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

Local Moran statistics are stable

Code
  df_local_i <- ww_local_moran_i(guerry_modeled, Crm_prs, predictions)
  df_local_i[1:3]
Output
  # A tibble: 85 x 3
     .metric       .estimator .estimate
     <chr>         <chr>          <dbl>
   1 local_moran_i standard      0.530 
   2 local_moran_i standard      0.858 
   3 local_moran_i standard      0.759 
   4 local_moran_i standard      0.732 
   5 local_moran_i standard      0.207 
   6 local_moran_i standard      0.860 
   7 local_moran_i standard      0.692 
   8 local_moran_i standard      1.69  
   9 local_moran_i standard     -0.0109
  10 local_moran_i standard      0.710 
  # i 75 more rows

Code
  df_local_i_p <- ww_local_moran_pvalue(guerry_modeled, Crm_prs, predictions)
  df_local_i_p[1:3]
Output
  # A tibble: 85 x 3
     .metric            .estimator .estimate
     <chr>              <chr>          <dbl>
   1 local_moran_pvalue standard     0.361  
   2 local_moran_pvalue standard     0.0127 
   3 local_moran_pvalue standard     0.0318 
   4 local_moran_pvalue standard     0.115  
   5 local_moran_pvalue standard     0.234  
   6 local_moran_pvalue standard     0.0935 
   7 local_moran_pvalue standard     0.531  
   8 local_moran_pvalue standard     0.109  
   9 local_moran_pvalue standard     0.335  
  10 local_moran_pvalue standard     0.00663
  # i 75 more rows

Code
  (vec_local_i <- ww_local_moran_i_vec(guerry_modeled$Crm_prs, guerry_modeled$
    predictions, weights))
Output
   [1]  0.529586027  0.857962397  0.759397482  0.731821184  0.207216255
   [6]  0.859824645  0.692480894  1.685682868 -0.010937577  0.709971045
  [11]  1.756476080  0.839390997 -0.208812822  0.311287253 -0.195850256
  [16] -0.046485425  0.219659575  0.072248473  0.911260991  0.796818074
  [21]  0.472218810 -0.047995949 -0.701165391  0.682001844 -0.114131742
  [26]  0.043283334  1.067791069  1.186850176  0.174554949  0.071132504
  [31]  0.014932487  1.014614517  0.258635858  0.385988835 -0.113213840
  [36]  0.016531123  0.601974328 -0.029051514  0.101963855 -0.098393898
  [41]  0.305211136 -0.057462330 -0.015702560  0.882089292 -0.163892577
  [46]  1.649695545  0.377330987  0.868476489 -0.465975751  0.303084203
  [51]  1.404344537 -0.370062874  0.440556284  0.289554503  0.035787495
  [56]  0.393521099  1.006384006  0.222959827  0.730981130  0.628215009
  [61] -0.183012992  0.227295946  0.284153229  2.316505472  0.494418600
  [66]  0.982994320 -0.124397352  0.160297076  1.039537767  1.231583113
  [71]  0.271055716 -0.168894660 -0.038283576  0.017831736 -0.052920056
  [76]  1.205308932  0.808428811  0.551329387  0.878044848  0.901458850
  [81]  0.022009901 -0.327876773 -0.318368758 -0.003280457 -0.124796245

Code
  (vec_local_i_p <- ww_local_moran_pvalue_vec(guerry_modeled$Crm_prs,
  guerry_modeled$predictions, weights))
Output
   [1] 0.361304795 0.012664975 0.031799252 0.115230513 0.234090293 0.093535973
   [7] 0.530618631 0.109289803 0.335060524 0.006632515 0.002278842 0.100115333
  [13] 0.247742772 0.003712388 0.526236804 0.541825841 0.021511546 0.773348351
  [19] 0.125986145 0.219825946 0.549732292 0.513555378 0.381564858 0.078983302
  [25] 0.695793884 0.602944660 0.244326204 0.001337467 0.021685082 0.326512972
  [31] 0.946696741 0.032650704 0.021272223 0.525113591 0.045656211 0.807490784
  [37] 0.121000471 0.863193762 0.128354731 0.818093330 0.195316218 0.278814034
  [43] 0.896063138 0.223229844 0.314659364 0.021844009 0.371216056 0.230792356
  [49] 0.042199799 0.382128483 0.003916122 0.446093710 0.127376322 0.171424332
  [55] 0.947231579 0.141533773 0.058387258 0.599378815 0.103085890 0.044587278
  [61] 0.251120319 0.359430944 0.619407669 0.063573829 0.314640714 0.389339642
  [67] 0.102639026 0.293931872 0.011789349 0.086786681 0.617302569 0.175776744
  [73] 0.738859695 0.940225426 0.575078121 0.123277217 0.055146249 0.054102586
  [79] 0.104150628 0.009218471 0.672565343 0.245960451 0.143659118 0.951709014
  [85] 0.277252686