class statsmodels.regression.linear_model.WLS(endog, exog, weights=1.0, missing='none', hasconst=None, **kwargs)
[source]
A regression model with diagonal but nonidentity 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 noconstant 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 reinitialize) a Model instance. 
loglike (params)  Returns the value of the gaussian loglikelihood 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 3clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.regression.linear_model.WLS.html