Package Bio :: Package Statistics :: Module lowess
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Module lowess

source code

Implements the Lowess function for nonparametric regression.

Functions: lowess Fit a smooth nonparametric regression curve to a scatterplot.

For more information, see

William S. Cleveland: "Robust locally weighted regression and smoothing scatterplots", Journal of the American Statistical Association, December 1979, volume 74, number 368, pp. 829-836.

William S. Cleveland and Susan J. Devlin: "Locally weighted regression: An approach to regression analysis by local fitting", Journal of the American Statistical Association, September 1988, volume 83, number 403, pp. 596-610.

Functions [hide private]
yest
lowess(x, y, f=2./3., iter=3)
Lowess smoother: Robust locally weighted regression.
source code
 
_test()
Run the Bio.Statistics.lowess module's doctests.
source code
Variables [hide private]
  __package__ = 'Bio.Statistics'
  x = ImportError('numpy.core.multiarray failed to import',)
Function Details [hide private]

lowess(x, y, f=2./3., iter=3)

source code 

Lowess smoother: Robust locally weighted regression. The lowess function fits a nonparametric regression curve to a scatterplot. The arrays x and y contain an equal number of elements; each pair (x[i], y[i]) defines a data point in the scatterplot. The function returns the estimated (smooth) values of y.

The smoothing span is given by f. A larger value for f will result in a smoother curve. The number of robustifying iterations is given by iter. The function will run faster with a smaller number of iterations.

x and y should be numpy float arrays of equal length. The return value is also a numpy float array of that length.

e.g. >>> import numpy >>> x = numpy.array([4, 4, 7, 7, 8, 9, 10, 10, 10, 11, 11, 12, 12, 12, ... 12, 13, 13, 13, 13, 14, 14, 14, 14, 15, 15, 15, 16, 16, ... 17, 17, 17, 18, 18, 18, 18, 19, 19, 19, 20, 20, 20, 20, ... 20, 22, 23, 24, 24, 24, 24, 25], numpy.float) >>> y = numpy.array([2, 10, 4, 22, 16, 10, 18, 26, 34, 17, 28, 14, 20, 24, ... 28, 26, 34, 34, 46, 26, 36, 60, 80, 20, 26, 54, 32, 40, ... 32, 40, 50, 42, 56, 76, 84, 36, 46, 68, 32, 48, 52, 56, ... 64, 66, 54, 70, 92, 93, 120, 85], numpy.float) >>> result = lowess(x, y) >>> len(result) 50 >>> print("[%0.2f, ..., %0.2f]" % (result[0], result[-1])) [4.85, ..., 84.98]

Returns: yest