diff --git a/articles/spatial-modeling-use-case.html b/articles/spatial-modeling-use-case.html index 007b1926..3bb45f2d 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: 0.54 % +## OOB prediction error: 0.66 %

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    3.34e-02 0.00112 3.39e-02 0.001037
-## train_accuracy 9.67e-01 0.00112 9.66e-01 0.001037
-## train_events   4.69e+03 0.00000 4.69e+03 0.000000
-## train_count    3.09e+04 0.00000 3.09e+04 0.000000
-## test_error     3.74e-02 0.00054 3.76e-02 0.000519
-## test_accuracy  9.63e-01 0.00054 9.62e-01 0.000519
-## test_events    1.17e+03 0.00000 1.17e+03 0.000000
-## test_count     7.71e+03 0.00000 7.71e+03 0.000000

+
##                    mean       sd   median      IQR
+## train_error    3.45e-02 0.000252 3.45e-02 0.000243
+## train_accuracy 9.65e-01 0.000252 9.66e-01 0.000243
+## 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     3.93e-02 0.002142 3.92e-02 0.002139
+## test_accuracy  9.61e-01 0.002142 9.61e-01 0.002139
+## 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
 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
@@ -400,16 +400,16 @@ 
Linear Discriminant Analysis (LDA)  benchmark = TRUE, progress = FALSE
 )
 res_lda_sp$benchmark$runtime_performance
-
## Time difference of 21.3 secs
+
## Time difference of 18.3 secs
 summary(res_lda_sp$error_rep)
##                    mean      sd   median     IQR
-## train_error    2.91e-02 0.00220 2.95e-02 0.00217
-## train_accuracy 9.71e-01 0.00220 9.71e-01 0.00217
+## train_error    3.03e-02 0.00375 3.07e-02 0.00373
+## train_accuracy 9.70e-01 0.00375 9.69e-01 0.00373
 ## 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     6.41e-02 0.00389 6.39e-02 0.00389
-## test_accuracy  9.36e-01 0.00389 9.36e-01 0.00389
+## test_error     6.49e-02 0.00967 6.75e-02 0.00940
+## test_accuracy  9.35e-01 0.00967 9.32e-01 0.00940
 ## test_events    1.17e+03 0.00000 1.17e+03 0.00000
 ## test_count     7.71e+03 0.00000 7.71e+03 0.00000
@@ -460,14 +460,14 @@

RandomForest## 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 8.45e-02 0.00234 8.52e-02 0.00227 -## test_accuracy 9.15e-01 0.00234 9.15e-01 0.00227 +## test_error 8.70e-02 0.00306 8.78e-02 0.00298 +## test_accuracy 9.13e-01 0.00306 9.12e-01 0.00298 ## test_events 1.17e+03 0.00000 1.17e+03 0.00000 ## test_count 7.71e+03 0.00000 7.71e+03 0.00000 
 summary(res_rf_sp$error_rep)["test_accuracy",]
##                mean      sd median     IQR
-## test_accuracy 0.915 0.00234  0.915 0.00227
+## test_accuracy 0.913 0.00306 0.912 0.00298

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 83e1b1e0..394b5d7c 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 f1430408..61684427 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: 2023-08-22T04:24Z +last_built: 2023-08-23T04:30Z 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 54a6a674..2de73a22 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]]$distance -#> [1] 63.62793 +#> [1] 55.09906 # spatial partitioning: more substantial distance, depending on number of # folds etc. sp.parti[[1]][[1]]$distance -#> [1] 400.8701 +#> [1] 392.9521 diff --git a/reference/as.resampling.html b/reference/as.resampling.html index 98d13713..c57f950a 100644 --- a/reference/as.resampling.html +++ b/reference/as.resampling.html @@ -164,19 +164,19 @@

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 715272 712722 712642 713542 714292 ... -#> $ y : num 9557702 9560152 9560232 9559972 9558982 ... -#> $ dem : num 2813 1835 1883 2184 2459 ... -#> $ slope : num 31 29.3 46.8 35.2 43.6 ... -#> $ hcurv : num -0.00123 -0.00333 0.00884 -0.00841 0.00214 ... -#> $ vcurv : num 0.00393 -0.01767 0.01345 -0.0001 0.00466 ... -#> $ carea : num 2081 3322 1121 1527 724 ... -#> $ cslope : num 37.6 31 36.6 20.1 38.8 ... -#> $ distroad : num 300 280 195 300 300 ... +#> $ x : num 713512 712992 712842 714042 715022 ... +#> $ y : num 9559092 9560672 9560152 9558902 9559332 ... +#> $ dem : num 2166 1926 1930 2332 2308 ... +#> $ slope : num 56 27.2 28.1 50.1 20.1 ... +#> $ hcurv : num 0.02056 -0.00199 0.00753 -0.00858 0.0094 ... +#> $ vcurv : num -0.06976 0.00659 0.01487 -0.00292 0.00679 ... +#> $ carea : num 301 3554 1078 1234 550 ... +#> $ cslope : num 49.4 27.8 28.3 42.2 30 ... +#> $ distroad : num 300 30 300 300 300 ... #> $ slides : Factor w/ 2 levels "FALSE","TRUE": 2 2 2 2 2 2 2 2 2 2 ... -#> $ distdeforest : num 300 21.4 47 247 300 ... -#> $ distslidespast: num 100 46 0 100 100 100 57 100 0 100 ... -#> $ log.carea : num 3.32 3.52 3.05 3.18 2.86 ... +#> $ distdeforest : num 300 183.39 0.56 300 300 ... +#> $ distslidespast: num 41 20 100 100 85 7 8 100 100 4 ... +#> $ log.carea : num 2.48 3.55 3.03 3.09 2.74 ... # the corresponding training sample - larger: str(ecuador[parti[[1]]$train, ]) #> 'data.frame': 676 obs. of 13 variables: @@ -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 ... -#> $ distslidespast: num 9 21 40 100 21 2 100 41 5 20 ... +#> $ distdeforest : num 15 300 300 300 300 9.15 300 300 0 300 ... +#> $ distslidespast: num 9 21 40 100 21 2 100 100 5 100 ... #> $ 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': 273 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 ... -#> $ distroad : num 300 300 300 300 300 300 300 300 300 300 ... +#> 'data.frame': 285 obs. of 13 variables: +#> $ x : num 715042 715382 714852 714922 712742 ... +#> $ y : num 9559312 9560142 9557902 9558862 9560482 ... +#> $ dem : num 2320 2021 2675 2536 1928 ... +#> $ slope : num 42.9 42 30.7 54.9 34.7 ... +#> $ hcurv : num -0.01106 0.00958 0.00221 0.01305 -0.00292 ... +#> $ vcurv : num -0.04634 0.02642 0.00969 -0.00555 0.00712 ... +#> $ carea : num 501 671 369 430 3637 ... +#> $ cslope : num 33.9 41.6 20.5 46.3 29 ... +#> $ distroad : num 300 300 300 300 60.5 ... #> $ slides : Factor w/ 2 levels "FALSE","TRUE": 2 2 2 2 2 2 2 2 2 2 ... -#> $ distdeforest : num 15 300 300 300 9.15 300 300 300 300 300 ... -#> $ distslidespast: num 9 21 40 100 2 100 10 100 2 100 ... -#> $ log.carea : num 3.75 3.15 5.55 2.7 2.8 ... +#> $ distdeforest : num 300 300 300 300 152 ... +#> $ distslidespast: num 100 21 10 100 35 100 2 100 41 100 ... +#> $ log.carea : num 2.7 2.83 2.57 2.63 3.56 ... # bootstrap training sample: same size as nrow(ecuador): str(ecuador[parti[[1]]$train, ]) #> 'data.frame': 751 obs. of 13 variables: -#> $ x : num 714962 715212 715192 714602 714112 ... -#> $ y : num 9559862 9557752 9559492 9560542 9558562 ... -#> $ dem : num 2282 2821 2219 2038 2374 ... -#> $ slope : num 22.2 39.2 21.8 25.6 29.2 ... -#> $ hcurv : num -0.00028 0.00017 0.00713 0.00065 0.00283 ... -#> $ vcurv : num 0.01318 0.01213 -0.00173 0.00005 0.00117 ... -#> $ carea : num 289 329 8967 769 1018 ... -#> $ cslope : num 15.6 35.2 28.6 24.1 33.1 ... -#> $ 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 1 ... -#> $ distdeforest : num 300 300 300 300 300 300 300 300 300 300 ... -#> $ distslidespast: num 2 100 40 89 25 100 0 26 41 100 ... -#> $ log.carea : num 2.46 2.52 3.95 2.89 3.01 ... +#> $ x : num 715592 715572 712842 714882 713302 ... +#> $ y : num 9558562 9558482 9560152 9559892 9560702 ... +#> $ dem : num 2655 2686 1930 2258 1845 ... +#> $ slope : num 33.2 16.8 28.1 21.9 39 ... +#> $ hcurv : num -0.00034 0.00282 0.00753 -0.00849 0.00129 ... +#> $ vcurv : num 0.00294 0.00458 0.01487 0.0015 0.0251 ... +#> $ carea : num 740 486 1078 504 3343 ... +#> $ cslope : num 25.5 16.6 28.3 20.9 29.5 ... +#> $ distroad : num 300 300 300 300 77.8 ... +#> $ slides : Factor w/ 2 levels "FALSE","TRUE": 2 1 2 1 2 2 1 2 1 2 ... +#> $ distdeforest : num 300 300 0.56 300 76.41 ... +#> $ distslidespast: num 100 100 100 42 54 100 100 100 92 18 ... +#> $ log.carea : num 2.87 2.69 3.03 2.7 3.52 ... diff --git a/reference/err_default.html b/reference/err_default.html index 5641ad79..af99eaa7 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] 0.5191458 +#> [1] 0.5236306 #> #> $error -#> [1] 0.493 +#> [1] 0.477 #> #> $accuracy -#> [1] 0.507 +#> [1] 0.523 #> #> $sensitivity -#> [1] 0.3103448 +#> [1] 0.3372781 #> #> $specificity -#> [1] 0.6982249 +#> [1] 0.7139959 #> #> $fpr70 -#> [1] 0.6706114 +#> [1] 0.673428 #> #> $fpr80 -#> [1] 0.7692308 +#> [1] 0.7768763 #> #> $fpr90 -#> [1] 0.8836292 +#> [1] 0.8843813 #> #> $tpr80 -#> [1] 0.1845842 +#> [1] 0.2327416 #> #> $tpr90 -#> [1] 0.1014199 +#> [1] 0.1301775 #> #> $tpr95 -#> [1] 0.05476673 +#> [1] 0.04536489 #> #> $events -#> [1] 493 +#> [1] 507 #> #> $count #> [1] 1000 #> err_default(obs > 0, obs + rnorm(1000)) # some discrimination #> $auroc -#> [1] 0.8527671 +#> [1] 0.8133234 #> #> $error -#> [1] 0.252 +#> [1] 0.287 #> #> $accuracy -#> [1] 0.748 +#> [1] 0.713 #> #> $sensitivity -#> [1] 0.6288032 +#> [1] 0.5680473 #> #> $specificity -#> [1] 0.8639053 +#> [1] 0.862069 #> #> $fpr70 -#> [1] 0.1755424 +#> [1] 0.2210953 #> #> $fpr80 -#> [1] 0.2445759 +#> [1] 0.3853955 #> #> $fpr90 -#> [1] 0.3925049 +#> [1] 0.5415822 #> #> $tpr80 -#> [1] 0.7403651 +#> [1] 0.678501 #> #> $tpr90 -#> [1] 0.5638945 +#> [1] 0.5009862 #> #> $tpr95 -#> [1] 0.4361055 +#> [1] 0.3747535 #> #> $events -#> [1] 493 +#> [1] 507 #> #> $count #> [1] 1000 @@ -196,44 +196,44 @@

