python - Extract coefficients from spreg.OLS results -

i'm trying recreate r spgwr notebook using pysal. using r, local coefficients can directly extracted dataframe so: (csv available here)

r

library(spgwr) df <- read.csv("file.csv") attach(df) # calibrate bandwidth bw <- gwr.sel(endog ~ x1+x2+x3, data=df, coords=cbind(x,y), adapt=t) # fit model gwr.model = gwr(endog ~ x1+x2+x3, data=df, coords=cbind(x,y), adapt=bw, hatmatrix=true, se.fit=true) # build results dataframe results<-as.data.frame(gwr.model$sdf) # results contains coefficients , standard errors: head(results) # columns: sum.w x.intercept, x1, x2, x3 … pred.se_edf x y # attach coefficients original dataframe df$coef_x1<-results$x1 df$coef_x2<-results$x2 df$coef_x3<-results$x3 

is there simple way can calculate these coefficients using results ps.spreg.ols?

python

import pandas pd import pysal ps  df = pd.read_csv("file.csv") # build spatial weights # give different weights r, that's ok # repackage variables convenience yxs = df.loc[:, ['endog', 'x1', 'x2', 'x3']]  spatial_weights = ps.knnw_from_array(     df.loc[yxs.index, ['x', 'y']].values ) # row-standarise weights spatial_weights.transform = 'r'  fit = ps.spreg.ols(     df.endog.values[:, none],     df[['x1', 'x2', 'x3']].values,     w=spatial_weights,     spat_diag=true, ) print(fit.summary)  regression ---------- summary of output: ordinary least squares ----------------------------------------- data set            :     unknown weights matrix      :     unknown dependent variable  :     dep_var                number of observations:         625 mean dependent var  :    345.8406                number of variables   :           4 s.d. dependent var  :     19.5388                degrees of freedom    :         621 r-squared           :      0.5163 adjusted r-squared  :      0.5139 sum squared residual:  115234.766                f-statistic           :    220.9246 sigma-square        :     185.563                prob(f-statistic)     :    1.68e-97 s.e. of regression  :      13.622                log likelihood        :   -2517.141 sigma-square ml     :     184.376                akaike info criterion :    5042.283 s.e of regression ml:     13.5785                schwarz criterion     :    5060.034  ------------------------------------------------------------------------------------             variable     coefficient       std.error     t-statistic     probability ------------------------------------------------------------------------------------             constant     361.8912240       3.0709296     117.8441935       0.0000000                var_1     -13.0082465       1.8701682      -6.9556560       0.0000000                var_2      -1.1944903       0.1067895     -11.1854632       0.0000000                var_3      23.3549680       2.1474242      10.8758055       0.0000000 ------------------------------------------------------------------------------------  regression diagnostics multicollinearity condition number           13.948  test on normality of errors test                             df        value           prob jarque-bera                       2        2160.476           0.0000  diagnostics heteroskedasticity random coefficients test                             df        value           prob breusch-pagan test                3          75.484           0.0000 koenker-bassett test              3          13.942           0.0030  diagnostics spatial dependence test                           mi/df       value           prob lagrange multiplier (lag)         1          43.543           0.0000 robust lm (lag)                   1           1.819           0.1775 lagrange multiplier (error)       1          46.989           0.0000 robust lm (error)                 1           5.264           0.0218 lagrange multiplier (sarma)       2          48.808           0.0000  ================================ end of report ===================================== 

i'm not sure how use data contained in fit calculate local coefficients, however.