From ddea0d3fe07e907da430d854cc909d06f6702992 Mon Sep 17 00:00:00 2001 From: GitHub Date: Sat, 16 Sep 2023 04:28:48 +0000 Subject: [PATCH] Deploy from Github Actions build 6205213726 [ci skip] Build URL: https://github.com/giscience-fsu/sperrorest/actions/runs/6205213726 Commit: b4d2a1426bfcbb2f5630c6182ff946b926f96e4a --- articles/spatial-modeling-use-case.html | 42 +- .../figure-html/unnamed-chunk-19-1.png | Bin 82522 -> 82478 bytes pkgdown.yml | 2 +- reference/add.distance.html | 4 +- reference/as.resampling.html | 96 +-- reference/err_default.html | 74 +-- reference/partition_cv.html | 604 +++++++++--------- reference/partition_cv_strat.html | 2 +- reference/partition_disc.html | 40 +- reference/sperrorest.html | 32 +- 10 files changed, 448 insertions(+), 448 deletions(-) diff --git a/articles/spatial-modeling-use-case.html b/articles/spatial-modeling-use-case.html index 541b6bfe..4a62c622 100644 --- a/articles/spatial-modeling-use-case.html +++ b/articles/spatial-modeling-use-case.html @@ -276,7 +276,7 @@

RandomForest## Target node size: 1 ## Variable importance mode: none ## Splitrule: gini -## OOB prediction error: 0.60 % +## OOB prediction error: 0.57 %

Let’s take a look at the MER achieved on the training sample:

 pred <- predict(fit, data = maipo, type = "response")
@@ -362,12 +362,12 @@ 
Linear Discriminant Analysis (LDA)
 summary(res_lda_nsp$error_rep)
##                    mean      sd   median      IQR
-## train_error    3.36e-02 0.00041 3.37e-02 0.000405
-## train_accuracy 9.66e-01 0.00041 9.66e-01 0.000405
+## train_error    3.41e-02 0.00087 3.39e-02 0.000859
+## train_accuracy 9.66e-01 0.00087 9.66e-01 0.000859
 ## train_events   4.69e+03 0.00000 4.69e+03 0.000000
 ## train_count    3.09e+04 0.00000 3.09e+04 0.000000
-## test_error     4.09e-02 0.00158 4.06e-02 0.001556
-## test_accuracy  9.59e-01 0.00158 9.59e-01 0.001556
+## test_error     4.05e-02 0.00027 4.06e-02 0.000259
+## test_accuracy  9.60e-01 0.00027 9.59e-01 0.000259
 ## test_events    1.17e+03 0.00000 1.17e+03 0.000000
 ## test_count     7.71e+03 0.00000 7.71e+03 0.000000

To run a spatial cross-validation at the field level, we can use @@ -400,16 +400,16 @@

Linear Discriminant Analysis (LDA) benchmark = TRUE, progress = FALSE ) res_lda_sp$benchmark$runtime_performance -
## Time difference of 17.6 secs
+
## Time difference of 16.8 secs
 summary(res_lda_sp$error_rep)
##                    mean      sd   median     IQR
-## train_error    2.92e-02 0.00269 2.99e-02 0.00261
-## train_accuracy 9.71e-01 0.00269 9.70e-01 0.00261
+## train_error    3.02e-02 0.00119 3.08e-02 0.00110
+## train_accuracy 9.70e-01 0.00119 9.69e-01 0.00110
 ## train_events   4.69e+03 0.00000 4.69e+03 0.00000
 ## train_count    3.09e+04 0.00000 3.09e+04 0.00000
-## test_error     6.06e-02 0.00781 6.44e-02 0.00707
-## test_accuracy  9.39e-01 0.00781 9.36e-01 0.00707
+## test_error     6.25e-02 0.00577 6.25e-02 0.00577
+## test_accuracy  9.37e-01 0.00577 9.38e-01 0.00577
 ## test_events    1.17e+03 0.00000 1.17e+03 0.00000
 ## test_count     7.71e+03 0.00000 7.71e+03 0.00000
@@ -455,19 +455,19 @@

RandomForest) 
 summary(res_rf_sp$error_rep)
-
##                    mean     sd   median    IQR
-## train_error    0.00e+00 0.0000 0.00e+00 0.0000
-## train_accuracy 1.00e+00 0.0000 1.00e+00 0.0000
-## train_events   4.69e+03 0.0000 4.69e+03 0.0000
-## train_count    3.09e+04 0.0000 3.09e+04 0.0000
-## test_error     8.90e-02 0.0148 9.18e-02 0.0147
-## test_accuracy  9.11e-01 0.0148 9.08e-01 0.0147
-## test_events    1.17e+03 0.0000 1.17e+03 0.0000
-## test_count     7.71e+03 0.0000 7.71e+03 0.0000
+
##                    mean      sd   median    IQR
+## train_error    0.00e+00 0.00000 0.00e+00 0.0000
+## train_accuracy 1.00e+00 0.00000 1.00e+00 0.0000
+## train_events   4.69e+03 0.00000 4.69e+03 0.0000
+## train_count    3.09e+04 0.00000 3.09e+04 0.0000
+## test_error     1.02e-01 0.00266 1.01e-01 0.0024
+## test_accuracy  8.98e-01 0.00266 8.99e-01 0.0024
+## test_events    1.17e+03 0.00000 1.17e+03 0.0000
+## test_count     7.71e+03 0.00000 7.71e+03 0.0000
 summary(res_rf_sp$error_rep)["test_accuracy",]
-
##                mean     sd median    IQR
-## test_accuracy 0.911 0.0148  0.908 0.0147
+
##                mean      sd median    IQR
+## test_accuracy 0.898 0.00266  0.899 0.0024

What a surprise! {ranger}‘s classification is not that good after all, if we acknowledge that in ’real life’ we wouldn’t be making predictions in situations where the class membership of other grid cells diff --git a/articles/spatial-modeling-use-case_files/figure-html/unnamed-chunk-19-1.png b/articles/spatial-modeling-use-case_files/figure-html/unnamed-chunk-19-1.png index bb9ddacc4bc443c3283d22929165a784f9fb8264..462c2c99c3a05bd2a208634e1b19503cb9bb02d7 100644 GIT binary patch literal 82478 zcmd?Ri93{E{69QnY*|7QLlhE{Fk|1dMUtIl&6u%o*>@^S43c&1vSt?{>y(|b6B)}y zvWJAp`rJOB@8|mmJlFMH&-J^0b6qvleeQFgbHC4dzs~FZI`7kKoyQMosaUB%AP}vF zx{4kMM5YJ=k>pa616Q6dPe}k@7oxXSoJZ0m8PIAXw720c-!*)H z$E}SX4*uZS_3e{^B5v~~wz&4b>STHGYIRjz`1WnP4fRJ7%({?wV>fe4%t(*tgNFRi zpL`s$X!f40ENyk1+&}coo?F?R@p7n=x7}W__~>A+sPccq=Zi0`oA6RS8}aVWjnI!-Z=2Vo06fN3IfU~HB1W#sg}VHPl7(qfiLx8F}qu7RB;kE{WQpk@f4g6 zdw!P%bs9;7f@5i@zAPK@nCR`aSfkL9a-2tUT)c%h;uSf{kgZ%qSgO=aeJPKHZpVb2 znUWpIjRv$CA)ecD`e%V0ki{Qeqa{4PawMs>$%f`_9^lLk{nBs 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sp.parti <- add.distance(sp.parti, data = ecuador) # non-spatial partioning: very small test-training distance: nsp.parti[[1]][[1]]$distance -#> [1] 56.80551 +#> [1] 39.88192 # spatial partitioning: more substantial distance, depending on number of # folds etc. sp.parti[[1]][[1]]$distance -#> [1] 388.3352 +#> [1] 400.8701 diff --git a/reference/as.resampling.html b/reference/as.resampling.html index 87743ac7..eb5bd45b 100644 --- a/reference/as.resampling.html +++ b/reference/as.resampling.html @@ -164,71 +164,71 @@

