Code
df_local_i <- ww_local_getis_ord_g(guerry_modeled, Crm_prs, predictions)
df_local_i[1:3]
Output
# A tibble: 85 x 3
.metric .estimator .estimate
<chr> <chr> <dbl>
1 local_getis_ord_g standard 0.913
2 local_getis_ord_g standard 2.49
3 local_getis_ord_g standard 2.15
4 local_getis_ord_g standard -1.58
5 local_getis_ord_g standard -1.19
6 local_getis_ord_g standard -1.68
7 local_getis_ord_g standard 0.627
8 local_getis_ord_g standard -1.60
9 local_getis_ord_g standard 0.964
10 local_getis_ord_g standard -2.71
# i 75 more rows
Code
df_local_i_p <- ww_local_getis_ord_g_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_getis_ord_g_pvalue standard 0.339
2 local_getis_ord_g_pvalue standard 0.0175
3 local_getis_ord_g_pvalue standard 0.0470
4 local_getis_ord_g_pvalue standard 0.136
5 local_getis_ord_g_pvalue standard 0.253
6 local_getis_ord_g_pvalue standard 0.0943
7 local_getis_ord_g_pvalue standard 0.541
8 local_getis_ord_g_pvalue standard 0.132
9 local_getis_ord_g_pvalue standard 0.361
10 local_getis_ord_g_pvalue standard 0.00787
# i 75 more rows
Code
(vec_local_i <- ww_local_getis_ord_g_vec(guerry_modeled$Crm_prs, guerry_modeled$
predictions, weights))
Output
[1] 0.91288164 2.49305353 2.14692501 -1.57511235 -1.18988831 -1.67703329
[7] 0.62706195 -1.60139345 0.96397088 -2.71475331 -3.05125861 -1.64429475
[13] 1.15584938 -2.90161984 -0.63376101 0.61005430 2.29888368 -0.28799789
[19] 1.53012357 1.22699107 0.59816133 -0.65331166 0.87501663 -1.75661589
[25] 0.39100454 0.52017062 1.16424138 -3.20781683 -2.29583912 -0.98116177
[31] 0.06685550 -2.13635236 2.30311768 0.63548278 1.99855789 -0.24366435
[37] 1.55058791 0.17231008 1.52062197 -0.22999799 -1.29501163 1.08298736
[43] 0.13063616 -1.21798455 -1.00549332 -2.29306942 0.89419782 1.19832028
[49] 2.03154442 0.87398122 2.88484032 -0.76194353 1.52453019 1.36764146
[55] -0.06618369 1.47010332 1.89277905 0.52529402 1.63007385 2.00852739
[61] 1.14763247 -0.91644996 -0.49669001 -1.85515687 -1.00553207 -0.86081555
[67] 1.63219209 1.04953518 2.51838763 1.71259713 -0.49967695 1.35387348
[73] -0.33336379 -0.07498653 0.56058846 1.54116260 -1.91772217 -1.92601432
[79] -1.62505600 -2.60384397 0.42262985 1.16021704 1.46229964 0.06056077
[85] 1.08651166
Code
(vec_local_i_p <- ww_local_getis_ord_g_pvalue_vec(guerry_modeled$Crm_prs,
guerry_modeled$predictions, weights))
Output
[1] 0.338551650 0.017476897 0.046989319 0.136306243 0.252759855 0.094280941
[7] 0.540950240 0.131507964 0.360573261 0.007868825 0.002910297 0.099812583
[13] 0.263145960 0.006073623 0.486840848 0.592079221 0.024897187 0.769018856
[19] 0.147064254 0.236079422 0.566712648 0.491109230 0.430473715 0.079265433
[25] 0.725081238 0.578327557 0.252363140 0.001922916 0.033158491 0.345911465
[31] 0.980042792 0.026524059 0.020365959 0.457589915 0.049929480 0.842223381
[37] 0.122974384 0.896634027 0.134780339 0.852933638 0.197999212 0.283637329
[43] 0.908861719 0.251729216 0.309816435 0.029894833 0.388820107 0.244965500
[49] 0.055290126 0.358699720 0.004749006 0.419371223 0.135285432 0.172833170
[55] 0.889256614 0.166182979 0.052600987 0.625967137 0.123280301 0.042396581
[61] 0.263286551 0.358596865 0.672797323 0.064221214 0.333500223 0.360499055
[67] 0.106951191 0.292784000 0.020687637 0.123175709 0.582501078 0.195554540
[73] 0.718377209 0.944328466 0.556059177 0.167383256 0.060941608 0.081390165
[79] 0.130453836 0.012380565 0.707571437 0.277582701 0.190935930 0.922672415
[85] 0.296789212
Code
df_local_i <- ww_local_getis_ord_g(guerry_modeled, Crm_prs, predictions,
weights)
df_local_i[1:3]
Output
