diff --git a/articles/spatial-modeling-use-case.html b/articles/spatial-modeling-use-case.html index e8d57072..f951a743 100644 --- a/articles/spatial-modeling-use-case.html +++ b/articles/spatial-modeling-use-case.html @@ -276,7 +276,7 @@

RandomForest## Target node size: 1 ## Variable importance mode: none ## Splitrule: gini -## OOB prediction error: 0.60 % +## OOB prediction error: 0.64 %

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

 pred <- predict(fit, data = maipo, type = "response")
@@ -362,12 +362,12 @@ 
Linear Discriminant Analysis (LDA)
 summary(res_lda_nsp$error_rep)
##                    mean       sd   median      IQR
-## train_error    3.43e-02 0.000717 3.44e-02 0.000713
-## train_accuracy 9.66e-01 0.000717 9.66e-01 0.000713
+## train_error    3.40e-02 0.000460 3.39e-02 0.000454
+## train_accuracy 9.66e-01 0.000460 9.66e-01 0.000454
 ## 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     3.84e-02 0.001883 3.85e-02 0.001880
-## test_accuracy  9.62e-01 0.001883 9.61e-01 0.001880
+## test_error     3.98e-02 0.000594 3.99e-02 0.000583
+## test_accuracy  9.60e-01 0.000594 9.60e-01 0.000583
 ## test_events    1.17e+03 0.000000 1.17e+03 0.000000
 ## test_count     7.71e+03 0.000000 7.71e+03 0.000000

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

Linear Discriminant Analysis (LDA) benchmark = TRUE, progress = FALSE ) res_lda_sp$benchmark$runtime_performance -
## Time difference of 16.7 secs
+
## Time difference of 17.6 secs
 summary(res_lda_sp$error_rep)
##                    mean      sd   median     IQR
-## train_error    2.81e-02 0.00176 2.89e-02 0.00162
-## train_accuracy 9.72e-01 0.00176 9.71e-01 0.00162
+## train_error    2.80e-02 0.00274 2.76e-02 0.00271
+## train_accuracy 9.72e-01 0.00274 9.72e-01 0.00271
 ## 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     7.31e-02 0.01287 7.92e-02 0.01173
-## test_accuracy  9.27e-01 0.01287 9.21e-01 0.01173
+## test_error     6.80e-02 0.01212 7.05e-02 0.01193
+## test_accuracy  9.32e-01 0.01212 9.29e-01 0.01193
 ## test_events    1.17e+03 0.00000 1.17e+03 0.00000
 ## test_count     7.71e+03 0.00000 7.71e+03 0.00000
@@ -455,19 +455,19 @@

RandomForest) 
 summary(res_rf_sp$error_rep)
-
##                    mean     sd   median   IQR
-## train_error    0.00e+00 0.0000 0.00e+00 0.000
-## train_accuracy 1.00e+00 0.0000 1.00e+00 0.000
-## train_events   4.69e+03 0.0000 4.69e+03 0.000
-## train_count    3.09e+04 0.0000 3.09e+04 0.000
-## test_error     8.22e-02 0.0167 9.06e-02 0.015
-## test_accuracy  9.18e-01 0.0167 9.09e-01 0.015
-## test_events    1.17e+03 0.0000 1.17e+03 0.000
-## test_count     7.71e+03 0.0000 7.71e+03 0.000
+
##                    mean      sd   median     IQR
+## train_error    0.00e+00 0.00000 0.00e+00 0.00000
+## train_accuracy 1.00e+00 0.00000 1.00e+00 0.00000
+## train_events   4.69e+03 0.00000 4.69e+03 0.00000
+## train_count    3.09e+04 0.00000 3.09e+04 0.00000
+## test_error     8.89e-02 0.00347 8.79e-02 0.00337
+## test_accuracy  9.11e-01 0.00347 9.12e-01 0.00337
+## test_events    1.17e+03 0.00000 1.17e+03 0.00000
+## test_count     7.71e+03 0.00000 7.71e+03 0.00000
 summary(res_rf_sp$error_rep)["test_accuracy",]
-
##                mean     sd median   IQR
-## test_accuracy 0.918 0.0167  0.909 0.015
+
##                mean      sd median     IQR
+## test_accuracy 0.911 0.00347  0.912 0.00337

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 a83fda06..9e2045a0 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 cf3217f4..16eabf86 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: 2023-11-01T04:27Z +last_built: 2023-11-02T04:26Z 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 3680f200..82774c19 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]]$distance -#> [1] 41.30321 +#> [1] 47.06544 # spatial partitioning: more substantial distance, depending on number of # folds etc. sp.parti[[1]][[1]]$distance -#> [1] 415.0488 +#> [1] 458.6762 diff --git a/reference/as.resampling.html b/reference/as.resampling.html index aec06e60..c15ee2ee 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 713512 714022 713832 714892 713852 ... -#> $ y : num 9559092 9558862 9559662 9559312 9558612 ... -#> $ dem : num 2166 2331 2288 2380 2309 ... -#> $ slope : num 56 45.1 18.3 32.8 52.2 ... -#> $ hcurv : num 0.02056 -0.00075 0.01917 -0.00266 -0.01059 ... -#> $ vcurv : num -0.06976 0.00475 0.00333 0.02896 -0.07431 ... -#> $ carea : num 301 1001 259 276 4079 ... -#> $ cslope : num 49.4 39.3 20.2 20.9 31.7 ... -#> $ distroad : num 300 300 300 300 300 ... +#> $ x : num 712742 715362 713162 715022 714712 ... +#> $ y : num 9560482 9560102 9559632 9559332 9561042 ... +#> $ dem : num 1928 2059 2042 2308 1839 ... +#> $ slope : num 34.7 49.1 46.8 20.1 44.1 ... +#> $ hcurv : num -0.00292 0.02059 -0.00857 0.0094 -0.00223 ... +#> $ vcurv : num 0.00712 -0.00628 0.03677 0.00679 0.01783 ... +#> $ carea : num 3637 556 1675 550 378 ... +#> $ cslope : num 29 43.5 34.6 30 16.7 ... +#> $ distroad : num 60.5 300 300 300 135 ... #> $ 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 41 100 41 31 1 100 100 77 100 100 ... -#> $ log.carea : num 2.48 3 2.41 2.44 3.61 ... +#> $ distdeforest : num 152 300 195 300 0 ... +#> $ distslidespast: num 35 26 2 85 2 100 100 35 6 19 ... +#> $ log.carea : num 3.56 2.75 3.22 2.74 2.58 ... # 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 2 100 100 5 20 ... +#> $ 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 ... #> $ 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': 271 obs. of 13 variables: -#> $ x : num 712882 714842 713512 712992 714852 ... -#> $ y : num 9560002 9558892 9559092 9560672 9557902 ... -#> $ dem : num 1912 2483 2166 1926 2675 ... -#> $ slope : num 25.6 68.8 56 27.2 30.7 ... -#> $ hcurv : num -0.00681 -0.04921 0.02056 -0.00199 0.00221 ... -#> $ vcurv : num -0.00029 -0.12438 -0.06976 0.00659 0.00969 ... -#> $ carea : num 5577 754 301 3554 369 ... -#> $ cslope : num 34.4 53.7 49.4 27.8 20.5 ... -#> $ distroad : num 300 300 300 30 300 ... +#> 'data.frame': 268 obs. of 13 variables: +#> $ x : num 712882 715392 715382 712802 714932 ... +#> $ y : num 9560002 9560172 9560142 9559952 9557982 ... +#> $ dem : num 1912 1989 2021 1838 2650 ... +#> $ slope : num 25.6 40.5 42 52.1 37.3 ... +#> $ hcurv : num -0.00681 -0.01919 0.00958 0.00183 0.01633 ... +#> $ vcurv : num -0.00029 -0.04051 0.02642 -0.09203 -0.01813 ... +#> $ carea : num 5577 351155 671 634 1131 ... +#> $ cslope : num 34.4 32.8 41.6 30.3 35.1 ... +#> $ 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 2 ... -#> $ distdeforest : num 15 300 300 183 300 ... -#> $ distslidespast: num 9 100 41 20 10 35 100 2 10 41 ... -#> $ log.carea : num 3.75 2.88 2.48 3.55 2.57 ... +#> $ distdeforest : num 15 300 300 9.15 300 300 300 300 300 300 ... +#> $ distslidespast: num 9 40 21 2 100 10 100 100 11 100 ... +#> $ log.carea : num 3.75 5.55 2.83 2.8 3.05 ... # bootstrap training sample: same size as nrow(ecuador): str(ecuador[parti[[1]]$train, ]) #> 'data.frame': 751 obs. of 13 variables: -#> $ x : num 712502 715022 714332 714272 714032 ... -#> $ y : num 9560202 9559952 9557502 9559622 9558502 ... -#> $ dem : num 2005 2208 2567 2257 2403 ... -#> $ slope : num 27.5 43.4 48.9 55.5 31.4 ... -#> $ hcurv : num 0.00115 -0.00643 -0.00452 -0.03224 0.01924 ... -#> $ vcurv : num 0.01015 0.00314 -0.01298 -0.02036 0.02606 ... -#> $ carea : num 790 2360 436 477 565 ... -#> $ cslope : num 16.5 29.5 36.7 41 26.3 ... -#> $ distroad : num 55.1 300 300 300 300 ... -#> $ slides : Factor w/ 2 levels "FALSE","TRUE": 2 2 1 1 2 2 2 2 1 2 ... -#> $ distdeforest : num 0 300 300 300 300 ... -#> $ distslidespast: num 1 65 100 100 8 100 100 100 100 40 ... -#> $ log.carea : num 2.9 3.37 2.64 2.68 2.75 ... +#> $ x : num 714672 714622 714862 712852 715852 ... +#> $ y : num 9560022 9559942 9560992 9560162 9558702 ... +#> $ dem : num 2224 2167 1859 1935 2652 ... +#> $ slope : num 38.9 33 21.3 23.4 22.9 ... +#> $ hcurv : num 0.0118 -0.02917 -0.00317 0.00071 0.00321 ... +#> $ vcurv : num 0.0117 -0.01993 0.00077 0.0015 0.00548 ... +#> $ carea : num 254 10309 3057 1274 533 ... +#> $ cslope : num 33.7 31.6 23.2 28.2 19.2 ... +#> $ distroad : num 300 300 204 300 300 ... +#> $ slides : Factor w/ 2 levels "FALSE","TRUE": 1 2 2 2 1 2 2 2 2 2 ... +#> $ distdeforest : num 300 300 10.4 0 300 ... +#> $ distslidespast: num 100 94 74 100 100 57 100 2 100 4 ... +#> $ log.carea : num 2.4 4.01 3.49 3.11 2.73 ... diff --git a/reference/err_default.html b/reference/err_default.html index 0da11d0d..895107bd 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] 0.4733952 +#> [1] 0.5197163 #> #> $error -#> [1] 0.505 +#> [1] 0.495 #> #> $accuracy -#> [1] 0.495 +#> [1] 0.505 #> #> $sensitivity -#> [1] 0.2862669 +#> [1] 0.3313253 #> #> $specificity -#> [1] 0.7184265 +#> [1] 0.6772908 #> #> $fpr70 -#> [1] 0.7494824 +#> [1] 0.6673307 #> #> $fpr80 -#> [1] 0.8198758 +#> [1] 0.7609562 #> #> $fpr90 -#> [1] 0.9171843 +#> [1] 0.9043825 #> #> $tpr80 -#> [1] 0.172147 +#> [1] 0.2188755 #> #> $tpr90 -#> [1] 0.09284333 +#> [1] 0.1004016 #> #> $tpr95 -#> [1] 0.04255319 +#> [1] 0.07228916 #> #> $events -#> [1] 517 +#> [1] 498 #> #> $count #> [1] 1000 #> err_default(obs > 0, obs + rnorm(1000)) # some discrimination #> $auroc -#> [1] 0.8206086 +#> [1] 0.8240732 #> #> $error -#> [1] 0.292 +#> [1] 0.279 #> #> $accuracy -#> [1] 0.708 +#> [1] 0.721 #> #> $sensitivity -#> [1] 0.5531915 +#> [1] 0.5722892 #> #> $specificity -#> [1] 0.873706 +#> [1] 0.8685259 #> #> $fpr70 -#> [1] 0.2277433 +#> [1] 0.2250996 #> #> $fpr80 -#> [1] 0.3416149 +#> [1] 0.3227092 #> #> $fpr90 -#> [1] 0.4927536 +#> [1] 0.5059761 #> #> $tpr80 -#> [1] 0.6537718 +#> [1] 0.6626506 #> #> $tpr90 -#> [1] 0.5145068 +#> [1] 0.4919679 #> #> $tpr95 -#> [1] 0.3713733 +#> [1] 0.3634538 #> #> $events -#> [1] 517 +#> [1] 498 #> #> $count #> [1] 1000 @@ -196,44 +196,44 @@

