- 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
- offset : array
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
- 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
n*n; spatial weights for each observation from each
calibration point
- S : array
n*n, hat matrix
- CCT : array
n*k, scaled variance-covariance matrix
ENP
: scalar
effective number of parameters
tr_S
: float
trace of S (hat) matrix
tr_STS
: float
trace of STS matrix
y_bar
: array
weighted mean of y
TSS
: array
geographically weighted total sum of squares
RSS
: array
geographically weighted residual sum of squares
- 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
localR2
: array
local R square
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.
adj_alpha
: array
Corrected alpha (critical) values to account for multiple testing during hypothesis testing.
- deviance : array
n*1, local model deviance for each calibration point
- resid_deviance : array
n*1, local sum of residual deviance for each
calibration point
- llf : scalar
log-likelihood of the full model; see
pysal.contrib.glm.family for damily-sepcific
log-likelihoods
pDev
: float
Local percentage of deviance accounted for.
- mu : array
n*, flat one dimensional array of predicted mean
response value from estimator
- fit_params : dict
parameters passed into fit method to define estimation
routine
- predictions : array
p*1, predicted values generated by calling the GWR
predict method to predict dependent variable at
unsampled points ()