diff --git a/articles/spatial-modeling-use-case.html b/articles/spatial-modeling-use-case.html index b7af82b6..0855c5a8 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: 0.58 % +## OOB prediction error: 0.53 %

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

 pred <- predict(fit, data = maipo, type = "response")
@@ -361,15 +361,15 @@ 
Linear Discriminant Analysis (LDA)So what have we got:

 
 summary(res_lda_nsp$error_rep)

-
##                    mean       sd   median      IQR
-## train_error    3.42e-02 0.000505 3.42e-02 0.000502
-## train_accuracy 9.66e-01 0.000505 9.66e-01 0.000502
-## 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     3.90e-02 0.001547 3.97e-02 0.001426
-## test_accuracy  9.61e-01 0.001547 9.60e-01 0.001426
-## test_events    1.17e+03 0.000000 1.17e+03 0.000000
-## test_count     7.71e+03 0.000000 7.71e+03 0.000000

+
##                    mean      sd   median     IQR
+## train_error    3.41e-02 0.00112 3.38e-02 0.00109
+## train_accuracy 9.66e-01 0.00112 9.66e-01 0.00109
+## 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     3.94e-02 0.00147 3.88e-02 0.00136
+## test_accuracy  9.61e-01 0.00147 9.61e-01 0.00136
+## test_events    1.17e+03 0.00000 1.17e+03 0.00000
+## test_count     7.71e+03 0.00000 7.71e+03 0.00000

 
To run a spatial cross-validation at the field level, we can use
 partition_factor_cv() as the sampling function. Since we
 are using 5 folds, we get a coarse 80/20 split of our data. 80% will be
@@ -400,18 +400,18 @@ 
Linear Discriminant Analysis (LDA)  benchmark = TRUE, progress = FALSE
 )
 res_lda_sp$benchmark$runtime_performance
-
## Time difference of 16.7 secs
+
## Time difference of 16.2 secs
 summary(res_lda_sp$error_rep)
-
##                    mean      sd   median    IQR
-## train_error    3.03e-02 0.00111 2.98e-02 0.0010
-## train_accuracy 9.70e-01 0.00111 9.70e-01 0.0010
-## 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     6.37e-02 0.00956 6.17e-02 0.0094
-## test_accuracy  9.36e-01 0.00956 9.38e-01 0.0094
-## test_events    1.17e+03 0.00000 1.17e+03 0.0000
-## test_count     7.71e+03 0.00000 7.71e+03 0.0000
+
##                    mean       sd   median      IQR
+## train_error    2.81e-02 0.000146 2.81e-02 0.000146
+## train_accuracy 9.72e-01 0.000146 9.72e-01 0.000146
+## 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     6.88e-02 0.006761 7.08e-02 0.006547
+## test_accuracy  9.31e-01 0.006761 9.29e-01 0.006547
+## test_events    1.17e+03 0.000000 1.17e+03 0.000000
+## test_count     7.71e+03 0.000000 7.71e+03 0.000000

RandomForest @@ -455,19 +455,19 @@

RandomForest)

 summary(res_rf_sp$error_rep)
-
##                    mean     sd   median    IQR
-## train_error    0.00e+00 0.0000 0.00e+00 0.0000
-## train_accuracy 1.00e+00 0.0000 1.00e+00 0.0000
-## train_events   4.69e+03 0.0000 4.69e+03 0.0000
-## train_count    3.09e+04 0.0000 3.09e+04 0.0000
-## test_error     9.05e-02 0.0146 8.36e-02 0.0133
-## test_accuracy  9.10e-01 0.0146 9.16e-01 0.0133
-## test_events    1.17e+03 0.0000 1.17e+03 0.0000
-## test_count     7.71e+03 0.0000 7.71e+03 0.0000
+
##                    mean     sd   median   IQR
+## train_error    0.00e+00 0.0000 0.00e+00 0.000
+## train_accuracy 1.00e+00 0.0000 1.00e+00 0.000
+## train_events   4.69e+03 0.0000 4.69e+03 0.000
+## train_count    3.09e+04 0.0000 3.09e+04 0.000
+## test_error     9.19e-02 0.0146 8.41e-02 0.013
+## test_accuracy  9.08e-01 0.0146 9.16e-01 0.013
+## test_events    1.17e+03 0.0000 1.17e+03 0.000
+## test_count     7.71e+03 0.0000 7.71e+03 0.000
 summary(res_rf_sp$error_rep)["test_accuracy",]
-
##               mean     sd median    IQR
-## test_accuracy 0.91 0.0146  0.916 0.0133
+
##                mean     sd median   IQR
+## test_accuracy 0.908 0.0146  0.916 0.013

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 6715c7ad..4617754b 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 d7632080..0581abe1 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: 2023-07-10T04:32Z +last_built: 2023-07-11T04:31Z 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 bfcc19f0..cb44c58d 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]]$distance -#> [1] 54.34323 +#> [1] 45.14667 # spatial partitioning: more substantial distance, depending on number of # folds etc. sp.parti[[1]][[1]]$distance -#> [1] 400.8701 +#> [1] 475.7608 diff --git a/reference/as.resampling.html b/reference/as.resampling.html index fc2b61cd..e729a972 100644 --- a/reference/as.resampling.html +++ b/reference/as.resampling.html @@ -163,23 +163,23 @@

Examples

parti <- parti[[1]] # the first (and only) resampling object in parti # data corresponding to the test sample of the first fold: str(ecuador[parti[[1]]$test, ]) -#> 'data.frame': 76 obs. of 13 variables: -#> $ x : num 715462 715532 713912 714972 714292 ... -#> $ y : num 9559372 9558762 9558552 9557762 9558982 ... -#> $ dem : num 2319 2549 2357 2687 2459 ... -#> $ slope : num 32.3 38.1 38.7 30.6 43.6 ... -#> $ hcurv : num -0.00661 -0.00373 -0.00645 -0.0073 0.00214 ... -#> $ vcurv : num -0.01339 -0.00057 0.00835 -0.01491 0.00466 ... -#> $ carea : num 1003 1630 2425 1844 724 ... -#> $ cslope : num 32.7 31 28.2 30.2 38.8 ... +#> 'data.frame': 75 obs. of 13 variables: +#> $ x : num 715272 713512 715232 715362 714852 ... +#> $ y : num 9557702 9559092 9559542 9560102 9557882 ... +#> $ dem : num 2813 2166 2212 2059 2680 ... +#> $ slope : num 31 56 26.1 49.1 23.3 ... +#> $ hcurv : num -0.00123 0.02056 0.00023 0.02059 0.00479 ... +#> $ vcurv : num 0.00393 -0.06976 -0.00043 -0.00628 0.01261 ... +#> $ carea : num 2081 301 1806 556 324 ... +#> $ cslope : num 37.6 49.4 34.1 43.5 15.7 ... #> $ 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 300 300 300 300 300 ... -#> $ distslidespast: num 11 100 7 100 100 100 20 56 45 100 ... -#> $ log.carea : num 3 3.21 3.38 3.27 2.86 ... +#> $ distdeforest : num 300 300 300 300 300 300 300 300 300 300 ... +#> $ distslidespast: num 100 41 2 26 10 100 6 100 89 1 ... +#> $ log.carea : num 3.32 2.48 3.26 2.75 2.51 ... # the corresponding training sample - larger: str(ecuador[parti[[1]]$train, ]) -#> 'data.frame': 675 obs. of 13 variables: +#> 'data.frame': 676 obs. of 13 variables: #> $ x : num 712882 715232 715392 715042 715382 ... #> $ y : num 9560002 9559582 9560172 9559312 9560142 ... #> $ dem : num 1912 2199 1989 2320 2021 ... @@ -190,8 +190,8 @@

Examples

#> $ cslope : num 34.4 30.7 32.8 33.9 41.6 ... #> $ 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 9.15 300 300 300 0 ... -#> $ distslidespast: num 9 21 40 100 21 2 100 100 41 5 ... +#> $ distdeforest : num 15 300 300 300 300 ... +#> $ distslidespast: num 9 21 40 100 21 2 100 5 20 100 ... #> $ log.carea : num 3.75 3.15 5.55 2.7 2.83 ... # Bootstrap training sets, out-of-bag test sets: @@ -199,36 +199,36 @@