Examples

# Three mock regression examples: err_default(obs, rnorm(1000)) # just noise, but no bias #> $bias -#> [1] 0.04707292 +#> [1] -0.0851432 #> #> $stddev -#> [1] 1.434161 +#> [1] 1.355774 #> #> $rmse -#> [1] 1.434216 +#> [1] 1.357768 #> #> $mad -#> [1] 1.355108 +#> [1] 1.259861 #> #> $median -#> [1] 0.05678635 +#> [1] -0.09292797 #> #> $iqr -#> [1] 1.822728 +#> [1] 1.700537 #> #> $count #> [1] 1000 #> err_default(obs, obs + rnorm(1000)) # some association, no bias #> $bias -#> [1] -0.0003633561 +#> [1] 0.00212445 #> #> $stddev -#> [1] 1.005297 +#> [1] 0.9941995 #> #> $rmse -#> [1] 1.004795 +#> [1] 0.9937045 #> #> $mad -#> [1] 0.9782847 +#> [1] 1.000384 #> #> $median -#> [1] -0.02316039 +#> [1] 0.0171636 #> #> $iqr -#> [1] 1.314692 +#> [1] 1.357693 #> #> $count #> [1] 1000 @@ -243,7 +243,7 @@

Examples

#> [1] -1 #> #> $stddev -#> [1] 6.448258e-17 +#> [1] 6.144523e-17 #> #> $rmse #> [1] 1 diff --git a/reference/partition_cv.html b/reference/partition_cv.html index e574edd0..12878f4a 100644 --- a/reference/partition_cv.html +++ b/reference/partition_cv.html @@ -155,310 +155,308 @@

