diff --git a/articles/spatial-modeling-use-case.html b/articles/spatial-modeling-use-case.html index a4acbdc0..49e965f9 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: 0.56 % +## OOB prediction error: 0.60 %

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    3.30e-02 0.000369 3.31e-02 0.000357
-## train_accuracy 9.67e-01 0.000369 9.67e-01 0.000357
+## train_error    3.36e-02 0.000472 3.38e-02 0.000438
+## train_accuracy 9.66e-01 0.000472 9.66e-01 0.000438
 ## train_events   4.69e+03 0.000000 4.69e+03 0.000000
 ## train_count    3.09e+04 0.000000 3.09e+04 0.000000
-## test_error     4.09e-02 0.000714 4.12e-02 0.000648
-## test_accuracy  9.59e-01 0.000714 9.59e-01 0.000648
+## test_error     4.08e-02 0.000782 4.07e-02 0.000778
+## test_accuracy  9.59e-01 0.000782 9.59e-01 0.000778
 ## test_events    1.17e+03 0.000000 1.17e+03 0.000000
 ## test_count     7.71e+03 0.000000 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 16.6 secs
+
## Time difference of 16.2 secs
 summary(res_lda_sp$error_rep)
##                    mean      sd   median     IQR
-## train_error    3.00e-02 0.00131 2.94e-02 0.00118
-## train_accuracy 9.70e-01 0.00131 9.71e-01 0.00118
+## train_error    2.80e-02 0.00100 2.76e-02 0.00094
+## train_accuracy 9.72e-01 0.00100 9.72e-01 0.00094
 ## 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     6.52e-02 0.00482 6.38e-02 0.00467
-## test_accuracy  9.35e-01 0.00482 9.36e-01 0.00467
+## test_error     6.33e-02 0.00104 6.34e-02 0.00104
+## test_accuracy  9.37e-01 0.00104 9.37e-01 0.00104
 ## 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$error_rep)
-
##                    mean      sd   median     IQR
-## train_error    0.00e+00 0.00000 0.00e+00 0.00000
-## train_accuracy 1.00e+00 0.00000 1.00e+00 0.00000
-## train_events   4.69e+03 0.00000 4.69e+03 0.00000
-## train_count    3.09e+04 0.00000 3.09e+04 0.00000
-## test_error     8.89e-02 0.00301 8.74e-02 0.00272
-## test_accuracy  9.11e-01 0.00301 9.13e-01 0.00272
-## test_events    1.17e+03 0.00000 1.17e+03 0.00000
-## test_count     7.71e+03 0.00000 7.71e+03 0.00000
+
##                    mean     sd   median    IQR
+## train_error    0.00e+00 0.0000 0.00e+00 0.0000
+## train_accuracy 1.00e+00 0.0000 1.00e+00 0.0000
+## train_events   4.69e+03 0.0000 4.69e+03 0.0000
+## train_count    3.09e+04 0.0000 3.09e+04 0.0000
+## test_error     8.56e-02 0.0113 8.76e-02 0.0112
+## test_accuracy  9.14e-01 0.0113 9.12e-01 0.0112
+## test_events    1.17e+03 0.0000 1.17e+03 0.0000
+## test_count     7.71e+03 0.0000 7.71e+03 0.0000
 summary(res_rf_sp$error_rep)["test_accuracy",]
-
##                mean      sd median     IQR
-## test_accuracy 0.911 0.00301  0.913 0.00272
+
##                mean     sd median    IQR
+## test_accuracy 0.914 0.0113  0.912 0.0112

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 a34f9abc..d38bde3e 100644 Binary files a/articles/spatial-modeling-use-case_files/figure-html/unnamed-chunk-19-1.png and b/articles/spatial-modeling-use-case_files/figure-html/unnamed-chunk-19-1.png differ diff --git a/pkgdown.yml b/pkgdown.yml index 9a56e8d9..354c2faa 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -4,7 +4,7 @@ pkgdown_sha: ~ articles: custom-pred-and-model-functions: custom-pred-and-model-functions.html spatial-modeling-use-case: spatial-modeling-use-case.html -last_built: 2023-09-03T04:26Z +last_built: 2023-09-04T04:25Z urls: reference: https://giscience-fsu.github.io/sperrorest/reference article: https://giscience-fsu.github.io/sperrorest/articles diff --git a/reference/add.distance.html b/reference/add.distance.html index df1f8e05..edfdf580 100644 --- a/reference/add.distance.html +++ b/reference/add.distance.html @@ -132,11 +132,11 @@

Examples

sp.parti <- add.distance(sp.parti, data = ecuador) # non-spatial partioning: very small test-training distance: nsp.parti[[1]][[1]]$distance -#> [1] 50.31624 +#> [1] 49.70341 # spatial partitioning: more substantial distance, depending on number of # folds etc. sp.parti[[1]][[1]]$distance -#> [1] 241.7747 +#> [1] 392.9521 diff --git a/reference/as.resampling.html b/reference/as.resampling.html index 6ec838b6..17c76018 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 715382 715062 713542 713552 714712 ... -#> $ y : num 9560142 9559392 9559972 9559962 9561042 ... -#> $ dem : num 2021 2279 2184 2192 1839 ... -#> $ slope : num 42 41.4 35.2 33.6 44.1 ... -#> $ hcurv : num 0.00958 0.0031 -0.00841 -0.00068 -0.00223 ... -#> $ vcurv : num 0.02642 0.0144 -0.0001 0.00588 0.01783 ... -#> $ carea : num 671 1089 1527 1295 378 ... -#> $ cslope : num 41.6 29.1 20.1 19.7 16.7 ... -#> $ distroad : num 300 300 300 300 135 300 300 300 300 300 ... +#> $ x : num 715042 712742 715552 715462 712812 ... +#> $ y : num 9559312 9560482 9558812 9559372 9559942 ... +#> $ dem : num 2320 1928 2518 2319 1861 ... +#> $ slope : num 42.86 34.67 7.98 32.33 63.34 ... +#> $ hcurv : num -0.01106 -0.00292 -0.01863 -0.00661 0.00514 ... +#> $ vcurv : num -0.04634 0.00712 -0.00507 -0.01339 -0.00644 ... +#> $ carea : num 501 3637 159065 1003 1179 ... +#> $ cslope : num 33.9 29 27.1 32.7 36.5 ... +#> $ distroad : num 300 60.5 300 300 300 ... #> $ slides : Factor w/ 2 levels "FALSE","TRUE": 2 2 2 2 2 2 2 2 2 2 ... -#> $ distdeforest : num 300 300 247 260 0 ... -#> $ distslidespast: num 21 100 100 100 2 6 100 45 83 65 ... -#> $ log.carea : num 2.83 3.04 3.18 3.11 2.58 ... +#> $ distdeforest : num 300 152.09 300 300 2.45 ... +#> $ distslidespast: num 100 35 100 11 0 46 100 21 100 100 ... +#> $ log.carea : num 2.7 3.56 5.2 3 3.07 ... # the corresponding training sample - larger: str(ecuador[parti[[1]]$train, ]) #> 'data.frame': 676 obs. of 13 variables: -#> $ x : num 712882 715232 715392 715042 712802 ... -#> $ y : num 9560002 9559582 9560172 9559312 9559952 ... -#> $ dem : num 1912 2199 1989 2320 1838 ... -#> $ slope : num 25.6 23.2 40.5 42.9 52.1 ... -#> $ hcurv : num -0.00681 -0.00501 -0.01919 -0.01106 0.00183 ... -#> $ vcurv : num -0.00029 -0.00649 -0.04051 -0.04634 -0.09203 ... -#> $ carea : num 5577 1399 351155 501 634 ... -#> $ cslope : num 34.4 30.7 32.8 33.9 30.3 ... +#> $ x : num 712882 715232 715392 715382 712802 ... +#> $ y : num 9560002 9559582 9560172 9560142 9559952 ... +#> $ dem : num 1912 2199 1989 2021 1838 ... +#> $ slope : num 25.6 23.2 40.5 42 52.1 ... +#> $ hcurv : num -0.00681 -0.00501 -0.01919 0.00958 0.00183 ... +#> $ vcurv : num -0.00029 -0.00649 -0.04051 0.02642 -0.09203 ... +#> $ carea : num 5577 1399 351155 671 634 ... +#> $ cslope : num 34.4 30.7 32.8 41.6 30.3 ... #> $ 