Examples

# data corresponding to the test sample of the first fold: str(ecuador[parti[[1]]$test, ]) #> 'data.frame': 75 obs. of 13 variables: -#> $ x : num 715392 713512 714932 715532 712812 ... -#> $ y : num 9560172 9559092 9557982 9558762 9559942 ... -#> $ dem : num 1989 2166 2650 2549 1861 ... -#> $ slope : num 40.5 56 37.3 38.1 63.3 ... -#> $ hcurv : num -0.01919 0.02056 0.01633 -0.00373 0.00514 ... -#> $ vcurv : num -0.04051 -0.06976 -0.01813 -0.00057 -0.00644 ... -#> $ carea : num 351155 301 1131 1630 1179 ... -#> $ cslope : num 32.8 49.4 35.1 31 36.5 ... -#> $ distroad : num 300 300 300 300 300 ... +#> $ x : num 714752 715302 714612 713552 714292 ... +#> $ y : num 9561022 9557472 9560552 9560652 9558982 ... +#> $ dem : num 1848 2857 2032 1852 2459 ... +#> $ slope : num 33.4 39.5 26.8 46 43.6 ... +#> $ hcurv : num -0.00347 -0.01021 0.00046 -0.00891 0.00214 ... +#> $ vcurv : num 0.02357 -0.01579 0.00324 -0.00949 0.00466 ... +#> $ carea : num 1752 1319 861 6218 724 ... +#> $ cslope : num 23.8 36.3 24.4 32.8 38.8 ... +#> $ distroad : num 159 300 300 280 300 ... #> $ slides : Factor w/ 2 levels "FALSE","TRUE": 2 2 2 2 2 2 2 2 2 2 ... -#> $ distdeforest : num 300 300 300 300 2.45 ... -#> $ distslidespast: num 40 41 100 100 0 46 100 56 5 0 ... -#> $ log.carea : num 5.55 2.48 3.05 3.21 3.07 ... +#> $ distdeforest : num 0 300 300 0 300 ... +#> $ distslidespast: num 5 100 89 100 100 39 5 46 100 0 ... +#> $ log.carea : num 3.24 3.12 2.94 3.79 2.86 ... # the corresponding training sample - larger: str(ecuador[parti[[1]]$train, ]) #> 'data.frame': 676 obs. of 13 variables: -#> $ x : num 712882 715232 715042 715382 712802 ... -#> $ y : num 9560002 9559582 9559312 9560142 9559952 ... -#> $ dem : num 1912 2199 2320 2021 1838 ... -#> $ slope : num 25.6 23.2 42.9 42 52.1 ... -#> $ hcurv : num -0.00681 -0.00501 -0.01106 0.00958 0.00183 ... -#> $ vcurv : num -0.00029 -0.00649 -0.04634 0.02642 -0.09203 ... -#> $ carea : num 5577 1399 501 671 634 ... -#> $ cslope : num 34.4 30.7 33.9 41.6 30.3 ... -#> $ distroad : num 300 300 300 300 300 ... +#> $ x : num 712882 715232 715392 715042 715382 ... +#> $ y : num 9560002 9559582 9560172 9559312 9560142 ... +#> $ dem : num 1912 2199 1989 2320 2021 ... +#> $ slope : num 25.6 23.2 40.5 42.9 42 ... +#> $ hcurv : num -0.00681 -0.00501 -0.01919 -0.01106 0.00958 ... +#> $ vcurv : num -0.00029 -0.00649 -0.04051 -0.04634 0.02642 ... +#> $ carea : num 5577 1399 351155 501 671 ... +#> $ cslope : num 34.4 30.7 32.8 33.9 41.6 ... +#> $ distroad : num 300 300 300 300 300 300 300 300 300 30 ... #> $ slides : Factor w/ 2 levels "FALSE","TRUE": 2 2 2 2 2 2 2 2 2 2 ... -#> $ distdeforest : num 15 300 300 300 9.15 ... -#> $ distslidespast: num 9 21 100 21 2 100 100 5 20 100 ... -#> $ log.carea : num 3.75 3.15 2.7 2.83 2.8 ... +#> $ distdeforest : num 15 300 300 300 300 ... +#> $ distslidespast: num 9 21 40 100 21 2 100 100 41 20 ... +#> $ log.carea : num 3.75 3.15 5.55 2.7 2.83 ... # Bootstrap training sets, out-of-bag test sets: parti <- represampling_bootstrap(ecuador, oob = TRUE) parti <- parti[[1]] # the first (and only) resampling object in parti # out-of-bag test sample: approx. one-third of nrow(ecuador): str(ecuador[parti[[1]]$test, ]) -#> 'data.frame': 259 obs. of 13 variables: -#> $ x : num 712882 715042 715382 715272 714932 ... -#> $ y : num 9560002 9559312 9560142 9557702 9557982 ... -#> $ dem : num 1912 2320 2021 2813 2650 ... -#> $ slope : num 25.6 42.9 42 31 37.3 ... -#> $ hcurv : num -0.00681 -0.01106 0.00958 -0.00123 0.01633 ... -#> $ vcurv : num -0.00029 -0.04634 0.02642 0.00393 -0.01813 ... -#> $ carea : num 5577 501 671 2081 1131 ... -#> $ cslope : num 34.4 33.9 41.6 37.6 35.1 ... +#> 'data.frame': 278 obs. of 13 variables: +#> $ x : num 712882 715232 715042 714842 714922 ... +#> $ y : num 9560002 9559582 9559312 9558892 9558862 ... +#> $ dem : num 1912 2199 2320 2483 2536 ... +#> $ slope : num 25.6 23.2 42.9 68.8 54.9 ... +#> $ hcurv : num -0.00681 -0.00501 -0.01106 -0.04921 0.01305 ... +#> $ vcurv : num -0.00029 -0.00649 -0.04634 -0.12438 -0.00555 ... +#> $ carea : num 5577 1399 501 754 430 ... +#> $ cslope : num 34.4 30.7 33.9 53.7 46.3 ... #> $ distroad : num 300 300 300 300 300 ... #> $ slides : Factor w/ 2 levels "FALSE","TRUE": 2 2 2 2 2 2 2 2 2 2 ... #> $ distdeforest : num 15 300 300 300 300 ... -#> $ distslidespast: num 9 100 21 100 100 35 100 6 11 89 ... -#> $ log.carea : num 3.75 2.7 2.83 3.32 3.05 ... +#> $ distslidespast: num 9 21 100 100 100 35 26 100 10 100 ... +#> $ log.carea : num 3.75 3.15 2.7 2.88 2.63 ... # bootstrap training sample: same size as nrow(ecuador): str(ecuador[parti[[1]]$train, ]) #> 'data.frame': 751 obs. of 13 variables: -#> $ x : num 712732 713122 715122 713882 715202 ... -#> $ y : num 9560462 9559052 9559402 9559182 9558872 ... -#> $ dem : num 1916 2169 2265 2350 2464 ... -#> $ slope : num 40.4 37.6 50 27.4 34.7 ... -#> $ hcurv : num 0.00143 -0.0211 -0.00589 -0.02608 -0.01267 ... -#> $ vcurv : num 0.00477 -0.0245 0.00849 0.00148 -0.05063 ... -#> $ carea : num 2945 2590 1121 10771 18734 ... -#> $ cslope : num 30.7 27.7 33.4 27.6 34.7 ... -#> $ distroad : num 73.7 300 300 300 300 ... -#> $ slides : Factor w/ 2 levels "FALSE","TRUE": 2 2 2 1 2 1 1 2 2 2 ... -#> $ distdeforest : num 138 300 300 300 300 ... -#> $ distslidespast: num 18 100 100 100 71 100 100 1 2 2 ... -#> $ log.carea : num 3.47 3.41 3.05 4.03 4.27 ... +#> $ x : num 713982 714902 712722 714402 714332 ... +#> $ y : num 9560462 9559262 9560272 9559252 9560262 ... +#> $ dem : num 2133 2363 1827 2326 2138 ... +#> $ slope : num 5.23 50.7 29.29 49.62 25.72 ... +#> $ hcurv : num 0.00366 -0.01407 0.00009 -0.00241 -0.00491 ... +#> $ vcurv : num 0.00525 0.00547 -0.00599 0.0002 0.00581 ... +#> $ carea : num 162 519 12874 673 1855 ... +#> $ cslope : num 4.44 35.25 22.88 36.82 23.83 ... +#> $ distroad : num 300 300 235 300 300 ... +#> $ slides : Factor w/ 2 levels "FALSE","TRUE": 1 2 1 1 2 2 2 2 1 2 ... +#> $ distdeforest : num 10 300 0 300 300 300 300 300 93.5 0 ... +#> $ distslidespast: num 100 59 43 50 10 100 100 100 70 25 ... +#> $ log.carea : num 2.21 2.72 4.11 2.83 3.27 ... diff --git a/reference/err_default.html b/reference/err_default.html index a574864d..c0d25b84 100644 --- a/reference/err_default.html +++ b/reference/err_default.html @@ -115,80 +115,80 @@

Examples

# Two mock (soft) classification examples: err_default(obs > 0, rnorm(1000)) # just noise #> $auroc -#> [1] 0.4993971 +#> [1] 0.5106984 #> #> $error -#> [1] 0.515 +#> [1] 0.494 #> #> $accuracy -#> [1] 0.485 +#> [1] 0.506 #> #> $sensitivity -#> [1] 0.2723493 +#> [1] 0.3259557 #> #> $specificity -#> [1] 0.6820809 +#> [1] 0.6838966 #> #> $fpr70 -#> [1] 0.6705202 +#> [1] 0.7017893 #> #> $fpr80 -#> [1] 0.7726397 +#> [1] 0.7932406 #> #> $fpr90 -#> [1] 0.8901734 +#> [1] 0.9045726 #> #> $tpr80 -#> [1] 0.1850312 +#> [1] 0.2132797 #> #> $tpr90 -#> [1] 0.1101871 +#> [1] 0.1187123 #> #> $tpr95 -#> [1] 0.04781705 +#> [1] 0.05231388 #> #> $events -#> [1] 481 +#> [1] 497 #> #> $count #> [1] 1000 #> err_default(obs > 0, obs + rnorm(1000)) # some discrimination #> $auroc -#> [1] 0.8419077 +#> [1] 0.8318179 #> #> $error -#> [1] 0.262 +#> [1] 0.26 #> #> $accuracy -#> [1] 0.738 +#> [1] 0.74 #> #> $sensitivity -#> [1] 0.5779626 +#> [1] 0.6096579 #> #> $specificity -#> [1] 0.8863198 +#> [1] 0.8687873 #> #> $fpr70 -#> [1] 0.1868979 +#> [1] 0.2067594 #> #> $fpr80 -#> [1] 0.2947977 +#> [1] 0.332008 #> #> $fpr90 -#> [1] 0.4373796 +#> [1] 0.4890656 #> #> $tpr80 -#> [1] 0.7151767 +#> [1] 0.6881288 #> #> $tpr90 -#> [1] 0.5259875 +#> [1] 0.5513078 #> #> $tpr95 -#> [1] 0.3617464 +#> [1] 0.4285714 #> #> $events -#> [1] 481 +#> [1] 497 #> #> $count #> [1] 1000 @@ -196,44 +196,44 @@

Examples

# Three mock regression examples: err_default(obs, rnorm(1000)) # just noise, but no bias #> $bias -#> [1] -0.02045545 +#> [1] -0.06419451 #> #> $stddev -#> [1] 1.426845 +#> [1] 1.398668 #> #> $rmse -#> [1] 1.426278 +#> [1] 1.399441 #> #> $mad -#> [1] 1.377789 +#> [1] 1.407359 #> #> $median -#> [1] 0.03078271 +#> [1] -0.08687487 #> #> $iqr -#> [1] 1.897468 +#> [1] 1.89035 #> #> $count #> [1] 1000 #> err_default(obs, obs + rnorm(1000)) # some association, no bias #> $bias -#> [1] -0.02554345 +#> [1] 0.01807706 #> #> $stddev -#> [1] 1.01092 +#> [1] 0.9660815 #> #> $rmse -#> [1] 1.010737 +#> [1] 0.9657676 #> #> $mad -#> [1] 1.002436 +#> [1] 0.9951806 #> #> $median -#> [1] -0.05534539 +#> [1] 0.0352395 #> #> $iqr -#> [1] 1.333508 +#> [1] 1.330368 #> #> $count #> [1] 1000 @@ -243,7 +243,7 @@