Examples

idx <- resamp[["1"]][[2]]$test # test sample used in this particular repetition and fold: ecuador[idx, ] -#> x y dem slope hcurv vcurv carea -#> 23178 715382.5 9560142 2021.07 41.9766069 0.00958 0.02642 671.1807 -#> 37912 712802.5 9559952 1838.40 52.1013441 0.00183 -0.09203 634.3320 -#> 15412 715272.5 9557702 2813.17 30.9523260 -0.00123 0.00393 2081.0056 -#> 16749 715072.5 9558242 2745.27 49.0537816 0.00097 0.01483 566.7674 -#> 40756 714022.5 9558862 2331.20 45.0854759 -0.00075 0.00475 1001.0861 -#> 24051 715462.5 9559372 2319.46 32.3279977 -0.00661 -0.01339 1002.5877 -#> 19963 714612.5 9560552 2031.70 26.8482293 0.00046 0.00324 861.3537 -#> 37265 712812.5 9559942 1860.77 63.3370465 0.00514 -0.00644 1179.0695 -#> 48187 712722.5 9560152 1835.29 29.3279907 -0.00333 -0.01767 3321.6113 -#> 24072 715282.5 9557602 2837.46 34.3940835 -0.02191 -0.00579 2246.7725 -#> 14456 713542.5 9559972 2184.39 35.2414881 -0.00841 -0.00010 1527.1028 -#> 1768 713912.5 9558552 2357.19 38.6924129 -0.00645 0.00835 2425.3115 -#> 43669 712392.5 9560162 2001.90 47.2839150 -0.00284 0.01804 628.5153 -#> 49372 714862.5 9560982 1863.12 20.9691094 -0.00295 0.00035 2697.5974 -#> 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 -#> 16811 714902.5 9559262 2362.67 50.7033271 -0.01407 0.00547 519.4228 -#> 23817 714842.5 9557832 2677.58 26.6574344 0.00922 0.01088 266.4355 -#> 7349 714032.5 9558502 2402.95 31.3528235 0.01924 0.02606 564.6876 -#> 31023 714712.5 9561042 1838.66 44.1200421 -0.00223 0.01783 377.8918 -#> 42598 713542.5 9559972 2184.39 35.2414881 -0.00841 -0.00010 1527.1028 -#> 38287 714872.5 9561162 1719.41 48.2355979 -0.03021 -0.09328 2235.5259 -#> 23925 715202.5 9557652 2782.99 41.9571264 -0.00542 0.01842 868.6137 -#> 10899 713852.5 9559652 2282.82 28.8140475 0.00952 0.01378 440.9104 -#> 30800 715182.5 9557582 2772.37 39.2040642 -0.02475 0.00665 25318.3555 -#> 43007 713372.5 9559192 2120.47 46.0050732 0.03696 0.02514 297.7842 -#> 18304 714582.5 9558932 2463.25 22.1946661 -0.02131 0.00501 3889.7292 -#> 35285 714602.5 9560112 2142.75 51.1078353 -0.00017 -0.00032 994.6740 -#> 29472 714792.5 9561072 1786.08 36.1731174 -0.01029 -0.02312 2657.6936 -#> 34482 715152.5 9557642 2753.52 4.9956190 -0.01126 -0.02044 129412.3984 -#> 35942 712862.5 9558902 2109.97 31.4003790 0.00151 0.00189 2317.1819 -#> 47210 714952.5 9557612 2744.35 40.8845494 -0.00493 0.00733 598.7983 -#> 18391 715112.5 9559432 2235.02 22.2576915 -0.02651 -0.03629 360379.1875 -#> 10320 714912.5 9558482 2724.21 25.2829086 0.00356 0.00774 527.1068 -#> 20194 714132.5 9558452 2447.22 24.0224015 -0.00987 0.02107 1392.5880 -#> 8073 712572.5 9560302 1917.62 52.0090979 0.01937 0.00083 994.7876 -#> 15390 713132.5 9560622 1873.26 38.4804185 0.00252 0.01118 2359.5059 -#> 19730 715332.5 9558452 2640.35 47.6425866 -0.00211 0.00642 613.6390 -#> 27013 713062.5 9559782 1976.81 41.6322593 0.01414 0.03416 1204.8629 -#> 34642 714962.5 9559862 2282.08 22.1608616 -0.00028 0.01318 289.0063 -#> 19128 714852.5 9557872 2681.49 20.7576880 0.00453 0.01077 300.7915 -#> 45104 715062.5 9559242 2345.02 24.1564736 -0.01743 0.01313 783.4548 -#> 12868 712532.5 9560332 1913.92 61.9373106 -0.03543 -0.05146 1347.6224 -#> 34359 712862.5 9558912 2111.37 32.8121470 0.00463 0.00167 1938.5389 -#> 37348 715632.5 9558182 2743.16 13.6203527 -0.00904 -0.00866 29195.6016 -#> 43092 715532.5 9558192 2770.31 36.8331648 -0.00442 -0.00178 800.1933 -#> 37141 714382.5 9559822 2219.36 10.1064025 0.01661 0.01199 196.4080 -#> 4031 715392.5 9560162 1998.16 46.2858225 0.00708 -0.02538 807.8237 -#> 9704 714642.5 9559652 2249.12 37.1431350 -0.00759 -0.00400 608.6523 -#> 462 713712.5 9561182 1785.08 47.7170711 -0.03260 -0.03310 2833.9001 -#> 18892 712682.5 9560202 1839.08 40.9235105 -0.01364 -0.03357 473.4709 -#> 32909 712812.5 9560452 1883.85 38.2512354 -0.00221 0.00121 4159.6738 -#> 13029 713542.5 9560242 2019.80 29.5451417 0.00576 -0.00166 650.6246 -#> 28845 714892.5 9559272 2374.42 45.7501070 -0.00209 0.02739 365.7626 -#> 29801 714392.5 9559162 2356.18 31.5625261 -0.00856 -0.01625 1063.9150 -#> 32735 714922.5 9559002 2414.49 40.0646468 0.00062 -0.00262 1760.2162 -#> 19966 715362.5 9559572 2235.06 26.0495262 0.00126 -0.00485 2240.4185 -#> 44650 715612.5 9558142 2764.88 39.3604817 -0.00550 -0.00941 1290.7709 -#> 3418 715372.5 9558492 2601.00 22.6272492 -0.01168 -0.04102 35685.0000 -#> 21013 712742.5 9560032 1845.53 29.3526278 0.00568 -0.01558 1504.9717 -#> 27695 714142.5 9558452 2449.28 20.4901167 -0.00539 0.00809 1102.9956 -#> 35014 713502.5 9559772 2190.48 44.2357795 0.00927 0.01643 670.2289 -#> 24501 713302.5 9559142 2125.66 27.1089251 0.01784 0.00596 842.0994 -#> 41721 715182.5 9557562 2784.85 31.7899903 -0.00588 0.01448 1258.8196 -#> 18897 712802.5 9559882 1851.71 50.2346476 -0.02627 -0.05393 8301.6094 -#> 40216 714602.5 9560592 2015.55 34.3688733 -0.00093 0.00664 1807.2659 -#> 39722 715022.5 9557712 2735.53 30.2607659 0.00133 0.00367 806.0966 -#> 39166 712832.5 9560142 1922.46 39.6601386 0.01469 0.02940 987.8085 -#> 8136 713242.5 9560602 1798.18 41.2403562 -0.01169 -0.03220 8936.2666 -#> 6845 713412.5 9559172 2123.32 47.1538536 -0.01556 -0.04394 587.8840 -#> 7041 713472.5 9559092 2137.03 18.3489734 -0.10372 -0.00008 825875.6875 -#> 1424 714012.5 9558902 2305.97 32.2237194 -0.03986 -0.02524 10861.9482 -#> 44767 714452.5 9560402 2092.27 12.4629780 -0.00195 -0.00205 1112.2626 -#> 33910 715202.5 9559572 2195.66 13.7916671 -0.04376 -0.01644 1057765.6250 -#> 10073 715572.5 9558612 2632.78 32.1612670 0.00129 0.00171 788.0004 -#> 31117 712892.5 9558922 2129.84 30.5506826 0.00521 -0.00870 1895.1399 -#> 30287 713992.5 9557822 2403.92 18.2154742 0.03890 -0.00911 173.2806 -#> 34615 713242.5 9560732 1879.60 27.5936474 0.00384 0.00175 1026.6818 -#> 15432 715632.5 9558922 2509.67 35.8276239 -0.00444 -0.00017 759.9937 -#> 22907 713172.5 9559612 2056.81 22.2725884 -0.00166 0.01476 1404.4454 -#> 5911 713952.5 9561282 1801.16 33.7896130 0.00466 -0.01776 1021.8267 -#> 30933 714672.5 9561082 1798.37 54.8102886 0.00542 -0.00722 1373.4147 -#> 17058 714032.5 9558502 2402.95 31.3528235 0.01924 0.02606 564.6876 -#> 7485 714362.5 9558042 2484.02 47.9588593 0.03169 0.01441 424.2680 -#> 14545 713562.5 9559602 2222.17 54.2390497 0.01011 0.00739 789.7573 -#> 11352 713002.5 9560562 1858.95 39.8039510 -0.00442 -0.00118 19557.6621 -#> 28173 714562.5 9560382 2048.26 27.6858936 -0.00124 -0.00456 1531.4274 -#> 2801 715252.5 9559612 2199.28 24.4034184 0.03051 0.02190 264.6404 -#> 14192 715162.5 9558122 2772.18 44.4626708 -0.02970 0.00200 1305.6691 -#> 1997 714752.5 9561012 1853.19 26.5663341 -0.00693 0.01133 2397.2681 -#> 48574 712692.5 9560412 1901.97 41.9783258 0.01364 -0.00094 1392.6008 -#> 44612 714932.5 9558822 2578.80 38.2552461 0.01257 0.00044 288.5468 -#> 39405 713122.5 9559052 2169.30 37.6461919 -0.02110 -0.02450 2589.9910 -#> 42617 714092.5 9558392 2469.21 42.7999473 -0.01397 0.00526 706.1463 -#> 28260 715312.5 9558302 2681.75 34.4840379 -0.01125 -0.02685 2568.4631 -#> 37115 715612.5 9559202 2358.15 9.7276774 -0.01020 0.00279 39495.1797 -#> 17674 713882.5 9558562 2339.20 41.0363832 -0.03675 -0.01905 6487.0010 -#> 14779 712502.5 9560202 2004.61 27.5226006 0.00115 0.01015 790.2421 -#> 9864 714952.5 9557592 2752.40 31.9893796 -0.00983 0.01253 528.1924 -#> 11332 712742.5 9560072 1850.34 22.7000149 0.00683 -0.00603 1562.0503 -#> 28253 714102.5 9559892 2288.88 2.9576081 0.00378 0.02382 100.0000 -#> 15186 713322.5 9561212 1936.88 36.0797253 0.00165 0.00565 1410.8372 -#> 748 715102.5 9559692 2301.39 27.0218355 -0.00759 0.00158 837.3730 -#> 25212 714272.5 9559012 2433.45 37.6714021 -0.01909 -0.01181 1286.3949 -#> 16464 715252.5 9559982 2143.24 23.1858831 -0.00298 -0.00712 1010.5097 -#> 13512 715152.5 9557992 2778.81 43.3895209 -0.00724 0.01464 4029.5947 -#> 39193 715332.5 9560852 1872.90 30.2905598 -0.00161 0.00171 756.8818 -#> 29125 714992.5 9558792 2584.41 24.6658331 0.01253 -0.00333 422.8840 -#> 20547 713732.5 9560822 1851.67 36.9683829 -0.01405 0.00476 11992.5273 -#> 36833 713322.5 9560572 1794.25 33.7540896 0.00527 -0.02077 8759.9961 -#> 40139 712852.5 9560072 1907.89 43.7040747 0.00001 0.00759 1880.4403 -#> 36126 715052.5 9558292 2759.47 29.0621382 0.00196 0.01734 335.0773 -#> 34616 714522.5 9558742 2568.77 34.9979810 0.00990 0.02790 209.6952 -#> 43403 712632.5 9559262 2072.53 34.4863297 -0.00069 -0.00900 614.5898 -#> 31201 714672.5 9560022 2224.47 38.8637272 0.01180 0.01170 253.5129 -#> 42934 713832.5 9558672 2270.74 27.6119821 -0.03193 -0.00267 31588.1953 -#> 20319 715282.5 9560482 1901.16 27.6767263 0.00706 -0.03035 1471.9783 -#> 8895 714132.5 9557692 2487.19 37.3642967 0.03423 -0.00983 270.7949 -#> 47059 713822.5 9558272 2413.20 38.1750320 -0.00687 -0.00223 1056.3701 -#> 1691 714222.5 9559022 2448.69 38.2168579 -0.01292 0.00462 716.5762 -#> 45486 712762.5 9560172 1853.82 56.7973062 0.01978 0.01282 2322.7944 -#> 7553 714642.5 9559132 2391.59 63.9678730 -0.00066 0.05257 549.3252 -#> 7983 713512.5 9560432 1907.38 50.0192155 0.03310 -0.07650 766.6254 -#> 41855 713182.5 9560452 1851.53 45.1782951 0.00667 0.00863 1098.3462 -#> 35038 715092.5 9558152 2715.69 40.1838220 0.00270 -0.00250 2225.4426 -#> 46509 712512.5 9559532 1860.09 89.9887513 -0.00270 1018.51947 842.1514 -#> 40909 712722.5 9560272 1827.15 29.2930402 0.00009 -0.00599 12873.8018 -#> 44823 714202.5 9557442 2548.13 43.2302386 -0.00233 0.01282 561.2366 -#> 34532 713812.5 9559502 2328.80 44.9376528 -0.00070 -0.00060 383.3547 -#> 43415 714722.5 9558642 2657.99 0.2314749 0.00799 0.00911 100.0000 -#> 1479 715622.5 9557952 2888.67 34.9538632 0.01635 -0.00105 868.7802 -#> 5861 713882.5 9559182 2350.43 27.3936851 -0.02608 0.00148 10771.1963 -#> 29716 715272.5 9559992 2144.94 25.7177836 0.00262 -0.00502 520.5218 -#> 17628 714032.5 9560252 2160.03 30.2945705 0.00537 -0.00577 423.5598 -#> 3868 714542.5 9560622 2024.15 29.4471659 0.00360 0.00750 1892.8978 -#> 32430 715832.5 9558112 2824.39 23.6940967 0.00944 0.00836 250.5111 -#> 38362 714402.5 9558042 2499.31 56.9010116 0.03044 -0.00964 543.0025 -#> 44434 713022.5 9558762 2161.07 31.4542370 -0.00273 -0.01047 2826.7700 -#> 14763 713462.5 9560852 1830.40 47.7399894 0.01354 0.00907 1281.5044 -#> 7986 712752.5 9560242 1857.41 39.8303070 0.01852 0.00248 1432.4019 -#> 6732 715482.5 9559232 2376.03 29.9055321 -0.01137 -0.01523 888.2811 -#> 34334 714642.5 9557912 2582.25 30.9912871 0.00253 -0.01233 968.7780 -#> 40653 714262.5 9561262 1791.94 12.2223357 0.00161 0.00709 1181.2555 -#> 39883 712732.5 9560842 2082.69 25.0909678 0.00732 0.01858 298.4490 -#> 47245 715372.5 9560792 1900.65 34.4708598 -0.00011 -0.00419 323.0790 -#> 15880 715892.5 9557572 3113.36 53.0432868 0.00962 -0.00172 351.5812 -#> 39234 712902.5 9559652 1953.39 29.7319259 0.07379 -0.00249 148.1857 -#> 48157 713142.5 9560992 1978.48 44.6213801 0.00208 0.00492 772.2691 -#> 40043 713322.5 9560562 1802.09 39.8520794 0.04456 -0.01586 941.0288 -#> 40852 715392.5 9557932 2939.71 18.2974072 0.00505 0.00855 229.6061 -#> 24516 713802.5 9560862 1873.21 23.3497490 -0.00771 0.00661 5642.7544 +#> x y dem slope hcurv vcurv carea +#> 37912 712802.5 9559952 1838.40 52.1013441 0.00183 -0.09203 634.3320 +#> 27864 712992.5 9560672 1926.38 27.2240896 -0.00199 0.00659 3553.7166 +#> 44302 714932.5 9557982 2649.56 37.3006984 0.01633 -0.01813 1130.5791 +#> 42632 714852.5 9557902 2674.80 30.6526691 0.00221 0.00969 368.5606 +#> 13748 715232.5 9559542 2212.12 26.1234377 0.00023 -0.00043 1806.4552 +#> 42733 715112.5 9559062 2399.70 36.0545152 -0.00041 0.01741 746.4155 +#> 1300 714042.5 9558482 2408.27 24.1484522 0.00659 0.01041 772.9746 +#> 24129 714602.5 9560542 2038.30 25.5848574 0.00065 0.00005 769.3426 +#> 21939 713642.5 9558712 2299.09 33.7506519 -0.00503 0.02433 1475.3191 +#> 20799 714792.5 9561002 1857.29 23.1291603 0.00090 0.00890 2301.0454 +#> 15843 714042.5 9558902 2332.30 50.0977107 -0.00858 -0.00292 1233.9299 +#> 9669 713852.5 9558612 2308.97 52.1804123 -0.01059 -0.07431 4079.0366 +#> 37270 714852.5 9561032 1838.96 35.1589821 -0.00854 0.01264 5057.1431 +#> 31023 714712.5 9561042 1838.66 44.1200421 -0.00223 0.01783 377.8918 +#> 22814 713862.5 9558582 2327.48 48.6120312 -0.02894 -0.03416 947.4878 +#> 10974 715312.5 9557502 2844.23 26.6648828 -0.00831 -0.00149 12632.7793 +#> 10899 713852.5 9559652 2282.82 28.8140475 0.00952 0.01378 440.9104 +#> 33792 714652.5 9558172 2598.36 59.4586952 -0.03525 -0.11024 394.8751 +#> 43007 713372.5 9559192 2120.47 46.0050732 0.03696 0.02514 297.7842 +#> 38410 713712.5 9561172 1776.08 36.3192217 -0.02678 -0.04272 4955.5586 +#> 48055 712862.5 9559972 1902.48 29.0168746 -0.00720 0.01250 3686.1240 +#> 30125 715162.5 9558992 2438.18 39.8566631 0.00471 -0.01382 560.8309 +#> 18005 714832.5 9557912 2660.76 31.9120303 0.00191 -0.00261 554.9243 +#> 36030 712852.5 9559572 1938.79 28.6788295 -0.01440 0.00710 843.7961 +#> 4827 714962.5 9559882 2274.42 29.2449118 0.00051 0.00889 362.8363 +#> 45575 715432.5 9559452 2305.28 24.2343959 0.00993 -0.00413 364.9788 +#> 32821 715022.5 9559952 2208.33 43.4164499 -0.00643 0.00314 2360.1501 +#> 27708 715222.5 9557782 2844.65 29.5428498 0.03206 0.02773 179.3077 +#> 10320 714912.5 9558482 2724.21 25.2829086 0.00356 0.00774 527.1068 +#> 15390 713132.5 9560622 1873.26 38.4804185 0.00252 0.01118 2359.5059 +#> 23031 714042.5 9558492 2404.94 28.4960559 0.01205 0.02205 629.4165 +#> 7879 714722.5 9559232 2356.19 36.8039440 -0.01097 0.00156 816.6036 +#> 2950 715042.5 9557712 2740.72 26.5250811 -0.00089 -0.00051 779.0555 +#> 22735 714492.5 9559772 2170.30 39.6887865 -0.00440 -0.02651 419.0927 +#> 46756 713372.5 9559192 2120.47 46.0050732 0.03696 0.02514 297.7842 +#> 47351 714222.5 9558792 2420.94 28.6685162 -0.09566 -0.03355 14919.6260 +#> 47429 714962.5 9557832 2705.12 32.0575616 0.02224 0.00177 291.5937 +#> 40738 714972.5 9557642 2745.48 42.5426892 -0.00294 -0.00096 520.5570 +#> 24100 715132.5 9558992 2420.18 29.2139721 -0.03229 -0.00511 361979.2812 +#> 20001 712702.5 9560132 1854.55 36.2911467 -0.00204 -0.00376 2048.3198 +#> 34563 715022.5 9558162 2675.70 18.8898455 -0.01993 0.01373 16031.7500 +#> 27084 713632.5 9558472 2380.37 23.1354628 -0.01752 -0.00517 1000.7119 +#> 46201 714832.5 9561022 1845.94 34.5739922 -0.00856 0.01796 4729.6382 +#> 18751 714882.5 9559182 2381.81 39.2991752 0.00066 -0.00036 516.4709 +#> 29926 714702.5 9557622 2637.00 44.6425796 0.01843 0.04407 344.8395 +#> 28743 715202.5 9558872 2463.87 34.6570711 -0.01267 -0.05063 18733.8848 +#> 37141 714382.5 9559822 2219.36 10.1064025 0.01661 0.01199 196.4080 +#> 835 715052.5 9558212 2707.65 37.7252601 -0.01399 -0.00731 