Examples

# Three mock regression examples: err_default(obs, rnorm(1000)) # just noise, but no bias #> $bias -#> [1] 0.002447058 +#> [1] -0.002810627 #> #> $stddev -#> [1] 1.375342 +#> [1] 1.441729 #> #> $rmse -#> [1] 1.374656 +#> [1] 1.441011 #> #> $mad -#> [1] 1.332189 +#> [1] 1.428686 #> #> $median -#> [1] -0.0528806 +#> [1] -0.01561067 #> #> $iqr -#> [1] 1.793704 +#> [1] 1.925516 #> #> $count #> [1] 1000 #> err_default(obs, obs + rnorm(1000)) # some association, no bias #> $bias -#> [1] -0.02626323 +#> [1] -0.02739735 #> #> $stddev -#> [1] 0.9617077 +#> [1] 1.014792 #> #> $rmse -#> [1] 0.9615854 +#> [1] 1.014655 #> #> $mad -#> [1] 0.8821401 +#> [1] 0.9683702 #> #> $median -#> [1] 0.00189562 +#> [1] -0.0445426 #> #> $iqr -#> [1] 1.199519 +#> [1] 1.298096 #> #> $count #> [1] 1000 @@ -243,7 +243,7 @@

Examples

#> [1] -1 #> #> $stddev -#> [1] 6.154555e-17 +#> [1] 6.073836e-17 #> #> $rmse #> [1] 1 diff --git a/reference/partition_cv.html b/reference/partition_cv.html index 8fde8422..28cae655 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 -#> 31358 712882.5 9560002 1911.52 25.5642309 -0.00681 -0.00029 5577.3916 -#> 4965 715042.5 9559312 2320.49 42.8578160 -0.01106 -0.04634 500.5027 -#> 23178 715382.5 9560142 2021.07 41.9766069 0.00958 0.02642 671.1807 -#> 36052 714842.5 9558892 2483.27 68.7898858 -0.04921 -0.12438 753.6304 -#> 16435 713512.5 9559092 2166.13 55.9739659 0.02056 -0.06976 301.2347 -#> 47223 715302.5 9557472 2857.45 39.5335149 -0.01021 -0.01579 1318.7560 -#> 28223 712742.5 9560482 1927.69 34.6679573 -0.00292 0.00712 3636.5303 -#> 25090 715362.5 9560102 2059.29 49.1196718 0.02059 -0.00628 556.0121 -#> 46178 714892.5 9559312 2379.54 32.7668833 -0.00266 0.02896 276.0310 -#> 1300 714042.5 9558482 2408.27 24.1484522 0.00659 0.01041 772.9746 -#> 8598 714002.5 9558872 2310.02 39.5839352 -0.00498 -0.00752 1321.5997 -#> 48187 712722.5 9560152 1835.29 29.3279907 -0.00333 -0.01767 3321.6113 -#> 34512 713162.5 9559632 2041.52 46.8152355 -0.00857 0.03677 1675.4679 -#> 21939 713642.5 9558712 2299.09 33.7506519 -0.00503 0.02433 1475.3191 -#> 9669 713852.5 9558612 2308.97 52.1804123 -0.01059 -0.07431 4079.0366 -#> 37104 713962.5 9561282 1795.56 25.7418478 0.00273 -0.02403 1787.3024 -#> 8869 715062.5 9559392 2278.56 41.4019303 0.00310 0.01440 1088.8639 -#> 20151 714602.5 9559922 2184.66 48.3415951 -0.00979 -0.01542 674.0147 -#> 26447 715632.5 9559102 2410.77 39.6567008 0.00730 0.01770 474.5059 -#> 23817 714842.5 9557832 2677.58 26.6574344 0.00922 0.01088 266.4355 -#> 38993 714202.5 9558402 2475.75 33.1593594 -0.01607 0.00677 1343.5006 -#> 22814 713862.5 9558582 2327.48 48.6120312 -0.02894 -0.03416 947.4878 -#> 10899 713852.5 9559652 2282.82 28.8140475 0.00952 0.01378 440.9104 -#> 3835 714142.5 9558552 2388.42 32.9003825 0.00979 -0.00269 491.8017 -#> 30800 715182.5 9557582 2772.37 39.2040642 -0.02475 0.00665 25318.3555 -#> 45869 714342.5 9560282 2127.11 27.6234412 -0.02169 -0.00160 9344.2930 -#> 30125 715162.5 9558992 2438.18 39.8566631 0.00471 -0.01382 560.8309 -#> 29472 714792.5 9561072 1786.08 36.1731174 -0.01029 -0.02312 2657.6936 -#> 22933 712712.5 9560432 1902.17 38.1234658 -0.00746 0.00096 3210.5781 -#> 36030 712852.5 9559572 1938.79 28.6788295 -0.01440 0.00710 843.7961 -#> 2942 713232.5 9560652 1851.75 34.5350311 0.00856 0.01114 4510.8833 -#> 47022 715362.5 9560102 2059.29 49.1196718 0.02059 -0.00628 556.0121 -#> 8288 714842.5 9557712 2679.04 28.9395253 0.00902 0.01358 802.7622 -#> 13585 714552.5 9560092 2123.69 57.5289733 -0.03613 -0.01898 588.3761 -#> 5749 712852.5 9560162 1934.78 23.3887102 0.00071 0.00150 1274.3594 -#> 34565 712972.5 9560172 2007.72 27.9471624 0.00598 0.01212 561.1948 -#> 3133 715042.5 9559252 2344.13 29.0208853 0.00422 0.02357 275.7474 -#> 1635 713102.5 9559052 2172.87 31.4840308 -0.00064 -0.00916 2009.1686 -#> 40738 714972.5 9557642 2745.48 42.5426892 -0.00294 -0.00096 520.5570 -#> 11687 714552.5 9560382 2052.66 29.5755084 -0.00255 -0.00365 1536.2647 -#> 48434 713352.5 9559042 2176.63 35.7021461 -0.01771 -0.01939 703.7817 -#> 19784 714982.5 9557722 2716.98 35.4362937 0.00888 0.00862 874.5408 -#> 20001 712702.5 9560132 1854.55 36.2911467 -0.00204 -0.00376 2048.3198 -#> 28743 715202.5 9558872 2463.87 34.6570711 -0.01267 -0.05063 18733.8848 -#> 17423 715592.5 9558562 2654.69 33.2195200 -0.00034 0.00294 739.5768 -#> 13134 714652.5 9559632 2265.33 49.5998104 -0.03552 -0.02058 431.4362 -#> 18394 715022.5 9559942 2216.16 42.1376081 -0.00613 0.00903 2360.1536 -#> 9704 714642.5 9559652 2249.12 37.1431350 -0.00759 -0.00400 608.6523 -#> 25244 713472.5 9559092 2137.03 18.3489734 -0.10372 -0.00008 825875.6875 -#> 5084 714912.5 9559302 2359.77 47.9680266 0.00107 0.02373 527.3528 -#> 49569 713232.5 9561122 1868.73 37.3121575 -0.00405 -0.01565 2232.6133 -#> 39520 715342.5 9557622 2889.48 41.6488751 -0.01282 0.00542 860.8840 -#> 18265 715522.5 9558782 2539.23 35.8562718 -0.00458 -0.00401 1722.6481 -#> 33350 713132.5 9560672 1896.89 24.6950539 0.00306 -0.00436 2602.7390 -#> 39732 715362.5 9559582 2230.97 23.3468842 -0.00161 -0.00409 2312.8413 -#> 28845 714892.5 9559272 2374.42 45.7501070 -0.00209 0.02739 365.7626 -#> 46783 713592.5 9560762 1832.60 36.0103974 0.00448 -0.00328 592.3326 -#> 44650 715612.5 9558142 2764.88 39.3604817 -0.00550 -0.00941 1290.7709 -#> 32280 712572.5 9560182 1956.86 50.6781170 -0.02554 0.03304 1569.7313 -#> 3418 715372.5 9558492 2601.00 22.6272492 -0.01168 -0.04102 35685.0000 -#> 664 714452.5 9559852 2174.83 44.8608765 0.00282 0.00109 767.4097 -#> 11618 713852.5 9558402 2432.14 54.0591409 -0.00410 0.03620 379.0855 -#> 28046 714882.5 9558492 2711.77 28.3058340 -0.00131 0.00201 788.0621 -#> 42765 713552.5 9559632 2186.95 44.0111801 0.00394 -0.00224 942.0322 -#> 31834 713492.5 9560682 1800.85 34.2370930 -0.00472 -0.00228 2262.3796 -#> 39166 712832.5 9560142 1922.46 39.6601386 0.01469 0.02940 987.8085 -#> 20368 715252.5 9560622 1865.83 33.5724620 0.00025 -0.01245 6353.3179 -#> 47718 712812.5 9559892 1867.55 54.9386311 -0.01491 0.01061 5744.7583 -#> 30287 713992.5 9557822 2403.92 18.2154742 0.03890 -0.00911 173.2806 -#> 20797 713012.5 9560392 1842.25 36.6498183 0.01299 0.00141 4611.0718 -#> 34615 713242.5 9560732 1879.60 27.5936474 0.00384 0.00175 1026.6818 -#> 43245 713722.5 9560012 2153.38 34.1912564 -0.01263 0.01322 