Examples

#> [1] -1 #> #> $stddev -#> [1] 6.673921e-17 +#> [1] 6.17457e-17 #> #> $rmse #> [1] 1 diff --git a/reference/partition_cv.html b/reference/partition_cv.html index 524bab84..48310845 100644 --- a/reference/partition_cv.html +++ b/reference/partition_cv.html @@ -155,308 +155,308 @@

Examples

idx <- resamp[["1"]][[2]]$test # test sample used in this particular repetition and fold: ecuador[idx, ] -#> x y dem slope hcurv vcurv carea -#> 37912 712802.5 9559952 1838.40 52.101344 0.00183 -0.09203 634.3320 -#> 25090 715362.5 9560102 2059.29 49.119672 0.02059 -0.00628 556.0121 -#> 30059 713832.5 9559662 2288.02 18.277927 0.01917 0.00333 258.6537 -#> 42733 715112.5 9559062 2399.70 36.054515 -0.00041 0.01741 746.4155 -#> 5999 715552.5 9558812 2518.03 7.984167 -0.01863 -0.00507 159065.0781 -#> 24072 715282.5 9557602 2837.46 34.394083 -0.02191 -0.00579 2246.7725 -#> 20799 714792.5 9561002 1857.29 23.129160 0.00090 0.00890 2301.0454 -#> 9669 713852.5 9558612 2308.97 52.180412 -0.01059 -0.07431 4079.0366 -#> 27973 715022.5 9559332 2308.28 20.063263 0.00940 0.00679 550.0344 -#> 8869 715062.5 9559392 2278.56 41.401930 0.00310 0.01440 1088.8639 -#> 43327 713962.5 9561082 1839.11 63.361684 -0.04951 -0.00529 3282.1186 -#> 26447 715632.5 9559102 2410.77 39.656701 0.00730 0.01770 474.5059 -#> 16811 714902.5 9559262 2362.67 50.703327 -0.01407 0.00547 519.4228 -#> 23817 714842.5 9557832 2677.58 26.657434 0.00922 0.01088 266.4355 -#> 24018 714292.5 9559282 2334.12 32.865432 0.00102 0.00388 1051.4222 -#> 2990 713542.5 9560372 1970.07 31.887966 0.01903 0.01447 331.4833 -#> 45869 714342.5 9560282 2127.11 27.623441 -0.02169 -0.00160 9344.2930 -#> 48498 714972.5 9557972 2681.66 38.346919 0.00528 0.01392 1255.0884 -#> 40307 713302.5 9560702 1845.07 39.004675 0.00129 0.02510 3342.5518 -#> 2107 714142.5 9558562 2382.67 35.553750 0.01412 0.00898 458.6938 -#> 29420 713592.5 9560772 1826.82 34.926934 -0.00070 -0.00530 9000.4356 -#> 34482 715152.5 9557642 2753.52 4.995619 -0.01126 -0.02044 129412.3984 -#> 27978 713552.5 9558532 2373.05 17.044921 0.00832 0.00838 238.6685 -#> 6676 715092.5 9559912 2217.44 41.707890 -0.00077 -0.00284 2010.4285 -#> 3453 713482.5 9559642 2145.11 35.125751 0.00108 -0.00678 1891.6584 -#> 36230 713322.5 9560662 1798.99 40.128245 -0.00033 -0.03917 116.1986 -#> 10320 714912.5 9558482 2724.21 25.282909 0.00356 0.00774 527.1068 -#> 20194 714132.5 9558452 2447.22 24.022401 -0.00987 0.02107 1392.5880 -#> 34280 713652.5 9560702 1896.37 41.273588 0.00382 0.00088 2322.1746 -#> 5749 712852.5 9560162 1934.78 23.388710 0.00071 0.00150 1274.3594 -#> 34565 712972.5 9560172 2007.72 27.947162 0.00598 0.01212 561.1948 -#> 3133 715042.5 9559252 2344.13 29.020885 0.00422 0.02357 275.7474 -#> 13847 715052.5 9559232 2337.87 26.114843 -0.01452 -0.01437 1155.1469 -#> 46125 713732.5 9560062 2115.18 37.593480 -0.04616 0.01276 9614.6924 -#> 7879 714722.5 9559232 2356.19 36.803944 -0.01097 0.00156 816.6036 -#> 19128 714852.5 9557872 2681.49 20.757688 0.00453 0.01077 300.7915 -#> 15990 712852.5 9559982 1894.77 37.694320 -0.01545 0.02405 5681.2412 -#> 2858 714292.5 9559332 2335.04 42.355905 0.04547 0.03323 183.1836 -#> 48434 713352.5 9559042 2176.63 35.702146 -0.01771 -0.01939 703.7817 -#> 36931 712852.5 9558892 2102.80 30.535786 0.00140 -0.00070 2562.7559 -#> 46235 712712.5 9561042 2023.11 41.277025 0.01458 0.00672 565.1107 -#> 18751 714882.5 9559182 2381.81 39.299175 0.00066 -0.00036 516.4709 -#> 5578 714962.5 9557612 2751.95 39.191459 0.00288 0.01452 437.3965 -#> 29926 714702.5 9557622 2637.00 44.642580 0.01843 0.04407 344.8395 -#> 2976 715122.5 9559142 2390.05 33.167954 0.01008 0.00102 483.4184 -#> 33240 714972.5 9557652 2741.04 39.434393 -0.00286 -0.00964 571.1364 -#> 4031 715392.5 9560162 1998.16 46.285823 0.00708 -0.02538 807.8237 -#> 39599 714112.5 9558562 2374.25 29.168135 0.00283 0.00117 1018.2141 -#> 20220 714052.5 9558522 2378.67 39.656128 -0.00855 -0.02175 1122.9120 -#> 835 715052.5 9558212 2707.65 37.725260 -0.01399 -0.00731 1384.7959 -#> 15766 715562.5 9558592 2646.16 29.020885 0.00194 0.00516 576.0335 -#> 4617 714292.5 9559312 2342.95 32.392169 0.03504 0.05825 162.6688 -#> 49569 713232.5 9561122 1868.73 37.312158 -0.00405 -0.01565 2232.6133 -#> 19887 714662.5 9559632 2266.47 45.179441 -0.02797 -0.00063 576.2819 -#> 38862 712952.5 9559892 1950.86 37.721822 -0.00048 -0.00332 1418.6622 -#> 7466 712962.5 9560392 1819.03 18.402831 -0.00140 -0.02330 1456.9246 -#> 18265 715522.5 9558782 2539.23 35.856272 -0.00458 -0.00401 1722.6481 -#> 33350 713132.5 9560672 1896.89 24.695054 0.00306 -0.00436 2602.7390 -#> 13029 713542.5 9560242 2019.80 29.545142 0.00576 -0.00166 650.6246 -#> 47100 712752.5 9560462 1910.00 41.911863 -0.00032 0.00542 3614.7334 -#> 19966 715362.5 9559572 2235.06 26.049526 0.00126 -0.00485 2240.4185 -#> 44825 714322.5 9557882 2534.80 40.688598 0.00114 0.03026 247.0894 -#> 664 714452.5 9559852 2174.83 44.860876 0.00282 0.00109 767.4097 -#> 11618 713852.5 9558402 2432.14 54.059141 -0.00410 0.03620 379.0855 -#> 16370 713852.5 9558632 2296.97 34.624413 -0.01972 0.03512 13178.3223 -#> 38197 712522.5 9560322 1942.04 62.777267 -0.01611 0.02961 1086.6963 -#> 26010 712852.5 9560542 1928.25 30.978109 0.00165 0.00555 4439.0967 -#> 21680 712802.5 9560462 1893.75 38.734239 -0.00481 -0.00399 3900.3445 -#> 7385 713752.5 9560122 2064.61 31.771083 -0.02605 -0.03594 16587.9980 -#> 47366 714892.5 9557612 2718.78 28.023939 0.00538 0.00112 745.3315 -#> 12137 712842.5 9558902 2097.88 30.098619 0.00024 -0.00474 2343.2100 -#> 40216 714602.5 9560592 2015.55 34.368873 -0.00093 0.00664 1807.2659 -#> 30268 714172.5 9558612 2328.84 54.950663 0.03743 -0.05242 507.9263 -#> 18958 714192.5 9558372 2495.72 34.677125 -0.00178 -0.00822 472.8748 -#> 7041 713472.5 9559092 2137.03 18.348973 -0.10372 -0.00008 825875.6875 -#> 43859 714892.5 9557602 2722.70 28.129363 0.00370 -0.00190 640.8842 -#> 33910 715202.5 9559572 2195.66 13.791667 -0.04376 -0.01644 1057765.6250 -#> 19892 714482.5 9560562 2059.63 19.815172 -0.00332 -0.00058 1817.2371 -#> 47718 712812.5 9559892 1867.55 54.938631 -0.01491 0.01061 5744.7583 -#> 17345 713922.5 9559682 2227.44 34.490913 -0.06509 -0.01211 49347.5547 -#> 27244 714162.5 9558622 2316.00 36.751232 -0.02556 -0.11694 102301.4609 -#> 18440 714862.5 9558932 2448.29 41.052426 -0.04989 -0.02781 29995.1543 -#> 43201 715712.5 9557922 2852.82 31.462258 -0.00949 0.00520 3390.5708 -#> 42201 714642.5 9557452 2592.29 13.225585 -0.02084 -0.02236 9755.3027 -#> 34887 714662.5 9557392 2609.02 51.020746 -0.01878 -0.04351 1442.9247 -#> 20797 713012.5 9560392 1842.25 36.649818 0.01299 0.00141 4611.0718 -#> 21683 714792.5 9561052 1807.84 55.732751 -0.02070 -0.02661 2508.5374 -#> 33875 712832.5 9559852 1892.81 37.554519 -0.01175 0.04195 1901.6743 -#> 19832 715182.5 9557662 2761.27 38.432863 -0.00666 -0.03583 1898.0105 -#> 29228 712782.5 9560422 1870.56 32.906112 -0.00218 -0.00372 4154.5786 -#> 5572 715062.5 9558262 2754.60 39.911667 0.00707 0.01833 369.3771 -#> 30933 714672.5 9561082 1798.37 54.810289 0.00542 -0.00722 1373.4147 -#> 36292 712862.5 9559602 1915.77 37.975070 0.00511 -0.01801 1071.6141 -#> 28009 715002.5 9559922 2237.68 34.603786 -0.01109 0.00298 1533.7925 -#> 47086 713692.5 9559972 2175.51 25.045704 -0.00789 -0.00261 3353.5422 -#> 28173 714562.5 9560382 2048.26 27.685894 -0.00124 -0.00456 1531.4274 -#> 332 714852.5 9561102 1787.11 45.358204 0.00540 0.00879 1200.4210 -#> 10617 713232.5 9560722 1879.95 26.497006 0.00258 0.00422 1159.2100 -#> 2801 715252.5 9559612 2199.28 24.403418 0.03051 0.02190 264.6404 -#> 23626 713742.5 9560032 2137.92 39.304905 -0.00605 0.00075 2170.2249 -#> 