Examples

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': 294 obs. of 13 variables: -#> $ x : num 715232 712992 714932 714852 714922 ... -#> $ y : num 9559582 9560672 9557982 9557902 9558862 ... -#> $ dem : num 2199 1926 2650 2675 2536 ... -#> $ slope : num 23.2 27.2 37.3 30.7 54.9 ... -#> $ hcurv : num -0.00501 -0.00199 0.01633 0.00221 0.01305 ... -#> $ vcurv : num -0.00649 0.00659 -0.01813 0.00969 -0.00555 ... -#> $ carea : num 1399 3554 1131 369 430 ... -#> $ cslope : num 30.7 27.8 35.1 20.5 46.3 ... -#> $ distroad : num 300 30 300 300 300 ... +#> 'data.frame': 282 obs. of 13 variables: +#> $ x : num 715232 712802 715272 712992 715302 ... +#> $ y : num 9559582 9559952 9557702 9560672 9557472 ... +#> $ dem : num 2199 1838 2813 1926 2857 ... +#> $ slope : num 23.2 52.1 31 27.2 39.5 ... +#> $ hcurv : num -0.00501 0.00183 -0.00123 -0.00199 -0.01021 ... +#> $ vcurv : num -0.00649 -0.09203 0.00393 0.00659 -0.01579 ... +#> $ carea : num 1399 634 2081 3554 1319 ... +#> $ cslope : num 30.7 30.3 37.6 27.8 36.3 ... +#> $ distroad : num 300 300 300 30 300 300 300 300 300 300 ... #> $ slides : Factor w/ 2 levels "FALSE","TRUE": 2 2 2 2 2 2 2 2 2 2 ... -#> $ distdeforest : num 300 183 300 300 300 ... -#> $ distslidespast: num 21 20 100 10 100 35 26 100 100 31 ... -#> $ log.carea : num 3.15 3.55 3.05 2.57 2.63 ... +#> $ distdeforest : num 300 9.15 300 183.39 300 ... +#> $ distslidespast: num 21 2 100 20 100 10 2 26 10 100 ... +#> $ log.carea : num 3.15 2.8 3.32 3.55 3.12 ... # bootstrap training sample: same size as nrow(ecuador): str(ecuador[parti[[1]]$train, ]) #> 'data.frame': 751 obs. of 13 variables: -#> $ x : num 714742 714792 715212 714322 714972 ... -#> $ y : num 9560142 9561072 9559222 9561182 9557642 ... -#> $ dem : num 2162 1786 2342 1754 2745 ... -#> $ slope : num 26.57 36.17 55.68 4.17 42.54 ... -#> $ hcurv : num -0.00978 -0.01029 0.00426 -0.03757 -0.00294 ... -#> $ vcurv : num 0.00367 -0.02312 -0.00367 -0.00003 -0.00096 ... -#> $ carea : num 3003 2658 794 5426606 521 ... -#> $ cslope : num 31.7 18.6 34.2 23.4 29 ... -#> $ distroad : num 300 111.1 300 86.4 300 ... -#> $ slides : Factor w/ 2 levels "FALSE","TRUE": 1 2 2 1 2 2 2 2 2 1 ... -#> $ distdeforest : num 300 0 300 172 300 ... -#> $ distslidespast: num 100 25 100 100 100 5 100 46 100 63 ... -#> $ log.carea : num 3.48 3.42 2.9 6.73 2.72 ... +#> $ x : num 714912 715042 714022 714862 713682 ... +#> $ y : num 9558822 9559252 9558862 9560982 9558582 ... +#> $ dem : num 2564 2344 2331 1863 2347 ... +#> $ slope : num 49.8 29 45.1 21 28.3 ... +#> $ hcurv : num -0.00099 0.00422 -0.00075 -0.00295 -0.02196 ... +#> $ vcurv : num 0.02169 0.02357 0.00475 0.00035 0.00336 ... +#> $ carea : num 496 276 1001 2698 2806 ... +#> $ cslope : num 39.2 18.8 39.3 23.5 27.2 ... +#> $ distroad : num 300 300 300 214 300 ... +#> $ slides : Factor w/ 2 levels "FALSE","TRUE": 2 2 2 2 1 1 1 2 1 2 ... +#> $ distdeforest : num 300 300 300 20.2 300 ... +#> $ distslidespast: num 100 100 100 68 61 100 65 5 65 6 ... +#> $ log.carea : num 2.7 2.44 3 3.43 3.45 ... diff --git a/reference/err_default.html b/reference/err_default.html index a82d0d35..70754cc8 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] 0.4937799 +#> [1] 0.4725721 #> #> $error -#> [1] 0.498 +#> [1] 0.537 #> #> $accuracy -#> [1] 0.502 +#> [1] 0.463 #> #> $sensitivity -#> [1] 0.3443983 +#> [1] 0.2730769 #> #> $specificity -#> [1] 0.6486486 +#> [1] 0.66875 #> #> $fpr70 -#> [1] 0.7297297 +#> [1] 0.7104167 #> #> $fpr80 -#> [1] 0.8243243 +#> [1] 0.7916667 #> #> $fpr90 -#> [1] 0.9150579 +#> [1] 0.9020833 #> #> $tpr80 -#> [1] 0.2012448 +#> [1] 0.1480769 #> #> $tpr90 -#> [1] 0.1120332 +#> [1] 0.08653846 #> #> $tpr95 -#> [1] 0.05394191 +#> [1] 0.03461538 #> #> $events -#> [1] 482 +#> [1] 520 #> #> $count #> [1] 1000 #> err_default(obs > 0, obs + rnorm(1000)) # some discrimination #> $auroc -#> [1] 0.8277127 +#> [1] 0.8505048 #> #> $error -#> [1] 0.26 +#> [1] 0.257 #> #> $accuracy -#> [1] 0.74 +#> [1] 0.743 #> #> $sensitivity -#> [1] 0.6286307 +#> [1] 0.5942308 #> #> $specificity -#> [1] 0.8436293 +#> [1] 0.9041667 #> #> $fpr70 -#> [1] 0.1988417 +#> [1] 0.1375 #> #> $fpr80 -#> [1] 0.3455598 +#> [1] 0.2708333 #> #> $fpr90 -#> [1] 0.4864865 +#> [1] 0.4729167 #> #> $tpr80 -#> [1] 0.7095436 +#> [1] 0.7576923 #> #> $tpr90 -#> [1] 0.4875519 +#> [1] 0.6076923 #> #> $tpr95 -#> [1] 0.3900415 +#> [1] 0.4461538 #> #> $events -#> [1] 482 +#> [1] 520 #> #> $count #> [1] 1000 @@ -196,44 +196,44 @@

Examples

# Three mock regression examples: err_default(obs, rnorm(1000)) # just noise, but no bias #> $bias -#> [1] -0.07933607 +#> [1] 0.05624215 #> #> $stddev -#> [1] 1.391702 +#> [1] 1.468124 #> #> $rmse -#> [1] 1.393266 +#> [1] 1.468468 #> #> $mad -#> [1] 1.47393 +#> [1] 1.457928 #> #> $median -#> [1] -0.1400077 +#> [1] 0.09018728 #> #> $iqr -#> [1] 1.954717 +#> [1] 1.957922 #> #> $count #> [1] 1000 #> err_default(obs, obs + rnorm(1000)) # some association, no bias #> $bias -#> [1] 0.02519863 +#> [1] -0.03691 #> #> $stddev -#> [1] 1.027117 +#> [1] 0.9947866 #> #> $rmse -#> [1] 1.026913 +#> [1] 0.994974 #> #> $mad -#> [1] 1.030814 +#> [1] 0.9323828 #> #> $median -#> [1] 0.04860829 +#> [1] -0.01063031 #> #> $iqr -#> [1] 1.388528 +#> [1] 1.246688 #> #> $count #> [1] 1000 @@ -243,7 +243,7 @@

