrule="keep")
b = runmed(x, k, endrule="keep")
stopifnot(all(a==b, na.rm=TRUE));
a = runquantile(x, k, probs=0.5, endrule="constant")
b = runmed(x, k, endrule="constant")
stopifnot(all(a==b, na.rm=TRUE));
# basic tests against apply/embed
a = runquantile(x,k, c(0.3, 0.7), endrule="trim")
b = t(apply(embed(x,k), 1, quantile, probs = c(0.3, 0.7)))
eps = .Machine$double.eps ^ 0.5
stopifnot(all(abs(a-b)<eps));
# test against loop approach
# this test works fine at the R prompt but fails during package check - need to investigate
k=25; n=200;
x = rnorm(n,sd=30) + abs(seq(n)-n/4) # create random data
x[seq(1,n,11)] = NaN; # add NANs
k2 = k
k1 = k-k2-1
a = runquantile(x, k, probs=c(0.3, 0.8) )
b = matrix(0,n,2);
for(j in 1:n) {
lo = max(1, j-k1)
hi = min(n, j+k2)
b[j,] = quantile(x[lo:hi], probs=c(0.3, 0.8), na.rm = TRUE)
}
#stopifnot(all(abs(a-b)<eps));
# compare calculation of array ends
a = runquantile(x, k, probs=0.4, endrule="quantile") # fast C code
b = runquantile(x, k, probs=0.4, endrule="func") # slow R code
stopifnot(all(abs(a-b)<eps));
# test if moving windows forward and backward gives the same results
k=51;
a = runquantile(x , k, probs=0.4)
b = runquantile(x[n:1], k, probs=0.4)
stopifnot(all(a[n:1]==b, na.rm=TRUE));
# test vector vs. matrix inputs, especially for the edge handling
nRow=200; k=25; nCol=10
x = rnorm(nRow,sd=30) + abs(seq(nRow)-n/4)
x[seq(1,nRow,10)] = NaN; # add NANs
X = matrix(rep(x, nCol ), nRow, nCol) # replicate x in columns of X
a = runquantile(x, k, probs=0.6)
b = runquantile(X, k, probs=0.6)
stopifnot(all(abs(a-b[,1])<eps)); # vector vs. 2D array
stopifnot(all(abs(b[,1]-b[,nCol])<eps)); # compare rows within 2D array
# Exhaustive testing of runquantile to standard R approach
numeric.test = function (x, k) {
probs=c(1, 25, 50, 75, 99)/100
a = runquantile(x,k, c(0.3, 0.7), endrule="trim")
b = t(apply(embed(x,k), 1, quantile, probs = c(0.3, 0.7), na.rm=TRUE))
eps = .Machine$double.eps ^ 0.5
stopifnot(all(abs(a-b)<eps));
}
n=50;
x = rnorm(n,sd=30) + abs(seq(n)-n/4) # nice behaving data
for(i in 2:5) numeric.test(x, i) # test small window sizes
for(i in 1:5) numeric.test(x, n-i+1) # test large window size
x[seq(1,50,10)] = NaN; # add NANs and repet the test
for(i in 2:5) numeric.test(x, i) # test small window sizes
for(i in 1:5) numeric.test(x, n-i+1) # test large window size
# Speed comparison
## Not run:
# x=runif(1e6); k=1e3+1;
# system.time(runquantile(x,k,0.5)) # Speed O(n*k)
# system.time(runmed(x,k)) # Speed O(n * log(k))
# ## End(Not run)
```

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