KDEUnivariate.fit()

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. fft : bool 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.