34984 715042.5 9559312 2320.49 42.857816 -0.01106 -0.04634 500.5027 -#> 48650 715592.5 9558612 2626.74 31.876507 0.00010 0.00140 1066.3957 -#> 42617 714092.5 9558392 2469.21 42.799947 -0.01397 0.00526 706.1463 -#> 16065 715242.5 9560622 1871.61 34.306421 -0.00019 0.00119 6589.5796 -#> 22847 714582.5 9560382 2040.49 25.336194 0.00181 -0.00341 1363.4000 -#> 46627 714852.5 9558932 2459.24 53.445503 0.02451 -0.03921 525.3245 -#> 18498 714842.5 9557712 2679.04 28.939525 0.00902 0.01358 802.7622 -#> 6212 714592.5 9560362 2034.54 12.522566 -0.01454 0.00144 317260.6875 -#> 41310 714452.5 9560402 2092.27 12.462978 -0.00195 -0.00205 1112.2626 -#> 17851 715362.5 9557482 2869.26 29.125737 -0.03251 -0.00389 14516.2002 -#> 23176 714012.5 9558892 2308.59 43.029130 -0.01084 -0.02996 1599.3491 -#> 4785 713792.5 9560472 2055.34 33.052789 -0.00586 0.00067 1123.7318 -#> 27884 714072.5 9559202 2446.27 19.756731 -0.00009 0.00109 605.1908 -#> 18286 714792.5 9557882 2650.57 28.253695 -0.00124 0.00204 853.2902 -#> 8635 713832.5 9557902 2397.71 47.119476 0.02295 0.02715 520.7355 -#> 18746 713292.5 9560952 1890.51 41.885507 -0.00402 -0.01328 1066.7134 -#> 20458 713612.5 9559562 2288.02 33.440109 0.00112 0.03538 265.4677 -#> 32869 715332.5 9558892 2533.17 20.635075 -0.00242 0.01132 737.5982 -#> 30267 714442.5 9557512 2612.30 28.765919 -0.00458 0.00479 604.0975 -#> 12716 713962.5 9557762 2402.60 18.932244 0.03480 0.01500 538.8209 -#> 16542 713772.5 9559622 2304.85 30.810232 -0.00712 0.00923 811.5580 -#> 44396 715512.5 9558102 2845.31 26.598420 0.01263 0.03598 186.3527 -#> 39685 715332.5 9558102 2797.04 47.788118 0.00167 0.01133 828.8604 -#> 34347 714742.5 9560142 2161.95 26.566907 -0.00978 0.00367 3002.8828 -#> 48183 713642.5 9558262 2314.44 50.023226 0.01338 -0.00399 1601.4852 -#> 12974 714662.5 9557332 2667.61 41.941657 -0.00065 0.00475 515.1661 -#> 8413 713242.5 9561232 1967.45 38.194513 0.00236 0.00483 1097.3082 -#> 42934 713832.5 9558672 2270.74 27.611982 -0.03193 -0.00267 31588.1953 -#> 1691 714222.5 9559022 2448.69 38.216858 -0.01292 0.00462 716.5762 -#> 22545 713402.5 9559562 2195.44 25.315567 0.04152 0.00098 164.0802 -#> 34228 715932.5 9558592 2690.97 9.535164 197.75496 -197.75366 267.5057 -#> 34345 715852.5 9557902 2874.08 43.655946 0.00379 -0.01018 937.6911 -#> 12044 713612.5 9559292 2247.76 23.074729 -0.00112 -0.00038 2017.3547 -#> 33410 715342.5 9560452 1942.88 29.093078 -0.00305 -0.00705 968.9738 -#> 20764 714542.5 9558152 2602.18 52.078999 0.02118 0.00162 290.2031 -#> 7553 714642.5 9559132 2391.59 63.967873 -0.00066 0.05257 549.3252 -#> 44823 714202.5 9557442 2548.13 43.230239 -0.00233 0.01282 561.2366 -#> 1479 715622.5 9557952 2888.67 34.953863 0.01635 -0.00105 868.7802 -#> 17628 714032.5 9560252 2160.03 30.294570 0.00537 -0.00577 423.5598 -#> 7986 712752.5 9560242 1857.41 39.830307 0.01852 0.00248 1432.4019 -#> 26283 715932.5 9557592 3075.48 32.766883 -0.00375 0.00215 917.5296 -#> 4269 714162.5 9560572 2087.45 7.334433 0.00293 0.00186 2751.4626 -#> 37783 713832.5 9557892 2405.28 40.886841 0.01572 0.03148 481.8292 -#> 1434 714672.5 9559902 2194.92 42.452162 -0.01196 -0.01935 903.6397 -#> 43972 713642.5 9558702 2304.16 24.023547 0.00340 0.01809 1158.0426 -#> 40653 714262.5 9561262 1791.94 12.222336 0.00161 0.00709 1181.2555 -#> 14881 715482.5 9559232 2376.03 29.905532 -0.01137 -0.01523 888.2811 -#> 26635 715092.5 9560412 2010.59 30.571882 -0.00463 0.00253 1237.0536 -#> 11025 714402.5 9559252 2325.64 49.620437 -0.00241 0.00020 673.2861 -#> 13548 715812.5 9558122 2821.65 15.508249 0.02751 0.00388 180.4414 -#> cslope distroad slides distdeforest distslidespast log.carea -#> 37912 30.29457046 300.00 TRUE 9.15 2 2.802317 -#> 25090 43.53161440 300.00 TRUE 300.00 26 2.745084 -#> 30059 20.19217862 300.00 TRUE 300.00 41 2.412719 -#> 42733 31.28292266 300.00 TRUE 300.00 100 2.872981 -#> 5999 27.14215667 300.00 TRUE 300.00 100 5.201575 -#> 24072 37.66681841 300.00 TRUE 300.00 100 3.351559 -#> 20799 24.80792661 180.67 TRUE 0.00 16 3.361925 -#> 9669 31.66451255 300.00 TRUE 300.00 1 3.610558 -#> 27973 30.00981044 300.00 TRUE 300.00 85 2.740390 -#> 8869 29.11026670 300.00 TRUE 300.00 100 3.036974 -#> 43327 29.05870050 173.40 TRUE 33.81 100 3.516154 -#> 26447 33.03674647 300.00 TRUE 300.00 100 2.676242 -#> 16811 35.25237426 300.00 TRUE 300.00 59 2.715521 -#> 23817 21.61139507 300.00 TRUE 300.00 4 2.425592 -#> 24018 29.30507235 300.00 TRUE 300.00 5 3.021777 -#> 2990 25.32129680 300.00 TRUE 123.56 5 2.520462 -#> 45869 23.45460030 300.00 TRUE 300.00 12 3.970546 -#> 48498 33.61829863 300.00 TRUE 300.00 100 3.098674 -#> 40307 29.51362898 77.82 TRUE 76.41 54 3.524078 -#> 2107 35.42368864 300.00 TRUE 300.00 45 2.661523 -#> 29420 28.85243569 253.02 TRUE 0.00 100 3.954264 -#> 34482 32.97142928 300.00 TRUE 300.00 100 5.111976 -#> 27978 14.32852854 300.00 TRUE 300.00 12 2.377795 -#> 6676 34.62154773 300.00 TRUE 300.00 100 3.303289 -#> 3453 40.08470031 300.00 TRUE 300.00 2 3.276843 -#> 36230 0.05614986 120.49 TRUE 38.30 46 2.065201 -#> 10320 16.45592083 300.00 TRUE 300.00 30 2.721899 -#> 20194 28.11733084 300.00 TRUE 300.00 41 3.143823 -#> 34280 33.62116342 300.00 TRUE 0.00 100 3.365895 -#> 5749 28.23306831 300.00 TRUE 0.00 100 3.105292 -#> 34565 20.38010877 300.00 TRUE 68.13 100 2.749114 -#> 3133 18.83426864 300.00 TRUE 300.00 100 2.440511 -#> 13847 20.57090372 300.00 TRUE 300.00 100 3.062637 -#> 46125 25.65705007 300.00 TRUE 300.00 100 3.982935 -#> 7879 28.58085369 300.00 TRUE 300.00 100 2.912011 -#> 19128 14.65969815 300.00 TRUE 300.00 15 2.478266 -#> 15990 34.14885755 300.00 TRUE 0.00 0 3.754443 -#> 2858 35.31941032 300.00 TRUE 300.00 2 2.262887 -#> 48434 29.93589888 300.00 TRUE 300.00 6 2.847438 -#> 36931 27.20919273 300.00 TRUE 300.00 100 3.408707 -#> 46235 30.10835918 300.00 TRUE 41.37 0 2.752133 -#> 18751 38.07591028 300.00 TRUE 300.00 100 2.713046 -#> 5578 25.74184782 300.00 TRUE 300.00 100 2.640875 -#> 29926 32.09480385 300.00 TRUE 300.00 100 2.537617 -#> 2976 26.88203383 300.00 TRUE 300.00 100 2.684323 -#> 33240 29.35491968 300.00 TRUE 300.00 100 2.756740 -#> 4031 42.76155912 300.00 TRUE 300.00 31 2.907317 -#> 39599 33.13472225 300.00 TRUE 300.00 25 3.007839 -#> 20220 32.17559090 300.00 TRUE 300.00 8 3.050346 -#> 835 36.65039128 300.00 TRUE 300.00 100 3.141386 -#> 15766 23.33714395 300.00 TRUE 300.00 100 2.760448 -#> 4617 27.95174604 300.00 TRUE 300.00 6 2.211304 -#> 49569 41.84941031 25.00 TRUE 35.00 100 3.348814 -#> 19887 38.06215929 300.00 TRUE 300.00 63 2.760635 -#> 38862 34.90000522 300.00 TRUE 104.48 92 3.151879 -#> 7466 28.79055625 255.23 TRUE 0.00 60 3.163437 -#> 18265 31.44564267 300.00 TRUE 300.00 100 3.236197 -#> 33350 28.95270330 10.00 TRUE 166.26 2 3.415431 -#> 13029 34.18781868 300.00 TRUE 96.65 4 2.813330 -#> 47100 29.64598224 82.80 TRUE 129.73 36 3.558076 -#> 19966 29.22886896 300.00 TRUE 300.00 23 3.350329 -#> 44825 29.02145824 300.00 TRUE 300.00 100 2.392854 -#> 664 30.84231811 300.00 TRUE 300.00 12 2.885027 -#> 11618 33.98556458 300.00 TRUE 300.00 1 2.578737 -#> 16370 31.15859081 300.00 TRUE 300.00 8 4.119860 -#> 38197 34.34251728 105.52 TRUE 65.32 50 3.036108 -#> 26010 25.22962355 69.52 TRUE 166.56 2 3.647295 -#> 21680 28.34823283 104.12 TRUE 110.76 4 3.591103 -#> 7385 28.71034216 300.00 TRUE 300.00 100 4.219794 -#> 47366 28.72753089 300.00 TRUE 300.00 100 2.872349 -#> 12137 27.58849079 300.00 TRUE 300.00 100 3.369811 -#> 40216 26.36407999 300.00 TRUE 300.00 100 3.257022 -#> 30268 47.27245585 300.00 TRUE 300.00 41 2.705801 -#> 18958 29.98975691 300.00 TRUE 300.00 5 2.674746 -#> 7041 33.21894705 300.00 TRUE 300.00 6 5.916915 -#> 43859 29.55774674 300.00 TRUE 300.00 100 