View source: R/gaussian.R View source: R/curveFitTools.R
gaussian | R Documentation |
Generate a vector of values that represent a Gaussian distribution. Secondly, apply a nonlinear least squares fit to gaussian data to determine the fitted gaussian function.
gaussian(x, mean = 0, sd = 1, height = NULL, offset = 0)
fit.gaussian( x, y, fit.offset = TRUE)
x |
vector of X locations to evaluate the gaussian at. For 'fit.gaussian', the locations at which the measured Y values are taken from. |
mean |
the center locatinon X for the generated gaussian curve |
sd |
the standard deviation for the generated gaussian curve |
height |
the maximum height of the generated gaussian curve. By default, the curve will have height such that its integral equals 1. |
offset |
the linear offset for the baseline of the generated gaussian curve |
y |
the raw data measurements at 'x', that are to be fit by a gaussian model. |
fit.offset |
logical, should the model include the offset term, to fit the baseline tails of the raw data. |
For 'gaussian', a vector of length length(x)
, that gives the amplitude
of that gaussian function evaluated at x
.
For fit.gaussian
, a list:
coefficients |
a vector of named coefficients (mean, sd, height, offset) giving the parameters of the best fit gaussian model. |
y |
a vector of the same length as |
Implemented via nls
Bob Morrison
based on code from: Earl F. Glynn, Stowers Inst for Medical Research
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