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 9.15 ... -#> $ distslidespast: num 9 21 40 100 2 100 100 41 5 20 ... -#> $ log.carea : num 3.75 3.15 5.55 2.7 2.8 ... +#> $ distslidespast: num 9 21 40 21 2 100 100 41 5 20 ... +#> $ log.carea : num 3.75 3.15 5.55 2.83 2.8 ... # 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': 283 obs. of 13 variables: -#> $ x : num 712882 714752 715302 714852 712742 ... -#> $ y : num 9560002 9561022 9557472 9557902 9560482 ... -#> $ dem : num 1912 1848 2857 2675 1928 ... -#> $ slope : num 25.6 33.4 39.5 30.7 34.7 ... -#> $ hcurv : num -0.00681 -0.00347 -0.01021 0.00221 -0.00292 ... -#> $ vcurv : num -0.00029 0.02357 -0.01579 0.00969 0.00712 ... -#> $ carea : num 5577 1752 1319 369 3637 ... -#> $ cslope : num 34.4 23.8 36.3 20.5 29 ... -#> $ distroad : num 300 158.9 300 300 60.5 ... +#> 'data.frame': 297 obs. of 13 variables: +#> $ x : num 715232 715392 713512 715302 714852 ... +#> $ y : num 9559582 9560172 9559092 9557472 9557902 ... +#> $ dem : num 2199 1989 2166 2857 2675 ... +#> $ slope : num 23.2 40.5 56 39.5 30.7 ... +#> $ hcurv : num -0.00501 -0.01919 0.02056 -0.01021 0.00221 ... +#> $ vcurv : num -0.00649 -0.04051 -0.06976 -0.01579 0.00969 ... +#> $ carea : num 1399 351155 301 1319 369 ... +#> $ cslope : num 30.7 32.8 49.4 36.3 20.5 ... +#> $ distroad : num 300 300 300 300 300 300 300 300 300 300 ... #> $ slides : Factor w/ 2 levels "FALSE","TRUE": 2 2 2 2 2 2 2 2 2 2 ... -#> $ distdeforest : num 15 0 300 300 152 ... -#> $ distslidespast: num 9 5 100 10 35 100 26 41 6 100 ... -#> $ log.carea : num 3.75 3.24 3.12 2.57 3.56 ... +#> $ distdeforest : num 300 300 300 300 300 300 300 300 300 300 ... +#> $ distslidespast: num 21 40 41 100 10 2 11 4 100 100 ... +#> $ log.carea : num 3.15 5.55 2.48 3.12 2.57 ... # bootstrap training sample: same size as nrow(ecuador): str(ecuador[parti[[1]]$train, ]) #> 'data.frame': 751 obs. of 13 variables: -#> $ x : num 712832 712862 714742 714732 715202 ... -#> $ y : num 9560142 9558902 9560082 9559502 9558872 ... -#> $ dem : num 1922 2110 2201 2284 2464 ... -#> $ slope : num 39.7 31.4 28.7 53.4 34.7 ... -#> $ hcurv : num 0.01469 0.00151 -0.00598 0.0039 -0.01267 ... -#> $ vcurv : num 0.0294 0.00189 0.02208 -0.02831 -0.05063 ... -#> $ carea : num 988 2317 842 455 18734 ... -#> $ cslope : num 29.8 27.2 31.4 50.3 34.7 ... -#> $ distroad : num 300 300 300 300 300 300 300 300 300 300 ... -#> $ slides : Factor w/ 2 levels "FALSE","TRUE": 2 2 1 1 2 2 2 2 2 2 ... -#> $ distdeforest : num 1.9 300 300 300 300 300 0 300 300 300 ... -#> $ distslidespast: num 90 100 100 45 71 1 100 100 90 100 ... -#> $ log.carea : num 2.99 3.36 2.93 2.66 4.27 ... +#> $ x : num 714332 713642 714042 715472 715122 ... +#> $ y : num 9560262 9558712 9558482 9558902 9559142 ... +#> $ dem : num 2138 2299 2408 2483 2390 ... +#> $ slope : num 25.7 33.8 24.1 30.7 33.2 ... +#> $ hcurv : num -0.00491 -0.00503 0.00659 -0.01102 0.01008 ... +#> $ vcurv : num 0.00581 0.02433 0.01041 -0.01778 0.00102 ... +#> $ carea : num 1855 1475 773 4594 483 ... +#> $ cslope : num 23.8 23.9 27.5 29 26.9 ... +#> $ distroad : num 300 300 300 300 300 ... +#> $ slides : Factor w/ 2 levels "FALSE","TRUE": 2 2 2 2 2 2 1 2 1 1 ... +#> $ distdeforest : num 300 300 300 300 300 ... +#> $ distslidespast: num 10 100 6 100 100 54 100 100 42 92 ... +#> $ log.carea : num 3.27 3.17 2.89 3.66 2.68 ... diff --git a/reference/err_default.html b/reference/err_default.html index 34cfc887..5d002d8f 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] 0.4406818 +#> [1] 0.5138262 #> #> $error -#> [1] 0.531 +#> [1] 0.48 #> #> $accuracy -#> [1] 0.469 +#> [1] 0.52 #> #> $sensitivity -#> [1] 0.2643443 +#> [1] 0.3326446 #> #> $specificity -#> [1] 0.6640625 +#> [1] 0.6957364 #> #> $fpr70 -#> [1] 0.7617188 +#> [1] 0.6705426 #> #> $fpr80 -#> [1] 0.8632812 +#> [1] 0.7829457 #> #> $fpr90 -#> [1] 0.9394531 +#> [1] 0.8895349 #> #> $tpr80 -#> [1] 0.1413934 +#> [1] 0.2128099 #> #> $tpr90 -#> [1] 0.09016393 +#> [1] 0.1136364 #> #> $tpr95 -#> [1] 0.05532787 +#> [1] 0.05371901 #> #> $events -#> [1] 488 +#> [1] 484 #> #> $count #> [1] 1000 #> err_default(obs > 0, obs + rnorm(1000)) # some discrimination #> $auroc -#> [1] 0.8389192 +#> [1] 0.8595882 #> #> $error -#> [1] 0.258 +#> [1] 0.244 #> #> $accuracy -#> [1] 0.742 +#> [1] 0.756 #> #> $sensitivity -#> [1] 0.6106557 +#> [1] 0.6157025 #> #> $specificity -#> [1] 0.8671875 +#> [1] 0.8875969 #> #> $fpr70 -#> [1] 0.2109375 +#> [1] 0.1666667 #> #> $fpr80 -#> [1] 0.296875 +#> [1] 0.2616279 #> #> $fpr90 -#> [1] 0.4433594 +#> [1] 0.3992248 #> #> $tpr80 -#> [1] 0.6946721 +#> [1] 0.7479339 #> #> $tpr90 -#> [1] 0.5696721 +#> [1] 0.5702479 #> #> $tpr95 -#> [1] 0.4036885 +#> [1] 0.3822314 #> #> $events -#> [1] 488 +#> [1] 484 #> #> $count #> [1] 1000 @@ -196,44 +196,44 @@

Examples

# Three mock regression examples: err_default(obs, rnorm(1000)) # just noise, but no bias #> $bias -#> [1] 0.0484288 +#> [1] 0.06380087 #> #> $stddev -#> [1] 1.424808 +#> [1] 1.465409 #> #> $rmse -#> [1] 1.424919 +#> [1] 1.466065 #> #> $mad -#> [1] 1.36595 +#> [1] 1.474986 #> #> $median -#> [1] -0.01119599 +#> [1] 0.06683879 #> #> $iqr -#> [1] 1.878506 +#> [1] 1.997871 #> #> $count #> [1] 1000 #> err_default(obs, obs + rnorm(1000)) # some association, no bias #> $bias -#> [1] -0.02326742 +#> [1] -0.02305671 #> #> $stddev -#> [1] 0.9718334 +#> [1] 0.9844829 #> #> $rmse -#> [1] 0.971626 +#> [1] 0.9842606 #> #> $mad -#> [1] 0.9728568 +#> [1] 0.9781223 #> #> $median -#> [1] -0.04646661 +#> [1] -0.03819017 #> #> $iqr -#> [1] 1.320561 +#> [1] 1.321501 #> #> $count #> [1] 1000 @@ -243,7 +243,7 @@

Examples

