statsmodels.nonparametric.kde.KDEUnivariate¶
-
class
statsmodels.nonparametric.kde.
KDEUnivariate
(endog)[source]¶ Univariate Kernel Density Estimator.
Parameters: endog : array_like
The variable for which the density estimate is desired.
See also
KDEMultivariate
,kdensity
,kdensityfft
Notes
If cdf, sf, cumhazard, or entropy are computed, they are computed based on the definition of the kernel rather than the FFT approximation, even if the density is fit with FFT = True.
KDEUnivariate is much faster than KDEMultivariate, due to its FFT-based implementation. It should be preferred for univariate, continuous data. KDEMultivariate also supports mixed data.
Examples
>>> import statsmodels.api as sm >>> import matplotlib.pyplot as plt
>>> nobs = 300 >>> np.random.seed(1234) # Seed random generator >>> dens = sm.nonparametric.KDEUnivariate(np.random.normal(size=nobs)) >>> dens.fit() >>> plt.plot(dens.cdf) >>> plt.show()
Methods
evaluate
(point)Evaluate density at a single point. fit
([kernel, bw, fft, weights, gridsize, …])Attach the density estimate to the KDEUnivariate class. Methods
evaluate
(point)Evaluate density at a single point. fit
([kernel, bw, fft, weights, gridsize, …])Attach the density estimate to the KDEUnivariate class. Properties
cdf
Returns the cumulative distribution function evaluated at the support. cumhazard
Returns the hazard function evaluated at the support. entropy
Returns the differential entropy evaluated at the support icdf
Inverse Cumulative Distribution (Quantile) Function sf
Returns the survival function evaluated at the support.