4605.1411 -#> 10689 713542.5 9559132 2211.95 31.9813582 0.05379 0.00801 172.7491 -#> 26428 713042.5 9560332 1895.15 33.9597815 -0.01439 -0.01161 3065.3694 -#> 17058 714032.5 9558502 2402.95 31.3528235 0.01924 0.02606 564.6876 -#> 14545 713562.5 9559602 2222.17 54.2390497 0.01011 0.00739 789.7573 -#> 19756 715572.5 9558972 2485.95 38.8127340 0.00546 0.01484 495.0292 -#> 2801 715252.5 9559612 2199.28 24.4034184 0.03051 0.02190 264.6404 -#> 49133 714162.5 9558462 2451.13 21.8904255 0.00042 0.00957 495.4453 -#> 49378 713252.5 9560732 1875.41 27.8354356 0.00181 -0.00001 1184.7952 -#> 4667 712652.5 9560472 1957.89 36.3243783 -0.00296 0.00426 1315.5334 -#> 20171 715732.5 9557962 2815.53 48.6578678 -0.01341 0.00171 4425.5410 -#> 9606 714392.5 9559172 2353.05 30.7833035 -0.00185 -0.01185 953.8614 -#> 22847 714582.5 9560382 2040.49 25.3361937 0.00181 -0.00341 1363.4000 -#> 42640 714332.5 9558522 2403.10 38.8711757 -0.00212 0.02732 4237.6934 -#> 17227 714812.5 9558892 2545.43 54.0717460 0.00458 0.04291 293.4231 -#> 47722 712732.5 9561022 1999.03 37.4926392 0.00004 -0.02384 947.2075 -#> 6212 714592.5 9560362 2034.54 12.5225656 -0.01454 0.00144 317260.6875 -#> 23832 714252.5 9560182 2188.12 30.6475125 0.00427 0.02403 474.8613 -#> 39466 714942.5 9557772 2680.35 16.7532859 0.00166 -0.01265 1148.6487 -#> 17851 715362.5 9557482 2869.26 29.1257366 -0.03251 -0.00389 14516.2002 -#> 13072 714862.5 9558932 2448.29 41.0524260 -0.04989 -0.02781 29995.1543 -#> 23176 714012.5 9558892 2308.59 43.0291304 -0.01084 -0.02996 1599.3491 -#> 4785 713792.5 9560472 2055.34 33.0527893 -0.00586 0.00067 1123.7318 -#> 31983 714302.5 9559912 2204.77 23.4649135 -0.01332 0.01842 246.5161 -#> 15211 715772.5 9558082 2801.07 36.2968763 -0.00079 -0.00331 699.3024 -#> 32405 713022.5 9558982 2208.85 15.0046187 0.00599 0.00301 338.9676 -#> 36561 715292.5 9560802 1844.64 31.9372405 0.00002 -0.01232 977.7320 -#> 46165 714392.5 9557912 2542.56 16.3298701 0.00325 -0.00505 351.4078 -#> 48601 713692.5 9560152 2058.14 59.5572439 -0.01433 -0.02177 997.3089 -#> 30809 712532.5 9560032 1928.24 40.8994463 0.00570 0.02230 714.7432 -#> 748 715102.5 9559692 2301.39 27.0218355 -0.00759 0.00158 837.3730 -#> 24836 714202.5 9558272 2507.87 38.4890128 -0.00226 0.00736 477.8591 -#> 25212 714272.5 9559012 2433.45 37.6714021 -0.01909 -0.01181 1286.3949 -#> 40459 712542.5 9559192 1992.60 39.8171290 -0.00162 -0.00517 1060.5494 -#> 14694 715032.5 9559482 2307.44 44.1733271 -0.00007 -0.00463 1050.7473 -#> 8635 713832.5 9557902 2397.71 47.1194761 0.02295 0.02715 520.7355 -#> 35852 715112.5 9559962 2173.61 39.3157909 -0.00738 0.00118 2273.4758 -#> 18746 713292.5 9560952 1890.51 41.8855067 -0.00402 -0.01328 1066.7134 -#> 20067 714242.5 9560442 2125.23 38.6402737 0.00363 -0.00363 389.2691 -#> 8066 715842.5 9558732 2636.38 33.7174203 -0.00494 0.01064 683.2795 -#> 49363 712772.5 9559552 1935.91 29.0369281 -0.03220 -0.03290 2481.5632 -#> 12716 713962.5 9557762 2402.60 18.9322444 0.03480 0.01500 538.8209 -#> 7538 714722.5 9558582 2660.25 15.9946898 0.00544 -0.01014 271.4219 -#> 1085 715722.5 9557532 3097.22 28.7842537 0.02327 0.01833 300.0125 -#> 36833 713322.5 9560572 1794.25 33.7540896 0.00527 -0.02077 8759.9961 -#> 18627 714292.5 9559252 2330.78 18.4962235 -0.00538 -0.00052 3508.5591 -#> 12974 714662.5 9557332 2667.61 41.9416565 -0.00065 0.00475 515.1661 -#> 43403 712632.5 9559262 2072.53 34.4863297 -0.00069 -0.00900 614.5898 -#> 42934 713832.5 9558672 2270.74 27.6119821 -0.03193 -0.00267 31588.1953 -#> 22545 713402.5 9559562 2195.44 25.3155672 0.04152 0.00098 164.0802 -#> 48869 712562.5 9560122 1972.89 24.9465824 0.00185 0.01655 560.8554 -#> 12519 713982.5 9560912 1956.98 28.8249337 -0.00452 -0.00948 1222.5433 -#> 33410 715342.5 9560452 1942.88 29.0930780 -0.00305 -0.00705 968.9738 -#> 5656 713132.5 9560142 2042.24 33.5953803 -0.00035 0.00955 713.4463 -#> 33666 713632.5 9561012 1775.88 21.1788119 -0.00950 -0.05130 107.5831 -#> 330 714232.5 9560362 2165.00 18.0607756 0.01142 0.00868 235.8511 -#> 42424 714692.5 9560902 1921.34 25.3310371 0.01154 -0.00204 618.2027 -#> 36889 714302.5 9559802 2243.60 21.1662069 0.01923 0.05547 128.2613 -#> 48988 714682.5 9560852 1942.97 20.2907274 0.00480 0.00740 909.4791 -#> 43415 714722.5 9558642 2657.99 0.2314749 0.00799 0.00911 100.0000 -#> 17628 714032.5 9560252 2160.03 30.2945705 0.00537 -0.00577 423.5598 -#> 3724 714322.5 9557402 2603.46 34.5974835 0.02523 0.02247 282.1076 -#> 40612 713542.5 9561152 1856.15 33.5684513 0.00420 0.01960 884.2003 -#> 23499 714272.5 9558232 2508.35 28.9676002 -0.00820 -0.00260 1484.6593 -#> 12300 712372.5 9560062 1903.64 50.0369772 0.00343 0.01177 1558.2231 -#> 44216 715772.5 9558402 2710.44 30.4446854 -0.00070 -0.00050 1471.4346 -#> 2999 714592.5 9558492 2515.83 47.0318136 0.01600 -0.01849 764.8193 -#> 24446 714742.5 9560082 2200.60 28.7218013 -0.00598 0.02208 842.4825 -#> 19767 714272.5 9558052 2431.67 55.7247292 0.00361 -0.01841 756.0055 -#> 48050 714022.5 9560572 2099.82 18.7139475 0.01182 -0.00972 331.7462 -#> 11394 713472.5 9561232 1889.48 34.3144424 0.00108 -0.00948 1538.1741 -#> 43972 713642.5 9558702 2304.16 24.0235474 0.00340 0.01809 1158.0426 -#> 9320 713872.5 9559262 2378.76 29.8585496 0.00309 0.01011 501.7867 -#> 9526 714432.5 9560582 2076.13 31.0800956 -0.00172 -0.00119 792.3544 -#> 39883 712732.5 9560842 2082.69 25.0909678 0.00732 0.01858 298.4490 -#> 6577 714142.5 9559902 2244.30 57.8905097 -0.06986 -0.05013 616.4128 -#> 36141 714222.5 9559422 2316.64 16.2771580 -0.07627 -0.00124 25826.6914 -#> 39234 712902.5 9559652 1953.39 29.7319259 0.07379 -0.00249 148.1857 -#> 30296 713322.5 9559782 2042.96 52.6210169 -0.02110 -0.02931 1812.3820 -#> cslope distroad slides distdeforest distslidespast log.carea -#> 31358 34.4278880 300.00 TRUE 15.00 9 3.746431 -#> 4965 33.9059234 300.00 TRUE 300.00 100 2.699406 -#> 23178 41.6001737 300.00 TRUE 300.00 21 2.826839 -#> 36052 53.7451601 300.00 TRUE 300.00 100 2.877158 -#> 16435 49.4439659 300.00 TRUE 300.00 41 2.478905 -#> 47223 36.2590611 300.00 TRUE 300.00 100 3.120164 -#> 28223 29.0226042 60.46 TRUE 152.09 35 3.560687 -#> 25090 43.5316144 300.00 TRUE 300.00 26 2.745084 -#> 46178 20.9312942 300.00 TRUE 300.00 31 2.440958 -#> 1300 27.5306221 300.00 TRUE 300.00 6 2.888165 -#> 8598 39.5982591 300.00 TRUE 300.00 100 3.121100 -#> 