Examples

#> [1] -1 #> #> $stddev -#> [1] 6.144523e-17 +#> [1] 6.981135e-17 #> #> $rmse #> [1] 1 diff --git a/reference/partition_cv.html b/reference/partition_cv.html index b159a789..eab578e4 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 52.10134 0.00183 -0.09203 634.3320 -#> 16435 713512.5 9559092 2166.13 55.97397 0.02056 -0.06976 301.2347 -#> 31357 714752.5 9561022 1848.22 33.44641 -0.00347 0.02357 1752.0375 -#> 44302 714932.5 9557982 2649.56 37.30070 0.01633 -0.01813 1130.5791 -#> 42632 714852.5 9557902 2674.80 30.65267 0.00221 0.00969 368.5606 -#> 40756 714022.5 9558862 2331.20 45.08548 -0.00075 0.00475 1001.0861 -#> 11088 715532.5 9558762 2549.28 38.05013 -0.00373 -0.00057 1630.3259 -#> 48187 712722.5 9560152 1835.29 29.32799 -0.00333 -0.01767 3321.6113 -#> 21939 713642.5 9558712 2299.09 33.75065 -0.00503 0.02433 1475.3191 -#> 20799 714792.5 9561002 1857.29 23.12916 0.00090 0.00890 2301.0454 -#> 9669 713852.5 9558612 2308.97 52.18041 -0.01059 -0.07431 4079.0366 -#> 38411 713552.5 9560652 1851.63 46.00507 -0.00891 -0.00949 6217.8213 -#> 26531 714462.5 9560452 2104.74 19.52411 0.00453 0.00257 357.1436 -#> 14456 713542.5 9559972 2184.39 35.24149 -0.00841 -0.00010 1527.1028 -#> 26447 715632.5 9559102 2410.77 39.65670 0.00730 0.01770 474.5059 -#> 16811 714902.5 9559262 2362.67 50.70333 -0.01407 0.00547 519.4228 -#> 23817 714842.5 9557832 2677.58 26.65743 0.00922 0.01088 266.4355 -#> 24018 714292.5 9559282 2334.12 32.86543 0.00102 0.00388 1051.4222 -#> 11463 713112.5 9558902 2244.08 21.89157 0.00160 0.00311 586.7354 -#> 42598 713542.5 9559972 2184.39 35.24149 -0.00841 -0.00010 1527.1028 -#> 3835 714142.5 9558552 2388.42 32.90038 0.00979 -0.00269 491.8017 -#> 8019 714672.5 9559272 2316.84 40.38149 -0.01966 -0.02234 849.0547 -#> 45869 714342.5 9560282 2127.11 27.62344 -0.02169 -0.00160 9344.2930 -#> 22283 713592.5 9560762 1832.60 36.01040 0.00448 -0.00328 592.3326 -#> 15109 713872.5 9559602 2288.64 25.18551 0.00602 0.01518 854.4269 -#> 30125 715162.5 9558992 2438.18 39.85666 0.00471 -0.01382 560.8309 -#> 23375 713162.5 9559632 2041.52 46.81524 -0.00857 0.03677 1675.4679 -#> 14522 715012.5 9557732 2721.34 35.18133 -0.00382 0.00672 1114.8801 -#> 6808 715192.5 9559492 2219.03 21.81766 0.00713 -0.00173 8967.1924 -#> 38077 714952.5 9557612 2744.35 40.88455 -0.00493 0.00733 598.7983 -#> 34642 714962.5 9559862 2282.08 22.16086 -0.00028 0.01318 289.0063 -#> 22735 714492.5 9559772 2170.30 39.68879 -0.00440 -0.02651 419.0927 -#> 45104 715062.5 9559242 2345.02 24.15647 -0.01743 0.01313 783.4548 -#> 12868 712532.5 9560332 1913.92 61.93731 -0.03543 -0.05146 1347.6224 -#> 12460 712762.5 9559622 1906.41 24.26190 0.01047 -0.00887 366.8537 -#> 6557 713312.5 9559022 2181.69 27.63433 -0.00669 -0.00991 1403.0193 -#> 40738 714972.5 9557642 2745.48 42.54269 -0.00294 -0.00096 520.5570 -#> 37828 713482.5 9558652 2330.58 20.44543 0.00606 -0.00556 660.3690 -#> 19784 714982.5 9557722 2716.98 35.43629 0.00888 0.00862 874.5408 -#> 20001 712702.5 9560132 1854.55 36.29115 -0.00204 -0.00376 2048.3198 -#> 18564 715552.5 9558592 2648.36 26.29131 0.00316 0.00714 475.3120 -#> 28743 715202.5 9558872 2463.87 34.65707 -0.01267 -0.05063 18733.8848 -#> 39278 714692.5 9561052 1830.60 48.39488 -0.01996 0.00626 1014.7159 -#> 651 714602.5 9560152 2111.23 53.85173 -0.01149 0.01949 1182.1506 -#> 4031 715392.5 9560162 1998.16 46.28582 0.00708 -0.02538 807.8237 -#> 39599 714112.5 9558562 2374.25 29.16814 0.00283 0.00117 1018.2141 -#> 835 715052.5 9558212 2707.65 37.72526 -0.01399 -0.00731 1384.7959 -#> 19887 714662.5 9559632 2266.47 45.17944 -0.02797 -0.00063 576.2819 -#> 2595 714912.5 9557662 2700.72 24.19486 -0.02681 -0.01319 3457.7519 -#> 10507 714892.5 9559282 2374.45 42.84235 -0.01057 0.02427 380.9472 -#> 39732 715362.5 9559582 2230.97 23.34688 -0.00161 -0.00409 2312.8413 -#> 35083 714612.5 9558152 2596.12 60.59831 0.04548 0.05543 352.1558 -#> 23213 715592.5 9558562 2654.69 33.21952 -0.00034 0.00294 739.5768 -#> 20065 713772.5 9558852 2221.21 28.01993 -0.00568 -0.02553 1750.4175 -#> 3418 715372.5 9558492 2601.00 22.62725 -0.01168 -0.04102 35685.0000 -#> 21013 712742.5 9560032 1845.53 29.35263 0.00568 -0.01558 1504.9717 -#> 16355 712962.5 9560212 1992.88 39.52607 0.01927 -0.00316 454.9674 -#> 28046 714882.5 9558492 2711.77 28.30583 -0.00131 0.00201 788.0621 -#> 2552 714942.5 9557652 2719.32 34.87365 -0.01238 -0.00851 1012.5963 -#> 17431 712802.5 9560022 1832.71 41.52512 -0.00478 -0.07132 42970.7891 -#> 34192 712702.5 9560102 1867.01 35.47468 0.00629 -0.00469 1021.5407 -#> 40216 714602.5 9560592 2015.55 34.36887 -0.00093 0.00664 1807.2659 -#> 37769 714412.5 9558562 2382.08 39.87672 -0.04601 -0.08389 47022.9688 -#> 37714 713192.5 9560692 1885.05 18.03442 0.00188 0.00832 3022.3462 -#> 12772 715172.5 9557642 2759.42 34.80547 -0.00567 -0.03793 864.9472 -#> 6845 713412.5 9559172 2123.32 47.15385 -0.01556 -0.04394 587.8840 -#> 43859 714892.5 9557602 2722.70 28.12936 0.00370 -0.00190 640.8842 -#> 1973 715252.5 9557822 2861.00 15.57070 0.01865 0.00335 195.0293 -#> 32879 713912.5 9559672 2238.00 31.97677 -0.00278 -0.00412 1350.3611 -#> 20368 715252.5 9560622 1865.83 33.57246 0.00025 -0.01245 6353.3179 -#> 17345 713922.5 9559682 2227.44 34.49091 -0.06509 -0.01211 49347.5547 -#> 42201 714642.5 9557452 2592.29 13.22558 -0.02084 -0.02236 9755.3027 -#> 20797 713012.5 9560392 1842.25 36.64982 0.01299 0.00141 4611.0718 -#> 46138 715202.5 9559582 2194.40 18.86005 -0.02143 -0.05078 622888.2500 -#> 34615 713242.5 9560732 1879.60 27.59365 0.00384 0.00175 1026.6818 -#> 43245 713722.5 9560012 2153.38 34.19126 -0.01263 0.01322 4605.1411 -#> 26428 713042.5 9560332 1895.15 33.95978 -0.01439 -0.01161 3065.3694 -#> 36292 712862.5 9559602 1915.77 37.97507 0.00511 -0.01801 1071.6141 -#> 47298 714602.5 9560572 2025.50 28.59002 -0.00253 0.00753 1337.7854 -#> 19756 715572.5 9558972 2485.95 38.81273 0.00546 0.01484 495.0292 -#> 13259 714862.5 9560992 1859.28 21.26762 -0.00317 0.00077 3056.9475 -#> 4667 712652.5 9560472 1957.89 36.32438 -0.00296 0.00426 1315.5334 -#> 39624 715222.5 9559552 2205.54 23.89750 0.00401 -0.00391 1794.7964 -#> 750 712812.5 9560032 1847.09 54.95926 -0.00767 -0.03713 6379.7974 -#> 34984 715042.5 9559312 2320.49 42.85782 -0.01106 -0.04634 500.5027 -#> 14494 715332.5 9558792 2557.22 20.72045 0.01267 0.00483 343.1384 -#> 14950 713382.5 9559172 2111.91 48.10668 0.00915 -0.02705 413.5702 -#> 22847 714582.5 9560382 2040.49 25.33619 0.00181 -0.00341 1363.4000 -#> 35458 715242.5 9558442 2623.01 38.95483 -0.00770 -0.03400 1982.7782 -#> 30378 714852.5 9557752 2651.70 41.29822 -0.00010 -0.00870 2099.7930 -#> 41408 713172.5 9559612 2056.81 22.27259 -0.00166 0.01476 1404.4454 -#> 17322 712612.5 9560402 1924.48 43.80778 -0.00486 0.02306 1718.3652 -#> 41310 714452.5 9560402 2092.27 12.46298 -0.00195 -0.00205 1112.2626 -#> 9984 712742.5 9560092 1848.56 23.61274 0.00614 -0.00093 118.1402 -#> 17851 715362.5 9557482 2869.26 29.12574 -0.03251 -0.00389 14516.2002 -#> 17374 715712.5 9557952 2832.22 37.87480 -0.01519 0.00609 4275.3457 -#> 31983 714302.5 9559912 2204.77 23.46491 -0.01332 0.01842 246.5161 -#> 32405 713022.5 9558982 2208.85 15.00462 0.00599 0.00301 338.9676 -#> 7317 713682.5 9558582 2347.46 28.34766 -0.02196 