#> [1] -1 #> #> $stddev -#> [1] 6.429096e-17 +#> [1] 6.380937e-17 #> #> $rmse #> [1] 1 diff --git a/reference/partition_cv.html b/reference/partition_cv.html index 81a91796..89488be3 100644 --- a/reference/partition_cv.html +++ b/reference/partition_cv.html @@ -155,310 +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 -#> 31358 712882.5 9560002 1911.52 25.564231 -0.00681 -0.00029 5577.3916 -#> 4965 715042.5 9559312 2320.49 42.857816 -0.01106 -0.04634 500.5027 -#> 27864 712992.5 9560672 1926.38 27.224090 -0.00199 0.00659 3553.7166 -#> 25090 715362.5 9560102 2059.29 49.119672 0.02059 -0.00628 556.0121 -#> 29391 712842.5 9560152 1930.16 28.095558 0.00753 0.01487 1077.7585 -#> 37265 712812.5 9559942 1860.77 63.337047 0.00514 -0.00644 1179.0695 -#> 20799 714792.5 9561002 1857.29 23.129160 0.00090 0.00890 2301.0454 -#> 39885 715062.5 9561022 1840.94 38.435728 0.00250 -0.01340 604.1032 -#> 25169 712642.5 9560232 1883.10 46.845029 0.00884 0.01345 1120.6929 -#> 49372 714862.5 9560982 1863.12 20.969109 -0.00295 0.00035 2697.5974 -#> 20151 714602.5 9559922 2184.66 48.341595 -0.00979 -0.01542 674.0147 -#> 26447 715632.5 9559102 2410.77 39.656701 0.00730 0.01770 474.5059 -#> 23817 714842.5 9557832 2677.58 26.657434 0.00922 0.01088 266.4355 -#> 10899 713852.5 9559652 2282.82 28.814048 0.00952 0.01378 440.9104 -#> 44328 713222.5 9558932 2227.94 29.155530 -0.00769 -0.00281 1325.6138 -#> 3835 714142.5 9558552 2388.42 32.900383 0.00979 -0.00269 491.8017 -#> 43964 714802.5 9558452 2684.33 37.982518 -0.02123 0.01164 895.6606 -#> 48498 714972.5 9557972 2681.66 38.346919 0.00528 0.01392 1255.0884 -#> 20290 714812.5 9561092 1775.73 34.626704 -0.01537 -0.00203 10263.2734 -#> 36030 712852.5 9559572 1938.79 28.678829 -0.01440 0.00710 843.7961 -#> 15340 715452.5 9558852 2523.95 34.165473 0.00151 0.00899 1394.2274 -#> 2942 713232.5 9560652 1851.75 34.535031 0.00856 0.01114 4510.8833 -#> 47210 714952.5 9557612 2744.35 40.884549 -0.00493 0.00733 598.7983 -#> 32821 715022.5 9559952 2208.33 43.416450 -0.00643 0.00314 2360.1501 -#> 13585 714552.5 9560092 2123.69 57.528973 -0.03613 -0.01898 588.3761 -#> 34280 713652.5 9560702 1896.37 41.273588 0.00382 0.00088 2322.1746 -#> 15390 713132.5 9560622 1873.26 38.480418 0.00252 0.01118 2359.5059 -#> 6763 714692.5 9561052 1830.60 48.394880 -0.01996 0.00626 1014.7159 -#> 3466 713232.5 9560612 1812.72 51.566202 -0.01804 -0.01596 5296.9014 -#> 7879 714722.5 9559232 2356.19 36.803944 -0.01097 0.00156 816.6036 -#> 17004 713102.5 9559032 2186.79 36.533508 -0.01383 0.00193 1809.3838 -#> 23141 715632.5 9558942 2495.99 34.376895 0.01275 0.00605 798.6775 -#> 19730 715332.5 9558452 2640.35 47.642587 -0.00211 0.00642 613.6390 -#> 2950 715042.5 9557712 2740.72 26.525081 -0.00089 -0.00051 779.0555 -#> 35142 714792.5 9558862 2575.17 25.664499 0.06765 0.00114 150.1498 -#> 29667 713892.5 9559712 2239.25 41.592725 0.00804 0.00346 543.0749 -#> 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 -#> 47429 714962.5 9557832 2705.12 32.057562 0.02224 0.00177 291.5937 -#> 40738 714972.5 9557642 2745.48 42.542689 -0.00294 -0.00096 520.5570 -#> 34359 712862.5 9558912 2111.37 32.812147 0.00463 0.00167 1938.5389 -#> 17594 714832.5 9561052 1818.04 46.167220 -0.01353 0.00063 5035.0000 -#> 5578 714962.5 9557612 2751.95 39.191459 0.00288 0.01452 437.3965 -#> 23029 713502.5 9559102 2174.07 56.457542 0.03395 0.01495 266.5888 -#> 20220 714052.5 9558522 2378.67 39.656128 -0.00855 -0.02175 1122.9120 -#> 48808 715032.5 9557792 2707.02 25.437607 -0.02329 -0.01181 11655.4316 -#> 2595 714912.5 9557662 2700.72 24.194862 -0.02681 -0.01319 3457.7519 -#> 10507 714892.5 9559282 2374.45 42.842346 -0.01057 0.02427 380.9472 -#> 33350 713132.5 9560672 1896.89 24.695054 0.00306 -0.00436 2602.7390 -#> 25700 715172.5 9559272 2324.49 35.534842 -0.00529 0.02009 1247.7061 -#> 47100 712752.5 9560462 1910.00 41.911863 -0.00032 0.00542 3614.7334 -#> 23213 715592.5 9558562 2654.69 33.219520 -0.00034 0.00294 739.5768 -#> 46783 713592.5 9560762 1832.60 36.010397 0.00448 -0.00328 592.3326 -#> 10364 714762.5 9561002 1856.21 25.274887 -0.00845 0.00454 3476.0818 -#> 31121 713462.5 9559132 2135.56 61.596974 -0.03800 -0.06020 1563.5549 -#> 13146 713792.5 9558552 2341.40 29.825318 -0.00104 0.01664 1898.5588 -#> 11618 713852.5 9558402 2432.14 54.059141 -0.00410 0.03620 379.0855 -#> 17728 713092.5 9560652 1900.14 30.279101 0.00645 -0.00395 2011.3270 -#> 5103 714612.5 9559892 2220.36 49.007945 -0.00078 0.00219 420.7032 -#> 24501 713302.5 9559142 2125.66 27.108925 0.01784 0.00596 842.0994 -#> 28046 714882.5 9558492 2711.77 28.305834 -0.00131 0.00201 788.0621 -#> 2552 714942.5 9557652 2719.32 34.873649 -0.01238 -0.00851 1012.5963 -#> 45592 714962.5 9559862 2282.08 22.160862 -0.00028 0.01318 289.0063 -#> 34192 712702.5 9560102 1867.01 35.474682 0.00629 -0.00469 1021.5407 -#> 40270 713312.5 9559012 2186.56 30.935710 -0.00771 -0.00599 1378.5760 -#> 5239 714832.5 9560992 1859.70 19.960131 -0.00320 0.00180 3904.5254 -#> 7385 713752.5 9560122 2064.61 31.771083 -0.02605 -0.03594 16587.9980 -#> 12772 715172.5 9557642 2759.42 34.805467 -0.00567 -0.03793 864.9472 -#> 6845 713412.5 9559172 2123.32 47.153854 -0.01556 -0.04394 587.8840 -#> 18958 714192.5 9558372 2495.72 34.677125 -0.00178 -0.00822 472.8748 -#> 10467 714642.5 9559652 2249.12 37.143135 -0.00759 -0.00400 608.6523 -#> 24135 712612.5 9559492 1903.67 46.756794 -0.06953 -0.01797 7557.7866 -#> 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 -#> 33875 712832.5 9559852 1892.81 37.554519 -0.01175 0.04195 1901.6743 -#> 34615 713242.5 9560732 1879.60 27.593647 0.00384 0.00175 1026.6818 -#> 43245 713722.5 9560012 2153.38 34.191256 -0.01263 0.01322 4605.1411 -#> 15981 713302.5 9559172 2106.91 35.997792 0.01354 0.00245 629.5983 -#> 28312 715712.5 9558032 2783.23 24.252731 -0.00007 -0.02132 1580.1621 -#> 5911 713952.5 9561282 1801.16 33.789613 0.00466 -0.01776 1021.8267 -#> 1036 713042.5 9558892 2205.38 31.509814 -0.00428 -0.00522 1355.7436 -#> 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 -#> 47244 714262.5 9559302 2350.47 17.059245 0.02213 -0.01363 565.5207 -#> 49378 713252.5 9560732 1875.41 27.835436 0.00181 -0.00001 1184.7952 -#> 14192 715162.5 9558122 2772.18 44.462671 -0.02970 0.00200 1305.6691 -#> 4667 712652.5 9560472 1957.89 36.324378 -0.00296 0.00426 1315.5334 -#> 1916 715202.5 9559512 2213.91 26.327984 -0.00292 -0.00109 2047.9031 -#> 39624 715222.5 9559552 2205.54 23.897497 0.00401 -0.00391 1794.7964 -#> 7409 714772.5 9559122 2404.90 30.215502 -0.01016 -0.00294 734.9908 -#> 44612 714932.5 9558822 2578.80 38.255246 0.01257 0.00044 288.5468 -#> 26058 713402.5 9560442 1880.12 31.377461 0.00730 -0.02610 1872.9276 -#> 17825 712732.5 9560462 1915.51 40.397535 0.00143 0.00477 2945.2100 -#> 46792 713862.5 9559672 2272.03 35.464942 0.00560 0.00610 454.8103 -#> 17674 713882.5 9558562 2339.20 41.036383 -0.03675 -0.01905 6487.0010 -#> 12074 714562.5 9560372 2045.17 26.287304 -0.00144 -0.00856 1789.1596 -#> 4479 713982.5 9557812 2399.24 41.231189 0.01895 0.04744 226.7911 -#> 14779 712502.5 9560202 2004.61 27.522601 0.00115 0.01015 790.2421 -#> 14140 714672.5 9559262 2326.36 45.792506 -0.01400 -0.01210 585.8992 -#> 9864 714952.5 9557592 2752.40 31.989380 -0.00983 0.01253 528.1924 -#> 49346 715322.5 9558782 2553.74 25.395208 0.00319 0.00291 487.3438 -#> 21490 714672.5 9560382 2021.86 32.804126 -0.00783 -0.00367 1966.2112 -#> 17851 715362.5 9557482 2869.26 29.125737 -0.03251 -0.00389 14516.2002 -#> 17374 715712.5 9557952 2832.22 37.874802 -0.01519 0.00609 4275.3457 -#> 23176 714012.5 9558892 2308.59 43.029130 -0.01084 -0.02996 1599.3491 -#> 16208 712882.5 9560332 1846.34 48.625209 0.00159 -0.01559 866.5775 -#> 27884 714072.5 9559202 2446.27 19.756731 -0.00009 0.00109 605.1908 -#> 15558 714732.5 9559502 2283.66 53.431752 0.00390 -0.02831 454.9113 -#> 11111 715142.5 9558482 2650.65 42.343300 -0.00533 0.01493 2212.0525 -#> 14717 715382.5 9558062 2799.05 50.590454 -0.00812 -0.02208 951.3401 -#> 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 -#> 12716 713962.5 9557762 2402.60 18.932244 0.03480 0.01500 538.8209 -#> 43426 713782.5 9560822 1876.35 23.823012 -0.00345 -0.00075 8141.7549 -#> 28162 714062.5 9561152 1785.47 48.444727 0.02309 -0.03409 1575.9425 -#> 47005 714942.5 9559192 2333.66 29.220848 -0.04937 -0.02273 72123.6172 -#> 44396 715512.5 9558102 2845.31 26.598420 0.01263 0.03598 186.3527 -#> 41753 715722.5 9558192 2771.39 26.085050 -0.00196 -0.00175 725.1355 -#> 20319 715282.5 9560482 1901.16 27.676726 0.00706 -0.03035 1471.9783 -#> 33175 715822.5 9558832 2563.13 36.622889 -0.00674 -0.00376 629.8236 -#> 33370 714202.5 9558962 2472.93 32.641406 -0.00307 0.02997 230.8641 -#> 39440 714222.5 9557872 2508.52 39.047647 0.02785 0.00645 234.0114 -#> 12044 713612.5 9559292 2247.76 23.074729 -0.00112 -0.00038 2017.3547 -#> 45486 712762.5 9560172 1853.82 56.797306 0.01978 0.01282 2322.7944 -#> 36889 714302.5 9559802 2243.60 21.166207 0.01923 0.05547 128.2613 -#> 41612 713952.5 9560002 2198.00 28.756179 0.00119 0.00361 1011.0248 -#> 15008 714492.5 9559012 2449.93 29.515348 0.00972 0.00308 422.5022 -#> 1479 715622.5 9557952 2888.67 34.953863 0.01635 -0.00105 868.7802 -#> 29716 715272.5 9559992 2144.94 25.717784 0.00262 -0.00502 520.5218 -#> 17628 714032.5 9560252 2160.03 30.294570 0.00537 -0.00577 423.5598 -#> 48184 714202.5 9558222 2489.17 36.113530 0.02236 -0.00066 306.0146 -#> 9007 714952.5 9560902 1863.75 32.770321 0.00074 -0.02904 608.3561 -#> 974 713402.5 9561062 1854.56 23.634509 0.00082 0.02578 1258.8865 -#> 42638 714322.5 9561182 1754.22 4.172279 -0.03757 -0.00003 5426606.0000 -#> 26283 715932.5 9557592 3075.48 32.766883 -0.00375 0.00215 917.5296 -#> 18246 713902.5 9561342 1818.52 19.110434 -0.01502 0.00282 1659.6211 -#> 19767 714272.5 9558052 2431.67 55.724729 0.00361 -0.01841 756.0055 -#> 7132 713192.5 9559612 2060.27 20.773731 0.00452 0.00368 1071.8734 -#> 10981 713832.5 9560022 2125.33 34.848439 -0.06909 -0.01151 143213.1562 -#> 18504 713372.5 9560672 1786.89 6.473850 -0.00278 -0.01172 111973.8906 -#> 40653 714262.5 9561262 1791.94 12.222336 0.00161 0.00709 1181.2555 -#> 37254 713482.5 9560892 1823.73 39.882446 0.01014 -0.00884 672.2466 -#> 33881 712992.5 9558822 2173.26 33.197748 -0.00595 0.03064 1485.5201 -#> 46639 715232.5 9560042 2107.15 39.657274 -0.01305 0.00585 5714.3267 -#> 49812 713392.5 9559222 2133.99 35.662612 -0.00983 0.00803 1058.5630 -#> 11570 715572.5 9558482 2685.72 16.814019 0.00282 0.00458 485.7418 -#> 24962 715152.5 9560472 1967.37 28.845560 0.00298 0.00222 991.8300 -#> 47245 715372.5 9560792 1900.65 34.470860 -0.00011 -0.00419 323.0790 -#> 126 715042.5 9561262 1733.49 30.110651 -0.00288 0.00448 941.8503 -#> 13548 715812.5 9558122 2821.65 15.508249 0.02751 0.00388 180.4414 -#> 24516 713802.5 9560862 1873.21 23.349749 -0.00771 0.00661 5642.7544 -#> cslope distroad slides distdeforest distslidespast log.carea -#> 31358 34.42788799 300.00 TRUE 15.00 9 3.746431 -#> 4965 33.90592344 300.00 TRUE 300.00 100 2.699406 -#> 27864 27.81538208 30.00 TRUE 183.39 20 3.550683 -#> 25090 43.53161440 300.00 TRUE 300.00 26 2.745084 -#> 29391 28.28348860 300.00 TRUE 0.56 100 3.032521 -#> 37265 36.51746507 300.00 TRUE 2.45 0 3.071539 -#> 20799 24.80792661 180.67 TRUE 0.00 16 3.361925 -#> 39885 6.08481178 210.57 TRUE 0.00 100 2.781111 -#> 25169 36.60627353 195.00 TRUE 47.05 0 3.049487 -#> 49372 23.54054397 213.54 TRUE 20.21 68 3.430977 -#> 20151 44.79555930 300.00 TRUE 300.00 100 2.828669 -#> 26447 33.03674647 300.00 TRUE 300.00 100 2.676242 -#> 23817 21.61139507 300.00 TRUE 300.00 4 2.425592 -#> 10899 24.77412210 300.00 TRUE 300.00 20 2.644350 -#> 44328 25.50407033 300.00 TRUE 300.00 100 3.122417 -#> 3835 35.13892862 300.00 TRUE 300.00 39 2.691790 -#> 43964 28.75732470 300.00 TRUE 300.00 46 2.952143 -#> 48498 33.61829863 300.00 TRUE 300.00 100 3.098674 -#> 20290 15.45668244 95.53 TRUE 0.00 49 4.011286 -#> 36030 29.19964811 300.00 TRUE 0.00 100 2.926238 -#> 15340 27.97523731 300.00 TRUE 300.00 100 3.144334 -#> 2942 29.82073436 68.13 TRUE 116.32 10 3.654262 -#> 47210 26.91526538 300.00 TRUE 300.00 100 2.777281 -#> 32821 29.48326222 300.00 TRUE 300.00 65 3.372940 -#> 13585 47.06733695 300.00 TRUE 300.00 100 2.769655 -#> 34280 33.62116342 300.00 TRUE 0.00 100 3.365895 -#> 15390 29.32226108 60.00 TRUE 118.92 2 3.372821 -#> 6763 16.21814335 125.47 TRUE 0.00 6 3.006344 -#> 3466 26.79494425 101.32 TRUE 95.02 19 3.724022 -#> 7879 28.58085369 300.00 TRUE 300.00 100 2.912011 -#> 17004 27.18913921 300.00 TRUE 300.00 100 3.257531 -#> 23141 30.29170567 300.00 TRUE 300.00 100 2.902371 -#> 19730 38.47869961 300.00 TRUE 300.00 100 2.787913 -#> 2950 28.20384747 300.00 TRUE 300.00 100 2.891568 -#> 35142 27.15304287 300.00 TRUE 300.00 100 2.176525 -#> 29667 34.25198995 300.00 TRUE 300.00 27 2.734860 -#> 5728 40.40842146 300.00 TRUE 300.00 2 3.325151 -#> 47351 34.87880578 300.00 TRUE 300.00 100 4.173758 -#> 47429 32.09766864 300.00 TRUE 300.00 100 2.464778 -#> 40738 28.96015175 300.00 TRUE 300.00 100 2.716468 -#> 34359 27.41603050 300.00 TRUE 294.31 100 3.287475 -#> 17594 19.23190135 138.35 TRUE 0.00 60 3.701999 -#> 5578 25.74184782 300.00 TRUE 300.00 100 2.640875 -#> 23029 46.60152227 300.00 TRUE 300.00 34 2.425842 -#> 20220 32.17559090 300.00 TRUE 300.00 8 3.050346 -#> 48808 33.77357019 300.00 TRUE 300.00 100 4.066528 -#> 2595 30.43952878 300.00 TRUE 300.00 100 3.538794 -#> 10507 27.16221019 300.00 TRUE 300.00 46 2.580865 -#> 33350 28.95270330 10.00 TRUE 166.26 2 3.415431 -#> 25700 32.54113797 300.00 TRUE 300.00 100 3.096112 -#> 47100 29.64598224 82.80 TRUE 129.73 36 3.558076 -#> 23213 25.46052554 300.00 TRUE 300.00 100 2.868983 -#> 46783 19.94236902 256.35 TRUE 0.00 100 2.772566 -#> 10364 26.09307095 179.84 TRUE 0.00 0 3.541090 -#> 31121 38.16013507 300.00 TRUE 300.00 45 3.194113 -#> 13146 33.57647271 300.00 TRUE 300.00 2 3.278424 -#> 11618 33.98556458 300.00 TRUE 300.00 1 2.578737 -#> 17728 28.55220580 30.14 TRUE 140.15 2 3.303483 -#> 5103 34.91318325 300.00 TRUE 300.00 91 2.623976 -#> 24501 27.15246991 300.00 TRUE 300.00 0 2.925363 -#> 28046 19.59114589 300.00 