48187 30.9666500 279.54 TRUE 21.35 46 3.521349 -#> 34512 34.6398824 300.00 TRUE 195.00 2 3.224136 -#> 21939 23.9324471 300.00 TRUE 300.00 100 3.168886 -#> 9669 31.6645125 300.00 TRUE 300.00 1 3.610558 -#> 37104 4.8661306 50.99 TRUE 0.00 21 3.252198 -#> 8869 29.1102667 300.00 TRUE 300.00 100 3.036974 -#> 20151 44.7955593 300.00 TRUE 300.00 100 2.828669 -#> 26447 33.0367465 300.00 TRUE 300.00 100 2.676242 -#> 23817 21.6113951 300.00 TRUE 300.00 4 2.425592 -#> 38993 29.5485794 300.00 TRUE 300.00 2 3.128238 -#> 22814 34.9120373 300.00 TRUE 300.00 6 2.976574 -#> 10899 24.7741221 300.00 TRUE 300.00 20 2.644350 -#> 3835 35.1389286 300.00 TRUE 300.00 39 2.691790 -#> 30800 33.2481679 300.00 TRUE 300.00 100 4.403435 -#> 45869 23.4546003 300.00 TRUE 300.00 12 3.970546 -#> 30125 40.7355804 300.00 TRUE 300.00 100 2.748832 -#> 29472 18.6486303 111.09 TRUE 0.00 25 3.424505 -#> 22933 33.0631025 87.56 TRUE 115.40 2 3.506583 -#> 36030 29.1996481 300.00 TRUE 0.00 100 2.926238 -#> 2942 29.8207344 68.13 TRUE 116.32 10 3.654262 -#> 47022 43.5316144 300.00 TRUE 300.00 26 2.745084 -#> 8288 22.7177766 300.00 TRUE 300.00 83 2.904587 -#> 13585 47.0673370 300.00 TRUE 300.00 100 2.769655 -#> 5749 28.2330683 300.00 TRUE 0.00 100 3.105292 -#> 34565 20.3801088 300.00 TRUE 68.13 100 2.749114 -#> 3133 18.8342686 300.00 TRUE 300.00 100 2.440511 -#> 1635 27.8268412 300.00 TRUE 300.00 100 3.303016 -#> 40738 28.9601518 300.00 TRUE 300.00 100 2.716468 -#> 11687 23.1939045 300.00 TRUE 300.00 68 3.186466 -#> 48434 29.9358989 300.00 TRUE 300.00 6 2.847438 -#> 19784 27.9557567 300.00 TRUE 300.00 100 2.941780 -#> 20001 34.6771246 264.46 TRUE 18.17 56 3.311398 -#> 28743 34.6530604 300.00 TRUE 300.00 71 4.272628 -#> 17423 25.4605255 300.00 TRUE 300.00 100 2.868983 -#> 13134 37.6439001 300.00 TRUE 300.00 67 2.634917 -#> 18394 28.6106475 300.00 TRUE 300.00 57 3.372940 -#> 9704 38.9960805 300.00 TRUE 300.00 90 2.784369 -#> 25244 33.2189470 300.00 TRUE 300.00 6 5.916915 -#> 5084 31.5951847 300.00 TRUE 300.00 18 2.722101 -#> 49569 41.8494103 25.00 TRUE 35.00 100 3.348814 -#> 39520 35.8940870 300.00 TRUE 300.00 100 2.934945 -#> 18265 31.4456427 300.00 TRUE 300.00 100 3.236197 -#> 33350 28.9527033 10.00 TRUE 166.26 2 3.415431 -#> 39732 29.1681354 300.00 TRUE 300.00 25 3.364146 -#> 28845 29.8425068 300.00 TRUE 300.00 54 2.563199 -#> 46783 19.9423690 256.35 TRUE 0.00 100 2.772566 -#> 44650 37.3625778 300.00 TRUE 300.00 100 3.110849 -#> 32280 27.8314249 126.66 TRUE 0.00 5 3.195825 -#> 3418 35.4534824 300.00 TRUE 300.00 73 4.552486 -#> 664 30.8423181 300.00 TRUE 300.00 12 2.885027 -#> 11618 33.9855646 300.00 TRUE 300.00 1 2.578737 -#> 28046 19.5911459 300.00 TRUE 300.00 15 2.896560 -#> 42765 42.3530402 300.00 TRUE 300.00 12 2.974066 -#> 31834 24.6469255 215.68 TRUE 0.00 100 3.354565 -#> 39166 29.8184425 300.00 TRUE 1.90 90 2.994673 -#> 20368 30.3444178 300.00 TRUE 166.26 100 3.803001 -#> 47718 32.3234140 300.00 TRUE 10.00 1 3.759272 -#> 30287 19.8484039 300.00 TRUE 300.00 65 2.238750 -#> 20797 32.0569886 285.07 TRUE 0.00 96 3.663802 -#> 34615 34.1895376 17.57 TRUE 142.95 16 3.011436 -#> 43245 24.4458173 300.00 TRUE 300.00 100 3.663243 -#> 10689 30.6480854 300.00 TRUE 300.00 61 2.237416 -#> 26428 35.4752548 300.00 TRUE 0.00 100 3.486483 -#> 17058 26.3228270 300.00 TRUE 300.00 8 2.751808 -#> 14545 39.3140721 300.00 TRUE 300.00 37 2.897494 -#> 19756 30.1163806 300.00 TRUE 300.00 100 2.694631 -#> 2801 25.7464315 300.00 TRUE 300.00 26 2.422656 -#> 49133 23.5193445 300.00 TRUE 300.00 38 2.694996 -#> 49378 33.9941589 22.68 TRUE 134.63 25 3.073643 -#> 4667 31.5172624 24.78 TRUE 172.24 38 3.119102 -#> 20171 42.1301596 300.00 TRUE 300.00 100 3.645966 -#> 9606 33.8239905 300.00 TRUE 300.00 0 2.979485 -#> 22847 24.7477660 300.00 TRUE 300.00 41 3.134623 -#> 42640 34.9097455 300.00 TRUE 300.00 100 3.627130 -#> 17227 43.5975045 300.00 TRUE 300.00 100 2.467494 -#> 47722 33.1158146 300.00 TRUE 65.02 1 2.976445 -#> 6212 31.9315109 300.00 TRUE 300.00 47 5.501416 -#> 23832 25.4851627 300.00 TRUE 300.00 8 2.676567 -#> 39466 27.2515916 300.00 TRUE 300.00 100 3.060187 -#> 17851 32.2632534 300.00 TRUE 300.00 100 4.161853 -#> 13072 34.9446959 300.00 TRUE 300.00 100 4.477051 -#> 23176 41.5583478 300.00 TRUE 300.00 100 3.203943 -#> 4785 26.4861200 300.00 FALSE 0.00 100 3.050663 -#> 31983 19.7756383 300.00 FALSE 300.00 65 2.391845 -#> 15211 30.8348697 300.00 FALSE 300.00 100 2.844665 -#> 32405 15.7620689 300.00 FALSE 300.00 100 2.530158 -#> 36561 25.8048732 300.00 FALSE 70.00 100 2.990220 -#> 46165 14.8184075 300.00 FALSE 300.00 100 2.545811 -#> 48601 35.8579907 300.00 FALSE 245.19 100 2.998830 -#> 30809 36.3662042 181.84 FALSE 0.00 100 2.854150 -#> 748 38.1326331 300.00 FALSE 300.00 12 2.922919 -#> 24836 25.5837115 300.00 FALSE 300.00 95 2.679300 -#> 25212 38.9829025 300.00 FALSE 300.00 100 3.109374 -#> 40459 36.1868684 300.00 FALSE 216.66 10 3.025531 -#> 14694 43.5115609 300.00 FALSE 300.00 100 3.021498 -#> 8635 27.6818829 300.00 FALSE 300.00 23 2.716617 -#> 35852 36.3621935 300.00 FALSE 300.00 100 3.356690 -#> 18746 39.1364552 4.48 FALSE 205.00 27 3.028048 -#> 20067 29.8235992 300.00 FALSE 268.03 100 2.590250 -#> 8066 23.5273659 300.00 FALSE 300.00 100 2.834598 -#> 49363 32.9576783 300.00 FALSE 0.00 100 3.394725 -#> 12716 24.3449767 300.00 FALSE 300.00 25 2.731444 -#> 7538 17.2649372 300.00 FALSE 300.00 87 2.433645 -#> 1085 25.8948276 300.00 FALSE 300.00 100 2.477139 -#> 36833 28.0755049 187.46 FALSE 18.49 45 3.942504 -#> 18627 31.2571396 300.00 FALSE 300.00 20 3.545129 -#> 12974 37.3385136 300.00 FALSE 300.00 100 2.711947 -#> 43403 31.0617609 300.00 FALSE 111.88 86 2.788585 -#> 42934 32.2271571 300.00 FALSE 300.00 22 4.499525 -#> 22545 23.6843564 300.00 FALSE 300.00 96 2.215056 -#> 48869 24.8755356 138.98 FALSE 0.00 63 2.748851 -#> 12519 30.4859384 300.00 FALSE 120.00 100 3.087264 -#> 33410 32.8797560 300.00 FALSE 300.00 100 2.986312 -#> 5656 31.2726094 300.00 FALSE 94.43 100 2.853361 -#> 33666 0.0217724 212.17 FALSE 103.16 100 2.031744 -#> 330 13.5722243 300.00 FALSE 275.17 100 2.372638 -#> 42424 22.6071957 275.21 FALSE 93.50 70 2.791131 -#> 36889 19.6450039 300.00 FALSE 300.00 48 2.108096 -#> 48988 18.9586005 300.00 FALSE 144.19 100 2.958793 -#> 43415 0.2314749 300.00 FALSE 300.00 100 2.000000 -#> 17628 26.9920417 300.00 FALSE 