1384.7959 +#> 36120 714622.5 9559942 2166.51 32.9863262 -0.02917 -0.01993 10308.6406 +#> 4617 714292.5 9559312 2342.95 32.3921689 0.03504 0.05825 162.6688 +#> 49569 713232.5 9561122 1868.73 37.3121575 -0.00405 -0.01565 2232.6133 +#> 48808 715032.5 9557792 2707.02 25.4376072 -0.02329 -0.01181 11655.4316 +#> 28845 714892.5 9559272 2374.42 45.7501070 -0.00209 0.02739 365.7626 +#> 35083 714612.5 9558152 2596.12 60.5983082 0.04548 0.05543 352.1558 +#> 29801 714392.5 9559162 2356.18 31.5625261 -0.00856 -0.01625 1063.9150 +#> 5103 714612.5 9559892 2220.36 49.0079450 -0.00078 0.00219 420.7032 +#> 28046 714882.5 9558492 2711.77 28.3058340 -0.00131 0.00201 788.0621 +#> 45592 714962.5 9559862 2282.08 22.1608616 -0.00028 0.01318 289.0063 +#> 31834 713492.5 9560682 1800.85 34.2370930 -0.00472 -0.00228 2262.3796 +#> 41721 715182.5 9557562 2784.85 31.7899903 -0.00588 0.01448 1258.8196 +#> 39722 715022.5 9557712 2735.53 30.2607659 0.00133 0.00367 806.0966 +#> 30268 714172.5 9558612 2328.84 54.9506633 0.03743 -0.05242 507.9263 +#> 39166 712832.5 9560142 1922.46 39.6601386 0.01469 0.02940 987.8085 +#> 8136 713242.5 9560602 1798.18 41.2403562 -0.01169 -0.03220 8936.2666 +#> 6845 713412.5 9559172 2123.32 47.1538536 -0.01556 -0.04394 587.8840 +#> 18958 714192.5 9558372 2495.72 34.6771246 -0.00178 -0.00822 472.8748 +#> 43859 714892.5 9557602 2722.70 28.1293630 0.00370 -0.00190 640.8842 +#> 1973 715252.5 9557822 2861.00 15.5707010 0.01865 0.00335 195.0293 +#> 17345 713922.5 9559682 2227.44 34.4909134 -0.06509 -0.01211 49347.5547 +#> 18440 714862.5 9558932 2448.29 41.0524260 -0.04989 -0.02781 29995.1543 +#> 6217 714442.5 9559932 2151.30 36.6704448 0.01489 0.00241 1752.9242 +#> 30287 713992.5 9557822 2403.92 18.2154742 0.03890 -0.00911 173.2806 +#> 18348 714042.5 9558882 2341.41 44.0638922 -0.00338 0.00728 1137.2450 +#> 24135 712612.5 9559492 1903.67 46.7567938 -0.06953 -0.01797 7557.7866 +#> 33875 712832.5 9559852 1892.81 37.5545187 -0.01175 0.04195 1901.6743 +#> 34615 713242.5 9560732 1879.60 27.5936474 0.00384 0.00175 1026.6818 +#> 28312 715712.5 9558032 2783.23 24.2527305 -0.00007 -0.02132 1580.1621 +#> 47298 714602.5 9560572 2025.50 28.5900210 -0.00253 0.00753 1337.7854 +#> 10363 713252.5 9559722 1999.55 41.8299297 -0.03669 -0.01981 3975.3608 +#> 49133 714162.5 9558462 2451.13 21.8904255 0.00042 0.00957 495.4453 +#> 14192 715162.5 9558122 2772.18 44.4626708 -0.02970 0.00200 1305.6691 +#> 39624 715222.5 9559552 2205.54 23.8974967 0.00401 -0.00391 1794.7964 +#> 20426 714702.5 9559712 2208.61 43.0463191 -0.08153 -0.02417 34908.6562 +#> 16065 715242.5 9560622 1871.61 34.3064209 -0.00019 0.00119 6589.5796 +#> 30363 715192.5 9557542 2796.31 25.3218698 -0.00157 -0.00152 850.4633 +#> 46792 713862.5 9559672 2272.03 35.4649416 0.00560 0.00610 454.8103 +#> 46627 714852.5 9558932 2459.24 53.4455031 0.02451 -0.03921 525.3245 +#> 13278 712752.5 9560452 1901.25 42.1135439 -0.00118 -0.00252 3631.7017 +#> 35458 715242.5 9558442 2623.01 38.9548275 -0.00770 -0.03400 1982.7782 +#> 23832 714252.5 9560182 2188.12 30.6475125 0.00427 0.02403 474.8613 +#> 34718 714802.5 9558802 2599.22 44.6500280 0.01996 0.04684 209.1778 +#> 36744 714922.5 9557722 2692.39 25.9807712 0.00316 -0.00427 866.9398 +#> 23176 714012.5 9558892 2308.59 43.0291304 -0.01084 -0.02996 1599.3491 +#> 10145 713782.5 9560932 1865.62 23.5955479 -0.00203 0.00642 1688.1782 +#> 32405 713022.5 9558982 2208.85 15.0046187 0.00599 0.00301 338.9676 +#> 34250 713822.5 9559142 2316.36 33.7598192 -0.02284 0.01085 2229.5105 +#> 40459 712542.5 9559192 1992.60 39.8171290 -0.00162 -0.00517 1060.5494 +#> 31071 713472.5 9558462 2298.33 29.1532385 0.00156 -0.00116 1817.5222 +#> 27965 715042.5 9560342 2063.39 29.1973563 0.04336 0.01403 241.2991 +#> 12716 713962.5 9557762 2402.60 18.9322444 0.03480 0.01500 538.8209 +#> 43426 713782.5 9560822 1876.35 23.8230122 -0.00345 -0.00075 8141.7549 +#> 28162 714062.5 9561152 1785.47 48.4447275 0.02309 -0.03409 1575.9425 +#> 45166 715262.5 9558722 2531.59 13.5533166 -0.00502 -0.00028 2394.7261 +#> 39796 714802.5 9560372 2046.48 40.1557471 0.00176 0.02973 215.5101 +#> 1085 715722.5 9557532 3097.22 28.7842537 0.02327 0.01833 300.0125 +#> 39685 715332.5 9558102 2797.04 47.7881179 0.00167 0.01133 828.8604 +#> 45071 712652.5 9560032 1900.80 14.4734869 0.00987 -0.00507 504.3085 +#> 32849 714302.5 9558222 2520.01 32.1572563 -0.00846 0.00216 1412.2295 +#> 38689 714622.5 9559342 2264.90 42.6481135 -0.00844 0.00514 897.9290 +#> 42934 713832.5 9558672 2270.74 27.6119821 -0.03193 -0.00267 31588.1953 +#> 14800 712952.5 9558682 2120.89 38.2735807 -0.00022 0.00662 1269.2650 +#> 8895 714132.5 9557692 2487.19 37.3642967 0.03423 -0.00983 270.7949 +#> 47059 713822.5 9558272 2413.20 38.1750320 -0.00687 -0.00223 1056.3701 +#> 36245 713142.5 9560572 1827.82 43.6759998 -0.00410 0.00400 4126.7812 +#> 34105 715432.5 9558592 2621.73 18.9563087 -0.00301 -0.01099 1146.2140 +#> 39440 714222.5 9557872 2508.52 39.0476467 0.02785 0.00645 234.0114 +#> 34345 715852.5 9557902 2874.08 43.6559462 0.00379 -0.01018 937.6911 +#> 44267 712972.5 9559042 2191.01 22.2691506 0.01462 0.00268 252.0072 +#> 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 +#> 7983 713512.5 9560432 1907.38 50.0192155 0.03310 -0.07650 766.6254 +#> 41855 713182.5 9560452 1851.53 45.1782951 0.00667 0.00863 1098.3462 +#> 40909 712722.5 9560272 1827.15 29.2930402 0.00009 -0.00599 12873.8018 +#> 35826 712912.5 9560302 1891.22 50.8694849 -0.00229 0.00099 909.0560 +#> 48845 713232.5 9561322 2041.83 29.9124076 -100.76649 100.79199 107.2986 +#> 43415 714722.5 9558642 2657.99 0.2314749 0.00799 0.00911 100.0000 +#> 15008 714492.5 9559012 2449.93 29.5153479 0.00972 0.00308 422.5022 +#> 1479 715622.5 9557952 2888.67 34.9538632 0.01635 -0.00105 868.7802 +#> 5861 713882.5 9559182 2350.43 27.3936851 -0.02608 0.00148 10771.1963 +#> 17628 714032.5 9560252 2160.03 30.2945705 0.00537 -0.00577 423.5598 +#> 40612 713542.5 9561152 1856.15 33.5684513 0.00420 0.01960 884.2003 +#> 44216 715772.5 9558402 2710.44 30.4446854 -0.00070 -0.00050 1471.4346 +#> 42638 714322.5 9561182 1754.22 4.1722787 -0.03757 -0.00003 5426606.0000 +#> 26283 715932.5 9557592 3075.48 32.7668833 -0.00375 0.00215 917.5296 +#> 18246 713902.5 9561342 1818.52 19.1104343 -0.01502 0.00282 1659.6211 +#> 48050 714022.5 9560572 2099.82 18.7139475 0.01182 -0.00972 331.7462 +#> 37783 713832.5 9557892 2405.28 40.8868412 0.01572 0.03148 481.8292 +#> 10981 713832.5 9560022 2125.33 34.8484390 -0.06909 -0.01151 143213.1562 +#> 34334 714642.5 9557912 2582.25 30.9912871 0.00253 -0.01233 968.7780 +#> 18504 713372.5 9560672 1786.89 6.4738501 -0.00278 -0.01172 111973.8906 +#> 16443 714472.5 9559532 2222.55 16.7441186 0.03263 -0.02012 305.3624 +#> 49812 713392.5 9559222 2133.99 35.6626120 -0.00983 0.00803 1058.5630 +#> 6577 714142.5 9559902 2244.30 57.8905097 -0.06986 -0.05013 616.4128 +#> 32342 714862.5 9559622 2294.74 36.9672369 0.01021 0.00109 915.4989 +#> 42990 712432.5 9560692 2102.11 32.4500377 -0.00087 -0.00573 1490.0312 +#> 11025 714402.5 9559252 2325.64 49.6204369 -0.00241 0.00020 673.2861 +#> 39234 712902.5 9559652 1953.39 29.7319259 0.07379 -0.00249 148.1857 +#> 46567 714472.5 9560352 2080.13 21.3816390 0.01336 0.01403 609.8665 +#> 6206 714122.5 9560992 1917.28 37.9699131 -0.01023 -0.00227 