TRUE 300.00 15 2.896560 -#> 2552 32.21569795 300.00 TRUE 300.00 100 3.005436 -#> 45592 15.64862330 300.00 TRUE 300.00 2 2.460907 -#> 34192 33.87498372 273.94 TRUE 4.48 85 3.009256 -#> 40270 27.59765812 300.00 TRUE 300.00 5 3.139431 -#> 5239 24.66526012 197.29 TRUE 4.67 57 3.591568 -#> 7385 28.71034216 300.00 TRUE 300.00 100 4.219794 -#> 12772 37.92006576 300.00 TRUE 300.00 100 2.936990 -#> 6845 44.59616998 300.00 TRUE 300.00 29 2.769292 -#> 18958 29.98975691 300.00 TRUE 300.00 5 2.674746 -#> 10467 38.99608049 300.00 TRUE 300.00 90 2.784369 -#> 24135 38.66834864 300.00 TRUE 122.87 100 3.878395 -#> 42201 39.55872505 300.00 TRUE 300.00 100 3.989241 -#> 34887 42.93573829 300.00 TRUE 300.00 100 3.159244 -#> 33875 27.56958319 300.00 TRUE 4.67 0 3.279136 -#> 34615 34.18953755 17.57 TRUE 142.95 16 3.011436 -#> 43245 24.44581729 300.00 TRUE 300.00 100 3.663243 -#> 15981 30.00350790 300.00 TRUE 300.00 0 2.799064 -#> 28312 41.64257255 300.00 TRUE 300.00 100 3.198702 -#> 5911 4.13847415 42.88 TRUE 0.00 13 3.009377 -#> 1036 25.72695092 300.00 TRUE 300.00 100 3.132178 -#> 36292 31.51554352 300.00 TRUE 16.03 100 3.030038 -#> 28009 27.01782483 300.00 TRUE 300.00 29 3.185767 -#> 47244 28.03883562 300.00 TRUE 300.00 2 2.752448 -#> 49378 33.99415894 22.68 TRUE 134.63 25 3.073643 -#> 14192 40.58661133 300.00 TRUE 300.00 100 3.115833 -#> 4667 31.51726239 24.78 TRUE 172.24 38 3.119102 -#> 1916 29.27355967 300.00 TRUE 300.00 21 3.311309 -#> 39624 33.29457748 300.00 TRUE 300.00 2 3.254015 -#> 7409 25.37057117 300.00 TRUE 300.00 100 2.866282 -#> 44612 34.69144858 300.00 TRUE 300.00 100 2.460216 -#> 26058 34.49836180 300.00 TRUE 76.02 39 3.272521 -#> 17825 30.73345613 73.66 TRUE 137.63 18 3.469116 -#> 46792 27.60109586 300.00 TRUE 300.00 18 2.657830 -#> 17674 30.48250062 300.00 TRUE 300.00 1 3.812044 -#> 12074 23.28557775 300.00 TRUE 300.00 63 3.252649 -#> 4479 32.01172497 300.00 TRUE 300.00 55 2.355626 -#> 14779 16.52123802 55.08 TRUE 0.00 1 2.897760 -#> 14140 35.48728696 300.00 TRUE 300.00 65 2.767823 -#> 9864 20.82071332 300.00 TRUE 300.00 100 2.722792 -#> 49346 23.34573832 300.00 TRUE 300.00 29 2.687835 -#> 21490 34.06004909 300.00 TRUE 300.00 1 3.293630 -#> 17851 32.26325344 300.00 TRUE 300.00 100 4.161853 -#> 17374 43.14544085 300.00 TRUE 300.00 100 3.630971 -#> 23176 41.55834775 300.00 TRUE 300.00 100 3.203943 -#> 16208 38.37384833 256.60 FALSE 0.00 67 2.937807 -#> 27884 15.49736244 300.00 FALSE 300.00 100 2.781892 -#> 15558 50.34408258 300.00 FALSE 300.00 45 2.657927 -#> 11111 33.38395889 300.00 FALSE 300.00 100 3.344795 -#> 14717 50.85630685 300.00 FALSE 300.00 100 2.978336 -#> 18746 39.13645515 4.48 FALSE 205.00 27 3.028048 -#> 20458 21.42976745 300.00 FALSE 300.00 28 2.424012 -#> 12716 24.34497672 300.00 FALSE 300.00 25 2.731444 -#> 43426 35.46264977 300.00 FALSE 82.11 100 3.910718 -#> 28162 18.66982975 130.00 FALSE 75.00 100 3.197540 -#> 47005 36.13530222 300.00 FALSE 300.00 100 4.858077 -#> 44396 21.44237252 300.00 FALSE 300.00 100 2.270336 -#> 41753 25.00731593 300.00 FALSE 300.00 100 2.860419 -#> 20319 32.07245850 300.00 FALSE 300.00 100 3.167901 -#> 33175 34.78369478 300.00 FALSE 300.00 100 2.799219 -#> 33370 26.41908393 300.00 FALSE 300.00 100 2.363356 -#> 39440 32.70614982 300.00 FALSE 300.00 92 2.369237 -#> 12044 32.23689739 300.00 FALSE 300.00 100 3.304782 -#> 45486 33.42177411 300.00 FALSE 0.00 68 3.366011 -#> 36889 19.64500392 300.00 FALSE 300.00 48 2.108096 -#> 41612 25.58714921 300.00 FALSE 300.00 100 3.004762 -#> 15008 26.90781693 300.00 FALSE 300.00 48 2.625829 -#> 1479 43.67657272 300.00 FALSE 300.00 100 2.938910 -#> 29716 32.69354475 300.00 FALSE 300.00 100 2.716439 -#> 17628 26.99204173 300.00 FALSE 205.59 100 2.626915 -#> 48184 30.95633671 300.00 FALSE 300.00 75 2.485742 -#> 9007 30.96435812 300.00 FALSE 14.98 35 2.784158 -#> 974 22.75845658 80.00 FALSE 90.05 100 3.099987 -#> 42638 23.42136875 86.41 FALSE 172.46 100 6.734528 -#> 26283 41.51079226 300.00 FALSE 300.00 100 2.962620 -#> 18246 7.98760462 18.49 FALSE 0.00 29 3.220009 -#> 19767 42.01041145 300.00 FALSE 300.00 100 2.878525 -#> 7132 34.04916289 300.00 FALSE 225.26 12 3.030143 -#> 10981 29.16641656 300.00 FALSE 300.00 100 5.155983 -#> 18504 23.09592872 144.96 FALSE 4.48 89 5.049117 -#> 40653 0.14209353 18.02 FALSE 140.15 100 3.072344 -#> 37254 4.19118627 131.59 FALSE 80.55 100 2.827529 -#> 33881 29.18589713 300.00 FALSE 300.00 100 3.171879 -#> 46639 33.56329468 300.00 FALSE 300.00 100 3.756965 -#> 49812 33.35817579 300.00 FALSE 300.00 65 3.024717 -#> 11570 16.60718169 300.00 FALSE 300.00 100 2.686405 -#> 24962 29.05010613 300.00 FALSE 300.00 100 2.996437 -#> 47245 34.97620860 300.00 FALSE 150.03 100 2.509309 -#> 126 0.02521014 9.90 FALSE 40.52 100 2.973982 -#> 13548 16.97845834 300.00 FALSE 300.00 100 2.256336 -#> 24516 36.25447744 300.00 FALSE 122.13 100 3.751491 +#> x y dem slope hcurv vcurv carea +#> 31358 712882.5 9560002 1911.52 25.564231 -0.00681 -0.00029 5577.3916 +#> 16435 713512.5 9559092 2166.13 55.973966 0.02056 -0.06976 301.2347 +#> 27864 712992.5 9560672 1926.38 27.224090 -0.00199 0.00659 3553.7166 +#> 25090 715362.5 9560102 2059.29 49.119672 0.02059 -0.00628 556.0121 +#> 40756 714022.5 9558862 2331.20 45.085476 -0.00075 0.00475 1001.0861 +#> 29391 712842.5 9560152 1930.16 28.095558 0.00753 0.01487 1077.7585 +#> 24072 715282.5 9557602 2837.46 34.394083 -0.02191 -0.00579 2246.7725 +#> 34512 713162.5 9559632 2041.52 46.815236 -0.00857 0.03677 1675.4679 +#> 21939 713642.5 9558712 2299.09 33.750652 -0.00503 0.02433 1475.3191 +#> 15843 714042.5 9558902 2332.30 50.097711 -0.00858 -0.00292 1233.9299 +#> 42917 714202.5 9557412 2544.08 39.877863 -0.02104 -0.02046 1024.1573 +#> 25169 712642.5 9560232 1883.10 46.845029 0.00884 0.01345 1120.6929 +#> 1768 713912.5 9558552 2357.19 38.692413 -0.00645 0.00835 2425.3115 +#> 32622 714972.5 9557762 2687.38 30.556412 -0.00730 -0.01491 1844.4353 +#> 43669 712392.5 9560162 2001.90 47.283915 -0.00284 0.01804 628.5153 +#> 49372 714862.5 9560982 1863.12 20.969109 -0.00295 0.00035 2697.5974 +#> 38827 713412.5 9560472 1869.10 16.871315 -0.00156 0.00406 2421.1145 +#> 26447 715632.5 9559102 2410.77 39.656701 0.00730 0.01770 474.5059 +#> 23817 714842.5 9557832 2677.58 26.657434 0.00922 0.01088 266.4355 +#> 31023 714712.5 9561042 1838.66 44.120042 -0.00223 0.01783 377.8918 +#> 22814 713862.5 9558582 2327.48 48.612031 -0.02894 -0.03416 947.4878 +#> 27375 714292.5 9558982 2458.96 43.600942 0.00214 0.00466 724.0713 +#> 10974 715312.5 9557502 2844.23 26.664883 -0.00831 -0.00149 12632.7793 +#> 23925 715202.5 9557652 2782.99 