statsmodels.nonparametric.kernel_density.KDEMultivariateConditional¶
- class statsmodels.nonparametric.kernel_density.KDEMultivariateConditional(endog, exog, dep_type, indep_type, bw, defaults=None)[source]¶
Conditional multivariate kernel density estimator.
Calculates
P(Y_1,Y_2,...Y_n | X_1,X_2...X_m) = P(X_1, X_2,...X_n, Y_1, Y_2,..., Y_m)/P(X_1, X_2,..., X_m)
. The conditional density is by definition the ratio of the two densities, see [1].- Parameters:
- endog
list
of
ndarrays
or 2-Dndarray
The training data for the dependent variables, used to determine the bandwidth(s). If a 2-D array, should be of shape (num_observations, num_variables). If a list, each list element is a separate observation.
- exog
list
of
ndarrays
or 2-Dndarray
The training data for the independent variable; same shape as endog.
- dep_type
str
The type of the dependent variables:
c : Continuous u : Unordered (Discrete) o : Ordered (Discrete)
The string should contain a type specifier for each variable, so for example
dep_type='ccuo'
.- indep_type
str
The type of the independent variables; specified like dep_type.
- bwarray_like or
str
,optional
If an array, it is a fixed user-specified bandwidth. If a string, should be one of:
normal_reference: normal reference rule of thumb (default)
cv_ml: cross validation maximum likelihood
cv_ls: cross validation least squares
- defaults
Instance
of
class
EstimatorSettings
The default values for the efficient bandwidth estimation
- endog
See also
References
Examples
>>> import statsmodels.api as sm >>> nobs = 300 >>> c1 = np.random.normal(size=(nobs,1)) >>> c2 = np.random.normal(2,1,size=(nobs,1))
>>> dens_c = sm.nonparametric.KDEMultivariateConditional(endog=[c1], ... exog=[c2], dep_type='c', indep_type='c', bw='normal_reference') >>> dens_c.bw # show computed bandwidth array([ 0.41223484, 0.40976931])
- Attributes:
- bwarray_like
The bandwidth parameters
Methods
cdf
([endog_predict, exog_predict])Cumulative distribution function for the conditional density.
imse
(bw)The integrated mean square error for the conditional KDE.
loo_likelihood
(bw[, func])Returns the leave-one-out conditional likelihood of the data.
pdf
([endog_predict, exog_predict])Evaluate the probability density function.