# A tibble: 85 x 3
.metric .estimator .estimate
<chr> <chr> <dbl>
1 local_getis_ord_gstar standard 1.35
2 local_getis_ord_gstar standard 2.64
3 local_getis_ord_gstar standard 2.33
4 local_getis_ord_gstar standard -1.84
5 local_getis_ord_gstar standard -1.19
6 local_getis_ord_gstar standard -2.06
7 local_getis_ord_gstar standard 1.58
8 local_getis_ord_gstar standard -2.32
9 local_getis_ord_gstar standard 0.880
10 local_getis_ord_gstar standard -2.74
# i 75 more rows
Code
df_local_i_p <- ww_local_getis_ord_g_pvalue(guerry_modeled, Crm_prs,
predictions, weights)
df_local_i_p[1:3]
Output
# A tibble: 85 x 3
.metric .estimator .estimate
<chr> <chr> <dbl>
1 local_getis_ord_gstar_pvalue standard 0.353
2 local_getis_ord_gstar_pvalue standard 0.0153
3 local_getis_ord_gstar_pvalue standard 0.0447
4 local_getis_ord_gstar_pvalue standard 0.124
5 local_getis_ord_gstar_pvalue standard 0.257
6 local_getis_ord_gstar_pvalue standard 0.104
7 local_getis_ord_gstar_pvalue standard 0.579
8 local_getis_ord_gstar_pvalue standard 0.100
9 local_getis_ord_gstar_pvalue standard 0.324
10 local_getis_ord_gstar_pvalue standard 0.00724
# i 75 more rows
Code
(vec_local_i <- ww_local_getis_ord_g_vec(guerry_modeled$Crm_prs, guerry_modeled$
predictions, weights))
Output
[1] 1.35371776 2.64470358 2.33101218 -1.83696218 -1.19214894 -2.06145107
[7] 1.58230958 -2.31764702 0.88028873 -2.74035690 -3.39292895 -1.87812026
[13] 0.85856419 -2.81583254 -0.31227470 0.49524512 2.24081985 -0.51676147
[19] 2.00481255 1.63825389 1.39112875 -0.55103244 0.08003347 -1.97182396
[25] 0.11106013 0.56430623 1.71460247 -3.33281642 -2.22026799 -0.98578659
[31] 0.30960568 -2.34956428 2.25994512 1.20029086 1.79507717 -0.29168508
[37] 1.78306056 0.01327693 1.48600121 0.13091399 -1.40966303 0.93339994
[43] 0.02027691 -1.82812173 -0.79364697 -2.75575017 1.26343283 1.74008875
[49] 1.69116164 1.14388681 3.09199200 -0.26702307 1.66914599 1.42530173
[55] -0.65613591 1.62256416 2.11228076 0.90733590 1.93759338 1.91124325
[61] 0.92968354 -1.01687632 -0.94802983 -2.54791742 -1.35286802 -1.66942606
[67] 1.40993406 1.10799183 2.75546130 2.26572376 -0.86272825 1.07976560
[73] -0.21434998 -0.43824366 0.43342398 2.13948993 -2.14931849 -2.06450852
[79] -1.88711642 -2.74970443 0.42882579 0.82915623 1.17030359 0.01011938
[85] 0.90033560
Code
(vec_local_i_p <- ww_local_getis_ord_g_pvalue_vec(guerry_modeled$Crm_prs,
guerry_modeled$predictions, weights))
Output
[1] 0.353320976 0.015320807 0.044659841 0.124494604 0.257380879 0.104374603
[7] 0.579386974 0.100499213 0.324430137 0.007238788 0.003024914 0.089786809
[13] 0.245293341 0.001554081 0.515887921 0.580670550 0.028218825 0.782467003
[19] 0.148454172 0.225639656 0.571064419 0.540603232 0.425964966 0.102290000
[25] 0.750018977 0.653242771 0.239989113 0.001853200 0.019877161 0.378631358
[31] 0.895027710 0.026860310 0.029574160 0.542892507 0.049082054 0.843613753
[37] 0.119831188 0.922323909 0.121905018 0.802914179 0.238752158 0.286284115
[43] 0.901178086 0.246053959 0.336073064 0.017506445 0.428596576 0.209104318
[49] 0.049489955 0.405297312 0.004827649 0.463676567 0.145834651 0.166071593
[55] 0.952182978 0.177860755 0.070212352 0.661584162 0.117573309 0.035904620
[61] 0.249850749 0.301292619 0.623718225 0.073175337 0.343285322 0.455803772
[67] 0.097245254 0.305824379 0.019794928 0.090672872 0.600765705 0.181230901
[73] 0.778825479 0.987438813 0.557938792 0.135621556 0.055019155 0.049909299
[79] 0.095463604 0.009723617 0.720748620 0.245515207 0.162155516 0.966175966
[85] 0.314680103
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