2.806780 -#> 33910 32.67349123 300.00 TRUE 300.00 6 6.024389 -#> 19892 27.02641920 300.00 TRUE 300.00 92 3.259412 -#> 47718 32.32341401 300.00 TRUE 10.00 1 3.759272 -#> 17345 29.82130732 300.00 TRUE 300.00 2 4.693266 -#> 27244 37.77510743 300.00 TRUE 300.00 54 5.009882 -#> 18440 34.94469593 300.00 TRUE 300.00 100 4.477051 -#> 43201 41.82821088 300.00 TRUE 300.00 100 3.530273 -#> 42201 39.55872505 300.00 TRUE 300.00 100 3.989241 -#> 34887 42.93573829 300.00 TRUE 300.00 100 3.159244 -#> 20797 32.05698864 285.07 TRUE 0.00 96 3.663802 -#> 21683 19.74527154 130.93 TRUE 0.00 21 3.399421 -#> 33875 27.56958319 300.00 TRUE 4.67 0 3.279136 -#> 19832 36.63377551 300.00 TRUE 300.00 100 3.278299 -#> 29228 30.39942174 131.25 TRUE 80.55 2 3.618527 -#> 5572 26.59784677 300.00 TRUE 300.00 100 2.567470 -#> 30933 12.84342194 99.29 TRUE 0.00 6 3.137802 -#> 36292 31.51554352 300.00 TRUE 16.03 100 3.030038 -#> 28009 27.01782483 300.00 TRUE 300.00 29 3.185767 -#> 47086 23.38699128 300.00 TRUE 300.00 100 3.525504 -#> 28173 23.88374569 300.00 TRUE 300.00 59 3.185096 -#> 332 0.55691498 94.82 TRUE 0.00 90 3.079334 -#> 10617 33.79820738 17.89 TRUE 145.83 4 3.064162 -#> 2801 25.74643148 300.00 TRUE 300.00 26 2.422656 -#> 23626 26.77832847 300.00 TRUE 300.00 100 3.336505 -#> 34984 33.90592344 300.00 TRUE 300.00 100 2.699406 -#> 48650 27.05220230 300.00 TRUE 300.00 100 3.027918 -#> 42617 31.23192941 300.00 TRUE 300.00 100 2.848895 -#> 16065 30.32035356 300.00 TRUE 162.92 100 3.818858 -#> 22847 24.74776605 300.00 TRUE 300.00 41 3.134623 -#> 46627 53.68328061 300.00 TRUE 300.00 100 2.720428 -#> 18498 22.71777658 300.00 TRUE 300.00 83 2.904587 -#> 6212 31.93151088 300.00 TRUE 300.00 47 5.501416 -#> 41310 17.29530400 300.00 TRUE 300.00 25 3.046207 -#> 17851 32.26325344 300.00 TRUE 300.00 100 4.161853 -#> 23176 41.55834775 300.00 TRUE 300.00 100 3.203943 -#> 4785 26.48612000 300.00 FALSE 0.00 100 3.050663 -#> 27884 15.49736244 300.00 FALSE 300.00 100 2.781892 -#> 18286 24.35070629 300.00 FALSE 300.00 42 2.931097 -#> 8635 27.68188291 300.00 FALSE 300.00 23 2.716617 -#> 18746 39.13645515 4.48 FALSE 205.00 27 3.028048 -#> 20458 21.42976745 300.00 FALSE 300.00 28 2.424012 -#> 32869 14.60011054 300.00 FALSE 300.00 78 2.867820 -#> 30267 25.27030355 300.00 FALSE 300.00 100 2.781107 -#> 12716 24.34497672 300.00 FALSE 300.00 25 2.731444 -#> 16542 21.11234883 300.00 FALSE 300.00 100 2.909320 -#> 44396 21.44237252 300.00 FALSE 300.00 100 2.270336 -#> 39685 47.87348857 300.00 FALSE 300.00 100 2.918481 -#> 34347 31.72008945 300.00 FALSE 300.00 100 3.477538 -#> 48183 30.94143981 300.00 FALSE 300.00 100 3.204523 -#> 12974 37.33851359 300.00 FALSE 300.00 100 2.711947 -#> 8413 39.88989465 80.73 FALSE 0.00 100 3.040329 -#> 42934 32.22715710 300.00 FALSE 300.00 22 4.499525 -#> 1691 27.00006314 300.00 FALSE 300.00 100 2.855262 -#> 22545 23.68435638 300.00 FALSE 300.00 96 2.215056 -#> 34228 10.74868824 300.00 FALSE 300.00 100 2.427333 -#> 34345 44.39506180 300.00 FALSE 300.00 100 2.972060 -#> 12044 32.23689739 300.00 FALSE 300.00 100 3.304782 -#> 33410 32.87975603 300.00 FALSE 300.00 100 2.986312 -#> 20764 39.85036057 300.00 FALSE 300.00 91 2.462702 -#> 7553 39.77129239 300.00 FALSE 300.00 100 2.739830 -#> 44823 36.10722729 300.00 FALSE 300.00 100 2.749146 -#> 1479 43.67657272 300.00 FALSE 300.00 100 2.938910 -#> 17628 26.99204173 300.00 FALSE 205.59 100 2.626915 -#> 7986 32.00313060 275.06 FALSE 0.00 65 3.156065 -#> 26283 41.51079226 300.00 FALSE 300.00 100 2.962620 -#> 4269 19.11673683 300.00 FALSE 194.53 100 3.439564 -#> 37783 24.25731417 300.00 FALSE 300.00 18 2.682893 -#> 1434 39.69279717 300.00 FALSE 300.00 35 2.955995 -#> 43972 22.85242166 300.00 FALSE 300.00 100 3.063725 -#> 40653 0.14209353 18.02 FALSE 140.15 100 3.072344 -#> 14881 26.85625073 300.00 FALSE 300.00 100 2.948550 -#> 26635 31.39464943 300.00 FALSE 300.00 100 3.092389 -#> 11025 36.81998679 300.00 FALSE 300.00 50 2.828200 -#> 13548 16.97845834 300.00 FALSE 300.00 100 2.256336 +#> x y dem slope hcurv vcurv carea +#> 21788 715392.5 9560172 1988.71 40.476603 -0.01919 -0.04051 351155.1250 +#> 37912 712802.5 9559952 1838.40 52.101344 0.00183 -0.09203 634.3320 +#> 27864 712992.5 9560672 1926.38 27.224090 -0.00199 0.00659 3553.7166 +#> 13748 715232.5 9559542 2212.12 26.123438 0.00023 -0.00043 1806.4552 +#> 30059 713832.5 9559662 2288.02 18.277927 0.01917 0.00333 258.6537 +#> 1300 714042.5 9558482 2408.27 24.148452 0.00659 0.01041 772.9746 +#> 8598 714002.5 9558872 2310.02 39.583935 -0.00498 -0.00752 1321.5997 +#> 15843 714042.5 9558902 2332.30 50.097711 -0.00858 -0.00292 1233.9299 +#> 28293 713762.5 9558812 2250.98 33.285410 0.00118 0.00761 928.6858 +#> 39885 715062.5 9561022 1840.94 38.435728 0.00250 -0.01340 604.1032 +#> 20151 714602.5 9559922 2184.66 48.341595 -0.00979 -0.01542 674.0147 +#> 38827 713412.5 9560472 1869.10 16.871315 -0.00156 0.00406 2421.1145 +#> 47255 713552.5 9559962 2191.77 33.578765 -0.00068 0.00588 1295.1610 +#> 21703 713842.5 9559672 2282.58 24.991846 0.01581 0.01189 287.5425 +#> 30800 715182.5 9557582 2772.37 39.204064 -0.02475 0.00665 25318.3555 +#> 45869 714342.5 9560282 2127.11 27.623441 -0.02169 -0.00160 9344.2930 +#> 40307 713302.5 9560702 1845.07 39.004675 0.00129 0.02510 3342.5518 +#> 48055 712862.5 9559972 1902.48 29.016875 -0.00720 0.01250 3686.1240 +#> 18005 714832.5 9557912 2660.76 31.912030 0.00191 -0.00261 554.9243 +#> 22933 712712.5 9560432 1902.17 38.123466 -0.00746 0.00096 3210.5781 +#> 31576 713942.5 9558492 2387.12 46.143156 -0.05634 -0.02626 4316.0977 +#> 4827 714962.5 9559882 2274.42 29.244912 0.00051 0.00889 362.8363 +#> 6676 715092.5 9559912 2217.44 41.707890 -0.00077 -0.00284 2010.4285 +#> 34280 713652.5 9560702 1896.37 41.273588 0.00382 0.00088 2322.1746 +#> 34565 712972.5 9560172 2007.72 27.947162 0.00598 0.01212 561.1948 +#> 6763 714692.5 9561052 1830.60 48.394880 -0.01996 0.00626 1014.7159 +#> 1635 713102.5 9559052 2172.87 31.484031 -0.00064 -0.00916 2009.1686 +#> 23141 715632.5 9558942 2495.99 34.376895 0.01275 0.00605 798.6775 +#> 2950 715042.5 9557712 2740.72 26.525081 -0.00089 -0.00051 779.0555 +#> 46756 713372.5 9559192 2120.47 46.005073 0.03696 0.02514 297.7842 +#> 12868 712532.5 9560332 1913.92 61.937311 -0.03543 -0.05146 1347.6224 +#> 15990 712852.5 9559982 1894.77 37.694320 -0.01545 0.02405 5681.2412 +#> 5728 713502.5 9559662 2140.27 34.307567 -0.00154 0.00124 2114.2253 +#> 47351 714222.5 9558792 2420.94 28.668516 -0.09566 -0.03355 14919.6260 +#> 40738 714972.5 9557642 2745.48 42.542689 -0.00294 -0.00096 520.5570 +#> 17594 714832.5 9561052 1818.04 46.167220 -0.01353 0.00063 5035.0000 +#> 36931 712852.5 9558892 2102.80 30.535786 0.00140 -0.00070 2562.7559 +#> 18564 715552.5 9558592 2648.36 26.291314 0.00316 0.00714 475.3120 +#> 6967 715342.5 9560192 1995.36 44.881503 -0.00666 -0.00504 1020.2766 +#> 43092 715532.5 9558192 2770.31 36.833165 -0.00442 -0.00178 800.1933 +#> 29926 714702.5 9557622 2637.00 44.642580 0.01843 0.04407 344.8395 +#> 41037 715532.5 9558792 2529.58 32.846524 -0.00585 -0.00974 2136.6939 +#> 44550 714842.5 9557642 2696.38 15.173068 0.00359 -0.00279 930.0871 +#> 23029 713502.5 9559102 2174.07 56.457542 0.03395 0.01495 266.5888 +#> 39599 714112.5 9558562 2374.25 29.168135 0.00283 0.00117 1018.2141 +#> 20220 714052.5 9558522 2378.67 39.656128 -0.00855 -0.02175 1122.9120 +#> 22299 713532.5 9560362 1975.58 27.709958 0.00478 0.01092 412.5849 +#> 2718 715062.5 9558222 2719.56 44.753160 -0.00119 -0.01560 977.4719 +#> 48808 715032.5 9557792 2707.02 25.437607 -0.02329 -0.01181 11655.4316 +#> 42941 713252.5 9559742 1990.10 28.954422 -0.04708 0.00408 76318.0156 +#> 30406 715312.5 