Description Usage Arguments Value Note Author(s)

View source: R/curveFitTools.R

Generate or fit by nonlinear least squares a family of classic distribution functions.

1 2 3 4 5 6 7 8 9 10 11 | ```
normal(x, mean = 0, sd = 1, height = NULL, floor = 0)
fit.normal(x, y, start.mean = 0, start.sd = 1, start.height = NULL, start.floor = 0)
gaussian(x, center = 0, width = 1, height = NULL, floor = 0)
fit.gaussian(x, y, start.center = 0, start.width = 1, start.height = NULL, start.floor = 0)
lorentzian(x, center = 0, width = 1, height = NULL, floor = 0)
fit.lorentzian(x, y, start.center = 0, start.width = 1, start.height = NULL, start.floor = 0)
gumbel(x, center = 0, width = 1, height = NULL, floor = 0)
fit.gumbel(x, y, start.center = 0, start.width = 1, start.height = NULL, start.floor = 0)
``` |

`x` |
a vector of values to evaluate or fit the function at |

`y` |
a vector of observed Y values to fit the named distribution to |

`mean, center` |
the central value for the function |

`sd, width` |
the nominal measure of the width of the distribution. Note that this is a signed value for gumbel distributions, affecting the direction of the asymmetric tail. |

`height` |
an optional multiplier for adjusting the magnitude of the Y values returned. By default, the height is defined by the underlying function. |

`floor` |
an optional floor value for the tails of the distribution. This has the effect of applying a linear offset to the Y values. |

`start.center, start.width, start.height, etc.` |
for the |

For the curve generation functions, a vector of Y values, from evaluating the function at all values in X.

For the curve fitting functions, a list containing:

`y ` |
a vector of best fit values of Y, at each location in X. |

`mean,center ` |
the best fit curve parameter of the distribution's central value. |

`sd,width ` |
the best fit curve parameter of the distribution's width. |

`height ` |
the best fit curve parameter of the distribution's height. |

`floor ` |
if fitted, the best fit curve parameter of the distribution's floor (linear offset of the function tails) |

While the functions are implemented differently, 'normal' an 'gaussian' are effectively the same.

Bob Morrison

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