class statsmodels.regression.linear_model.WLS(endog, exog, weights=1.0, missing='none', hasconst=None, **kwargs)
[source]
A regression model with diagonal but non-identity covariance structure.
The weights are presumed to be (proportional to) the inverse of the variance of the observations. That is, if the variables are to be transformed by 1/sqrt(W) you must supply weights = 1/W.
Parameters: |
|
---|
weights
array – The stored weights supplied as an argument.
See regression.GLS
>>> import numpy as np >>> import statsmodels.api as sm >>> Y = [1,3,4,5,2,3,4] >>> X = range(1,8) >>> X = sm.add_constant(X) >>> wls_model = sm.WLS(Y,X, weights=list(range(1,8))) >>> results = wls_model.fit() >>> results.params array([ 2.91666667, 0.0952381 ]) >>> results.tvalues array([ 2.0652652 , 0.35684428]) >>> print(results.t_test([1, 0])) <T test: effect=array([ 2.91666667]), sd=array([[ 1.41224801]]), t=array([[ 2.0652652]]), p=array([[ 0.04690139]]), df_denom=5> >>> print(results.f_test([0, 1])) <F test: F=array([[ 0.12733784]]), p=[[ 0.73577409]], df_denom=5, df_num=1>
If the weights are a function of the data, then the post estimation statistics such as fvalue and mse_model might not be correct, as the package does not yet support no-constant regression.
fit ([method, cov_type, cov_kwds, use_t]) | Full fit of the model. |
fit_regularized ([method, alpha, L1_wt, …]) | Return a regularized fit to a linear regression model. |
from_formula (formula, data[, subset, drop_cols]) | Create a Model from a formula and dataframe. |
get_distribution (params, scale[, exog, …]) | Returns a random number generator for the predictive distribution. |
hessian (params) | The Hessian matrix of the model |
hessian_factor (params[, scale, observed]) | Weights for calculating Hessian |
information (params) | Fisher information matrix of model |
initialize () | Initialize (possibly re-initialize) a Model instance. |
loglike (params) | Returns the value of the gaussian log-likelihood function at params. |
predict (params[, exog]) | Return linear predicted values from a design matrix. |
score (params) | Score vector of model. |
whiten (X) | Whitener for WLS model, multiplies each column by sqrt(self.weights) |
df_model | The model degree of freedom, defined as the rank of the regressor matrix minus 1 if a constant is included. |
df_resid | The residual degree of freedom, defined as the number of observations minus the rank of the regressor matrix. |
endog_names | Names of endogenous variables |
exog_names | Names of exogenous variables |
© 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.regression.linear_model.WLS.html