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
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