9560152 2029.10 34.137398 0.00953 -0.02283 590.1536 +#> 34005 714922.5 9558842 2559.23 52.831292 0.03658 0.04602 345.8933 +#> 2595 714912.5 9557662 2700.72 24.194862 -0.02681 -0.01319 3457.7519 +#> 33350 713132.5 9560672 1896.89 24.695054 0.00306 -0.00436 2602.7390 +#> 28845 714892.5 9559272 2374.42 45.750107 -0.00209 0.02739 365.7626 +#> 44650 715612.5 9558142 2764.88 39.360482 -0.00550 -0.00941 1290.7709 +#> 31561 713302.5 9559192 2092.11 36.527778 0.00406 -0.00055 773.0359 +#> 16355 712962.5 9560212 1992.88 39.526066 0.01927 -0.00316 454.9674 +#> 13146 713792.5 9558552 2341.40 29.825318 -0.00104 0.01664 1898.5588 +#> 2552 714942.5 9557652 2719.32 34.873649 -0.01238 -0.00851 1012.5963 +#> 41783 715572.5 9558652 2609.51 32.408212 0.00291 -0.00081 940.6002 +#> 12137 712842.5 9558902 2097.88 30.098619 0.00024 -0.00474 2343.2100 +#> 39722 715022.5 9557712 2735.53 30.260766 0.00133 0.00367 806.0966 +#> 39166 712832.5 9560142 1922.46 39.660139 0.01469 0.02940 987.8085 +#> 32879 713912.5 9559672 2238.00 31.976775 -0.00278 -0.00412 1350.3611 +#> 44767 714452.5 9560402 2092.27 12.462978 -0.00195 -0.00205 1112.2626 +#> 10073 715572.5 9558612 2632.78 32.161267 0.00129 0.00171 788.0004 +#> 47718 712812.5 9559892 1867.55 54.938631 -0.01491 0.01061 5744.7583 +#> 17345 713922.5 9559682 2227.44 34.490913 -0.06509 -0.01211 49347.5547 +#> 6217 714442.5 9559932 2151.30 36.670445 0.01489 0.00241 1752.9242 +#> 30287 713992.5 9557822 2403.92 18.215474 0.03890 -0.00911 173.2806 +#> 43201 715712.5 9557922 2852.82 31.462258 -0.00949 0.00520 3390.5708 +#> 42201 714642.5 9557452 2592.29 13.225585 -0.02084 -0.02236 9755.3027 +#> 34887 714662.5 9557392 2609.02 51.020746 -0.01878 -0.04351 1442.9247 +#> 20797 713012.5 9560392 1842.25 36.649818 0.01299 0.00141 4611.0718 +#> 46138 715202.5 9559582 2194.40 18.860052 -0.02143 -0.05078 622888.2500 +#> 33875 712832.5 9559852 1892.81 37.554519 -0.01175 0.04195 1901.6743 +#> 15432 715632.5 9558922 2509.67 35.827624 -0.00444 -0.00017 759.9937 +#> 22907 713172.5 9559612 2056.81 22.272588 -0.00166 0.01476 1404.4454 +#> 5911 713952.5 9561282 1801.16 33.789613 0.00466 -0.01776 1021.8267 +#> 28009 715002.5 9559922 2237.68 34.603786 -0.01109 0.00298 1533.7925 +#> 16071 714562.5 9560062 2166.69 28.696018 0.02281 0.04759 158.9370 +#> 48574 712692.5 9560412 1901.97 41.978326 0.01364 -0.00094 1392.6008 +#> 14494 715332.5 9558792 2557.22 20.720446 0.01267 0.00483 343.1384 +#> 44612 714932.5 9558822 2578.80 38.255246 0.01257 0.00044 288.5468 +#> 9606 714392.5 9559172 2353.05 30.783303 -0.00185 -0.01185 953.8614 +#> 24723 712942.5 9560202 1983.18 33.071697 0.00285 -0.01325 595.0426 +#> 42617 714092.5 9558392 2469.21 42.799947 -0.01397 0.00526 706.1463 +#> 7771 715232.5 9560922 1816.88 37.180377 0.00124 0.01166 1813.7841 +#> 37115 715612.5 9559202 2358.15 9.727677 -0.01020 0.00279 39495.1797 +#> 24532 715202.5 9560922 1799.62 4.488551 -0.02312 -0.01848 1890804.2500 +#> 47722 712732.5 9561022 1999.03 37.492639 0.00004 -0.02384 947.2075 +#> 10213 715542.5 9558782 2532.10 35.413375 -0.00893 -0.00867 2247.8748 +#> 48461 714672.5 9559272 2316.84 40.381492 -0.01966 -0.02234 849.0547 +#> 21490 714672.5 9560382 2021.86 32.804126 -0.00783 -0.00367 1966.2112 +#> 9984 712742.5 9560092 1848.56 23.612737 0.00614 -0.00093 118.1402 +#> 13072 714862.5 9558932 2448.29 41.052426 -0.04989 -0.02781 29995.1543 +#> 32405 713022.5 9558982 2208.85 15.004619 0.00599 0.00301 338.9676 +#> 7317 713682.5 9558582 2347.46 28.347660 -0.02196 0.00336 2806.2644 +#> 29431 714882.5 9559892 2258.03 21.917927 -0.00849 0.00150 503.5590 +#> 25584 715062.5 9558532 2675.93 37.688591 0.00534 0.01476 1275.9446 +#> 11288 712822.5 9560442 1873.42 39.609718 -0.00465 -0.00105 4432.8760 +#> 15186 713322.5 9561212 1936.88 36.079725 0.00165 0.00565 1410.8372 +#> 16464 715252.5 9559982 2143.24 23.185883 -0.00298 -0.00712 1010.5097 +#> 15558 714732.5 9559502 2283.66 53.431752 0.00390 -0.02831 454.9113 +#> 8635 713832.5 9557902 2397.71 47.119476 0.02295 0.02715 520.7355 +#> 30267 714442.5 9557512 2612.30 28.765919 -0.00458 0.00479 604.0975 +#> 441 714312.5 9560972 1916.59 31.302976 0.00540 0.00650 1133.1501 +#> 42329 715852.5 9558702 2651.92 22.870756 0.00321 0.00548 532.8210 +#> 16542 713772.5 9559622 2304.85 30.810232 -0.00712 0.00923 811.5580 +#> 49835 713272.5 9560372 1898.68 32.825898 0.00846 0.00794 1363.5321 +#> 16878 714232.5 9558172 2485.33 22.808304 0.00818 0.00502 1428.8546 +#> 13286 713662.5 9559702 2268.14 41.996660 -0.01341 0.04431 315.9084 +#> 4007 714132.5 9558412 2464.44 43.720118 0.00174 -0.03065 704.4843 +#> 31497 714032.5 9560672 2060.77 16.216997 0.00973 -0.00943 371.5514 +#> 12974 714662.5 9557332 2667.61 41.941657 -0.00065 0.00475 515.1661 +#> 8831 714372.5 9559852 2200.77 48.760427 -0.00457 0.01697 412.1185 +#> 31201 714672.5 9560022 2224.47 38.863727 0.01180 0.01170 253.5129 +#> 36245 713142.5 9560572 1827.82 43.676000 -0.00410 0.00400 4126.7812 +#> 48869 712562.5 9560122 1972.89 24.946582 0.00185 0.01655 560.8554 +#> 20135 714772.5 9560232 2107.52 37.850738 -0.00782 -0.00048 1550.6704 +#> 3733 713762.5 9559632 2297.63 34.214748 -0.01338 0.00518 1137.0179 +#> 29107 713172.5 9559272 2067.58 28.264008 0.02413 0.00528 355.3475 +#> 14148 714162.5 9560352 2149.57 37.133395 0.00424 0.00196 576.1450 +#> 44715 714932.5 9560132 2195.78 30.393692 0.03479 0.02251 168.1733 +#> 42424 714692.5 9560902 1921.34 25.331037 0.01154 -0.00204 618.2027 +#> 43222 714472.5 9560712 2039.25 22.190655 0.01858 0.00312 254.0656 +#> 1281 715102.5 9560032 2120.64 34.649050 -0.00930 -0.00360 4288.7329 +#> 26768 715092.5 9560582 1960.72 36.656121 0.00980 -0.00710 472.0236 +#> 14671 712672.5 9560372 1872.21 39.197189 -0.00873 -0.01417 1520.6630 +#> 17628 714032.5 9560252 2160.03 30.294570 0.00537 -0.00577 423.5598 +#> 484 713812.5 9560592 2016.41 44.301097 -0.00719 0.00079 1647.6451 +#> 31098 714352.5 9559042 2410.15 30.336396 0.00537 -0.00347 1893.3645 +#> 2999 714592.5 9558492 2515.83 47.031814 0.01600 -0.01849 764.8193 +#> 18308 714842.5 9558742 2568.20 26.017441 -0.02552 0.00532 16771.3066 +#> 18246 713902.5 9561342 1818.52 19.110434 -0.01502 0.00282 1659.6211 +#> 15138 713702.5 9561122 1780.68 49.221658 0.00607 -0.04566 12862.4922 +#> 24446 714742.5 9560082 2200.60 28.721801 -0.00598 0.02208 842.4825 +#> 18504 713372.5 9560672 1786.89 6.473850 -0.00278 -0.01172 111973.8906 +#> 16443 714472.5 9559532 2222.55 16.744119 0.03263 -0.02012 305.3624 +#> 40653 714262.5 9561262 1791.94 12.222336 0.00161 0.00709 1181.2555 +#> 9320 713872.5 9559262 2378.76 29.858550 0.00309 0.01011 501.7867 +#> 9526 714432.5 9560582 2076.13 31.080096 -0.00172 -0.00119 792.3544 +#> 35257 713822.5 9557982 2350.80 29.307937 0.01549 -0.00169 649.9975 +#> 31440 714842.5 9558392 2704.41 25.801435 0.00992 -0.01052 451.7209 +#> 6577 714142.5 9559902 2244.30 57.890510 -0.06986 -0.05013 616.4128 +#> 41373 715332.5 9560862 1873.10 29.124591 -0.00166 0.00156 758.4840 +#> 39234 712902.5 9559652 1953.39 29.731926 0.07379 -0.00249 148.1857 +#> 46567 714472.5 9560352 2080.13 21.381639 0.01336 0.01403 609.8665 +#> 24516 713802.5 9560862 1873.21 23.349749 -0.00771 0.00661 5642.7544 +#> cslope distroad slides distdeforest distslidespast log.carea +#> 21788 32.8144388 300.00 TRUE 300.00 40 5.545499 +#> 37912 30.2945705 300.00 TRUE 9.15 2 2.802317 +#> 27864 27.8153821 30.00 TRUE 183.39 20 3.550683 +#> 13748 34.1190637 300.00 TRUE 300.00 2 3.256827 +#> 30059 20.1921786 300.00 TRUE 300.00 41 2.412719 +#> 1300 27.5306221 300.00 TRUE 300.00 6 2.888165 +#> 8598 39.5982591 300.00 TRUE 300.00 100 3.121100 +#> 15843 42.2218329 300.00 TRUE 300.00 100 3.091290 +#> 28293 