41.957126 -0.00542 0.01842 868.6137 +#> 10899 713852.5 9559652 2282.82 28.814048 0.00952 0.01378 440.9104 +#> 29420 713592.5 9560772 1826.82 34.926934 -0.00070 -0.00530 9000.4356 +#> 29472 714792.5 9561072 1786.08 36.173117 -0.01029 -0.02312 2657.6936 +#> 38279 714192.5 9558612 2325.14 28.252549 -0.04430 -0.06070 138869.3906 +#> 34482 715152.5 9557642 2753.52 4.995619 -0.01126 -0.02044 129412.3984 +#> 23375 713162.5 9559632 2041.52 46.815236 -0.00857 0.03677 1675.4679 +#> 20290 714812.5 9561092 1775.73 34.626704 -0.01537 -0.00203 10263.2734 +#> 2942 713232.5 9560652 1851.75 34.535031 0.00856 0.01114 4510.8833 +#> 25081 713392.5 9558452 2242.24 46.538497 -0.02716 0.03696 4143.4019 +#> 40828 714842.5 9557632 2698.65 17.536519 0.00490 -0.00450 873.0448 +#> 32821 715022.5 9559952 2208.33 43.416450 -0.00643 0.00314 2360.1501 +#> 13585 714552.5 9560092 2123.69 57.528973 -0.03613 -0.01898 588.3761 +#> 48364 715582.5 9558222 2746.23 25.072060 0.01257 0.00593 939.0466 +#> 27957 715012.5 9559332 2306.35 20.480376 0.00225 0.00325 616.8652 +#> 13847 715052.5 9559232 2337.87 26.114843 -0.01452 -0.01437 1155.1469 +#> 47429 714962.5 9557832 2705.12 32.057562 0.02224 0.00177 291.5937 +#> 6310 713802.5 9559752 2238.38 19.651306 -0.02379 0.01338 10787.6299 +#> 18751 714882.5 9559182 2381.81 39.299175 0.00066 -0.00036 516.4709 +#> 17036 712832.5 9560582 1951.38 27.645787 0.00060 0.00270 4057.5637 +#> 47884 715032.5 9557712 2738.31 28.289218 0.00006 0.00175 804.5679 +#> 37141 714382.5 9559822 2219.36 10.106403 0.01661 0.01199 196.4080 +#> 39278 714692.5 9561052 1830.60 48.394880 -0.01996 0.00626 1014.7159 +#> 22332 715482.5 9558922 2475.28 19.936639 -0.02617 0.00307 147470.5312 +#> 33240 714972.5 9557652 2741.04 39.434393 -0.00286 -0.00964 571.1364 +#> 40733 712862.5 9558892 2108.60 29.994914 0.00175 0.00236 2715.4138 +#> 462 713712.5 9561182 1785.08 47.717071 -0.03260 -0.03310 2833.9001 +#> 18892 712682.5 9560202 1839.08 40.923511 -0.01364 -0.03357 473.4709 +#> 7466 712962.5 9560392 1819.03 18.402831 -0.00140 -0.02330 1456.9246 +#> 30406 715312.5 9560152 2029.10 34.137398 0.00953 -0.02283 590.1536 +#> 18265 715522.5 9558782 2539.23 35.856272 -0.00458 -0.00401 1722.6481 +#> 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 +#> 7668 713372.5 9559062 2164.82 37.934963 -0.01807 -0.01883 616.1993 +#> 19966 715362.5 9559572 2235.06 26.049526 0.00126 -0.00485 2240.4185 +#> 664 714452.5 9559852 2174.83 44.860876 0.00282 0.00109 767.4097 +#> 16355 712962.5 9560212 1992.88 39.526066 0.01927 -0.00316 454.9674 +#> 7064 715592.5 9558662 2597.69 34.222196 0.00099 0.00240 1201.4724 +#> 45500 712812.5 9560052 1852.16 56.770377 0.00904 -0.02013 4229.5708 +#> 41783 715572.5 9558652 2609.51 32.408212 0.00291 -0.00081 940.6002 +#> 40270 713312.5 9559012 2186.56 30.935710 -0.00771 -0.00599 1378.5760 +#> 12137 712842.5 9558902 2097.88 30.098619 0.00024 -0.00474 2343.2100 +#> 37714 713192.5 9560692 1885.05 18.034420 0.00188 0.00832 3022.3462 +#> 7041 713472.5 9559092 2137.03 18.348973 -0.10372 -0.00008 825875.6875 +#> 11612 715212.5 9557752 2820.51 39.162238 0.00017 0.01213 328.7922 +#> 46461 712682.5 9560032 1892.30 22.977899 0.01453 0.01337 486.7551 +#> 10467 714642.5 9559652 2249.12 37.143135 -0.00759 -0.00400 608.6523 +#> 20368 715252.5 9560622 1865.83 33.572462 0.00025 -0.01245 6353.3179 +#> 18440 714862.5 9558932 2448.29 41.052426 -0.04989 -0.02781 29995.1543 +#> 30287 713992.5 9557822 2403.92 18.215474 0.03890 -0.00911 173.2806 +#> 7086 712762.5 9560962 2022.00 44.913589 -0.01259 -0.00281 1178.4420 +#> 43245 713722.5 9560012 2153.38 34.191256 -0.01263 0.01322 4605.1411 +#> 15432 715632.5 9558922 2509.67 35.827624 -0.00444 -0.00017 759.9937 +#> 28312 715712.5 9558032 2783.23 24.252731 -0.00007 -0.02132 1580.1621 +#> 10689 713542.5 9559132 2211.95 31.981358 0.05379 0.00801 172.7491 +#> 1036 713042.5 9558892 2205.38 31.509814 -0.00428 -0.00522 1355.7436 +#> 37439 714602.5 9560672 1982.99 18.906461 -0.00070 0.00010 3467.2690 +#> 19756 715572.5 9558972 2485.95 38.812734 0.00546 0.01484 495.0292 +#> 1916 715202.5 9559512 2213.91 26.327984 -0.00292 -0.00109 2047.9031 +#> 39624 715222.5 9559552 2205.54 23.897497 0.00401 -0.00391 1794.7964 +#> 20426 714702.5 9559712 2208.61 43.046319 -0.08153 -0.02417 34908.6562 +#> 48650 715592.5 9558612 2626.74 31.876507 0.00010 0.00140 1066.3957 +#> 20171 715732.5 9557962 2815.53 48.657868 -0.01341 0.00171 4425.5410 +#> 14494 715332.5 9558792 2557.22 20.720446 0.01267 0.00483 343.1384 +#> 1223 713632.5 9559632 2226.22 50.808751 -0.01881 0.00121 1783.0658 +#> 9606 714392.5 9559172 2353.05 30.783303 -0.00185 -0.01185 953.8614 +#> 38103 715392.5 9560162 1998.16 46.285823 0.00708 -0.02538 807.8237 +#> 17825 712732.5 9560462 1915.51 40.397535 0.00143 0.00477 2945.2100 +#> 10456 712442.5 9560292 2010.91 26.891774 0.00445 0.00875 597.2893 +#> 11057 714962.5 9557642 2737.48 42.685929 -0.00488 -0.00632 620.0229 +#> 4479 713982.5 9557812 2399.24 41.231189 0.01895 0.04744 226.7911 +#> 10213 715542.5 9558782 2532.10 35.413375 -0.00893 -0.00867 2247.8748 +#> 9864 714952.5 9557592 2752.40 31.989380 -0.00983 0.01253 528.1924 +#> 41310 714452.5 9560402 2092.27 12.462978 -0.00195 -0.00205 1112.2626 +#> 11332 712742.5 9560072 1850.34 22.700015 0.00683 -0.00603 1562.0503 +#> 10785 714052.5 9557852 2377.76 20.984006 -0.00025 -0.05345 2417.1389 +#> 17851 715362.5 9557482 2869.26 29.125737 -0.03251 -0.00389 14516.2002 +#> 31253 712612.5 9559492 1903.67 46.756794 -0.06953 -0.01797 7557.7866 +#> 31983 714302.5 9559912 2204.77 23.464914 -0.01332 0.01842 246.5161 +#> 36561 715292.5 9560802 1844.64 31.937240 0.00002 -0.01232 977.7320 +#> 11288 712822.5 9560442 1873.42 39.609718 -0.00465 -0.00105 4432.8760 +#> 30809 712532.5 9560032 1928.24 40.899446 0.00570 0.02230 714.7432 +#> 25212 714272.5 9559012 2433.45 37.671402 -0.01909 -0.01181 1286.3949 +#> 27965 715042.5 9560342 2063.39 29.197356 0.04336 0.01403 241.2991 +#> 14694 715032.5 9559482 2307.44 44.173327 -0.00007 -0.00463 1050.7473 +#> 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 +#> 26023 715832.5 9557632 3097.56 41.710182 0.00133 -0.00452 578.5847 +#> 20067 714242.5 9560442 2125.23 38.640274 0.00363 -0.00363 389.2691 +#> 46930 713642.5 9560602 1945.03 34.509821 0.00165 0.00875 1165.3953 +#> 8066 715842.5 9558732 2636.38 33.717420 -0.00494 0.01064 683.2795 +#> 9665 713362.5 9558812 2275.28 31.494917 0.01306 -0.00626 450.7346 +#> 39193 715332.5 9560852 1872.90 30.290560 -0.00161 0.00171 756.8818 +#> 43243 715682.5 9558462 2693.23 27.406290 -0.00031 -0.00199 836.4196 +#> 29125 