841.2025 +#> 40852 715392.5 9557932 2939.71 18.2974072 0.00505 0.00855 229.6061 #> cslope distroad slides distdeforest distslidespast log.carea -#> 23178 41.6001737 300.00 TRUE 300.00 21 2.826839 #> 37912 30.2945705 300.00 TRUE 9.15 2 2.802317 -#> 15412 37.5929069 300.00 TRUE 300.00 100 3.318273 -#> 16749 32.1830394 300.00 TRUE 300.00 100 2.753405 -#> 40756 39.3352715 300.00 TRUE 300.00 100 3.000471 -#> 24051 32.7388084 300.00 TRUE 300.00 11 3.001122 -#> 19963 24.4377959 300.00 TRUE 300.00 89 2.935182 -#> 37265 36.5174651 300.00 TRUE 2.45 0 3.071539 -#> 48187 30.9666500 279.54 TRUE 21.35 46 3.521349 -#> 24072 37.6668184 300.00 TRUE 300.00 100 3.351559 -#> 14456 20.0689927 300.00 TRUE 247.02 100 3.183868 -#> 1768 28.1855128 300.00 TRUE 300.00 7 3.384768 -#> 43669 28.4765754 24.22 TRUE 0.00 89 2.798316 -#> 49372 23.5405440 213.54 TRUE 20.21 68 3.430977 -#> 20151 44.7955593 300.00 TRUE 300.00 100 2.828669 -#> 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 -#> 7349 26.3228270 300.00 TRUE 300.00 8 2.751808 +#> 27864 27.8153821 30.00 TRUE 183.39 20 3.550683 +#> 44302 35.1011134 300.00 TRUE 300.00 100 3.053301 +#> 42632 20.4780846 300.00 TRUE 300.00 10 2.566509 +#> 13748 34.1190637 300.00 TRUE 300.00 2 3.256827 +#> 42733 31.2829227 300.00 TRUE 300.00 100 2.872981 +#> 1300 27.5306221 300.00 TRUE 300.00 6 2.888165 +#> 24129 24.1289716 300.00 TRUE 300.00 89 2.886120 +#> 21939 23.9324471 300.00 TRUE 300.00 100 3.168886 +#> 20799 24.8079266 180.67 TRUE 0.00 16 3.361925 +#> 15843 42.2218329 300.00 TRUE 300.00 100 3.091290 +#> 9669 31.6645125 300.00 TRUE 300.00 1 3.610558 +#> 37270 18.9465684 162.65 TRUE 0.00 77 3.703905 #> 31023 16.6776555 135.00 TRUE 0.00 2 2.577367 -#> 42598 20.0689927 300.00 TRUE 247.02 100 3.183868 -#> 38287 14.8969027 41.43 TRUE 1.90 100 3.349380 -#> 23925 37.5333192 300.00 TRUE 300.00 100 2.938827 +#> 22814 34.9120373 300.00 TRUE 300.00 6 2.976574 +#> 10974 32.4655075 300.00 TRUE 300.00 100 4.101499 #> 10899 24.7741221 300.00 TRUE 300.00 20 2.644350 -#> 30800 33.2481679 300.00 TRUE 300.00 100 4.403435 +#> 33792 51.2081029 300.00 TRUE 300.00 5 2.596460 #> 43007 38.9617030 300.00 TRUE 300.00 37 2.473902 -#> 18304 44.9084320 300.00 TRUE 300.00 57 3.589919 -#> 35285 42.4664859 300.00 TRUE 300.00 100 2.997681 -#> 29472 18.6486303 111.09 TRUE 0.00 25 3.424505 -#> 34482 32.9714293 300.00 TRUE 300.00 100 5.111976 -#> 35942 27.1645020 300.00 TRUE 300.00 100 3.364960 -#> 47210 26.9152654 300.00 TRUE 300.00 100 2.777281 -#> 18391 34.5075291 300.00 TRUE 300.00 100 5.556760 +#> 38410 8.7610976 69.52 TRUE 47.61 65 3.695093 +#> 48055 33.8257094 300.00 TRUE 1.67 0 3.566570 +#> 30125 40.7355804 300.00 TRUE 300.00 100 2.748832 +#> 18005 24.9059024 300.00 TRUE 300.00 0 2.744234 +#> 36030 29.1996481 300.00 TRUE 0.00 100 2.926238 +#> 4827 21.0739607 300.00 TRUE 300.00 0 2.559711 +#> 45575 25.7280968 300.00 TRUE 300.00 59 2.562268 +#> 32821 29.4832622 300.00 TRUE 300.00 65 3.372940 +#> 27708 25.7109081 300.00 TRUE 300.00 100 2.253599 #> 10320 16.4559208 300.00 TRUE 300.00 30 2.721899 -#> 20194 28.1173308 300.00 TRUE 300.00 41 3.143823 -#> 8073 36.0757146 138.71 TRUE 76.41 35 2.997730 #> 15390 29.3222611 60.00 TRUE 118.92 2 3.372821 -#> 19730 38.4786996 300.00 TRUE 300.00 100 2.787913 -#> 27013 30.9185215 300.00 TRUE 168.05 18 3.080938 -#> 34642 15.6486233 300.00 TRUE 300.00 2 2.460907 -#> 19128 14.6596981 300.00 TRUE 300.00 15 2.478266 -#> 45104 17.9925936 300.00 TRUE 300.00 100 2.894014 -#> 12868 37.5883232 119.34 TRUE 79.16 61 3.129568 -#> 34359 27.4160305 300.00 TRUE 294.31 100 3.287475 -#> 37348 39.2877160 300.00 TRUE 300.00 100 4.465317 -#> 43092 39.4785110 300.00 TRUE 300.00 100 2.903195 +#> 23031 26.7410862 300.00 TRUE 300.00 2 2.798938 +#> 7879 28.5808537 300.00 TRUE 300.00 100 2.912011 +#> 2950 28.2038475 300.00 TRUE 300.00 100 2.891568 +#> 22735 36.5168921 300.00 TRUE 300.00 90 2.622310 +#> 46756 38.9617030 300.00 TRUE 300.00 37 2.473902 +#> 47351 34.8788058 300.00 TRUE 300.00 100 4.173758 +#> 47429 32.0976686 300.00 TRUE 300.00 100 2.464778 +#> 40738 28.9601518 300.00 TRUE 300.00 100 2.716468 +#> 24100 34.2978266 300.00 TRUE 300.00 100 5.558684 +#> 20001 34.6771246 264.46 TRUE 18.17 56 3.311398 +#> 34563 38.1566973 300.00 TRUE 300.00 100 4.204981 +#> 27084 21.6727016 300.00 TRUE 300.00 81 3.000309 +#> 46201 22.1837799 167.75 TRUE 0.00 55 3.674828 +#> 18751 38.0759103 300.00 TRUE 300.00 100 2.713046 +#> 29926 32.0948039 300.00 TRUE 300.00 100 2.537617 +#> 28743 34.6530604 300.00 TRUE 300.00 71 4.272628 #> 37141 12.3884934 300.00 TRUE 300.00 1 2.293159 -#> 4031 42.7615591 300.00 TRUE 300.00 31 2.907317 -#> 9704 38.9960805 300.00 TRUE 300.00 90 2.784369 -#> 462 9.8124752 61.17 TRUE 57.16 74 3.452385 -#> 18892 27.0630885 235.02 TRUE 27.24 0 2.675293 -#> 32909 28.3791726 118.07 TRUE 98.15 0 3.619059 -#> 13029 34.1878187 300.00 TRUE 96.65 4 2.813330 +#> 835 36.6503913 300.00 TRUE 300.00 100 3.141386 +#> 36120 31.5630990 300.00 TRUE 300.00 94 4.013201 +#> 4617 27.9517460 300.00 TRUE 300.00 6 2.211304 +#> 49569 41.8494103 25.00 TRUE 35.00 100 3.348814 +#> 48808 33.7735702 300.00 TRUE 300.00 100 4.066528 #> 28845 29.8425068 300.00 TRUE 300.00 54 2.563199 +#> 35083 43.5407817 300.00 TRUE 300.00 23 2.546735 #> 29801 33.2418654 300.00 TRUE 300.00 2 3.026907 -#> 32735 39.3581898 300.00 TRUE 300.00 100 3.245566 -#> 19966 29.2288690 300.00 TRUE 300.00 23 3.350329 -#> 44650 37.3625778 300.00 TRUE 300.00 100 3.110849 -#> 3418 35.4534824 300.00 TRUE 300.00 73 4.552486 -#> 21013 24.9631982 300.00 TRUE 13.47 64 3.177528 -#> 27695 27.3679020 300.00 TRUE 300.00 41 3.042574 -#> 35014 32.5302518 300.00 TRUE 300.00 57 2.826223 -#> 24501 27.1524699 300.00 TRUE 300.00 0 2.925363 +#> 5103 34.9131832 300.00 TRUE 300.00 91 2.623976 +#> 28046 19.5911459 300.00 TRUE 300.00 15 2.896560 +#> 45592 15.6486233 300.00 TRUE 300.00 2 2.460907 +#> 31834 24.6469255 215.68 TRUE 0.00 100 3.354565 #> 41721 29.9794437 300.00 TRUE 300.00 100 3.099963 -#> 18897 31.7882714 300.00 TRUE 20.00 5 3.919162 -#> 40216 26.3640800 300.00 TRUE 300.00 100 3.257022 #> 39722 26.5416969 300.00 TRUE 300.00 100 2.906387 +#> 30268 47.2724558 300.00 TRUE 300.00 41 2.705801 #> 39166 29.8184425 300.00 TRUE 1.90 90 2.994673 #> 8136 25.4290129 115.18 TRUE 84.59 30 3.951156 #> 6845 44.5961700 300.00 TRUE 300.00 29 2.769292 -#> 7041 33.2189470 300.00 TRUE 300.00 6 5.916915 -#> 1424 39.1542168 300.00 TRUE 300.00 100 4.035908 -#> 44767 17.2953040 300.00 TRUE 300.00 25 3.046207 -#> 33910 32.6734912 300.00 TRUE 300.00 6 6.024389 -#> 10073 25.7613284 300.00 TRUE 300.00 100 2.896526 -#> 31117 26.6459752 300.00 TRUE 291.23 100 3.277641 +#> 18958 29.9897569 300.00 TRUE 300.00 5 2.674746 +#> 43859 29.5577467 300.00 TRUE 300.00 100 2.806780 +#> 1973 19.3258664 300.00 TRUE 300.00 100 2.290100 +#> 17345 29.8213073 300.00 TRUE 300.00 2 4.693266 +#> 18440 34.9446959 300.00 TRUE 300.00 100 4.477051 +#> 6217 30.9718066 300.00 TRUE 300.00 1 3.243763 #> 30287 19.8484039 300.00 TRUE 300.00 65 2.238750 +#> 18348 41.0106001 300.00 TRUE 300.00 100 3.055854 +#> 24135 38.6683486 300.00 TRUE 122.87 100 3.878395 +#> 33875 27.5695832 300.00 TRUE 4.67 0 3.279136 #> 34615 34.1895376 17.57 TRUE 142.95 16 3.011436 -#> 15432 28.7676379 300.00 TRUE 300.00 100 2.880810 -#> 22907 33.8136772 300.00 TRUE 205.28 1 3.147505 -#> 5911 4.1384742 42.88 TRUE 0.00 13 3.009377 -#> 30933 12.8434219 99.29 TRUE 0.00 6 3.137802 -#> 17058 26.3228270 300.00 TRUE 300.00 8 2.751808 -#> 7485 39.4905431 300.00 TRUE 300.00 100 2.627640 -#> 14545 39.3140721 300.00 TRUE 300.00 37 2.897494 -#> 11352 27.9981556 135.89 TRUE 82.11 4 4.291317 -#> 28173 23.8837457 300.00 TRUE 300.00 59 3.185096 -#> 2801 25.7464315 300.00 TRUE 300.00 26 2.422656 +#> 28312 41.6425726 300.00 TRUE 300.00 100 3.198702 +#> 47298 25.3361937 300.00 TRUE 300.00 100 3.126386 +#> 10363 45.0934974 300.00 TRUE 297.43 6 3.599377 +#> 49133 23.5193445 300.00 TRUE 300.00 38 2.694996 #> 14192 40.5866113 300.00 TRUE 300.00 100 3.115833 -#> 1997 25.2359261 168.72 TRUE 0.00 5 3.379717 -#> 48574 34.4244502 96.21 TRUE 101.32 10 3.143827 -#> 44612 34.6914486 300.00 TRUE 300.00 100 2.460216 -#> 39405 27.7202711 300.00 TRUE 300.00 100 3.413298 -#> 42617 31.2319294 300.00 TRUE 300.00 100 2.848895 -#> 28260 31.8335351 300.00 TRUE 300.00 100 3.409673 -#> 37115 32.1870501 300.00 TRUE 300.00 100 4.596544 -#> 17674 30.4825006 300.00 TRUE 300.00 1 3.812044 -#> 14779 16.5212380 55.08 TRUE 0.00 1 2.897760 -#> 9864 20.8207133 300.00 TRUE 300.00 100 2.722792 -#> 11332 25.6816873 300.00 TRUE 27.04 68 3.193695 -#> 28253 2.9576081 300.00 FALSE 300.00 100 2.000000 -#> 15186 38.5486005 60.00 FALSE 0.00 100 3.149477 -#> 748 38.1326331 300.00 FALSE 300.00 12 2.922919 -#> 25212 38.9829025 300.00 FALSE 300.00 100 3.109374 -#> 16464 35.0461095 300.00 FALSE 300.00 100 3.004540 -#> 13512 35.9347670 300.00 FALSE 300.00 100 3.605261 -#> 39193 31.4800201 300.00 FALSE 117.12 100 2.879028 -#> 29125 23.9450522 300.00 FALSE 300.00 100 2.626221 -#> 20547 34.2611573 300.00 FALSE 42.56 100 4.078911 -#> 36833 28.0755049 187.46 FALSE 18.49 45 3.942504 -#> 40139 33.1433166 300.00 FALSE 1.11 21 3.274260 -#> 36126 19.2954997 300.00 FALSE 300.00 100 2.525145 -#> 34616 31.4995007 300.00 FALSE 300.00 100 2.321589 -#> 43403 31.0617609 300.00 FALSE 111.88 86 2.788585 -#> 31201 33.7225769 300.00 FALSE 300.00 100 2.404000 +#> 39624 33.2945775 300.00 TRUE 300.00 2 3.254015 +#> 20426 33.4085961 300.00 TRUE 300.00 76 4.542933 +#> 16065 30.3203536 300.00 TRUE 162.92 100 3.818858 +#> 30363 29.8029727 300.00 TRUE 300.00 100 2.929656 +#> 46792 27.6010959 300.00 TRUE 300.00 18 2.657830 +#> 46627 53.6832806 300.00 TRUE 300.00 100 2.720428 +#> 13278 30.2252426 91.73 TRUE 120.78 34 3.560110 +#> 35458 37.4731587 300.00 TRUE 300.00 100 3.297274 +#> 23832 25.4851627 300.00 TRUE 300.00 8 2.676567 +#> 34718 34.4095533 300.00 TRUE 300.00 100 2.320516 +#> 36744 29.4047670 300.00 TRUE 300.00 100 2.937989 +#> 23176 41.5583478 300.00 TRUE 300.00 100 3.203943 +#> 10145 27.4160305 276.16 FALSE 91.33 100 3.227418 +#> 32405 15.7620689 300.00 FALSE 300.00 100 2.530158 +#> 34250 30.8927384 300.00 FALSE 300.00 100 3.348210 +#> 40459 36.1868684 300.00 FALSE 216.66 10 3.025531 +#> 31071 31.7378511 300.00 FALSE 300.00 76 3.259480 +#> 27965 32.2953391 300.00 FALSE 300.00 100 2.382556 +#> 12716 24.3449767 300.00 FALSE 300.00 25 2.731444 +#> 43426 35.4626498 300.00 FALSE 82.11 100 3.910718 +#> 28162 18.6698298 130.00 FALSE 75.00 100 3.197540 +#> 45166 23.4477248 300.00 FALSE 300.00 1 3.379256 +#> 39796 34.5676897 300.00 FALSE 300.00 71 2.333468 +#> 1085 25.8948276 300.00 FALSE 300.00 100 2.477139 +#> 39685 47.8734886 300.00 FALSE 300.00 100 2.918481 +#> 45071 27.8050688 264.04 FALSE 0.00 100 2.702696 +#> 32849 29.5520172 300.00 FALSE 300.00 45 3.149905 +#> 38689 36.0843090 300.00 FALSE 300.00 18 2.953242 #> 42934 32.2271571 300.00 FALSE 300.00 22 4.499525 -#> 20319 32.0724585 300.00 FALSE 300.00 100 3.167901 +#> 14800 31.5229920 300.00 FALSE 300.00 100 3.103552 #> 8895 38.0776291 300.00 FALSE 300.00 90 2.432641 #> 47059 25.7596095 300.00 FALSE 300.00 100 3.023816 -#> 1691 27.0000631 300.00 FALSE 300.00 100 2.855262 -#> 45486 33.4217741 300.00 FALSE 0.00 68 3.366011 -#> 7553 39.7712924 300.00 FALSE 300.00 100 2.739830 +#> 36245 25.8535746 110.19 FALSE 81.50 20 3.615611 +#> 34105 20.0770141 300.00 FALSE 300.00 26 3.059266 +#> 39440 32.7061498 300.00 FALSE 300.00 92 2.369237 +#> 34345 44.3950618 300.00 FALSE 300.00 100 2.972060 +#> 44267 19.7378231 300.00 FALSE 226.30 100 2.401413 +#> 5656 31.2726094 300.00 FALSE 94.43 100 2.853361 +#> 33666 0.0217724 212.17 FALSE 103.16 100 2.031744 #> 7983 38.5062016 300.00 FALSE 57.15 22 2.884583 #> 41855 28.7017478 234.40 FALSE 47.05 100 3.040739 -#> 35038 40.2038755 300.00 FALSE 300.00 100 3.347416 -#> 46509 45.5787926 300.00 FALSE 162.53 100 2.925390 #> 40909 22.8782048 234.82 FALSE 0.00 43 4.109707 -#> 44823 36.1072273 300.00 FALSE 300.00 100 2.749146 -#> 34532 31.2067193 300.00 FALSE 300.00 92 2.583601 +#> 35826 44.2277581 298.23 FALSE 16.35 96 2.958591 +#> 48845 31.5590883 169.72 FALSE 0.00 100 2.030594 #> 43415 0.2314749 300.00 FALSE 300.00 100 2.000000 +#> 15008 26.9078169 300.00 FALSE 300.00 48 2.625829 #> 1479 43.6765727 300.00 FALSE 300.00 100 2.938910 #> 5861 27.6125550 300.00 FALSE 300.00 100 4.032264 -#> 29716 32.6935447 300.00 FALSE 300.00 100 2.716439 #> 17628 26.9920417 300.00 FALSE 205.59 100 2.626915 -#> 3868 26.1475019 300.00 FALSE 300.00 100 3.277127 -#> 32430 22.8604431 300.00 FALSE 300.00 100 2.398827 -#> 38362 38.2678511 300.00 FALSE 300.00 100 2.734802 -#> 44434 36.3197946 300.00 FALSE 300.00 100 3.451290 -#> 14763 10.1447907 110.04 FALSE 60.35 100 3.107720 -#> 7986 32.0031306 275.06 FALSE 0.00 65 3.156065 -#> 6732 26.8562507 300.00 FALSE 300.00 100 2.948550 +#> 40612 21.9270948 55.73 FALSE 125.03 100 2.946551 +#> 44216 26.0649960 300.00 FALSE 300.00 100 3.167741 +#> 42638 23.4213687 86.41 FALSE 172.46 100 6.734528 +#> 26283 41.5107923 300.00 FALSE 300.00 100 2.962620 +#> 18246 7.9876046 18.49 FALSE 0.00 29 3.220009 +#> 48050 23.3612082 300.00 FALSE 72.23 100 2.520806 +#> 37783 24.2573142 300.00 FALSE 300.00 18 2.682893 +#> 10981 29.1664166 300.00 FALSE 300.00 100 5.155983 #> 34334 25.0898218 300.00 FALSE 300.00 100 2.986224 -#> 40653 0.1420935 18.02 FALSE 140.15 100 3.072344 -#> 39883 17.0443485 233.81 FALSE 219.23 100 2.474870 -#> 47245 34.9762086 300.00 FALSE 150.03 100 2.509309 -#> 15880 48.8967912 300.00 FALSE 300.00 100 2.546026 +#> 18504 23.0959287 144.96 FALSE 4.48 89 5.049117 +#> 16443 26.5382592 300.00 FALSE 300.00 75 2.484816 +#> 49812 33.3581758 300.00 FALSE 300.00 65 3.024717 +#> 6577 42.4074712 300.00 FALSE 300.00 70 2.789872 +#> 32342 37.6198359 300.00 FALSE 300.00 81 2.961658 +#> 42990 22.2370650 205.00 FALSE 0.00 100 3.173195 +#> 11025 36.8199868 300.00 FALSE 300.00 50 2.828200 #> 39234 33.4859454 300.00 FALSE 0.00 100 2.170806 -#> 48157 33.5266254 57.15 FALSE 157.06 45 2.887769 -#> 40043 23.9966184 194.79 FALSE 24.89 54 2.973603 +#> 46567 16.9480916 300.00 FALSE 300.00 78 2.785235 +#> 6206 32.4632157 294.31 FALSE 35.90 100 2.924901 #> 40852 21.5180030 300.00 FALSE 300.00 100 2.360983 -#> 24516 36.2544774 300.00 FALSE 122.13 100 3.751491 diff --git a/reference/partition_cv_strat.html b/reference/partition_cv_strat.html index fd257ec6..98d3fa01 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] 0.9921697 +#> [1] 1.001333 # 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 ebe22fe6..0e74bbb7 100644 --- a/reference/partition_disc.html +++ b/reference/partition_disc.html @@ -192,19 +192,19 @@