28.9022830 300.00 TRUE 300.00 4 2.967869 +#> 39885 6.0848118 210.57 TRUE 0.00 100 2.781111 +#> 20151 44.7955593 300.00 TRUE 300.00 100 2.828669 +#> 38827 31.0359778 300.00 TRUE 52.52 39 3.384015 +#> 47255 19.6776625 300.00 TRUE 260.03 100 3.112324 +#> 21703 22.2691506 300.00 TRUE 300.00 34 2.458702 +#> 30800 33.2481679 300.00 TRUE 300.00 100 4.403435 +#> 45869 23.4546003 300.00 TRUE 300.00 12 3.970546 +#> 40307 29.5136290 77.82 TRUE 76.41 54 3.524078 +#> 48055 33.8257094 300.00 TRUE 1.67 0 3.566570 +#> 18005 24.9059024 300.00 TRUE 300.00 0 2.744234 +#> 22933 33.0631025 87.56 TRUE 115.40 2 3.506583 +#> 31576 26.9353189 300.00 TRUE 300.00 4 3.635091 +#> 4827 21.0739607 300.00 TRUE 300.00 0 2.559711 +#> 6676 34.6215477 300.00 TRUE 300.00 100 3.303289 +#> 34280 33.6211634 300.00 TRUE 0.00 100 3.365895 +#> 34565 20.3801088 300.00 TRUE 68.13 100 2.749114 +#> 6763 16.2181433 125.47 TRUE 0.00 6 3.006344 +#> 1635 27.8268412 300.00 TRUE 300.00 100 3.303016 +#> 23141 30.2917057 300.00 TRUE 300.00 100 2.902371 +#> 2950 28.2038475 300.00 TRUE 300.00 100 2.891568 +#> 46756 38.9617030 300.00 TRUE 300.00 37 2.473902 +#> 12868 37.5883232 119.34 TRUE 79.16 61 3.129568 +#> 15990 34.1488575 300.00 TRUE 0.00 0 3.754443 +#> 5728 40.4084215 300.00 TRUE 300.00 2 3.325151 +#> 47351 34.8788058 300.00 TRUE 300.00 100 4.173758 +#> 40738 28.9601518 300.00 TRUE 300.00 100 2.716468 +#> 17594 19.2319014 138.35 TRUE 0.00 60 3.701999 +#> 36931 27.2091927 300.00 TRUE 300.00 100 3.408707 +#> 18564 21.7002035 300.00 TRUE 300.00 100 2.676979 +#> 6967 41.1194621 300.00 TRUE 300.00 85 3.008718 +#> 43092 39.4785110 300.00 TRUE 300.00 100 2.903195 +#> 29926 32.0948039 300.00 TRUE 300.00 100 2.537617 +#> 41037 31.5705475 300.00 TRUE 300.00 100 3.329742 +#> 44550 24.8262613 300.00 TRUE 300.00 100 2.968524 +#> 23029 46.6015223 300.00 TRUE 300.00 34 2.425842 +#> 39599 33.1347223 300.00 TRUE 300.00 25 3.007839 +#> 20220 32.1755909 300.00 TRUE 300.00 8 3.050346 +#> 22299 21.7191111 300.00 TRUE 127.54 2 2.615513 +#> 2718 36.7328972 300.00 TRUE 300.00 100 2.990104 +#> 48808 33.7735702 300.00 TRUE 300.00 100 4.066528 +#> 42941 39.0631166 300.00 TRUE 300.00 17 4.882627 +#> 30406 43.1007501 300.00 TRUE 300.00 85 2.770965 +#> 34005 41.7336728 300.00 TRUE 300.00 100 2.538942 +#> 2595 30.4395288 300.00 TRUE 300.00 100 3.538794 +#> 33350 28.9527033 10.00 TRUE 166.26 2 3.415431 +#> 28845 29.8425068 300.00 TRUE 300.00 54 2.563199 +#> 44650 37.3625778 300.00 TRUE 300.00 100 3.110849 +#> 31561 31.4427779 300.00 TRUE 300.00 0 2.888200 +#> 16355 28.2468193 300.00 TRUE 58.52 100 2.657980 +#> 13146 33.5764727 300.00 TRUE 300.00 2 3.278424 +#> 2552 32.2156979 300.00 TRUE 300.00 100 3.005436 +#> 41783 28.1860858 300.00 TRUE 300.00 100 2.973405 +#> 12137 27.5884908 300.00 TRUE 300.00 100 3.369811 +#> 39722 26.5416969 300.00 TRUE 300.00 100 2.906387 +#> 39166 29.8184425 300.00 TRUE 1.90 90 2.994673 +#> 32879 29.7382284 300.00 TRUE 300.00 6 3.130450 +#> 44767 17.2953040 300.00 TRUE 300.00 25 3.046207 +#> 10073 25.7613284 300.00 TRUE 300.00 100 2.896526 +#> 47718 32.3234140 300.00 TRUE 10.00 1 3.759272 +#> 17345 29.8213073 300.00 TRUE 300.00 2 4.693266 +#> 6217 30.9718066 300.00 TRUE 300.00 1 3.243763 +#> 30287 19.8484039 300.00 TRUE 300.00 65 2.238750 +#> 43201 41.8282109 300.00 TRUE 300.00 100 3.530273 +#> 42201 39.5587250 300.00 TRUE 300.00 100 3.989241 +#> 34887 42.9357383 300.00 TRUE 300.00 100 3.159244 +#> 20797 32.0569886 285.07 TRUE 0.00 96 3.663802 +#> 46138 32.6946907 300.00 TRUE 300.00 8 5.794410 +#> 33875 27.5695832 300.00 TRUE 4.67 0 3.279136 +#> 15432 28.7676379 300.00 TRUE 300.00 100 2.880810 +#> 22907 33.8136772 300.00 TRUE 205.28 1 3.147505 +#> 5911 4.1384742 42.88 TRUE 0.00 13 3.009377 +#> 28009 27.0178248 300.00 TRUE 300.00 29 3.185767 +#> 16071 29.6213451 300.00 TRUE 300.00 100 2.201225 +#> 48574 34.4244502 96.21 TRUE 101.32 10 3.143827 +#> 14494 21.1851145 300.00 TRUE 300.00 28 2.535469 +#> 44612 34.6914486 300.00 TRUE 300.00 100 2.460216 +#> 9606 33.8239905 300.00 TRUE 300.00 0 2.979485 +#> 24723 28.1734807 300.00 TRUE 36.63 100 2.774548 +#> 42617 31.2319294 300.00 TRUE 300.00 100 2.848895 +#> 7771 16.5791068 300.00 TRUE 35.59 100 3.258586 +#> 37115 32.1870501 300.00 TRUE 300.00 100 4.596544 +#> 24532 32.5462946 300.00 TRUE 7.49 100 6.276647 +#> 47722 33.1158146 300.00 TRUE 65.02 1 2.976445 +#> 10213 31.5986224 300.00 TRUE 300.00 100 3.351772 +#> 48461 35.7800684 300.00 TRUE 300.00 61 2.928936 +#> 21490 34.0600491 300.00 TRUE 300.00 1 3.293630 +#> 9984 3.9465333 300.00 TRUE 36.46 76 2.072398 +#> 13072 34.9446959 300.00 TRUE 300.00 100 4.477051 +#> 32405 15.7620689 300.00 FALSE 300.00 100 2.530158 +#> 7317 27.2258085 300.00 FALSE 300.00 61 3.448129 +#> 29431 20.8917601 300.00 FALSE 300.00 42 2.702050 +#> 25584 27.1467403 300.00 FALSE 300.00 100 3.105832 +#> 11288 28.5373089 132.07 FALSE 85.50 1 3.646686 +#> 15186 38.5486005 60.00 FALSE 0.00 100 3.149477 +#> 16464 35.0461095 300.00 FALSE 300.00 100 3.004540 +#> 15558 50.3440826 300.00 FALSE 300.00 45 2.657927 +#> 8635 27.6818829 300.00 FALSE 300.00 23 2.716617 +#> 30267 25.2703036 300.00 FALSE 300.00 100 2.781107 +#> 441 25.1133131 294.31 FALSE 152.09 100 3.054287 +#> 42329 19.2278906 300.00 FALSE 300.00 100 2.726581 +#> 16542 21.1123488 300.00 FALSE 300.00 100 2.909320 +#> 49835 32.3612292 300.00 FALSE 0.00 100 3.134665 +#> 16878 25.3889058 300.00 FALSE 300.00 18 3.154988 +#> 13286 25.8386777 300.00 FALSE 300.00 76 2.499561 +#> 4007 32.0925120 300.00 FALSE 300.00 68 2.847871 +#> 31497 21.2704852 300.00 FALSE 154.49 100 2.570019 +#> 12974 37.3385136 300.00 FALSE 300.00 100 2.711947 +#> 8831 32.5554619 300.00 FALSE 300.00 24 2.615022 +#> 31201 33.7225769 300.00 FALSE 300.00 100 2.404000 +#> 36245 25.8535746 110.19 FALSE 81.50 20 3.615611 +#> 48869 24.8755356 138.98 FALSE 0.00 63 2.748851 +#> 20135 32.3325813 300.00 FALSE 300.00 100 3.190519 +#> 3733 22.1001281 300.00 FALSE 300.00 100 3.055767 +#> 29107 25.2307695 300.00 FALSE 300.00 100 2.550653 +#> 14148 21.8778204 300.00 FALSE 214.75 100 2.760532 +#> 44715 28.4502193 300.00 FALSE 300.00 100 2.225757 +#> 42424 22.6071957 275.21 FALSE 93.50 70 2.791131 +#> 43222 21.2653286 300.00 FALSE 300.00 100 2.404946 +#> 1281 35.3938948 300.00 FALSE 300.00 100 3.632329 +#> 26768 32.2821611 300.00 FALSE 207.22 100 2.673964 +#> 14671 37.8822505 123.04 FALSE 75.11 0 3.182033 +#> 17628 26.9920417 300.00 FALSE 205.59 100 2.626915 +#> 484 32.1457971 300.00 FALSE 55.63 100 3.216864 +#> 31098 34.6576441 300.00 FALSE 300.00 45 3.277234 +#> 2999 45.5839492 300.00 FALSE 300.00 100 2.883559 +#> 18308 30.0206966 300.00 FALSE 300.00 55 4.224567 +#> 18246 7.9876046 18.49 FALSE 0.00 29 3.220009 +#> 15138 20.6459612 116.69 FALSE 19.61 40 4.109325 +#> 24446 31.3631367 300.00 FALSE 300.00 100 2.925561 +#> 18504 23.0959287 144.96 FALSE 4.48 89 5.049117 +#> 16443 26.5382592 300.00 FALSE 300.00 75 2.484816 +#> 40653 0.1420935 18.02 FALSE 140.15 100 3.072344 +#> 9320 18.1392708 300.00 FALSE 300.00 93 2.700519 +#> 9526 27.5695832 300.00 FALSE 300.00 100 2.898919 +#> 35257 30.8818522 300.00 FALSE 300.00 79 2.812912 +#> 31440 24.0350065 300.00 FALSE 300.00 96 2.654870 +#> 6577 42.4074712 300.00 FALSE 300.00 70 2.789872 +#> 41373 31.1287970 300.00 FALSE 120.90 100 2.879946 +#> 39234 33.4859454 300.00 FALSE 0.00 100 2.170806 +#> 46567 16.9480916 300.00 FALSE 300.00 78 2.785235 +#> 24516 36.2544774 300.00 FALSE 122.13 100 3.751491 diff --git a/reference/partition_cv_strat.html b/reference/partition_cv_strat.html index 3aaad42d..39d5012d 100644 --- a/reference/partition_cv_strat.html +++ b/reference/partition_cv_strat.html @@ -152,7 +152,7 @@