205.59 100 2.626915 -#> 3724 25.7269509 300.00 FALSE 300.00 100 2.450415 -#> 40612 21.9270948 55.73 FALSE 125.03 100 2.946551 -#> 23499 28.5149635 300.00 FALSE 300.00 50 3.171627 -#> 12300 38.4173931 124.35 FALSE 0.00 100 3.192630 -#> 44216 26.0649960 300.00 FALSE 300.00 100 3.167741 -#> 2999 45.5839492 300.00 FALSE 300.00 100 2.883559 -#> 24446 31.3631367 300.00 FALSE 300.00 100 2.925561 -#> 19767 42.0104115 300.00 FALSE 300.00 100 2.878525 -#> 48050 23.3612082 300.00 FALSE 72.23 100 2.520806 -#> 11394 40.6038001 46.04 FALSE 55.00 100 3.187005 -#> 43972 22.8524217 300.00 FALSE 300.00 100 3.063725 -#> 9320 18.1392708 300.00 FALSE 300.00 93 2.700519 -#> 9526 27.5695832 300.00 FALSE 300.00 100 2.898919 -#> 39883 17.0443485 233.81 FALSE 219.23 100 2.474870 -#> 6577 42.4074712 300.00 FALSE 300.00 70 2.789872 -#> 36141 27.3060226 300.00 FALSE 300.00 85 4.412069 -#> 39234 33.4859454 300.00 FALSE 0.00 100 2.170806 -#> 30296 40.1586119 300.00 FALSE 300.00 68 3.258250 +#> x y dem slope hcurv vcurv carea +#> 37912 712802.5 9559952 1838.40 52.101344 0.00183 -0.09203 634.3320 +#> 15412 715272.5 9557702 2813.17 30.952326 -0.00123 0.00393 2081.0056 +#> 28223 712742.5 9560482 1927.69 34.667957 -0.00292 0.00712 3636.5303 +#> 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 +#> 20799 714792.5 9561002 1857.29 23.129160 0.00090 0.00890 2301.0454 +#> 9669 713852.5 9558612 2308.97 52.180412 -0.01059 -0.07431 4079.0366 +#> 38411 713552.5 9560652 1851.63 46.005073 -0.00891 -0.00949 6217.8213 +#> 1768 713912.5 9558552 2357.19 38.692413 -0.00645 0.00835 2425.3115 +#> 43669 712392.5 9560162 2001.90 47.283915 -0.00284 0.01804 628.5153 +#> 20151 714602.5 9559922 2184.66 48.341595 -0.00979 -0.01542 674.0147 +#> 125 713992.5 9561062 1878.28 49.971087 0.00183 0.03387 1989.3012 +#> 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 +#> 39629 714602.5 9558912 2474.89 40.258307 -0.06175 -0.08226 2325.2053 +#> 38287 714872.5 9561162 1719.41 48.235598 -0.03021 -0.09328 2235.5259 +#> 30800 715182.5 9557582 2772.37 39.204064 -0.02475 0.00665 25318.3555 +#> 45869 714342.5 9560282 2127.11 27.623441 -0.02169 -0.00160 9344.2930 +#> 22283 713592.5 9560762 1832.60 36.010397 0.00448 -0.00328 592.3326 +#> 40307 713302.5 9560702 1845.07 39.004675 0.00129 0.02510 3342.5518 +#> 2107 714142.5 9558562 2382.67 35.553750 0.01412 0.00898 458.6938 +#> 15109 713872.5 9559602 2288.64 25.185506 0.00602 0.01518 854.4269 +#> 38032 714762.5 9560442 1988.33 35.169868 -0.00905 -0.01385 674.0721 +#> 14522 715012.5 9557732 2721.34 35.181327 -0.00382 0.00672 1114.8801 +#> 2942 713232.5 9560652 1851.75 34.535031 0.00856 0.01114 4510.8833 +#> 26215 714332.5 9560262 2138.08 25.721221 -0.00491 0.00581 1855.1630 +#> 34275 715332.5 9558442 2650.77 45.536967 -0.00043 0.00973 514.5488 +#> 4827 714962.5 9559882 2274.42 29.244912 0.00051 0.00889 362.8363 +#> 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 +#> 3453 713482.5 9559642 2145.11 35.125751 0.00108 -0.00678 1891.6584 +#> 13585 714552.5 9560092 2123.69 57.528973 -0.03613 -0.01898 588.3761 +#> 27708 715222.5 9557782 2844.65 29.542850 0.03206 0.02773 179.3077 +#> 10320 714912.5 9558482 2724.21 25.282909 0.00356 0.00774 527.1068 +#> 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 +#> 6763 714692.5 9561052 1830.60 48.394880 -0.01996 0.00626 1014.7159 +#> 19128 714852.5 9557872 2681.49 20.757688 0.00453 0.01077 300.7915 +#> 45104 715062.5 9559242 2345.02 24.156474 -0.01743 0.01313 783.4548 +#> 46756 713372.5 9559192 2120.47 46.005073 0.03696 0.02514 297.7842 +#> 29667 713892.5 9559712 2239.25 41.592725 0.00804 0.00346 543.0749 +#> 6557 713312.5 9559022 2181.69 27.634327 -0.00669 -0.00991 1403.0193 +#> 34359 712862.5 9558912 2111.37 32.812147 0.00463 0.00167 1938.5389 +#> 38686 714292.5 9557832 2496.22 38.480991 -0.00509 0.00499 885.0064 +#> 46235 712712.5 9561042 2023.11 41.277025 0.01458 0.00672 565.1107 +#> 18564 715552.5 9558592 2648.36 26.291314 0.00316 0.00714 475.3120 +#> 18751 714882.5 9559182 2381.81 39.299175 0.00066 -0.00036 516.4709 +#> 29926 714702.5 9557622 2637.00 44.642580 0.01843 0.04407 344.8395 +#> 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 +#> 13134 714652.5 9559632 2265.33 49.599810 -0.03552 -0.02058 431.4362 +#> 39278 714692.5 9561052 1830.60 48.394880 -0.01996 0.00626 1014.7159 +#> 41037 715532.5 9558792 2529.58 32.846524 -0.00585 -0.00974 2136.6939 +#> 23029 713502.5 9559102 2174.07 56.457542 0.03395 0.01495 266.5888 +#> 34707 713962.5 9561092 1819.49 65.686173 -0.04749 0.04779 2972.0193 +#> 21952 715352.5 9560172 2013.44 40.488063 0.02409 0.02771 674.3859 +#> 4617 714292.5 9559312 2342.95 32.392169 0.03504 0.05825 162.6688 +#> 375 715622.5 9558922 2510.87 37.366589 -0.01017 0.00197 711.1091 +#> 48808 715032.5 9557792 2707.02 25.437607 -0.02329 -0.01181 11655.4316 +#> 39520 715342.5 9557622 2889.48 41.648875 -0.01282 0.00542 860.8840 +#> 34005 714922.5 9558842 2559.23 52.831292 0.03658 0.04602 345.8933 +#> 32909 712812.5 9560452 1883.85 38.251235 -0.00221 0.00121 4159.6738 +#> 18265 715522.5 9558782 2539.23 35.856272 -0.00458 -0.00401 1722.6481 +#> 39732 715362.5 9559582 2230.97 23.346884 -0.00161 -0.00409 2312.8413 +#> 7668 713372.5 9559062 2164.82 37.934963 -0.01807 -0.01883 616.1993 +#> 35083 714612.5 9558152 2596.12 60.598308 0.04548 0.05543 352.1558 +#> 19966 715362.5 9559572 2235.06 26.049526 0.00126 -0.00485 2240.4185 +#> 46783 713592.5 9560762 1832.60 36.010397 0.00448 -0.00328 592.3326 +#> 44650 715612.5 9558142 2764.88 39.360482 -0.00550 -0.00941 1290.7709 +#> 3418 715372.5 9558492 2601.00 22.627249 -0.01168 -0.04102 35685.0000 +#> 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 +#> 41783 715572.5 9558652 2609.51 32.408212 0.00291 -0.00081 940.6002 +#> 18897 712802.5 9559882 1851.71 50.234648 -0.02627 -0.05393 8301.6094 +#> 39166 712832.5 9560142 1922.46 39.660139 0.01469 0.02940 987.8085 +#> 10467 714642.5 9559652 2249.12 37.143135 -0.00759 -0.00400 608.6523 +#> 10073 715572.5 9558612 2632.78 32.161267 0.00129 0.00171 788.0004 +#> 18440 714862.5 9558932 2448.29 41.052426 -0.04989 -0.02781 29995.1543 +#> 43201 715712.5 9557922 2852.82 31.462258 -0.00949 0.00520 3390.5708 +#> 42201 714642.5 9557452 2592.29 13.225585 -0.02084 -0.02236 9755.3027 +#> 34887 714662.5 9557392 2609.02 51.020746 -0.01878 -0.04351 1442.9247 +#> 20797 713012.5 9560392 1842.25 36.649818 0.01299 0.00141 4611.0718 +#> 33875 712832.5 9559852 1892.81 37.554519 -0.01175 0.04195 1901.6743 +#> 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 +#> 7485 714362.5 9558042 2484.02 47.958859 0.03169 0.01441 424.2680 +#> 47244 714262.5 9559302 2350.47 17.059245 0.02213 -0.01363 565.5207 +#> 39325 714142.5 9558592 2357.72 40.792876 -0.00246 0.00396 704.7076 +#> 23626 713742.5 9560032 2137.92 39.304905 -0.00605 0.00075 2170.2249 +#> 1997 714752.5 9561012 1853.19 26.566334 -0.00693 0.01133 2397.2681 +#> 34984 715042.5 9559312 2320.49 42.857816 -0.01106 -0.04634 500.5027 +#> 20171 715732.5 9557962 2815.53 48.657868 -0.01341 0.00171 4425.5410 +#> 9606 714392.5 9559172 2353.05 30.783303 -0.00185 -0.01185 953.8614 +#> 29024 713492.5 9559112 2173.47 53.432325 0.04864 0.04226 338.3474 +#> 28260 715312.5 9558302 2681.75 34.484038 -0.01125 -0.02685 2568.4631 +#> 10456 712442.5 9560292 2010.91 26.891774 0.00445 0.00875 597.2893 +#> 30363 715192.5 9557542 2796.31 25.321870 -0.00157 -0.00152 850.4633 +#> 46792 713862.5 9559672 2272.03 35.464942 0.00560 0.00610 454.8103 +#> 42640 714332.5 9558522 2403.10 38.871176 -0.00212 0.02732 4237.6934 +#> 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 +#> 49346 715322.5 9558782 2553.74 25.395208 0.00319 0.00291 487.3438 +#> 17322 712612.5 9560402 1924.48 43.807780 -0.00486 0.02306 1718.3652 +#> 11332 712742.5 9560072 1850.34 22.700015 0.00683 -0.00603 1562.0503 +#> 21490 714672.5 9560382 2021.86 32.804126 -0.00783 -0.00367 1966.2112 +#> 9984 712742.5 9560092 1848.56 23.612737 0.00614 -0.00093 118.1402 +#> 17851 715362.5 9557482 2869.26 29.125737 -0.03251 -0.00389 14516.2002 +#> 36744 714922.5 9557722 2692.39 25.980771 0.00316 -0.00427 866.9398 +#> 15211 715772.5 9558082 2801.07 36.296876 -0.00079 -0.00331 699.3024 +#> 36561 715292.5 9560802 1844.64 31.937240 0.00002 -0.01232 977.7320 +#> 27884 714072.5 9559202 2446.27 19.756731 -0.00009 0.00109 605.1908 +#> 15186 713322.5 9561212 1936.88 36.079725 0.00165 0.00565 1410.8372 +#> 24836 714202.5 9558272 2507.87 38.489013 -0.00226 0.00736 477.8591 +#> 25212 714272.5 9559012 2433.45 37.671402 -0.01909 -0.01181 1286.3949 +#> 11111 715142.5 9558482 2650.65 42.343300 -0.00533 0.01493 2212.0525 +#> 32869 715332.5 9558892 2533.17 20.635075 -0.00242 0.01132 737.5982 +#> 19493 714252.5 9560372 2156.93 24.879546 0.00825 0.00445 355.5078 +#> 441 714312.5 9560972 1916.59 31.302976 0.00540 0.00650 1133.1501 +#> 43243 715682.5 9558462 2693.23 27.406290 -0.00031 -0.00199 836.4196 +#> 20547 713732.5 9560822 1851.67 36.968383 -0.01405 0.00476 11992.5273 +#> 43403 712632.5 9559262 2072.53 34.486330 -0.00069 -0.00900 614.5898 +#> 29944 713562.5 9558952 2209.55 17.339995 0.01432 -0.00362 283.7090 +#> 8413 713242.5 9561232 1967.45 38.194513 0.00236 0.00483 1097.3082 +#> 47059 713822.5 9558272 2413.20 38.175032 -0.00687 -0.00223 1056.3701 +#> 22545 713402.5 9559562 2195.44 25.315567 0.04152 0.00098 164.0802 +#> 24009 714042.5 9558962 2357.58 34.361998 0.05041 -0.00011 201.1272 +#> 34810 714112.5 9559572 2337.51 10.288030 -0.00393 0.00032 994.9224 +#> 14148 714162.5 9560352 2149.57 37.133395 0.00424 0.00196 576.1450 +#> 49831 715262.5 9557332 2884.30 22.294934 -0.00097 0.01487 1769.3392 +#> 35038 715092.5 9558152 2715.69 40.183822 0.00270 -0.00250 2225.4426 +#> 44715 714932.5 9560132 2195.78 30.393692 0.03479 0.02251 168.1733 +#> 26724 714252.5 9560862 1975.16 28.286353 0.00165 0.00555 1882.0179 +#> 5861 713882.5 9559182 2350.43 27.393685 -0.02608 0.00148 10771.1963 +#> 3868 714542.5 9560622 2024.15 29.447166 0.00360 0.00750 1892.8978 +#> 44434 713022.5 9558762 2161.07 31.454237 -0.00273 -0.01047 2826.7700 +#> 7986 712752.5 9560242 1857.41 39.830307 0.01852 0.00248 1432.4019 +#> 2999 714592.5 9558492 2515.83 47.031814 0.01600 -0.01849 764.8193 +#> 4943 712452.5 9559172 1927.46 34.465130 0.00105 -0.01576 1045.1074 +#> 4269 714162.5 9560572 2087.45 7.334433 0.00293 0.00186 2751.4626 +#> 16845 712772.5 9560022 1831.76 21.399974 -0.00865 -0.00805 7489.7417 +#> 18504 713372.5 9560672 1786.89 6.473850 -0.00278 -0.01172 111973.8906 +#> 43972 713642.5 9558702 2304.16 24.023547 0.00340 0.01809 1158.0426 +#> 9320 713872.5 9559262 2378.76 29.858550 0.00309 0.01011 501.7867 +#> 35257 713822.5 9557982 2350.80 29.307937 0.01549 -0.00169 649.9975 +#> 46148 715082.5 9558042 2725.39 42.836044 -0.00181 -0.00799 1036.6606 +#> 23808 715172.5 9558702 2533.50 22.345927 0.00811 -0.02281 492.4785 +#> 26635 715092.5 9560412 2010.59 30.571882 -0.00463 0.00253 1237.0536 +#> 30296 713322.5 9559782 2042.96 52.621017 -0.02110 -0.02931 1812.3820 +#> cslope distroad slides distdeforest distslidespast log.carea +#> 37912 30.294570 300.00 TRUE 9.15 2 2.802317 +#> 15412 37.592907 300.00 TRUE 300.00 100 3.318273 +#> 28223 29.022604 60.46 TRUE 152.09 35 3.560687 +#> 25090 43.531614 300.00 TRUE 300.00 26 2.745084 +#> 40756 39.335272 300.00 TRUE 300.00 100 3.000471 +#> 20799 24.807927 180.67 TRUE 0.00 16 3.361925 +#> 9669 31.664513 300.00 TRUE 300.00 1 3.610558 +#> 38411 32.758289 279.54 TRUE 0.00 100 3.793638 +#> 1768 28.185513 300.00 TRUE 300.00 7 3.384768 +#> 43669 28.476575 24.22 TRUE 0.00 89 2.798316 +#> 20151 44.795559 300.00 TRUE 300.00 100 2.828669 +#> 125 29.596708 207.04 TRUE 21.03 100 3.298701 +#> 26447 33.036746 300.00 TRUE 300.00 100 2.676242 +#> 23817 21.611395 300.00 TRUE 300.00 4 2.425592 +#> 31023 16.677656 135.00 TRUE 0.00 2 2.577367 +#> 39629 47.548622 300.00 TRUE 300.00 85 3.366461 +#> 38287 14.896903 41.43 TRUE 1.90 100 3.349380 +#> 30800 33.248168 300.00 TRUE 300.00 100 4.403435 +#> 45869 23.454600 300.00 TRUE 300.00 12 3.970546 +#> 22283 19.942369 256.35 TRUE 0.00 100 2.772566 +#> 40307 29.513629 77.82 TRUE 76.41 54 3.524078 +#> 2107 35.423689 300.00 TRUE 300.00 45 2.661523 +#> 15109 22.635271 300.00 TRUE 300.00 40 2.931675 +#> 38032 35.008294 300.00 TRUE 300.00 60 2.828706 +#> 14522 28.398653 300.00 TRUE 300.00 100 3.047228 +#> 2942 29.820734 68.13 TRUE 116.32 10 3.654262 +#> 26215 23.831607 300.00 TRUE 300.00 10 3.268382 +#> 34275 36.829154 300.00 TRUE 300.00 100 2.711427 +#> 4827 21.073961 300.00 TRUE 300.00 0 2.559711 +#> 40828 25.599754 300.00 TRUE 300.00 100 2.941037 +#> 32821 29.483262 300.00 TRUE 300.00 65 3.372940 +#> 3453 40.084700 300.00 TRUE 300.00 