0.00336 2806.2644 -#> 27884 714072.5 9559202 2446.27 19.75673 -0.00009 0.00109 605.1908 -#> 34250 713822.5 9559142 2316.36 33.75982 -0.02284 0.01085 2229.5105 -#> 14717 715382.5 9558062 2799.05 50.59045 -0.00812 -0.02208 951.3401 -#> 26023 715832.5 9557632 3097.56 41.71018 0.00133 -0.00452 578.5847 -#> 8066 715842.5 9558732 2636.38 33.71742 -0.00494 0.01064 683.2795 -#> 1488 714562.5 9560002 2150.93 11.28383 0.00197 0.00463 851.6306 -#> 12716 713962.5 9557762 2402.60 18.93224 0.03480 0.01500 538.8209 -#> 16542 713772.5 9559622 2304.85 30.81023 -0.00712 0.00923 811.5580 -#> 45166 715262.5 9558722 2531.59 13.55332 -0.00502 -0.00028 2394.7261 -#> 29125 714992.5 9558792 2584.41 24.66583 0.01253 -0.00333 422.8840 -#> 16878 714232.5 9558172 2485.33 22.80830 0.00818 0.00502 1428.8546 -#> 44396 715512.5 9558102 2845.31 26.59842 0.01263 0.03598 186.3527 -#> 40139 712852.5 9560072 1907.89 43.70407 0.00001 0.00759 1880.4403 -#> 12974 714662.5 9557332 2667.61 41.94166 -0.00065 0.00475 515.1661 -#> 34616 714522.5 9558742 2568.77 34.99798 0.00990 0.02790 209.6952 -#> 8831 714372.5 9559852 2200.77 48.76043 -0.00457 0.01697 412.1185 -#> 8413 713242.5 9561232 1967.45 38.19451 0.00236 0.00483 1097.3082 -#> 41753 715722.5 9558192 2771.39 26.08505 -0.00196 -0.00175 725.1355 -#> 42934 713832.5 9558672 2270.74 27.61198 -0.03193 -0.00267 31588.1953 -#> 14800 712952.5 9558682 2120.89 38.27358 -0.00022 0.00662 1269.2650 -#> 47059 713822.5 9558272 2413.20 38.17503 -0.00687 -0.00223 1056.3701 -#> 36245 713142.5 9560572 1827.82 43.67600 -0.00410 0.00400 4126.7812 -#> 22545 713402.5 9559562 2195.44 25.31557 0.04152 0.00098 164.0802 -#> 48869 712562.5 9560122 1972.89 24.94658 0.00185 0.01655 560.8554 -#> 33370 714202.5 9558962 2472.93 32.64141 -0.00307 0.02997 230.8641 -#> 34345 715852.5 9557902 2874.08 43.65595 0.00379 -0.01018 937.6911 -#> 13908 715022.5 9559372 2290.81 27.24300 -0.03004 0.00714 480535.5000 -#> 5656 713132.5 9560142 2042.24 33.59538 -0.00035 0.00955 713.4463 -#> 20764 714542.5 9558152 2602.18 52.07900 0.02118 0.00162 290.2031 -#> 7553 714642.5 9559132 2391.59 63.96787 -0.00066 0.05257 549.3252 -#> 42424 714692.5 9560902 1921.34 25.33104 0.01154 -0.00204 618.2027 -#> 35826 712912.5 9560302 1891.22 50.86948 -0.00229 0.00099 909.0560 -#> 14671 712672.5 9560372 1872.21 39.19719 -0.00873 -0.01417 1520.6630 -#> 23499 714272.5 9558232 2508.35 28.96760 -0.00820 -0.00260 1484.6593 -#> 44434 713022.5 9558762 2161.07 31.45424 -0.00273 -0.01047 2826.7700 -#> 14763 713462.5 9560852 1830.40 47.73999 0.01354 0.00907 1281.5044 -#> 31098 714352.5 9559042 2410.15 30.33640 0.00537 -0.00347 1893.3645 -#> 280 713182.5 9560632 1862.83 39.00410 0.00745 0.00875 2156.9358 -#> 37783 713832.5 9557892 2405.28 40.88684 0.01572 0.03148 481.8292 -#> 1577 713322.5 9559742 2027.47 29.56749 -0.03284 -0.00886 43249.3945 -#> 20950 714912.5 9557782 2667.71 30.81138 -0.04770 0.00270 35950.5508 -#> 41873 714832.5 9559942 2253.59 10.59800 0.01508 -0.00177 184.1042 -#> 43972 713642.5 9558702 2304.16 24.02355 0.00340 0.01809 1158.0426 -#> 40653 714262.5 9561262 1791.94 12.22234 0.00161 0.00709 1181.2555 -#> 14881 715482.5 9559232 2376.03 29.90553 -0.01137 -0.01523 888.2811 -#> 22628 714942.5 9557732 2696.19 26.21167 0.00788 -0.01029 894.2672 -#> 46639 715232.5 9560042 2107.15 39.65727 -0.01305 0.00585 5714.3267 -#> 49812 713392.5 9559222 2133.99 35.66261 -0.00983 0.00803 1058.5630 -#> 32342 714862.5 9559622 2294.74 36.96724 0.01021 0.00109 915.4989 -#> 32618 715132.5 9559642 2266.36 34.53274 -0.00305 -0.01135 1293.4855 -#> 11025 714402.5 9559252 2325.64 49.62044 -0.00241 0.00020 673.2861 -#> cslope distroad slides distdeforest distslidespast log.carea -#> 37912 30.2945705 300.00 TRUE 9.15 2 2.802317 -#> 16435 49.4439659 300.00 TRUE 300.00 41 2.478905 -#> 31357 23.8172826 158.92 TRUE 0.00 5 3.243543 -#> 44302 35.1011134 300.00 TRUE 300.00 100 3.053301 -#> 42632 20.4780846 300.00 TRUE 300.00 10 2.566509 -#> 40756 39.3352715 300.00 TRUE 300.00 100 3.000471 -#> 11088 30.9987356 300.00 TRUE 300.00 100 3.212274 -#> 48187 30.9666500 279.54 TRUE 21.35 46 3.521349 -#> 21939 23.9324471 300.00 TRUE 300.00 100 3.168886 -#> 20799 24.8079266 180.67 TRUE 0.00 16 3.361925 -#> 9669 31.6645125 300.00 TRUE 300.00 1 3.610558 -#> 38411 32.7582890 279.54 TRUE 0.00 100 3.793638 -#> 26531 15.9878143 300.00 TRUE 300.00 2 2.552843 -#> 14456 20.0689927 300.00 TRUE 247.02 100 3.183868 -#> 26447 33.0367465 300.00 TRUE 300.00 100 2.676242 -#> 16811 35.2523743 300.00 TRUE 300.00 59 2.715521 -#> 23817 21.6113951 300.00 TRUE 300.00 4 2.425592 -#> 24018 29.3050723 300.00 TRUE 300.00 5 3.021777 -#> 11463 20.1337369 300.00 TRUE 300.00 100 2.768442 -#> 42598 20.0689927 300.00 TRUE 247.02 100 3.183868 -#> 3835 35.1389286 300.00 TRUE 300.00 39 2.691790 -#> 8019 35.7800684 300.00 TRUE 300.00 61 2.928936 -#> 45869 23.4546003 300.00 TRUE 300.00 12 3.970546 -#> 22283 19.9423690 256.35 TRUE 0.00 100 2.772566 -#> 15109 22.6352707 300.00 TRUE 300.00 40 2.931675 -#> 30125 40.7355804 300.00 TRUE 300.00 100 2.748832 -#> 23375 34.6398824 300.00 TRUE 195.00 2 3.224136 -#> 14522 28.3986531 300.00 TRUE 300.00 100 3.047228 -#> 6808 28.5659568 300.00 TRUE 300.00 40 3.952656 -#> 38077 26.9152654 300.00 TRUE 300.00 100 2.777281 -#> 34642 15.6486233 300.00 TRUE 300.00 2 2.460907 -#> 22735 36.5168921 300.00 TRUE 300.00 90 2.622310 -#> 45104 17.9925936 300.00 TRUE 300.00 100 2.894014 -#> 12868 37.5883232 119.34 TRUE 79.16 61 3.129568 -#> 12460 36.2567693 300.00 TRUE 0.00 100 2.564493 -#> 6557 27.4194682 300.00 TRUE 300.00 5 3.147064 -#> 40738 28.9601518 300.00 TRUE 300.00 100 2.716468 -#> 37828 18.7637948 300.00 TRUE 300.00 2 2.819787 -#> 19784 27.9557567 300.00 TRUE 300.00 100 2.941780 -#> 20001 34.6771246 264.46 TRUE 18.17 56 3.311398 -#> 18564 21.7002035 300.00 TRUE 300.00 100 2.676979 -#> 28743 34.6530604 300.00 TRUE 300.00 71 4.272628 -#> 39278 16.2181433 125.47 TRUE 0.00 6 3.006344 -#> 651 45.5243616 300.00 TRUE 300.00 100 3.072673 -#> 4031 42.7615591 300.00 TRUE 300.00 31 2.907317 -#> 39599 33.1347223 300.00 TRUE 300.00 25 3.007839 -#> 835 36.6503913 300.00 TRUE 300.00 100 3.141386 -#> 19887 38.0621593 300.00 TRUE 300.00 63 2.760635 -#> 2595 30.4395288 300.00 TRUE 300.00 100 3.538794 -#> 10507 27.1622102 300.00 TRUE 300.00 46 2.580865 -#> 39732 29.1681354 300.00 TRUE 300.00 25 3.364146 -#> 35083 43.5407817 300.00 TRUE 300.00 23 2.546735 -#> 23213 25.4605255 300.00 TRUE 300.00 100 2.868983 -#> 20065 30.3896815 300.00 TRUE 300.00 5 3.243142 -#> 3418 35.4534824 300.00 TRUE 300.00 73 4.552486 -#> 21013 24.9631982 300.00 TRUE 13.47 64 3.177528 -#> 16355 28.2468193 300.00 TRUE 58.52 100 2.657980 -#> 28046 19.5911459 300.00 TRUE 300.00 15 2.896560 -#> 2552 32.2156979 300.00 TRUE 300.00 100 3.005436 -#> 17431 25.3253075 300.00 TRUE 1.11 5 4.633173 -#> 34192 33.8749837 273.94 TRUE 4.48 85 3.009256 -#> 40216 26.3640800 300.00 TRUE 300.00 100 3.257022 -#> 37769 37.4250302 300.00 TRUE 300.00 100 4.672310 -#> 37714 30.7317373 13.57 TRUE 170.00 4 3.480344 -#> 12772 37.9200658 300.00 TRUE 300.00 100 2.936990 -#> 6845 44.5961700 300.00 TRUE 300.00 29 2.769292 -#> 43859 29.5577467 300.00 TRUE 300.00 100 2.806780 -#> 1973 19.3258664 300.00 TRUE 300.00 100 2.290100 -#> 32879 