mgwr.gwr.MGWRResults

class mgwr.gwr.MGWRResults(model, params, predy, CCT, ENP_j, w, R)

Class including common properties for a MGWR model.

Parameters

modelMGWR object

pointer to MGWR object with estimation parameters

paramsarray

n*k, estimated coefficients

predyarray

n*1, predicted y values

Sarray

n*n, model hat matrix (if MGWR(hat_matrix=True))

Rarray

n*n*k, covariate-specific hat matrices (if MGWR(hat_matrix=True))

CCTarray

n*k, scaled variance-covariance matrix

warray

n*1, final weight used for iteratively re-weighted least sqaures; default is None

Attributes

modelGWR Object

points to GWR object for which parameters have been estimated

paramsarray

n*k, parameter estimates

predyarray

n*1, predicted value of y

yarray

n*1, dependent variable

Xarray

n*k, independent variable, including constant

familyfamily object

underlying probability model; provides distribution-specific calculations

ninteger

number of observations

kinteger

number of independent variables

df_modelinteger

model degrees of freedom

df_residinteger

residual degrees of freedom

scalefloat

sigma squared used for subsequent computations

warray

n*1, final weights from iteratively re-weighted least sqaures routine

resid_responsearray

n*1, residuals of the repsonse

resid_ssscalar

residual sum of sqaures

Warray-like

list of n*n arrays, spatial weights matrices for weighting all observations from each calibration point: one for each covariate (k)

Sarray

n*n, model hat matrix (if MGWR(hat_matrix=True))

Rarray

n*n*k, covariate-specific hat matrices (if MGWR(hat_matrix=True))

CCTarray

n*k, scaled variance-covariance matrix

ENPscalar

effective number of parameters

ENP_jarray-like

effective number of paramters, which depends on sigma2, for each covariate in the model

adj_alphaarray

Corrected alpha (critical) values to account for multiple testing during hypothesis testing.

adj_alpha_jarray

Corrected alpha (critical) values to account for multiple testing during hypothesis testing.

tr_Sfloat

trace of S (hat) matrix

tr_STSfloat

trace of STS matrix

R2float

Global r-squared value for a Gaussian model.

adj_R2float

Adjusted global r-squared for a Gaussian model.

aicfloat

Akaike information criterion

aiccfloat

corrected Akaike information criterion to account to account for model complexity (smaller bandwidths)

bicfloat

Bayesian information criterio

sigma2float

residual variance

std_resarray

standardized residuals

bsearray

standard errors of Betas

influarray

n*1, leading diagonal of S matrix

CooksDarray

n*1, Cook’s D

tvaluesarray

Return the t-statistic for a given parameter estimate.

llfscalar

log-likelihood of the full model; see pysal.contrib.glm.family for damily-sepcific log-likelihoods

muarray

n*, flat one dimensional array of predicted mean response value from estimator

Methods

D2(self)	Percentage of deviance explanied.
ENP(self)	effective number of parameters
R2(self)	Global r-squared value for a Gaussian model.
RSS(self)	geographically weighted residual sum of squares
TSS(self)	geographically weighted total sum of squares
adj_D2(self)	Adjusted percentage of deviance explanied.
adj_R2(self)	Adjusted global r-squared for a Gaussian model.
adj_alpha(self)	Corrected alpha (critical) values to account for multiple testing during hypothesis testing.
adj_alpha_j(self)	Corrected alpha (critical) values to account for multiple testing during hypothesis testing.
bse(self)	standard errors of Betas
conf_int(self)	Returns the confidence interval of the fitted parameters.
cooksD(self)	Influence: leading diagonal of S Matrix
cov_params(self, cov[, exog_scale])	Returns scaled covariance parameters
critical_tval(self[, alpha])	Utility function to derive the critial t-value based on given alpha that are needed for hypothesis testing
filter_tvals(self[, critical_t, alpha])	Utility function to set tvalues with an absolute value smaller than the absolute value of the alpha (critical) value to 0.
get_bws_intervals(self, selector[, level])	Computes bandwidths confidence intervals (CIs) for MGWR.
localR2(self)	local R square
local_collinearity(self)	Computes several indicators of multicollinearity within a geographically weighted design matrix, including:
pDev(self)	Local percentage of deviance accounted for.
sigma2(self)	residual variance
spatial_variability(self, selector[, …])	Method to compute a Monte Carlo test of spatial variability for each estimated coefficient surface.
std_res(self)	standardized residuals
summary(self)	Print out MGWR summary
tr_S(self)	trace of S (hat) matrix
tvalues(self)	Return the t-statistic for a given parameter estimate.
use_t(self)	bool(x) -> bool
y_bar(self)	weighted mean of y

W
adj_pseudoR2
aic
aicc
bic
deviance
df_model
df_resid
global_deviance
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__(self, model, params, predy, CCT, ENP_j, w, R)

Initialize class