Examples

summary(parti) #> $`1` #> n.train n.test -#> 457 711 23 -#> 418 714 11 -#> 655 731 6 -#> 179 710 22 -#> 645 682 28 +#> 150 703 22 +#> 394 718 12 +#> 430 710 17 +#> 691 726 10 +#> 719 742 3 #> #> $`2` #> n.train n.test -#> 72 699 16 -#> 733 737 5 -#> 386 714 19 -#> 584 738 3 -#> 486 719 6 +#> 406 719 12 +#> 613 710 7 +#> 401 702 20 +#> 55 727 8 +#> 128 690 30 #> # leave-one-out with buffer: @@ -212,19 +212,19 @@

Examples

summary(parti) #> $`1` #> n.train n.test -#> 457 711 23 -#> 418 714 11 -#> 655 731 6 -#> 179 710 22 -#> 645 682 28 +#> 150 703 22 +#> 394 718 12 +#> 430 710 17 +#> 691 726 10 +#> 719 742 3 #> #> $`2` #> n.train n.test -#> 72 699 16 -#> 733 737 5 -#> 386 714 19 -#> 584 738 3 -#> 486 719 6 +#> 406 719 12 +#> 613 710 7 +#> 401 702 20 +#> 55 727 8 +#> 128 690 30 #> diff --git a/reference/sperrorest.html b/reference/sperrorest.html index 768def27..cac61b6b 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) ) -#> Wed Nov 1 04:28:07 2023 Repetition 1 -#> Wed Nov 1 04:28:07 2023 Repetition - Fold 1 -#> Wed Nov 1 04:28:07 2023 Repetition - Fold 2 -#> Wed Nov 1 04:28:07 2023 Repetition - Fold 3 -#> Wed Nov 1 04:28:07 2023 Repetition 2 -#> Wed Nov 1 04:28:07 2023 Repetition - Fold 1 -#> Wed Nov 1 04:28:07 2023 Repetition - Fold 2 -#> Wed Nov 1 04:28:07 2023 Repetition - Fold 3 +#> Thu Nov 2 04:27:10 2023 Repetition 1 +#> Thu Nov 2 04:27:10 2023 Repetition - Fold 1 +#> Thu Nov 2 04:27:11 2023 Repetition - Fold 2 +#> Thu Nov 2 04:27:11 2023 Repetition - Fold 3 +#> Thu Nov 2 04:27:11 2023 Repetition 2 +#> Thu Nov 2 04:27:11 2023 Repetition - Fold 1 +#> Thu Nov 2 04:27:11 2023 Repetition - Fold 2 +#> Thu Nov 2 04:27:11 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) ) -#> Wed Nov 1 04:28:08 2023 Repetition 1 -#> Wed Nov 1 04:28:08 2023 Repetition - Fold 1 -#> Wed Nov 1 04:28:08 2023 Repetition - Fold 2 -#> Wed Nov 1 04:28:08 2023 Repetition - Fold 3 -#> Wed Nov 1 04:28:08 2023 Repetition 2 -#> Wed Nov 1 04:28:08 2023 Repetition - Fold 1 -#> Wed Nov 1 04:28:08 2023 Repetition - Fold 2 -#> Wed Nov 1 04:28:08 2023 Repetition - Fold 3 +#> Thu Nov 2 04:27:11 2023 Repetition 1 +#> Thu Nov 2 04:27:11 2023 Repetition - Fold 1 +#> Thu Nov 2 04:27:11 2023 Repetition - Fold 2 +#> Thu Nov 2 04:27:12 2023 Repetition - Fold 3 +#> Thu Nov 2 04:27:12 2023 Repetition 2 +#> Thu Nov 2 04:27:12 2023 Repetition - Fold 1 +#> Thu Nov 2 04:27:12 2023 Repetition - Fold 2 +#> Thu Nov 2 04:27:12 2023 Repetition - Fold 3 summary(sp_res$error_rep) #> mean sd median IQR #> train_auroc 0.8472530 0.017474834 0.8472530 0.012356574