View source: R/curveFitTools.R
normal | R Documentation |
Generate or fit by nonlinear least squares a family of classic distribution functions.
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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