2 3.276843 +#> 13585 47.067337 300.00 TRUE 300.00 100 2.769655 +#> 27708 25.710908 300.00 TRUE 300.00 100 2.253599 +#> 10320 16.455921 300.00 TRUE 300.00 30 2.721899 +#> 48364 32.075896 300.00 TRUE 300.00 100 2.972687 +#> 27957 30.060231 300.00 TRUE 300.00 75 2.790190 +#> 6763 16.218143 125.47 TRUE 0.00 6 3.006344 +#> 19128 14.659698 300.00 TRUE 300.00 15 2.478266 +#> 45104 17.992594 300.00 TRUE 300.00 100 2.894014 +#> 46756 38.961703 300.00 TRUE 300.00 37 2.473902 +#> 29667 34.251990 300.00 TRUE 300.00 27 2.734860 +#> 6557 27.419468 300.00 TRUE 300.00 5 3.147064 +#> 34359 27.416030 300.00 TRUE 294.31 100 3.287475 +#> 38686 31.188958 300.00 TRUE 300.00 75 2.946946 +#> 46235 30.108359 300.00 TRUE 41.37 0 2.752133 +#> 18564 21.700204 300.00 TRUE 300.00 100 2.676979 +#> 18751 38.075910 300.00 TRUE 300.00 100 2.713046 +#> 29926 32.094804 300.00 TRUE 300.00 100 2.537617 +#> 17036 24.928248 25.33 TRUE 208.61 15 3.608265 +#> 47884 27.054494 300.00 TRUE 300.00 100 2.905563 +#> 13134 37.643900 300.00 TRUE 300.00 67 2.634917 +#> 39278 16.218143 125.47 TRUE 0.00 6 3.006344 +#> 41037 31.570547 300.00 TRUE 300.00 100 3.329742 +#> 23029 46.601522 300.00 TRUE 300.00 34 2.425842 +#> 34707 27.702509 165.13 TRUE 39.56 100 3.473052 +#> 21952 40.091576 300.00 TRUE 300.00 64 2.828908 +#> 4617 27.951746 300.00 TRUE 300.00 6 2.211304 +#> 375 27.974091 300.00 TRUE 300.00 100 2.851936 +#> 48808 33.773570 300.00 TRUE 300.00 100 4.066528 +#> 39520 35.894087 300.00 TRUE 300.00 100 2.934945 +#> 34005 41.733673 300.00 TRUE 300.00 100 2.538942 +#> 32909 28.379173 118.07 TRUE 98.15 0 3.619059 +#> 18265 31.445643 300.00 TRUE 300.00 100 3.236197 +#> 39732 29.168135 300.00 TRUE 300.00 25 3.364146 +#> 7668 36.320368 300.00 TRUE 300.00 5 2.789721 +#> 35083 43.540782 300.00 TRUE 300.00 23 2.546735 +#> 19966 29.228869 300.00 TRUE 300.00 23 3.350329 +#> 46783 19.942369 256.35 TRUE 0.00 100 2.772566 +#> 44650 37.362578 300.00 TRUE 300.00 100 3.110849 +#> 3418 35.453482 300.00 TRUE 300.00 73 4.552486 +#> 10364 26.093071 179.84 TRUE 0.00 0 3.541090 +#> 31121 38.160135 300.00 TRUE 300.00 45 3.194113 +#> 41783 28.186086 300.00 TRUE 300.00 100 2.973405 +#> 18897 31.788271 300.00 TRUE 20.00 5 3.919162 +#> 39166 29.818443 300.00 TRUE 1.90 90 2.994673 +#> 10467 38.996080 300.00 TRUE 300.00 90 2.784369 +#> 10073 25.761328 300.00 TRUE 300.00 100 2.896526 +#> 18440 34.944696 300.00 TRUE 300.00 100 4.477051 +#> 43201 41.828211 300.00 TRUE 300.00 100 3.530273 +#> 42201 39.558725 300.00 TRUE 300.00 100 3.989241 +#> 34887 42.935738 300.00 TRUE 300.00 100 3.159244 +#> 20797 32.056989 285.07 TRUE 0.00 96 3.663802 +#> 33875 27.569583 300.00 TRUE 4.67 0 3.279136 +#> 43245 24.445817 300.00 TRUE 300.00 100 3.663243 +#> 15432 28.767638 300.00 TRUE 300.00 100 2.880810 +#> 28312 41.642573 300.00 TRUE 300.00 100 3.198702 +#> 7485 39.490543 300.00 TRUE 300.00 100 2.627640 +#> 47244 28.038836 300.00 TRUE 300.00 2 2.752448 +#> 39325 37.448521 300.00 TRUE 300.00 60 2.848009 +#> 23626 26.778328 300.00 TRUE 300.00 100 3.336505 +#> 1997 25.235926 168.72 TRUE 0.00 5 3.379717 +#> 34984 33.905923 300.00 TRUE 300.00 100 2.699406 +#> 20171 42.130160 300.00 TRUE 300.00 100 3.645966 +#> 9606 33.823990 300.00 TRUE 300.00 0 2.979485 +#> 29024 42.573056 300.00 TRUE 300.00 32 2.529363 +#> 28260 31.833535 300.00 TRUE 300.00 100 3.409673 +#> 10456 21.668691 24.34 TRUE 0.00 18 2.776185 +#> 30363 29.802973 300.00 TRUE 300.00 100 2.929656 +#> 46792 27.601096 300.00 TRUE 300.00 18 2.657830 +#> 42640 34.909745 300.00 TRUE 300.00 100 3.627130 +#> 4479 32.011725 300.00 TRUE 300.00 55 2.355626 +#> 10213 31.598622 300.00 TRUE 300.00 100 3.351772 +#> 49346 23.345738 300.00 TRUE 300.00 29 2.687835 +#> 17322 31.665086 75.77 TRUE 138.35 1 3.235115 +#> 11332 25.681687 300.00 TRUE 27.04 68 3.193695 +#> 21490 34.060049 300.00 TRUE 300.00 1 3.293630 +#> 9984 3.946533 300.00 TRUE 36.46 76 2.072398 +#> 17851 32.263253 300.00 TRUE 300.00 100 4.161853 +#> 36744 29.404767 300.00 TRUE 300.00 100 2.937989 +#> 15211 30.834870 300.00 FALSE 300.00 100 2.844665 +#> 36561 25.804873 300.00 FALSE 70.00 100 2.990220 +#> 27884 15.497362 300.00 FALSE 300.00 100 2.781892 +#> 15186 38.548600 60.00 FALSE 0.00 100 3.149477 +#> 24836 25.583711 300.00 FALSE 300.00 95 2.679300 +#> 25212 38.982902 300.00 FALSE 300.00 100 3.109374 +#> 11111 33.383959 300.00 FALSE 300.00 100 3.344795 +#> 32869 14.600111 300.00 FALSE 300.00 78 2.867820 +#> 19493 18.572427 300.00 FALSE 291.23 100 2.550849 +#> 441 25.113313 294.31 FALSE 152.09 100 3.054287 +#> 43243 27.986124 300.00 FALSE 300.00 100 2.922424 +#> 20547 34.261157 300.00 FALSE 42.56 100 4.078911 +#> 43403 31.061761 300.00 FALSE 111.88 86 2.788585 +#> 29944 18.628004 300.00 FALSE 300.00 48 2.452873 +#> 8413 39.889895 80.73 FALSE 0.00 100 3.040329 +#> 47059 25.759610 300.00 FALSE 300.00 100 3.023816 +#> 22545 23.684356 300.00 FALSE 300.00 96 2.215056 +#> 24009 38.497034 300.00 FALSE 300.00 100 2.303471 +#> 34810 15.554658 300.00 FALSE 300.00 100 2.997789 +#> 14148 21.877820 300.00 FALSE 214.75 100 2.760532 +#> 49831 29.837923 300.00 FALSE 300.00 100 3.247811 +#> 35038 40.203876 300.00 FALSE 300.00 100 3.347416 +#> 44715 28.450219 300.00 FALSE 300.00 100 2.225757 +#> 26724 21.777553 300.00 FALSE 185.65 100 3.274624 +#> 5861 27.612555 300.00 FALSE 300.00 100 4.032264 +#> 3868 26.147502 300.00 FALSE 300.00 100 3.277127 +#> 44434 36.319795 300.00 FALSE 300.00 100 3.451290 +#> 7986 32.003131 275.06 FALSE 0.00 65 3.156065 +#> 2999 45.583949 300.00 FALSE 300.00 100 2.883559 +#> 4943 37.771670 300.00 FALSE 300.00 100 3.019161 +#> 4269 19.116737 300.00 FALSE 194.53 100 3.439564 +#> 16845 22.882788 300.00 FALSE 25.55 33 3.874467 +#> 18504 23.095929 144.96 FALSE 4.48 89 5.049117 +#> 43972 22.852422 300.00 FALSE 300.00 100 3.063725 +#> 9320 18.139271 300.00 FALSE 300.00 93 2.700519 +#> 35257 30.881852 300.00 FALSE 300.00 79 2.812912 +#> 46148 38.910710 300.00 FALSE 300.00 100 3.015637 +#> 23808 37.533892 300.00 FALSE 300.00 72 2.692387 +#> 26635 31.394649 300.00 FALSE 300.00 100 3.092389 +#> 30296 40.158612 300.00 FALSE 300.00 68 3.258250 diff --git a/reference/partition_cv_strat.html b/reference/partition_cv_strat.html index 4f3f4799..8274b40d 100644 --- a/reference/partition_cv_strat.html +++ b/reference/partition_cv_strat.html @@ -146,13 +146,13 @@

