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: | endog : array-like
exog : array-like
weights : array-like, optional
missing : str
hasconst : None or bool
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Notes
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.
Examples
>>> 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>
Attributes
weights | array | The stored weights supplied as an argument. |
See regression.GLS |
Methods
fit([method, cov_type, cov_kwds, use_t]) | Full fit of the 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 |
information(params) | Fisher information matrix of model |
initialize() | |
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) |
Methods
fit([method, cov_type, cov_kwds, use_t]) | Full fit of the 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 |
information(params) | Fisher information matrix of model |
initialize() | |
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) |
Attributes
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 |