statsmodels.regression.linear_model.OLS¶
-
class
statsmodels.regression.linear_model.
OLS
(endog, exog=None, missing='none', hasconst=None, **kwargs)[source]¶ Ordinary Least Squares
- Parameters
- endogarray_like
A 1-d endogenous response variable. The dependent variable.
- exogarray_like
A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user. See
statsmodels.tools.add_constant
.- missing
str
Available options are ‘none’, ‘drop’, and ‘raise’. If ‘none’, no nan checking is done. If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised. Default is ‘none’.
- hasconst
None
or bool Indicates whether the RHS includes a user-supplied constant. If True, a constant is not checked for and k_constant is set to 1 and all result statistics are calculated as if a constant is present. If False, a constant is not checked for and k_constant is set to 0.
- **kwargs
Extra arguments that are used to set model properties when using the formula interface.
See also
Notes
No constant is added by the model unless you are using formulas.
Examples
>>> import statsmodels.api as sm >>> Y = [1,3,4,5,2,3,4] >>> X = range(1,8) >>> X = sm.add_constant(X) >>> model = sm.OLS(Y,X) >>> results = model.fit() >>> results.params array([ 2.14285714, 0.25 ])
>>> results.tvalues array([ 1.87867287, 0.98019606])
>>> print(results.t_test([1, 0])) <T test: effect=array([ 2.14285714]), sd=array([[ 1.14062282]]), t=array([[ 1.87867287]]), p=array([[ 0.05953974]]), df_denom=5> >>> print(results.f_test(np.identity(2))) <F test: F=array([[ 19.46078431]]), p=[[ 0.00437251]], df_denom=5, df_num=2>
- Attributes
- weightsscalar
Has an attribute weights = array(1.0) due to inheritance from WLS.
Methods
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, …])Construct a random number generator for the predictive distribution.
hessian
(params[, scale])Evaluate the Hessian function at a given point.
hessian_factor
(params[, scale, observed])Calculate the weights for the Hessian.
information
(params)Fisher information matrix of model.
Initialize model components.
loglike
(params[, scale])The likelihood function for the OLS model.
predict
(params[, exog])Return linear predicted values from a design matrix.
score
(params[, scale])Evaluate the score function at a given point.
whiten
(x)OLS model whitener does nothing.
Properties
The model degree of freedom.
The residual degree of freedom.
Names of endogenous variables.
Names of exogenous variables.