Examples

parti <- partition_cv(ecuador, nfold = 5, repetition = 1) idx <- parti[["1"]][[1]]$train mean(ecuador$slides[idx] == "TRUE") / mean(ecuador$slides == "TRUE") -#> [1] 0.99132 +#> [1] 1.012163 # close to 1 because of large sample size, but with some random variation diff --git a/reference/partition_disc.html b/reference/partition_disc.html index 1f07fe2b..45fd97fd 100644 --- a/reference/partition_disc.html +++ b/reference/partition_disc.html @@ -192,19 +192,19 @@

Examples

summary(parti) #> $`1` #> n.train n.test -#> 480 710 9 -#> 397 719 13 -#> 59 712 8 -#> 26 706 19 -#> 351 699 25 +#> 262 692 32 +#> 342 693 31 +#> 363 738 6 +#> 278 722 13 +#> 349 735 8 #> #> $`2` #> n.train n.test -#> 173 713 25 -#> 632 687 36 -#> 599 737 5 -#> 700 723 15 -#> 479 727 10 +#> 542 738 3 +#> 731 737 1 +#> 121 711 20 +#> 163 689 36 +#> 633 711 9 #> # leave-one-out with buffer: @@ -212,19 +212,19 @@

Examples

summary(parti) #> $`1` #> n.train n.test -#> 480 710 9 -#> 397 719 13 -#> 59 712 8 -#> 26 706 19 -#> 351 699 25 +#> 262 692 32 +#> 342 693 31 +#> 363 738 6 +#> 278 722 13 +#> 349 735 8 #> #> $`2` #> n.train n.test -#> 173 713 25 -#> 632 687 36 -#> 599 737 5 -#> 700 723 15 -#> 479 727 10 +#> 542 738 3 +#> 731 737 1 +#> 121 711 20 +#> 163 689 36 +#> 633 711 9 #> diff --git a/reference/sperrorest.html b/reference/sperrorest.html index 8875dad5..2de4e897 100644 --- a/reference/sperrorest.html +++ b/reference/sperrorest.html @@ -346,14 +346,14 @@

Examples

smp_fun = partition_cv, smp_args = list(repetition = 1:2, nfold = 3) ) -#> Fri Sep 15 04:28:26 2023 Repetition 1 -#> Fri Sep 15 04:28:26 2023 Repetition - Fold 1 -#> Fri Sep 15 04:28:27 2023 Repetition - Fold 2 -#> Fri Sep 15 04:28:27 2023 Repetition - Fold 3 -#> Fri Sep 15 04:28:27 2023 Repetition 2 -#> Fri Sep 15 04:28:27 2023 Repetition - Fold 1 -#> Fri Sep 15 04:28:27 2023 Repetition - Fold 2 -#> Fri Sep 15 04:28:27 2023 Repetition - Fold 3 +#> Sat Sep 16 04:26:54 2023 Repetition 1 +#> Sat Sep 16 04:26:54 2023 Repetition - Fold 1 +#> Sat Sep 16 04:26:55 2023 Repetition - Fold 2 +#> Sat Sep 16 04:26:55 2023 Repetition - Fold 3 +#> Sat Sep 16 04:26:55 2023 Repetition 2 +#> Sat Sep 16 04:26:55 2023 Repetition - Fold 1 +#> Sat Sep 16 04:26:55 2023 Repetition - Fold 2 +#> Sat Sep 16 04:26:55 2023 Repetition - Fold 3 summary(nsp_res$error_rep) #> mean sd median IQR #> train_auroc 0.8413531 0.0002190341 0.8413531 0.0001548805 @@ -436,14 +436,14 @@

Examples

smp_fun = partition_kmeans, smp_args = list(repetition = 1:2, nfold = 3) ) -#> Fri Sep 15 04:28:27 2023 Repetition 1 -#> Fri Sep 15 04:28:27 2023 Repetition - Fold 1 -#> Fri Sep 15 04:28:27 2023 Repetition - Fold 2 -#> Fri Sep 15 04:28:27 2023 Repetition - Fold 3 -#> Fri Sep 15 04:28:28 2023 Repetition 2 -#> Fri Sep 15 04:28:28 2023 Repetition - Fold 1 -#> Fri Sep 15 04:28:28 2023 Repetition - Fold 2 -#> Fri Sep 15 04:28:28 2023 Repetition - Fold 3 +#> Sat Sep 16 04:26:56 2023 Repetition 1 +#> Sat Sep 16 04:26:56 2023 Repetition - Fold 1 +#> Sat Sep 16 04:26:56 2023 Repetition - Fold 2 +#> Sat Sep 16 04:26:56 2023 Repetition - Fold 3 +#> Sat Sep 16 04:26:56 2023 Repetition 2 +#> Sat Sep 16 04:26:56 2023 Repetition - Fold 1 +#> Sat Sep 16 04:26:56 2023 Repetition - Fold 2 +#> Sat Sep 16 04:26:56 2023 Repetition - Fold 3 summary(sp_res$error_rep) #> mean sd median IQR #> train_auroc 0.8472530 0.017474834 0.8472530 0.012356574