mgwr.gwr.GWRResults¶
-
class
mgwr.gwr.
GWRResults
(model, params, predy, S, CCT, influ, tr_STS=None, w=None)[source]¶ Basic class including common properties for all GWR regression models
- Parameters
- modelGWR object
pointer to GWR object with estimation parameters
- paramsarray
n*k, estimated coefficients
- predyarray
n*1, predicted y values
- Sarray
n*n, hat matrix
- 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
- offsetarray
n*1, the offset variable at the ith location. For Poisson model this term is often the size of the population at risk or the expected size of the outcome in spatial epidemiology; Default is None where Ni becomes 1.0 for all locations
- 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
n*n; spatial weights for each observation from each calibration point
- Sarray
n*n, hat matrix
- CCTarray
n*k, scaled variance-covariance matrix
ENP
scalareffective number of parameters
tr_S
floattrace of S (hat) matrix
- tr_STSfloat
trace of STS matrix
y_bar
arrayweighted mean of y
TSS
arraygeographically weighted total sum of squares
RSS
arraygeographically weighted residual sum of squares
R2
floatGlobal r-squared value for a Gaussian model.
adj_R2
floatAdjusted 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
localR2
arraylocal R square
sigma2
floatresidual variance
std_res
arraystandardized residuals
bse
arraystandard errors of Betas
- influarray
n*1, leading diagonal of S matrix
- CooksDarray
n*1, Cook’s D
tvalues
arrayReturn the t-statistic for a given parameter estimate.
adj_alpha
arrayCorrected alpha (critical) values to account for multiple testing during hypothesis testing.
- deviancearray
n*1, local model deviance for each calibration point
- resid_deviancearray
n*1, local sum of residual deviance for each calibration point
- llfscalar
log-likelihood of the full model; see pysal.contrib.glm.family for damily-sepcific log-likelihoods
pDev
floatLocal percentage of deviance accounted for.
D2
floatPercentage of deviance explanied.
adj_D2
floatAdjusted percentage of deviance explanied.
- muarray
n*, flat one dimensional array of predicted mean response value from estimator
- fit_paramsdict
parameters passed into fit method to define estimation routine
- predictionsarray
p*1, predicted values generated by calling the GWR predict method to predict dependent variable at unsampled points ()
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.
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 critical 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 interval (CI) for GWR.
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 GWR 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