statsmodels.nonparametric.kernel_density.EstimatorSettings¶
-
class
statsmodels.nonparametric.kernel_density.
EstimatorSettings
(efficient=False, randomize=False, n_res=25, n_sub=50, return_median=True, return_only_bw=False, n_jobs=-1)[source]¶ Object to specify settings for density estimation or regression.
EstimatorSettings has several properties related to how bandwidth estimation for the KDEMultivariate, KDEMultivariateConditional, KernelReg and CensoredKernelReg classes behaves.
- Parameters
- efficientbool,
optional
If True, the bandwidth estimation is to be performed efficiently – by taking smaller sub-samples and estimating the scaling factor of each subsample. This is useful for large samples (nobs >> 300) and/or multiple variables (k_vars > 3). If False (default), all data is used at the same time.
- randomizebool,
optional
If True, the bandwidth estimation is to be performed by taking n_res random resamples (with replacement) of size n_sub from the full sample. If set to False (default), the estimation is performed by slicing the full sample in sub-samples of size n_sub so that all samples are used once.
- n_sub
int
,optional
Size of the sub-samples. Default is 50.
- n_res
int
,optional
The number of random re-samples used to estimate the bandwidth. Only has an effect if
randomize == True
. Default value is 25.- return_medianbool,
optional
If True (default), the estimator uses the median of all scaling factors for each sub-sample to estimate the bandwidth of the full sample. If False, the estimator uses the mean.
- return_only_bwbool,
optional
If True, the estimator is to use the bandwidth and not the scaling factor. This is not theoretically justified. Should be used only for experimenting.
- n_jobs
int
,optional
The number of jobs to use for parallel estimation with
joblib.Parallel
. Default is -1, meaningn_cores - 1
, withn_cores
the number of available CPU cores. See the joblib documentation for more details.
- efficientbool,
Examples
>>> settings = EstimatorSettings(randomize=True, n_jobs=3) >>> k_dens = KDEMultivariate(data, var_type, defaults=settings)
Methods