```
ts <- rep(61:65, 10)
approx_entropy(ts, edim = 2) # -0.0004610253
sample_entropy(ts, edim = 2) # 0
set.seed(8237)
approx_entropy(rnorm(500), edim = 2) # 1.351439 high, random
approx_entropy(sin(seq(1,100,by=0.2)), edim = 2) # 0.171806 low, deterministic
sample_entropy(sin(seq(1,100,by=0.2)), edim = 2) # 0.2359326
## Not run: (Careful: This will take several minutes.)
# # generate simulated data
# N <- 1000; t <- 0.001*(1:N)
# sint <- sin(2*pi*10*t); sd1 <- sd(sint) # sine curve
# whitet <- rnorm(N); sd2 <- sd(whitet) # white noise
# chirpt <- sint + 0.1*whitet; sd3 <- sd(chirpt) # chirp signal
#
# # calculate approximate entropy
# rnum <- 30; result <- zeros(3, rnum)
# for (i in 1:rnum) {
# r <- 0.02 * i
# result[1, i] <- approx_entropy(sint, 2, r*sd1)
# result[2, i] <- approx_entropy(chirpt, 2, r*sd2)
# result[3, i] <- approx_entropy(whitet, 2, r*sd3)
# }
#
# # plot curves
# r <- 0.02 * (1:rnum)
# plot(c(0, 0.6), c(0, 2), type="n",
# xlab = "", ylab = "", main = "Approximate Entropy")
# points(r, result[1, ], col="red"); lines(r, result[1, ], col="red")
# points(r, result[2, ], col="green"); lines(r, result[2, ], col="green")
# points(r, result[3, ], col="blue"); lines(r, result[3, ], col="blue")
# grid()## End(Not run)
```

Run the code above in your browser using DataLab