class statsmodels.nonparametric.kde.KDEUnivariate(endog)
[source]
Univariate Kernel Density Estimator.
Parameters: | endog (array-like) – The variable for which the density estimate is desired. |
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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.
See also
KDEMultivariate
, kdensity
, kdensityfft
>>> 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()
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 |
evaluate (point) | Evaluate density at a single point. |
fit ([kernel, bw, fft, weights, gridsize, …]) | Attach the density estimate to the KDEUnivariate class. |
icdf () | Inverse Cumulative Distribution (Quantile) Function |
sf () | Returns the survival function evaluated at the support. |
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.nonparametric.kde.KDEUnivariate.html