714992.5 9558792 2584.41 24.665833 0.01253 -0.00333 422.8840 +#> 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 +#> 4007 714132.5 9558412 2464.44 43.720118 0.00174 -0.03065 704.4843 +#> 43403 712632.5 9559262 2072.53 34.486330 -0.00069 -0.00900 614.5898 +#> 41753 715722.5 9558192 2771.39 26.085050 -0.00196 -0.00175 725.1355 +#> 38689 714622.5 9559342 2264.90 42.648113 -0.00844 0.00514 897.9290 +#> 20319 715282.5 9560482 1901.16 27.676726 0.00706 -0.03035 1471.9783 +#> 14800 712952.5 9558682 2120.89 38.273581 -0.00022 0.00662 1269.2650 +#> 36245 713142.5 9560572 1827.82 43.676000 -0.00410 0.00400 4126.7812 +#> 34228 715932.5 9558592 2690.97 9.535164 197.75496 -197.75366 267.5057 +#> 13737 714272.5 9559622 2257.05 55.457158 -0.03224 -0.02036 476.9504 +#> 3733 713762.5 9559632 2297.63 34.214748 -0.01338 0.00518 1137.0179 +#> 34810 714112.5 9559572 2337.51 10.288030 -0.00393 0.00032 994.9224 +#> 39440 714222.5 9557872 2508.52 39.047647 0.02785 0.00645 234.0114 +#> 44267 712972.5 9559042 2191.01 22.269151 0.01462 0.00268 252.0072 +#> 33666 713632.5 9561012 1775.88 21.178812 -0.00950 -0.05130 107.5831 +#> 36721 714092.5 9559752 2301.81 28.933223 0.03418 0.00513 161.9878 +#> 49831 715262.5 9557332 2884.30 22.294934 -0.00097 0.01487 1769.3392 +#> 41612 713952.5 9560002 2198.00 28.756179 0.00119 0.00361 1011.0248 +#> 24819 714032.5 9560882 1981.86 33.284264 0.00804 -0.00165 805.1403 +#> 5861 713882.5 9559182 2350.43 27.393685 -0.02608 0.00148 10771.1963 +#> 484 713812.5 9560592 2016.41 44.301097 -0.00719 0.00079 1647.6451 +#> 18246 713902.5 9561342 1818.52 19.110434 -0.01502 0.00282 1659.6211 +#> 24446 714742.5 9560082 2200.60 28.721801 -0.00598 0.02208 842.4825 +#> 10127 713722.5 9558712 2311.10 19.481138 0.01129 0.00491 261.7371 +#> 37783 713832.5 9557892 2405.28 40.886841 0.01572 0.03148 481.8292 +#> 34334 714642.5 9557912 2582.25 30.991287 0.00253 -0.01233 968.7780 +#> 14881 715482.5 9559232 2376.03 29.905532 -0.01137 -0.01523 888.2811 +#> 9526 714432.5 9560582 2076.13 31.080096 -0.00172 -0.00119 792.3544 +#> 11570 715572.5 9558482 2685.72 16.814019 0.00282 0.00458 485.7418 +#> 14645 715072.5 9560262 2034.04 38.169302 -0.00716 -0.03234 1707.3130 +#> 28978 713982.5 9560462 2133.10 5.229386 0.00366 0.00525 162.0592 +#> cslope distroad slides distdeforest distslidespast log.carea +#> 31358 34.4278880 300.00 TRUE 15.00 9 3.746431 +#> 16435 49.4439659 300.00 TRUE 300.00 41 2.478905 +#> 27864 27.8153821 30.00 TRUE 183.39 20 3.550683 +#> 25090 43.5316144 300.00 TRUE 300.00 26 2.745084 +#> 40756 39.3352715 300.00 TRUE 300.00 100 3.000471 +#> 29391 28.2834886 300.00 TRUE 0.56 100 3.032521 +#> 24072 37.6668184 300.00 TRUE 300.00 100 3.351559 +#> 34512 34.6398824 300.00 TRUE 195.00 2 3.224136 +#> 21939 23.9324471 300.00 TRUE 300.00 100 3.168886 +#> 15843 42.2218329 300.00 TRUE 300.00 100 3.091290 +#> 42917 38.8035667 300.00 TRUE 300.00 100 3.010367 +#> 25169 36.6062735 195.00 TRUE 47.05 0 3.049487 +#> 1768 28.1855128 300.00 TRUE 300.00 7 3.384768 +#> 32622 30.1713845 300.00 TRUE 300.00 100 3.265863 +#> 43669 28.4765754 24.22 TRUE 0.00 89 2.798316 +#> 49372 23.5405440 213.54 TRUE 20.21 68 3.430977 +#> 38827 31.0359778 300.00 TRUE 52.52 39 3.384015 +#> 26447 33.0367465 300.00 TRUE 300.00 100 2.676242 +#> 23817 21.6113951 300.00 TRUE 300.00 4 2.425592 +#> 31023 16.6776555 135.00 TRUE 0.00 2 2.577367 +#> 22814 34.9120373 300.00 TRUE 300.00 6 2.976574 +#> 27375 38.7703351 300.00 TRUE 300.00 100 2.859781 +#> 10974 32.4655075 300.00 TRUE 300.00 100 4.101499 +#> 23925 37.5333192 300.00 TRUE 300.00 100 2.938827 +#> 10899 24.7741221 300.00 TRUE 300.00 20 2.644350 +#> 29420 28.8524357 253.02 TRUE 0.00 100 3.954264 +#> 29472 18.6486303 111.09 TRUE 0.00 25 3.424505 +#> 38279 37.6284302 300.00 TRUE 300.00 24 5.142607 +#> 34482 32.9714293 300.00 TRUE 300.00 100 5.111976 +#> 23375 34.6398824 300.00 TRUE 195.00 2 3.224136 +#> 20290 15.4566824 95.53 TRUE 0.00 49 4.011286 +#> 2942 29.8207344 68.13 TRUE 116.32 10 3.654262 +#> 25081 34.8753680 300.00 TRUE 300.00 100 3.617357 +#> 40828 25.5997543 300.00 TRUE 300.00 100 2.941037 +#> 32821 29.4832622 300.00 TRUE 300.00 65 3.372940 +#> 13585 47.0673370 300.00 TRUE 300.00 100 2.769655 +#> 48364 32.0758962 300.00 TRUE 300.00 100 2.972687 +#> 27957 30.0602307 300.00 TRUE 300.00 75 2.790190 +#> 13847 20.5709037 300.00 TRUE 300.00 100 3.062637 +#> 47429 32.0976686 300.00 TRUE 300.00 100 2.464778 +#> 6310 23.8264499 300.00 TRUE 300.00 100 4.032926 +#> 18751 38.0759103 300.00 TRUE 300.00 100 2.713046 +#> 17036 24.9282478 25.33 TRUE 208.61 15 3.608265 +#> 47884 27.0544941 300.00 TRUE 300.00 100 2.905563 +#> 37141 12.3884934 300.00 TRUE 300.00 1 2.293159 +#> 39278 16.2181433 125.47 TRUE 0.00 6 3.006344 +#> 22332 27.0688181 300.00 TRUE 300.00 100 5.168705 +#> 33240 29.3549197 300.00 TRUE 300.00 100 2.756740 +#> 40733 27.0138141 300.00 TRUE 300.00 100 3.433836 +#> 462 9.8124752 61.17 TRUE 57.16 74 3.452385 +#> 18892 27.0630885 235.02 TRUE 27.24 0 2.675293 +#> 7466 28.7905562 255.23 TRUE 0.00 60 3.163437 +#> 30406 43.1007501 300.00 TRUE 300.00 85 2.770965 +#> 18265 31.4456427 300.00 TRUE 300.00 100 3.236197 +#> 13029 34.1878187 300.00 TRUE 96.65 4 2.813330 +#> 47100 29.6459822 82.80 TRUE 129.73 36 3.558076 +#> 7668 36.3203676 300.00 TRUE 300.00 5 2.789721 +#> 19966 29.2288690 300.00 TRUE 300.00 23 3.350329 +#> 664 30.8423181 300.00 TRUE 300.00 12 2.885027 +#> 16355 28.2468193 300.00 TRUE 58.52 100 2.657980 +#> 7064 29.1326121 300.00 TRUE 300.00 100 3.079714 +#> 45500 34.9561551 300.00 TRUE 0.00 2 3.626296 +#> 41783 28.1860858 300.00 TRUE 300.00 100 2.973405 +#> 40270 27.5976581 300.00 TRUE 300.00 5 3.139431 +#> 12137 27.5884908 300.00 TRUE 300.00 100 3.369811 +#> 37714 30.7317373 13.57 TRUE 170.00 4 3.480344 +#> 7041 33.2189470 300.00 TRUE 300.00 6 5.916915 +#> 11612 35.1670035 300.00 TRUE 300.00 100 2.516921 +#> 46461 24.9414258 288.47 TRUE 0.00 100 2.687311 +#> 10467 38.9960805 300.00 TRUE 300.00 90 2.784369 +#> 20368 30.3444178 300.00 TRUE 166.26 100 3.803001 +#> 18440 34.9446959 300.00 TRUE 300.00 100 4.477051 +#> 30287 19.8484039 300.00 TRUE 300.00 65 2.238750 +#> 7086 34.2044345 300.00 TRUE 128.61 63 3.071308 +#> 43245 24.4458173 300.00 TRUE 300.00 100 3.663243 +#> 15432 28.7676379 300.00 TRUE 300.00 100 2.880810 +#> 28312 41.6425726 300.00 TRUE 300.00 100 3.198702 +#> 10689 30.6480854 300.00 TRUE 300.00 61 2.237416 +#> 1036 25.7269509 300.00 TRUE 300.00 100 3.132178 +#> 37439 26.5926901 300.00 TRUE 300.00 100 3.539988 +#> 19756 30.1163806 300.00 TRUE 300.00 100 2.694631 +#> 1916 29.2735597 300.00 