- 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