29.7382284 300.00 TRUE 300.00 6 3.130450 -#> 20368 30.3444178 300.00 TRUE 166.26 100 3.803001 -#> 17345 29.8213073 300.00 TRUE 300.00 2 4.693266 -#> 42201 39.5587250 300.00 TRUE 300.00 100 3.989241 -#> 20797 32.0569886 285.07 TRUE 0.00 96 3.663802 -#> 46138 32.6946907 300.00 TRUE 300.00 8 5.794410 -#> 34615 34.1895376 17.57 TRUE 142.95 16 3.011436 -#> 43245 24.4458173 300.00 TRUE 300.00 100 3.663243 -#> 26428 35.4752548 300.00 TRUE 0.00 100 3.486483 -#> 36292 31.5155435 300.00 TRUE 16.03 100 3.030038 -#> 47298 25.3361937 300.00 TRUE 300.00 100 3.126386 -#> 19756 30.1163806 300.00 TRUE 300.00 100 2.694631 -#> 13259 23.1606730 203.85 TRUE 10.45 74 3.485288 -#> 4667 31.5172624 24.78 TRUE 172.24 38 3.119102 -#> 39624 33.2945775 300.00 TRUE 300.00 2 3.254015 -#> 750 34.3471009 300.00 TRUE 0.00 0 3.804807 -#> 34984 33.9059234 300.00 TRUE 300.00 100 2.699406 -#> 14494 21.1851145 300.00 TRUE 300.00 28 2.535469 -#> 14950 41.8740475 300.00 TRUE 300.00 15 2.616549 -#> 22847 24.7477660 300.00 TRUE 300.00 41 3.134623 -#> 35458 37.4731587 300.00 TRUE 300.00 100 3.297274 -#> 30378 28.5693945 300.00 TRUE 300.00 57 3.322176 -#> 41408 33.8136772 300.00 TRUE 205.28 1 3.147505 -#> 17322 31.6650855 75.77 TRUE 138.35 1 3.235115 -#> 41310 17.2953040 300.00 TRUE 300.00 25 3.046207 -#> 9984 3.9465333 300.00 TRUE 36.46 76 2.072398 -#> 17851 32.2632534 300.00 TRUE 300.00 100 4.161853 -#> 17374 43.1454408 300.00 TRUE 300.00 100 3.630971 -#> 31983 19.7756383 300.00 FALSE 300.00 65 2.391845 -#> 32405 15.7620689 300.00 FALSE 300.00 100 2.530158 -#> 7317 27.2258085 300.00 FALSE 300.00 61 3.448129 -#> 27884 15.4973624 300.00 FALSE 300.00 100 2.781892 -#> 34250 30.8927384 300.00 FALSE 300.00 100 3.348210 -#> 14717 50.8563069 300.00 FALSE 300.00 100 2.978336 -#> 26023 45.0728709 300.00 FALSE 300.00 100 2.762367 -#> 8066 23.5273659 300.00 FALSE 300.00 100 2.834598 -#> 1488 29.9215749 300.00 FALSE 300.00 100 2.930251 -#> 12716 24.3449767 300.00 FALSE 300.00 25 2.731444 -#> 16542 21.1123488 300.00 FALSE 300.00 100 2.909320 -#> 45166 23.4477248 300.00 FALSE 300.00 1 3.379256 -#> 29125 23.9450522 300.00 FALSE 300.00 100 2.626221 -#> 16878 25.3889058 300.00 FALSE 300.00 18 3.154988 -#> 44396 21.4423725 300.00 FALSE 300.00 100 2.270336 -#> 40139 33.1433166 300.00 FALSE 1.11 21 3.274260 -#> 12974 37.3385136 300.00 FALSE 300.00 100 2.711947 -#> 34616 31.4995007 300.00 FALSE 300.00 100 2.321589 -#> 8831 32.5554619 300.00 FALSE 300.00 24 2.615022 -#> 8413 39.8898947 80.73 FALSE 0.00 100 3.040329 -#> 41753 25.0073159 300.00 FALSE 300.00 100 2.860419 -#> 42934 32.2271571 300.00 FALSE 300.00 22 4.499525 -#> 14800 31.5229920 300.00 FALSE 300.00 100 3.103552 -#> 47059 25.7596095 300.00 FALSE 300.00 100 3.023816 -#> 36245 25.8535746 110.19 FALSE 81.50 20 3.615611 -#> 22545 23.6843564 300.00 FALSE 300.00 96 2.215056 -#> 48869 24.8755356 138.98 FALSE 0.00 63 2.748851 -#> 33370 26.4190839 300.00 FALSE 300.00 100 2.363356 -#> 34345 44.3950618 300.00 FALSE 300.00 100 2.972060 -#> 13908 34.3774677 300.00 FALSE 300.00 96 5.681725 -#> 5656 31.2726094 300.00 FALSE 94.43 100 2.853361 -#> 20764 39.8503606 300.00 FALSE 300.00 91 2.462702 -#> 7553 39.7712924 300.00 FALSE 300.00 100 2.739830 -#> 42424 22.6071957 275.21 FALSE 93.50 70 2.791131 -#> 35826 44.2277581 298.23 FALSE 16.35 96 2.958591 -#> 14671 37.8822505 123.04 FALSE 75.11 0 3.182033 -#> 23499 28.5149635 300.00 FALSE 300.00 50 3.171627 -#> 44434 36.3197946 300.00 FALSE 300.00 100 3.451290 -#> 14763 10.1447907 110.04 FALSE 60.35 100 3.107720 -#> 31098 34.6576441 300.00 FALSE 300.00 45 3.277234 -#> 280 29.3234070 60.55 FALSE 127.54 29 3.333837 -#> 37783 24.2573142 300.00 FALSE 300.00 18 2.682893 -#> 1577 38.9296174 300.00 FALSE 300.00 63 4.635980 -#> 20950 32.7118794 300.00 FALSE 300.00 81 4.555706 -#> 41873 11.0386049 300.00 FALSE 300.00 100 2.265064 -#> 43972 22.8524217 300.00 FALSE 300.00 100 3.063725 -#> 40653 0.1420935 18.02 FALSE 140.15 100 3.072344 -#> 14881 26.8562507 300.00 FALSE 300.00 100 2.948550 -#> 22628 29.1211529 300.00 FALSE 300.00 100 2.951467 -#> 46639 33.5632947 300.00 FALSE 300.00 100 3.756965 -#> 49812 33.3581758 300.00 FALSE 300.00 65 3.024717 -#> 32342 37.6198359 300.00 FALSE 300.00 81 2.961658 -#> 32618 39.3828270 300.00 FALSE 300.00 56 3.111762 -#> 11025 36.8199868 300.00 FALSE 300.00 50 2.828200 +#> x y dem slope hcurv vcurv carea +#> 37912 712802.5 9559952 1838.40 52.101344 0.00183 -0.09203 634.3320 +#> 36052 714842.5 9558892 2483.27 68.789886 -0.04921 -0.12438 753.6304 +#> 47223 715302.5 9557472 2857.45 39.533515 -0.01021 -0.01579 1318.7560 +#> 15762 714922.5 9558862 2535.97 54.910556 0.01305 -0.00555 430.4604 +#> 37265 712812.5 9559942 1860.77 63.337047 0.00514 -0.00644 1179.0695 +#> 34512 713162.5 9559632 2041.52 46.815236 -0.00857 0.03677 1675.4679 +#> 9669 713852.5 9558612 2308.97 52.180412 -0.01059 -0.07431 4079.0366 +#> 39885 715062.5 9561022 1840.94 38.435728 0.00250 -0.01340 604.1032 +#> 42917 714202.5 9557412 2544.08 39.877863 -0.02104 -0.02046 1024.1573 +#> 26531 714462.5 9560452 2104.74 19.524110 0.00453 0.00257 357.1436 +#> 20151 714602.5 9559922 2184.66 48.341595 -0.00979 -0.01542 674.0147 +#> 23817 714842.5 9557832 2677.58 26.657434 0.00922 0.01088 266.4355 +#> 39629 714602.5 9558912 2474.89 40.258307 -0.06175 -0.08226 2325.2053 +#> 27375 714292.5 9558982 2458.96 43.600942 0.00214 0.00466 724.0713 +#> 42598 713542.5 9559972 2184.39 35.241488 -0.00841 -0.00010 1527.1028 +#> 2990 713542.5 9560372 1970.07 31.887966 0.01903 0.01447 331.4833 +#> 30800 715182.5 9557582 2772.37 39.204064 -0.02475 0.00665 25318.3555 +#> 18843 715192.5 9559332 2265.75 41.153267 -0.07664 -0.05766 140591.4062 +#> 45869 714342.5 9560282 2127.11 27.623441 -0.02169 -0.00160 9344.2930 +#> 18304 714582.5 9558932 2463.25 22.194666 -0.02131 0.00501 3889.7292 +#> 18005 714832.5 9557912 2660.76 31.912030 0.00191 -0.00261 554.9243 +#> 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 +#> 25081 713392.5 9558452 2242.24 46.538497 -0.02716 0.03696 4143.4019 +#> 47022 715362.5 9560102 2059.29 49.119672 0.02059 -0.00628 556.0121 +#> 6676 715092.5 9559912 2217.44 41.707890 -0.00077 -0.00284 2010.4285 +#> 4477 715432.5 9559392 2325.25 37.029116 -0.00521 -0.00739 558.2286 +#> 10320 714912.5 9558482 2724.21 25.282909 0.00356 0.00774 527.1068 +#> 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 +#> 23031 714042.5 9558492 2404.94 28.496056 0.01205 0.02205 629.4165 +#> 46125 713732.5 9560062 2115.18 37.593480 -0.04616 0.01276 9614.6924 +#> 12868 712532.5 9560332 1913.92 61.937311 -0.03543 -0.05146 1347.6224 +#> 5728 713502.5 9559662 2140.27 34.307567 -0.00154 0.00124 2114.2253 +#> 12460 712762.5 9559622 1906.41 24.261898 0.01047 -0.00887 366.8537 +#> 47429 714962.5 9557832 2705.12 32.057562 0.02224 0.00177 291.5937 +#> 11687 714552.5 9560382 2052.66 29.575508 -0.00255 -0.00365 1536.2647 +#> 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 +#> 34563 715022.5 9558162 2675.70 18.889846 -0.01993 0.01373 16031.7500 +#> 46235 712712.5 9561042 2023.11 41.277025 0.01458 0.00672 565.1107 +#> 6967 715342.5 9560192 1995.36 44.881503 -0.00666 -0.00504 1020.2766 +#> 29926 714702.5 9557622 2637.00 44.642580 0.01843 0.04407 344.8395 +#> 17423 715592.5 9558562 2654.69 33.219520 -0.00034 0.00294 739.5768 +#> 18394 715022.5 9559942 2216.16 42.137608 -0.00613 0.00903 2360.1536 +#> 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 +#> 26567 714022.5 9558872 2327.42 46.665694 0.00290 0.00230 1056.5876 +#> 2976 715122.5 9559142 2390.05 33.167954 0.01008 0.00102 483.4184 +#> 15766 715562.5 9558592 2646.16 29.020885 0.00194 0.00516 576.0335 +#> 21053 712862.5 9559992 1901.01 30.950607 -0.00762 0.01252 5761.3486 +#> 38862 712952.5 9559892 1950.86 37.721822 -0.00048 -0.00332 1418.6622 +#> 27875 714832.5 9561032 1838.22 41.212281 -0.01160 0.01690 5121.6924 +#> 6317 715042.5 9557702 2745.36 27.873824 0.00090 -0.00299 688.0706 +#> 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 +#> 31561 713302.5 9559192 2092.11 36.527778 0.00406 -0.00055 773.0359 +#> 46065 713072.5 9559162 2109.39 16.264553 -0.00195 -0.00656 3036.7383 +#> 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 +#> 21680 712802.5 9560462 1893.75 38.734239 -0.00481 -0.00399 3900.3445 +#> 12137 712842.5 9558902 2097.88 30.098619 0.00024 -0.00474 2343.2100 +#> 37769 714412.5 9558562 2382.08 39.876717 -0.04601 -0.08389 47022.9688 +#> 30268 714172.5 9558612 2328.84 54.950663 0.03743 -0.05242 507.9263 +#> 39166 712832.5 9560142 1922.46 39.660139 0.01469 0.02940 987.8085 +#> 47320 713422.5 9559182 2143.03 56.325189 0.01246 0.00534 344.1447 +#> 1424 714012.5 9558902 2305.97 32.223719 -0.03986 -0.02524 10861.9482 +#> 10467 714642.5 9559652 2249.12 37.143135 -0.00759 -0.00400 608.6523 +#> 10933 712702.5 9560132 1854.55 36.291147 -0.00204 -0.00376 2048.3198 +#> 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 +#> 31117 712892.5 9558922 2129.84 30.550683 0.00521 -0.00870 1895.1399 +#> 7 714952.5 9559862 2284.19 16.106417 0.00516 0.01574 183.3220 +#> 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 +#> 34615 713242.5 9560732 1879.60 27.593647 0.00384 0.00175 1026.6818 +#> 19832 715182.5 9557662 2761.27 38.432863 -0.00666 -0.03583 1898.0105 +#> 5911 713952.5 9561282 1801.16 33.789613 0.00466 -0.01776 1021.8267 +#> 17058 714032.5 9558502 2402.95 31.352824 0.01924 0.02606 564.6876 +#> 28009 715002.5 9559922 2237.68 34.603786 -0.01109 0.00298 1533.7925 +#> 39325 714142.5 9558592 2357.72 40.792876 -0.00246 0.00396 704.7076 +#> 19756 715572.5 9558972 2485.95 38.812734 0.00546 0.01484 495.0292 +#> 38668 715582.5 9558242 2735.55 35.446034 0.01003 0.01196 1052.5341 +#> 49072 715472.5 9558902 2482.92 30.689338 -0.01102 -0.01778 4594.4351 +#> 49133 714162.5 9558462 2451.13 21.890426 0.00042 0.00957 495.4453 +#> 4667 712652.5 9560472 1957.89 36.324378 -0.00296 0.00426 1315.5334 +#> 23783 713062.5 9560252 1983.09 36.429803 0.00774 -0.00224 1137.3363 +#> 1997 714752.5 9561012 1853.19 26.566334 -0.00693 0.01133 2397.2681 +#> 14494 715332.5 9558792 2557.22 20.720446 0.01267 0.00483 343.1384 +#> 39405 713122.5 9559052 2169.30 37.646192 -0.02110 -0.02450 2589.9910 +#> 24723 712942.5 9560202 1983.18 33.071697 0.00285 -0.01325 595.0426 +#> 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 +#> 4479 713982.5 9557812 2399.24 41.231189 0.01895 0.04744 226.7911 +#> 41310 714452.5 9560402 2092.27 12.462978 -0.00195 -0.00205 1112.2626 +#> 43536 713782.5 9558732 2281.50 32.749122 0.00840 0.00189 556.5211 +#> 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 +#> 13072 714862.5 9558932 2448.29 41.052426 -0.04989 -0.02781 29995.1543 +#> 7317 713682.5 9558582 2347.46 28.347660 -0.02196 0.00336 2806.2644 +#> 46165 714392.5 9557912 2542.56 16.329870 0.00325 -0.00505 351.4078 +#> 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 +#> 30809 712532.5 9560032 1928.24 40.899446 0.00570 0.02230 714.7432 +#> 748 715102.5 9559692 2301.39 27.021836 -0.00759 0.00158 837.3730 +#> 25212 714272.5 9559012 2433.45 37.671402 -0.01909 -0.01181 1286.3949 +#> 16464 715252.5 9559982 2143.24 23.185883 -0.00298 -0.00712 1010.5097 +#> 40459 712542.5 9559192 1992.60 39.817129 -0.00162 -0.00517 1060.5494 +#> 31071 713472.5 9558462 2298.33 29.153239 0.00156 -0.00116 1817.5222 +#> 18286 714792.5 9557882 2650.57 28.253695 -0.00124 0.00204 853.2902 +#> 11111 715142.5 9558482 2650.65 42.343300 -0.00533 0.01493 2212.0525 +#> 20067 714242.5 9560442 2125.23 38.640274 0.00363 -0.00363 389.2691 +#> 1488 714562.5 9560002 2150.93 11.283831 0.00197 0.00463 851.6306 +#> 37832 712552.5 9559192 1998.10 41.550899 -0.00195 -0.00995 1026.7981 +#> 13512 715152.5 9557992 2778.81 43.389521 -0.00724 0.01464 4029.5947 +#> 16542 713772.5 9559622 2304.85 30.810232 -0.00712 0.00923 811.5580 +#> 45166 715262.5 9558722 2531.59 13.553317 -0.00502 -0.00028 2394.7261 +#> 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 +#> 39796 714802.5 9560372 2046.48 40.155747 0.00176 0.02973 215.5101 +#> 4007 714132.5 9558412 2464.44 43.720118 0.00174 -0.03065 704.4843 +#> 31884 715782.5 9558612 2632.60 30.279674 0.00084 -0.00083 1309.2347 +#> 14800 712952.5 9558682 2120.89 38.273581 -0.00022 0.00662 1269.2650 +#> 29527 714332.5 9557502 2567.02 48.904813 -0.00452 -0.01298 436.1459 +#> 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 +#> 34345 715852.5 9557902 2874.08 43.655946 0.00379 -0.01018 937.6911 +#> 26389 713002.5 9559712 1926.02 24.037298 0.00586 0.00654 1576.2065 +#> 33410 715342.5 9560452 1942.88 29.093078 -0.00305 -0.00705 968.9738 +#> 7553 714642.5 9559132 2391.59 63.967873 -0.00066 0.05257 549.3252 +#> 43222 714472.5 9560712 2039.25 22.190655 0.01858 0.00312 254.0656 +#> 34532 713812.5 9559502 2328.80 44.937653 -0.00070 -0.00060 383.3547 +#> 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 +#> 23499 714272.5 9558232 2508.35 28.967600 -0.00820 -0.00260 1484.6593 +#> 34336 713792.5 9559072 2288.95 36.932859 -0.00952 -0.00438 1276.5448 +#> 18308 714842.5 9558742 2568.20 26.017441 -0.02552 0.00532 16771.3066 +#> 10127 713722.5 9558712 2311.10 19.481138 0.01129 0.00491 261.7371 +#> 20950 714912.5 9557782 2667.71 30.811378 -0.04770 0.00270 35950.5508 +#> 36016 715262.5 9557682 2814.30 37.002187 0.00203 0.00238 787.0053 +#> 16443 714472.5 9559532 2222.55 16.744119 0.03263 -0.02012 305.3624 +#> 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 +#> 31440 714842.5 9558392 2704.41 25.801435 0.00992 -0.01052 451.7209 +#> 36141 714222.5 9559422 2316.64 16.277158 -0.07627 -0.00124 25826.6914 +#> 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 +#> cslope distroad slides distdeforest distslidespast log.carea +#> 37912 30.29457046 300.00 TRUE 9.15 2 2.802317 +#> 36052 53.74516006 300.00 TRUE 300.00 100 2.877158 +#> 47223 36.25906111 300.00 TRUE 300.00 100 3.120164 +#> 15762 46.30186534 300.00 TRUE 300.00 100 2.633933 +#> 37265 36.51746507 300.00 TRUE 2.45 0 3.071539 +#> 34512 34.63988238 300.00 TRUE 195.00 2 3.224136 +#> 9669 31.66451255 300.00 TRUE 300.00 1 3.610558 +#> 39885 6.08481178 210.57 TRUE 0.00 100 2.781111 +#> 42917 38.80356668 300.00 TRUE 300.00 100 3.010367 +#> 26531 15.98781432 300.00 TRUE 300.00 2 2.552843 +#> 20151 44.79555930 300.00 TRUE 300.00 100 2.828669 +#> 23817 21.61139507 300.00 TRUE 300.00 4 2.425592 +#> 39629 47.54862150 300.00 TRUE 300.00 85 3.366461 +#> 27375 38.77033512 300.00 TRUE 300.00 100 2.859781 +#> 42598 20.06899269 300.00 TRUE 247.02 100 3.183868 +#> 2990 25.32129680 300.00 TRUE 123.56 5 2.520462 +#> 