statsmodels.nonparametric.kde.KDEUnivariate.fit¶
-
KDEUnivariate.
fit
(kernel='gau', bw='normal_reference', fft=True, weights=None, gridsize=None, adjust=1, cut=3, clip=(-inf, inf))[source]¶ Attach the density estimate to the KDEUnivariate class.
- Parameters
- kernel
str
The Kernel to be used. Choices are:
“biw” for biweight
“cos” for cosine
“epa” for Epanechnikov
“gau” for Gaussian.
“tri” for triangular
“triw” for triweight
“uni” for uniform
- bw
str
,float
The bandwidth to use. Choices are:
“scott” - 1.059 * A * nobs ** (-1/5.), where A is min(std(X),IQR/1.34)
“silverman” - .9 * A * nobs ** (-1/5.), where A is min(std(X),IQR/1.34)
“normal_reference” - C * A * nobs ** (-1/5.), where C is calculated from the kernel. Equivalent (up to 2 dp) to the “scott” bandwidth for gaussian kernels. See bandwidths.py
If a float is given, it is the bandwidth.
- fftbool
Whether or not to use FFT. FFT implementation is more computationally efficient. However, only the Gaussian kernel is implemented. If FFT is False, then a ‘nobs’ x ‘gridsize’ intermediate array is created.
- gridsize
int
If gridsize is None, max(len(X), 50) is used.
- cut
float
Defines the length of the grid past the lowest and highest values of X so that the kernel goes to zero. The end points are -/+ cut*bw*{min(X) or max(X)}
- adjust
float
An adjustment factor for the bw. Bandwidth becomes bw * adjust.
- kernel