SlimResults
Class
SlimResults(glm_results=None)
Description
Create a version of the dictionary of results objects that uses less memory. This is the format of results dictionaries generated under the default options in EstimationModel (i.e. the following argument is executed: EstimationModel(..., full_results=False)). The SlimResults object is a smaller subset of the GLMResultsWrapper object in the statsmodels package(for more info, see statsmodels' GLMResults). Large attributes, such as copies of the estimating data, are removed from the results to cut back on memory size. The results most commonly referenced are retained, though.
The SlimResults object retains only the attributes listed below. For additional information see the documentation for the GLMResultsWrapper in the statsmodels package.
Arguments
glm_results: statsmodels.genmod.generalized_linear_model.GLMResultsWrapper
An instance of the statsmodels.GLM.fit() results object
Atributes
params: Pandas Series
Estimated parameter values
aic: float
Akaike Information Criterion
bic: float
Bayes Information Criterion
llf: float
Value of log-likelihood function
nobs: float
number of observations
bse: Pandas Series
Beta standard errors for parameter estimates
pvalues: Pandas Series
Two-tailed pvalues for parameter estimates
family_name: str
Name of distribution family used
family_link: str
Estimation link function
method: str
Estimation method
fit_history: int
Number of iterations completed
scale: float
The estimate of the scale / dispersion for the model fit
deviance: float
Deviance measure
pearson_chi2: Pandas Series
Chi-squared statistic
cov_type: str
Covariance type
yname: str
Column name of endogenous variable
xname: List[str]
Column names of exogenous variables
model: str
Model used for fit
df_resid: float
df_model: float
tvalues: Pandas Series
T statistics
fittedvalues: Pandas Series
Linear predicted values
Methods
The SlimResults object replicates two methods from the original GLMResultsWrapper object from statsmodels.
conf_int: array
alpha: (optional) float
The significance level for the confidence interval.
I.e., The default alpha = .05 returns a 95% confidence interval.
cols: (optional) array-like
cols specifies which confidence intervals to return
summary: object
print a table summarizing estimation results (replicates statsmodels summary method
for GLM).