local_geary.md
In waywiser: Ergonomic Methods for Assessing Spatial Models

Local geary statistics are stable

Code
  df_local_c <- ww_local_geary_c(guerry_modeled, Crm_prs, predictions)
  df_local_c[1:3]
Output
  # A tibble: 85 x 3
     .metric       .estimator .estimate
     <chr>         <chr>          <dbl>
   1 local_geary_c standard       0.981
   2 local_geary_c standard       0.836
   3 local_geary_c standard       0.707
   4 local_geary_c standard       0.108
   5 local_geary_c standard       0.264
   6 local_geary_c standard       1.36 
   7 local_geary_c standard       3.64 
   8 local_geary_c standard       1.57 
   9 local_geary_c standard       0.867
  10 local_geary_c standard       0.737
  # i 75 more rows

Code
  df_local_c_p <- ww_local_geary_pvalue(guerry_modeled, Crm_prs, predictions)
  df_local_c_p[1:3]
Output
  # A tibble: 85 x 3
     .metric            .estimator .estimate
     <chr>              <chr>          <dbl>
   1 local_geary_pvalue standard       0.202
   2 local_geary_pvalue standard       0.255
   3 local_geary_pvalue standard       0.164
   4 local_geary_pvalue standard       0.141
   5 local_geary_pvalue standard       0.277
   6 local_geary_pvalue standard       0.138
   7 local_geary_pvalue standard       0.422
   8 local_geary_pvalue standard       0.208
   9 local_geary_pvalue standard       0.802
  10 local_geary_pvalue standard       0.412
  # i 75 more rows

Code
  (vec_local_c <- ww_local_geary_c_vec(guerry_modeled$Crm_prs, guerry_modeled$
    predictions, weights))
Output
   [1] 0.981119438 0.836402177 0.707464373 0.108332465 0.264075824 1.361485477
   [7] 3.641239412 1.571824022 0.867252524 0.737094462 0.573376555 0.001605731
  [13] 1.891988440 1.152840284 1.029320931 0.297642850 1.219953394 1.934113868
  [19] 1.632566652 0.441916658 5.202733790 0.921953310 3.084515822 0.237218594
  [25] 1.346684045 1.051652204 0.419414691 0.217280214 0.794409207 0.243971372
  [31] 0.376678958 0.139152907 0.711305633 3.096840680 1.974463944 0.922230710
  [37] 1.032031759 0.339464386 0.933794842 1.910440700 0.937597672 0.625628647
  [43] 0.376707677 2.692250283 1.288784962 0.798443065 1.671895951 1.310183326
  [49] 2.347513577 0.845204889 0.302940809 2.291804447 0.881999216 0.412051312
  [55] 2.006031605 0.561239582 0.375776092 1.853716391 1.191472387 1.146802970
  [61] 1.857618679 0.149044974 0.614228825 0.755373475 1.287962784 1.447534518
  [67] 1.236607966 0.962394651 0.338400653 1.914478855 0.641340157 2.146993342
  [73] 0.703881855 1.417638272 0.692636715 1.765618175 0.246058853 0.700262130
  [79] 0.002876896 0.057575267 0.420878038 2.025012395 2.525093274 1.053335832
  [85] 1.030009749

Code
  (vec_local_c_p <- ww_local_geary_pvalue_vec(guerry_modeled$Crm_prs,
  guerry_modeled$predictions, weights))
Output
   [1] 0.20234363 0.25519649 0.16396588 0.14117561 0.27656178 0.13803991
   [7] 0.42226813 0.20763065 0.80158334 0.41221634 0.02279793 0.16590372
  [13] 0.36280952 0.94038689 0.57012896 0.20065488 0.74279801 0.56659279
  [19] 0.07231964 0.16050628 0.50930118 0.74413041 0.72654516 0.14156081
  [25] 0.82683764 0.96497971 0.23835208 0.09164608 0.65695357 0.14596483
  [31] 0.21116270 0.11062386 0.47581719 0.52089308 0.09617881 0.83006042
  [37] 0.37021871 0.27543027 0.84760567 0.81945612 0.64406665 0.55516421
  [43] 0.16365565 0.21954679 0.78645827 0.07224471 0.24248642 0.17178516
  [49] 0.16197800 0.21527791 0.06550740 0.68776553 0.44107979 0.22441812
  [55] 0.91939521 0.16998047 0.19854332 0.64059101 0.23958631 0.96021717
  [61] 0.29282034 0.23743981 0.33944054 0.19039950 0.54411569 0.33254192
  [67] 0.76919602 0.81252824 0.03109512 0.10399128 0.47133467 0.17643651
  [73] 0.37037862 0.56606511 0.60793474 0.10983655 0.11452305 0.32746708
  [79] 0.17184942 0.06669041 0.29892492 0.54538910 0.09744018 0.91852500
  [85] 0.91349534