30800 33.24816789 300.00 TRUE 300.00 100 4.403435 +#> 18843 28.52642270 300.00 TRUE 300.00 100 5.147959 +#> 45869 23.45460030 300.00 TRUE 300.00 12 3.970546 +#> 18304 44.90843198 300.00 TRUE 300.00 57 3.589919 +#> 18005 24.90590240 300.00 TRUE 300.00 0 2.744234 +#> 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 +#> 25081 34.87536803 300.00 TRUE 300.00 100 3.617357 +#> 47022 43.53161440 300.00 TRUE 300.00 26 2.745084 +#> 6676 34.62154773 300.00 TRUE 300.00 100 3.303289 +#> 4477 32.16986132 300.00 TRUE 300.00 11 2.746812 +#> 10320 16.45592083 300.00 TRUE 300.00 30 2.721899 +#> 34280 33.62116342 300.00 TRUE 0.00 100 3.365895 +#> 15390 29.32226108 60.00 TRUE 118.92 2 3.372821 +#> 23031 26.74108621 300.00 TRUE 300.00 2 2.798938 +#> 46125 25.65705007 300.00 TRUE 300.00 100 3.982935 +#> 12868 37.58832319 119.34 TRUE 79.16 61 3.129568 +#> 5728 40.40842146 300.00 TRUE 300.00 2 3.325151 +#> 12460 36.25676928 300.00 TRUE 0.00 100 2.564493 +#> 47429 32.09766864 300.00 TRUE 300.00 100 2.464778 +#> 11687 23.19390450 300.00 TRUE 300.00 68 3.186466 +#> 48434 29.93589888 300.00 TRUE 300.00 6 2.847438 +#> 36931 27.20919273 300.00 TRUE 300.00 100 3.408707 +#> 34563 38.15669732 300.00 TRUE 300.00 100 4.204981 +#> 46235 30.10835918 300.00 TRUE 41.37 0 2.752133 +#> 6967 41.11946208 300.00 TRUE 300.00 85 3.008718 +#> 29926 32.09480385 300.00 TRUE 300.00 100 2.537617 +#> 17423 25.46052554 300.00 TRUE 300.00 100 2.868983 +#> 18394 28.61064750 300.00 TRUE 300.00 57 3.372940 +#> 41037 31.57054747 300.00 TRUE 300.00 100 3.329742 +#> 44550 24.82626126 300.00 TRUE 300.00 100 2.968524 +#> 26567 40.43706935 300.00 TRUE 300.00 100 3.023906 +#> 2976 26.88203383 300.00 TRUE 300.00 100 2.684323 +#> 15766 23.33714395 300.00 TRUE 300.00 100 2.760448 +#> 21053 34.25141699 300.00 TRUE 0.00 2 3.760524 +#> 38862 34.90000522 300.00 TRUE 104.48 92 3.151879 +#> 27875 21.26704744 157.93 TRUE 0.00 57 3.709413 +#> 6317 27.32321133 300.00 TRUE 300.00 100 2.837633 +#> 47100 29.64598224 82.80 TRUE 129.73 36 3.558076 +#> 23213 25.46052554 300.00 TRUE 300.00 100 2.868983 +#> 31561 31.44277788 300.00 TRUE 300.00 0 2.888200 +#> 46065 29.77088703 300.00 TRUE 251.11 100 3.482407 +#> 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 +#> 21680 28.34823283 104.12 TRUE 110.76 4 3.591103 +#> 12137 27.58849079 300.00 TRUE 300.00 100 3.369811 +#> 37769 37.42503022 300.00 TRUE 300.00 100 4.672310 +#> 30268 47.27245585 300.00 TRUE 300.00 41 2.705801 +#> 39166 29.81844253 300.00 TRUE 1.90 90 2.994673 +#> 47320 45.66416331 300.00 TRUE 300.00 43 2.536741 +#> 1424 39.15421685 300.00 TRUE 300.00 100 4.035908 +#> 10467 38.99608049 300.00 TRUE 300.00 90 2.784369 +#> 10933 34.67712463 264.46 TRUE 18.17 56 3.311398 +#> 47718 32.32341401 300.00 TRUE 10.00 1 3.759272 +#> 17345 29.82130732 300.00 TRUE 300.00 2 4.693266 +#> 31117 26.64597522 300.00 TRUE 291.23 100 3.277641 +#> 7 13.22042816 300.00 TRUE 300.00 0 2.263215 +#> 46138 32.69469066 300.00 TRUE 300.00 8 5.794410 +#> 33875 27.56958319 300.00 TRUE 4.67 0 3.279136 +#> 34615 34.18953755 17.57 TRUE 142.95 16 3.011436 +#> 19832 36.63377551 300.00 TRUE 300.00 100 3.278299 +#> 5911 4.13847415 42.88 TRUE 0.00 13 3.009377 +#> 17058 26.32282702 300.00 TRUE 300.00 8 2.751808 +#> 28009 27.01782483 300.00 TRUE 300.00 29 3.185767 +#> 39325 37.44852149 300.00 TRUE 300.00 60 2.848009 +#> 19756 30.11638059 300.00 TRUE 300.00 100 2.694631 +#> 38668 32.92272787 300.00 TRUE 300.00 100 3.022236 +#> 49072 28.98765373 300.00 TRUE 300.00 100 3.662232 +#> 49133 23.51934453 300.00 TRUE 300.00 38 2.694996 +#> 4667 31.51726239 24.78 TRUE 172.24 38 3.119102 +#> 23783 27.60052291 300.00 TRUE 0.00 100 3.055889 +#> 1997 25.23592609 168.72 TRUE 0.00 5 3.379717 +#> 14494 21.18511447 300.00 TRUE 300.00 28 2.535469 +#> 39405 27.72027109 300.00 TRUE 300.00 100 3.413298 +#> 24723 28.17348070 300.00 TRUE 36.63 100 2.774548 +#> 26058 34.49836180 300.00 TRUE 76.02 39 3.272521 +#> 17825 30.73345613 73.66 TRUE 137.63 18 3.469116 +#> 4479 32.01172497 300.00 TRUE 300.00 55 2.355626 +#> 41310 17.29530400 300.00 TRUE 300.00 25 3.046207 +#> 43536 29.09479684 300.00 TRUE 300.00 8 2.745482 +#> 17851 32.26325344 300.00 TRUE 300.00 100 4.161853 +#> 31253 38.66834864 300.00 TRUE 122.87 100 3.878395 +#> 13072 34.94469593 300.00 TRUE 300.00 100 4.477051 +#> 7317 27.22580851 300.00 FALSE 300.00 61 3.448129 +#> 46165 14.81840746 300.00 FALSE 300.00 100 2.545811 +#> 25584 27.14674033 300.00 FALSE 300.00 100 3.105832 +#> 11288 28.53730890 132.07 FALSE 85.50 1 3.646686 +#> 30809 36.36620421 181.84 FALSE 0.00 100 2.854150 +#> 748 38.13263310 300.00 FALSE 300.00 12 2.922919 +#> 25212 38.98290247 300.00 FALSE 300.00 100 3.109374 +#> 16464 35.04610945 300.00 FALSE 300.00 100 3.004540 +#> 40459 36.18686842 300.00 FALSE 216.66 10 3.025531 +#> 31071 31.73785115 300.00 FALSE 300.00 76 3.259480 +#> 18286 24.35070629 300.00 FALSE 300.00 42 2.931097 +#> 11111 33.38395889 300.00 FALSE 300.00 100 3.344795 +#> 20067 29.82359915 300.00 FALSE 268.03 100 2.590250 +#> 1488 29.92157494 300.00 FALSE 300.00 100 2.930251 +#> 37832 36.44641831 300.00 FALSE 207.18 2 3.011485 +#> 13512 35.93476700 300.00 FALSE 300.00 100 3.605261 +#> 16542 21.11234883 300.00 FALSE 300.00 100 2.909320 +#> 45166 23.44772481 300.00 FALSE 300.00 1 3.379256 +#> 43243 27.98612350 300.00 FALSE 300.00 100 2.922424 +#> 29125 23.94505217 300.00 FALSE 300.00 100 2.626221 +#> 39796 34.56768970 300.00 FALSE 300.00 71 2.333468 +#> 4007 32.09251202 300.00 FALSE 300.00 68 2.847871 +#> 31884 24.13756599 300.00 FALSE 300.00 100 3.117018 +#> 14800 31.52299197 300.00 FALSE 300.00 100 3.103552 +#> 29527 36.72659467 300.00 FALSE 300.00 100 2.639632 +#> 34228 10.74868824 300.00 FALSE 300.00 100 2.427333 +#> 13737 40.96017982 300.00 FALSE 300.00 100 2.678473 +#> 34345 44.39506180 300.00 FALSE 300.00 100 2.972060 +#> 26389 36.12212419 300.00 FALSE 76.38 26 3.197613 +#> 33410 32.87975603 300.00 FALSE 300.00 100 2.986312 +#> 7553 39.77129239 300.00 FALSE 300.00 100 2.739830 +#> 43222 21.26532857 300.00 FALSE 300.00 100 2.404946 +#> 34532 31.20671927 300.00 FALSE 300.00 92 2.583601 +#> 14671 37.88225054 123.04 FALSE 75.11 0 3.182033 +#> 17628 26.99204173 300.00 FALSE 205.59 100 2.626915 +#> 23499 28.51496355 300.00 FALSE 300.00 50 3.171627 +#> 34336 32.45118360 300.00 FALSE 300.00 100 3.106036 +#> 18308 30.02069663 300.00 FALSE 300.00 55 4.224567 +#> 10127 18.61940947 300.00 FALSE 300.00 49 2.417865 +#> 20950 32.71187940 300.00 FALSE 300.00 81 4.555706 +#> 36016 39.52377462 300.00 FALSE 300.00 100 2.895978 +#> 16443 26.53825915 300.00 FALSE 300.00 75 2.484816 +#> 33881 29.18589713 300.00 FALSE 300.00 100 3.171879 +#> 46639 33.56329468 300.00 FALSE 300.00 100 3.756965 +#> 31440 24.03500655 300.00 FALSE 300.00 96 2.654870 +#> 36141 27.30602260 300.00 FALSE 300.00 85 4.412069 +#> 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 diff --git a/reference/partition_cv_strat.html b/reference/partition_cv_strat.html index cf107135..bbc1dc4a 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] 0.9971681 +#> [1] 1.002166 # 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 66ee5f4f..5f5c50c6 100644 --- a/reference/partition_disc.html +++ b/reference/partition_disc.html @@ -192,19 +192,19 @@