TRUE 300.00 21 3.311309 +#> 39624 33.2945775 300.00 TRUE 300.00 2 3.254015 +#> 20426 33.4085961 300.00 TRUE 300.00 76 4.542933 +#> 48650 27.0522023 300.00 TRUE 300.00 100 3.027918 +#> 20171 42.1301596 300.00 TRUE 300.00 100 3.645966 +#> 14494 21.1851145 300.00 TRUE 300.00 28 2.535469 +#> 1223 40.4582688 300.00 TRUE 300.00 4 3.251167 +#> 9606 33.8239905 300.00 TRUE 300.00 0 2.979485 +#> 38103 42.7615591 300.00 TRUE 300.00 31 2.907317 +#> 17825 30.7334561 73.66 TRUE 137.63 18 3.469116 +#> 10456 21.6686909 24.34 TRUE 0.00 18 2.776185 +#> 11057 31.2250539 300.00 TRUE 300.00 100 2.792408 +#> 4479 32.0117250 300.00 TRUE 300.00 55 2.355626 +#> 10213 31.5986224 300.00 TRUE 300.00 100 3.351772 +#> 9864 20.8207133 300.00 TRUE 300.00 100 2.722792 +#> 41310 17.2953040 300.00 TRUE 300.00 25 3.046207 +#> 11332 25.6816873 300.00 TRUE 27.04 68 3.193695 +#> 10785 33.2453031 300.00 TRUE 300.00 100 3.383302 +#> 17851 32.2632534 300.00 TRUE 300.00 100 4.161853 +#> 31253 38.6683486 300.00 TRUE 122.87 100 3.878395 +#> 31983 19.7756383 300.00 FALSE 300.00 65 2.391845 +#> 36561 25.8048732 300.00 FALSE 70.00 100 2.990220 +#> 11288 28.5373089 132.07 FALSE 85.50 1 3.646686 +#> 30809 36.3662042 181.84 FALSE 0.00 100 2.854150 +#> 25212 38.9829025 300.00 FALSE 300.00 100 3.109374 +#> 27965 32.2953391 300.00 FALSE 300.00 100 2.382556 +#> 14694 43.5115609 300.00 FALSE 300.00 100 3.021498 +#> 8635 27.6818829 300.00 FALSE 300.00 23 2.716617 +#> 18746 39.1364552 4.48 FALSE 205.00 27 3.028048 +#> 26023 45.0728709 300.00 FALSE 300.00 100 2.762367 +#> 20067 29.8235992 300.00 FALSE 268.03 100 2.590250 +#> 46930 31.9206247 300.00 FALSE 0.00 100 3.066473 +#> 8066 23.5273659 300.00 FALSE 300.00 100 2.834598 +#> 9665 34.0812485 300.00 FALSE 300.00 64 2.653921 +#> 39193 31.4800201 300.00 FALSE 117.12 100 2.879028 +#> 43243 27.9861235 300.00 FALSE 300.00 100 2.922424 +#> 29125 23.9450522 300.00 FALSE 300.00 100 2.626221 +#> 39685 47.8734886 300.00 FALSE 300.00 100 2.918481 +#> 34347 31.7200895 300.00 FALSE 300.00 100 3.477538 +#> 4007 32.0925120 300.00 FALSE 300.00 68 2.847871 +#> 43403 31.0617609 300.00 FALSE 111.88 86 2.788585 +#> 41753 25.0073159 300.00 FALSE 300.00 100 2.860419 +#> 38689 36.0843090 300.00 FALSE 300.00 18 2.953242 +#> 20319 32.0724585 300.00 FALSE 300.00 100 3.167901 +#> 14800 31.5229920 300.00 FALSE 300.00 100 3.103552 +#> 36245 25.8535746 110.19 FALSE 81.50 20 3.615611 +#> 34228 10.7486882 300.00 FALSE 300.00 100 2.427333 +#> 13737 40.9601798 300.00 FALSE 300.00 100 2.678473 +#> 3733 22.1001281 300.00 FALSE 300.00 100 3.055767 +#> 34810 15.5546582 300.00 FALSE 300.00 100 2.997789 +#> 39440 32.7061498 300.00 FALSE 300.00 92 2.369237 +#> 44267 19.7378231 300.00 FALSE 226.30 100 2.401413 +#> 33666 0.0217724 212.17 FALSE 103.16 100 2.031744 +#> 36721 26.6683206 300.00 FALSE 300.00 100 2.209482 +#> 49831 29.8379231 300.00 FALSE 300.00 100 3.247811 +#> 41612 25.5871492 300.00 FALSE 300.00 100 3.004762 +#> 24819 29.9370448 300.00 FALSE 150.00 100 2.905872 +#> 5861 27.6125550 300.00 FALSE 300.00 100 4.032264 +#> 484 32.1457971 300.00 FALSE 55.63 100 3.216864 +#> 18246 7.9876046 18.49 FALSE 0.00 29 3.220009 +#> 24446 31.3631367 300.00 FALSE 300.00 100 2.925561 +#> 10127 18.6194095 300.00 FALSE 300.00 49 2.417865 +#> 37783 24.2573142 300.00 FALSE 300.00 18 2.682893 +#> 34334 25.0898218 300.00 FALSE 300.00 100 2.986224 +#> 14881 26.8562507 300.00 FALSE 300.00 100 2.948550 +#> 9526 27.5695832 300.00 FALSE 300.00 100 2.898919 +#> 11570 16.6071817 300.00 FALSE 300.00 100 2.686405 +#> 14645 45.2132455 300.00 FALSE 300.00 100 3.232313 +#> 28978 4.4398500 300.00 FALSE 10.00 100 2.209674 diff --git a/reference/partition_cv_strat.html b/reference/partition_cv_strat.html index f22ff483..9c6040fc 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] 1.007165 +#> [1] 0.9846722 # 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 f38cd801..50210fdf 100644 --- a/reference/partition_disc.html +++ b/reference/partition_disc.html @@ -192,19 +192,19 @@

Examples

summary(parti) #> $`1` #> n.train n.test -#> 456 716 11 -#> 699 692 34 -#> 687 742 3 -#> 311 711 9 -#> 48 704 21 +#> 170 721 10 +#> 45 718 5 +#> 575 726 9 +#> 637 706 11 +#> 587 709 7 #> #> $`2` #> n.train n.test -#> 221 715 14 -#> 293 715 25 -#> 536 721 9 -#> 195 734 4 -#> 409 712 9 +#> 467 739 7 +#> 111 723 12 +#> 185 726 9 +#> 554 696 7 +#> 584 738 3 #> # leave-one-out with buffer: @@ -212,19 +212,19 @@

Examples

summary(parti) #> $`1` #> n.train n.test -#> 456 716 11 -#> 699 692 34 -#> 687 742 3 -#> 311 711 9 -#> 48 704 21 +#> 170 721 10 +#> 45 718 5 +#> 575 726 9 +#> 637 706 11 +#> 587 709 7 #> #> $`2` #> n.train n.test -#> 221 715 14 -#> 293 715 25 -#> 536 721 9 -#> 195 734 4 -#> 409 712 9 +#> 467 739 7 +#> 111 723 12 +#> 185 726 9 +#> 554 696 7 +#> 584 738 3 #> diff --git a/reference/sperrorest.html b/reference/sperrorest.html index 33b083d2..bddcd360 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) ) -#> Sun Sep 3 04:26:53 2023 Repetition 1 -#> Sun Sep 3 04:26:53 2023 Repetition - Fold 1 -#> Sun Sep 3 04:26:54 2023 Repetition - Fold 2 -#> Sun Sep 3 04:26:54 2023 Repetition - Fold 3 -#> Sun Sep 3 04:26:54 2023 Repetition 2 -#> Sun Sep 3 04:26:54 2023 Repetition - Fold 1 -#> Sun Sep 3 04:26:54 2023 Repetition - Fold 2 -#> Sun Sep 3 04:26:54 2023 Repetition - Fold 3 +#> Mon Sep 4 04:25:32 2023 Repetition 1 +#> Mon Sep 4 04:25:32 2023 Repetition - Fold 1 +#> Mon Sep 4 04:25:32 2023 Repetition - Fold 2 +#> Mon Sep 4 04:25:32 2023 Repetition - Fold 3 +#> Mon Sep 4 04:25:32 2023 Repetition 2 +#> Mon Sep 4 04:25:32 2023 Repetition - Fold 1 +#> Mon Sep 4 04:25:32 2023 Repetition - Fold 2 +#> Mon Sep 4 04:25:32 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) ) -#> Sun Sep 3 04:26:54 2023 Repetition 1 -#> Sun Sep 3 04:26:54 2023 Repetition - Fold 1 -#> Sun Sep 3 04:26:54 2023 Repetition - Fold 2 -#> Sun Sep 3 04:26:55 2023 Repetition - Fold 3 -#> Sun Sep 3 04:26:55 2023 Repetition 2 -#> Sun Sep 3 04:26:55 2023 Repetition - Fold 1 -#> Sun Sep 3 04:26:55 2023 Repetition - Fold 2 -#> Sun Sep 3 04:26:55 2023 Repetition - Fold 3 +#> Mon Sep 4 04:25:33 2023 Repetition 1 +#> Mon Sep 4 04:25:33 2023 Repetition - Fold 1 +#> Mon Sep 4 04:25:33 2023 Repetition - Fold 2 +#> Mon Sep 4 04:25:33 2023 Repetition - Fold 3 +#> Mon Sep 4 04:25:33 2023 Repetition 2 +#> Mon Sep 4 04:25:33 2023 Repetition - Fold 1 +#> Mon Sep 4 04:25:33 2023 Repetition - Fold 2 +#> Mon Sep 4 04:25:33 2023 Repetition - Fold 3 summary(sp_res$error_rep) #> mean sd median IQR #> train_auroc 0.8472530 0.017474834 0.8472530 0.012356574