mgwr.gwr.MGWRResults¶

class mgwr.gwr.MGWRResults(model, params, predy, S, CCT, R, w)[source]¶

Class including common properties for a MGWR model.

Parameters:

model : MGWR object pointer to MGWR object with estimation parameters params : array n*k, estimated coefficients predy : array n*1, predicted y values S : array n*n, hat matrix R : array n*n*k, partial hat matrices for each covariate CCT : array n*k, scaled variance-covariance matrix w : array n*1, final weight used for iteratively re-weighted least sqaures; default is None

Attributes:

model : GWR Object points to GWR object for which parameters have been estimated params : array n*k, parameter estimates predy : array n*1, predicted value of y y : array n*1, dependent variable X : array n*k, independent variable, including constant family : family object underlying probability model; provides distribution-specific calculations n : integer number of observations k : integer number of independent variables df_model : integer model degrees of freedom df_resid : integer residual degrees of freedom scale : float sigma squared used for subsequent computations w : array n*1, final weights from iteratively re-weighted least sqaures routine resid_response : array n*1, residuals of the repsonse resid_ss : scalar residual sum of sqaures W : array-like list of n*n arrays, spatial weights matrices for weighting all observations from each calibration point: one for each covariate (k) S : array n*n, hat matrix R : array n*n*k, partial hat matrices for each covariate CCT : array n*k, scaled variance-covariance matrix ENP : scalar effective number of parameters ENP_j : array-like effective number of paramters, which depends on sigma2, for each covariate in the model adj_alpha : array Corrected alpha (critical) values to account for multiple testing during hypothesis testing. adj_alpha_j : array Corrected alpha (critical) values to account for multiple testing during hypothesis testing. tr_S : float trace of S (hat) matrix tr_STS : float trace of STS matrix R2 : float R-squared for the entire model (1- RSS/TSS) aic : float Akaike information criterion aicc : float corrected Akaike information criterion to account to account for model complexity (smaller bandwidths) bic : float Bayesian information criterio sigma2 : float residual variance std_res : array standardized residuals bse : array standard errors of Betas influ : array Influence: leading diagonal of S Matrix CooksD : array n*1, Cook’s D tvalues : array Return the t-statistic for a given parameter estimate. llf : scalar log-likelihood of the full model; see pysal.contrib.glm.family for damily-sepcific log-likelihoods mu : array n*, flat one dimensional array of predicted mean response value from estimator

Methods

ENP()	effective number of parameters
RSS()	geographically weighted residual sum of squares
TSS()	geographically weighted total sum of squares
adj_alpha()	Corrected alpha (critical) values to account for multiple testing during hypothesis testing.
adj_alpha_j()	Corrected alpha (critical) values to account for multiple testing during hypothesis testing.
bse()	standard errors of Betas
conf_int()	Returns the confidence interval of the fitted parameters.
cooksD()	Influence: leading diagonal of S Matrix
cov_params(cov[, exog_scale])	Returns scaled covariance parameters
critical_tval([alpha])	Utility function to derive the critial t-value based on given alpha that are needed for hypothesis testing
filter_tvals([critical_t, alpha])	Utility function to set tvalues with an absolute value smaller than the absolute value of the alpha (critical) value to 0.
influ()	Influence: leading diagonal of S Matrix
localR2()	local R square
local_collinearity()	Computes several indicators of multicollinearity within a geographically weighted design matrix, including:
pDev()	Local percentage of deviance accounted for.
sigma2()	residual variance
spatial_variability(selector[, n_iters, seed])	Method to compute a Monte Carlo test of spatial variability for each estimated coefficient surface.
std_res()	standardized residuals
summary()	Print out MGWR summary
tr_S()	trace of S (hat) matrix
tr_STS()	trace of STS matrix
tvalues()	Return the t-statistic for a given parameter estimate.
use_t()	bool(x) -> bool
y_bar()	weighted mean of y

D2
ENP_j
R2
adj_D2
adj_pseudoR2
aic
aicc
bic
deviance
df_model
df_resid
initialize
llf
llnull
normalized_cov_params
null
null_deviance
pearson_chi2
predictions
pseudoR2
pvalues
resid_anscombe
resid_deviance
resid_pearson
resid_response
resid_ss
resid_working
scale

__init__(model, params, predy, S, CCT, R, w)[source]¶

Initialize class