Examples

) idx <- parti[["1"]][[1]]$train mean(ecuador$slides[idx] == "TRUE") / mean(ecuador$slides == "TRUE") -#> [1] 0.9996672 +#> [1] 1.001333 # always == 1 # Non-stratified cross-validation: parti <- partition_cv(ecuador, nfold = 5, repetition = 1) idx <- parti[["1"]][[1]]$train mean(ecuador$slides[idx] == "TRUE") / mean(ecuador$slides == "TRUE") -#> [1] 1.011347 +#> [1] 1.007165 # 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 e65d8363..7c4c68e3 100644 --- a/reference/partition_disc.html +++ b/reference/partition_disc.html @@ -192,19 +192,19 @@

Examples

summary(parti) #> $`1` #> n.train n.test -#> 674 725 7 -#> 92 713 25 -#> 439 680 17 -#> 79 692 28 -#> 311 711 9 +#> 398 727 9 +#> 271 705 16 +#> 61 722 23 +#> 153 703 23 +#> 40 710 10 #> #> $`2` #> n.train n.test -#> 305 721 7 -#> 400 724 14 -#> 219 722 21 -#> 46 745 4 -#> 568 717 15 +#> 260 725 13 +#> 264 694 17 +#> 396 713 9 +#> 566 741 3 +#> 625 718 17 #> # leave-one-out with buffer: @@ -212,19 +212,19 @@

Examples

summary(parti) #> $`1` #> n.train n.test -#> 674 725 7 -#> 92 713 25 -#> 439 680 17 -#> 79 692 28 -#> 311 711 9 +#> 398 727 9 +#> 271 705 16 +#> 61 722 23 +#> 153 703 23 +#> 40 710 10 #> #> $`2` #> n.train n.test -#> 305 721 7 -#> 400 724 14 -#> 219 722 21 -#> 46 745 4 -#> 568 717 15 +#> 260 725 13 +#> 264 694 17 +#> 396 713 9 +#> 566 741 3 +#> 625 718 17 #> diff --git a/reference/sperrorest.html b/reference/sperrorest.html index 8fcf6d29..c265b8e7 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) ) -#> Tue Aug 22 04:25:13 2023 Repetition 1 -#> Tue Aug 22 04:25:13 2023 Repetition - Fold 1 -#> Tue Aug 22 04:25:13 2023 Repetition - Fold 2 -#> Tue Aug 22 04:25:13 2023 Repetition - Fold 3 -#> Tue Aug 22 04:25:13 2023 Repetition 2 -#> Tue Aug 22 04:25:13 2023 Repetition - Fold 1 -#> Tue Aug 22 04:25:13 2023 Repetition - Fold 2 -#> Tue Aug 22 04:25:13 2023 Repetition - Fold 3 +#> Wed Aug 23 04:30:39 2023 Repetition 1 +#> Wed Aug 23 04:30:39 2023 Repetition - Fold 1 +#> Wed Aug 23 04:30:40 2023 Repetition - Fold 2 +#> Wed Aug 23 04:30:40 2023 Repetition - Fold 3 +#> Wed Aug 23 04:30:40 2023 Repetition 2 +#> Wed Aug 23 04:30:40 2023 Repetition - Fold 1 +#> Wed Aug 23 04:30:40 2023 Repetition - Fold 2 +#> Wed Aug 23 04:30:40 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) ) -#> Tue Aug 22 04:25:14 2023 Repetition 1 -#> Tue Aug 22 04:25:14 2023 Repetition - Fold 1 -#> Tue Aug 22 04:25:14 2023 Repetition - Fold 2 -#> Tue Aug 22 04:25:14 2023 Repetition - Fold 3 -#> Tue Aug 22 04:25:14 2023 Repetition 2 -#> Tue Aug 22 04:25:14 2023 Repetition - Fold 1 -#> Tue Aug 22 04:25:14 2023 Repetition - Fold 2 -#> Tue Aug 22 04:25:14 2023 Repetition - Fold 3 +#> Wed Aug 23 04:30:41 2023 Repetition 1 +#> Wed Aug 23 04:30:41 2023 Repetition - Fold 1 +#> Wed Aug 23 04:30:41 2023 Repetition - Fold 2 +#> Wed Aug 23 04:30:41 2023 Repetition - Fold 3 +#> Wed Aug 23 04:30:42 2023 Repetition 2 +#> Wed Aug 23 04:30:42 2023 Repetition - Fold 1 +#> Wed Aug 23 04:30:42 2023 Repetition - Fold 2 +#> Wed Aug 23 04:30:42 2023 Repetition - Fold 3 summary(sp_res$error_rep) #> mean sd median IQR #> train_auroc 0.8472530 0.017474834 0.8472530 0.012356574