Examples

summary(parti) #> $`1` #> n.train n.test -#> 259 713 17 -#> 324 699 25 -#> 39 723 25 -#> 702 737 4 -#> 635 718 6 +#> 318 715 10 +#> 444 734 8 +#> 342 693 31 +#> 412 711 13 +#> 422 709 17 #> #> $`2` #> n.train n.test -#> 354 707 15 -#> 636 734 5 -#> 242 718 10 -#> 410 746 5 -#> 102 712 5 +#> 294 691 31 +#> 464 729 7 +#> 649 715 8 +#> 228 714 19 +#> 149 698 18 #> # leave-one-out with buffer: @@ -212,19 +212,19 @@

Examples

summary(parti) #> $`1` #> n.train n.test -#> 259 713 17 -#> 324 699 25 -#> 39 723 25 -#> 702 737 4 -#> 635 718 6 +#> 318 715 10 +#> 444 734 8 +#> 342 693 31 +#> 412 711 13 +#> 422 709 17 #> #> $`2` #> n.train n.test -#> 354 707 15 -#> 636 734 5 -#> 242 718 10 -#> 410 746 5 -#> 102 712 5 +#> 294 691 31 +#> 464 729 7 +#> 649 715 8 +#> 228 714 19 +#> 149 698 18 #> diff --git a/reference/sperrorest.html b/reference/sperrorest.html index 5110af1d..a301f424 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) ) -#> Mon Jul 10 04:32:21 2023 Repetition 1 -#> Mon Jul 10 04:32:21 2023 Repetition - Fold 1 -#> Mon Jul 10 04:32:22 2023 Repetition - Fold 2 -#> Mon Jul 10 04:32:22 2023 Repetition - Fold 3 -#> Mon Jul 10 04:32:22 2023 Repetition 2 -#> Mon Jul 10 04:32:22 2023 Repetition - Fold 1 -#> Mon Jul 10 04:32:22 2023 Repetition - Fold 2 -#> Mon Jul 10 04:32:22 2023 Repetition - Fold 3 +#> Tue Jul 11 04:31:49 2023 Repetition 1 +#> Tue Jul 11 04:31:49 2023 Repetition - Fold 1 +#> Tue Jul 11 04:31:49 2023 Repetition - Fold 2 +#> Tue Jul 11 04:31:49 2023 Repetition - Fold 3 +#> Tue Jul 11 04:31:50 2023 Repetition 2 +#> Tue Jul 11 04:31:50 2023 Repetition - Fold 1 +#> Tue Jul 11 04:31:50 2023 Repetition - Fold 2 +#> Tue Jul 11 04:31:50 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) ) -#> Mon Jul 10 04:32:22 2023 Repetition 1 -#> Mon Jul 10 04:32:22 2023 Repetition - Fold 1 -#> Mon Jul 10 04:32:22 2023 Repetition - Fold 2 -#> Mon Jul 10 04:32:22 2023 Repetition - Fold 3 -#> Mon Jul 10 04:32:23 2023 Repetition 2 -#> Mon Jul 10 04:32:23 2023 Repetition - Fold 1 -#> Mon Jul 10 04:32:23 2023 Repetition - Fold 2 -#> Mon Jul 10 04:32:23 2023 Repetition - Fold 3 +#> Tue Jul 11 04:31:50 2023 Repetition 1 +#> Tue Jul 11 04:31:50 2023 Repetition - Fold 1 +#> Tue Jul 11 04:31:50 2023 Repetition - Fold 2 +#> Tue Jul 11 04:31:50 2023 Repetition - Fold 3 +#> Tue Jul 11 04:31:50 2023 Repetition 2 +#> Tue Jul 11 04:31:50 2023 Repetition - Fold 1 +#> Tue Jul 11 04:31:50 2023 Repetition - Fold 2 +#> Tue Jul 11 04:31:50 2023 Repetition - Fold 3 summary(sp_res$error_rep) #> mean sd median IQR #> train_auroc 0.8472530 0.017474834 0.8472530 0.012356574