spglm.glm.GLMResults¶

class spglm.glm.GLMResults(model, params, mu, w)[source]¶

Results of estimated GLM and diagnostics.

Parameters:	model : GLM object Pointer to GLM object with estimation parameters. params : array k*1, estimared coefficients mu : array n*1, predicted y values. w : array n*1, final weight used for iwls

Examples

>>> import libpysal
>>> from spglm.glm import GLM, GLMResults
>>> from spglm.family import Gaussian
>>> db = libpysal.io.open(libpysal.examples.get_path('columbus.dbf'),'r')
>>> y = np.array(db.by_col("HOVAL"))
>>> y = np.reshape(y, (49,1))
>>> X = []
>>> X.append(db.by_col("INC"))
>>> X.append(db.by_col("CRIME"))
>>> X = np.array(X).T
>>> model = GLM(y, X, family=Gaussian())
>>> results1 = model.fit()
>>> results1.aic
408.73548964604873
>>> model = results1.model
>>> params = results1.params.flatten()
>>> predy = results1.mu
>>> w = results1.w
>>> results2 = GLMResults(model, params, predy, w)
>>> results2.aic
408.73548964604873

Attributes:

model : GLM Object Points to GLM object for which parameters have been estimated. y : array n*1, dependent variable. x : array n*k, independent variable, including constant. family : string Model type: ‘Gaussian’, ‘Poisson’, ‘Logistic’ n : integer Number of observations k : integer Number of independent variables df_model : float k-1, where k is the number of variables (including intercept) df_residual : float observations minus variables (n-k) fit_params : dict parameters passed into fit method to define estimation routine. scale : float sigma squared used for subsequent computations. params : array n*k, estimared beta coefficients w : array n*1, final weight values of x mu : array n*1, predicted value of y (i.e., fittedvalues) cov_params : array Variance covariance matrix (kxk) of betas bse : array k*1, standard errors of betas pvalues : array k*1, two-tailed pvalues of parameters tvalues : array k*1, the tvalues of the standard errors null : array n*1, predicted values of y for null model deviance : float value of the deviance function evalued at params; see family.py for distribution-specific deviance null_deviance : float value of the deviance function for the model fit with a constant as the only regressor llf : float value of the loglikelihood function evalued at params; see family.py for distribution-specific loglikelihoods llnull : float value of log-likelihood function evaluated at null aic : float AIC bic : float BIC D2 : float percent deviance explained adj_D2 : float adjusted percent deviance explained pseudo_R2 : float McFadden’s pseudo R2 (coefficient of determination) adj_pseudoR2 : float adjusted McFadden’s pseudo R2 tr_S : trace of the hat matrix S resid_response : array response residuals; defined as y-mu resid_pearson : array Pearson residuals; defined as (y-mu)/sqrt(VAR(mu)) where VAR is the distribution specific variance function; see family.py and varfuncs.py for more information. resid_working : array Working residuals; the working residuals are defined as resid_response/link’(mu); see links.py for the derivatives of the link functions. resid_anscombe : array Anscombe residuals; see family.py for distribution-specific Anscombe residuals. resid_deviance : array deviance residuals; see family.py for distribution-specific deviance residuals. pearson_chi2 : float chi-Squared statistic is defined as the sum of the squares of the Pearson residuals normalized_cov_params : array k*k, approximates [X.T*X]-1

Methods

conf_int([alpha, cols, method])	Returns the confidence interval of the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, …])	Returns the variance/covariance matrix.
tvalues()	Return the t-statistic for a given parameter estimate.

D2
adj_D2
adj_pseudoR2
aic
bic
bse
deviance
df_model
df_resid
initialize
llf
llnull
normalized_cov_params
null
null_deviance
pearson_chi2
pseudoR2
pvalues
resid_anscombe
resid_deviance
resid_pearson
resid_response
resid_working
scale
tr_S

__init__(model, params, mu, w)[source]¶

Initialize self. See help(type(self)) for accurate signature.

Methods

D2()
__init__(model, params, mu, w)	Initialize self.
adj_D2()
adj_pseudoR2()
aic()
bic()
bse()
conf_int([alpha, cols, method])	Returns the confidence interval of the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, …])	Returns the variance/covariance matrix.
deviance()
df_model()
df_resid()
initialize(model, params, **kwd)
llf()
llnull()
normalized_cov_params()
null()
null_deviance()
pearson_chi2()
pseudoR2()
pvalues()
resid_anscombe()
resid_deviance()
resid_pearson()
resid_response()
resid_working()
scale()
tr_S()
tvalues()	Return the t-statistic for a given parameter estimate.

Attributes

use_t