`test.signal`

creates a random function that consists of peaks, a smooth background, and a Gaussian noise.

1 | ```
test.signal(x, lambda, sigma, x.delta, knots.n, peaks.widthRange, peaks.n)
``` |

`x` |
numeric vector, the x-points where data should be generated. |

`lambda` |
numeric, the mean signal magnitude. |

`sigma` |
numeric, the noise level. |

`x.delta` |
numeric, the minimum spacing allowed between spline knots. Defines background smoothness. |

`knots.n` |
numeric, a number of spline knots to generate. |

`peaks.widthRange` |
numeric vector, specifies range in peak widths. |

`peaks.n` |
numeric, the number of peaks to generate. |

The background is calculated as a sum of fundamental splines on the randomly generated knots. The function is a sum of the background, random peaks, and Gaussian noise.

An object of type `data`

(see `set.data`

) with the following elements added:

`knots` |
list with elements |

`bkg` |
numeric vector containing the generated background. |

1 2 3 4 5 6 7 8 9 10 11 | ```
# 1. Create test function
f <- test.signal(x=seq(0,30,0.01), lambda=5,
sigma=0.1, x.delta=1.0, knots.n=5,
peaks.widthRange=c(0.1, 0.3), peaks.n=7)
# 2. Plot results
plot(f$x, f$y, t="l", xlab="x", ylab="f")
lines(f$x, f$bkg, col=2)
lines(f$x, f$y - f$bkg, col="gray")
legend(20, .9*max(f$y), c("test function", "background",
"peaks+noise"), lty=1, col=c(1,2,"gray"))
``` |

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