diff --git a/articles/spatial-modeling-use-case.html b/articles/spatial-modeling-use-case.html index 4a62c622..7893e61d 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: 0.57 % +## OOB prediction error: 0.61 %

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    3.41e-02 0.00087 3.39e-02 0.000859
-## train_accuracy 9.66e-01 0.00087 9.66e-01 0.000859
-## train_events   4.69e+03 0.00000 4.69e+03 0.000000
-## train_count    3.09e+04 0.00000 3.09e+04 0.000000
-## test_error     4.05e-02 0.00027 4.06e-02 0.000259
-## test_accuracy  9.60e-01 0.00027 9.59e-01 0.000259
-## test_events    1.17e+03 0.00000 1.17e+03 0.000000
-## test_count     7.71e+03 0.00000 7.71e+03 0.000000

+
##                    mean       sd   median      IQR
+## train_error    3.39e-02 0.000257 3.38e-02 0.000243
+## train_accuracy 9.66e-01 0.000257 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     4.01e-02 0.001963 4.10e-02 0.001815
+## test_accuracy  9.60e-01 0.001963 9.59e-01 0.001815
+## 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 16.8 secs
+
## Time difference of 17.4 secs
 summary(res_lda_sp$error_rep)
##                    mean      sd   median     IQR
-## train_error    3.02e-02 0.00119 3.08e-02 0.00110
-## train_accuracy 9.70e-01 0.00119 9.69e-01 0.00110
+## train_error    2.86e-02 0.00105 2.83e-02 0.00102
+## train_accuracy 9.71e-01 0.00105 9.72e-01 0.00102
 ## 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     6.25e-02 0.00577 6.25e-02 0.00577
-## test_accuracy  9.37e-01 0.00577 9.38e-01 0.00577
+## test_error     6.72e-02 0.00517 6.99e-02 0.00460
+## test_accuracy  9.33e-01 0.00517 9.30e-01 0.00460
 ## test_events    1.17e+03 0.00000 1.17e+03 0.00000
 ## test_count     7.71e+03 0.00000 7.71e+03 0.00000
@@ -455,19 +455,19 @@

RandomForest) 
 summary(res_rf_sp$error_rep)
-
##                    mean      sd   median    IQR
-## train_error    0.00e+00 0.00000 0.00e+00 0.0000
-## train_accuracy 1.00e+00 0.00000 1.00e+00 0.0000
-## train_events   4.69e+03 0.00000 4.69e+03 0.0000
-## train_count    3.09e+04 0.00000 3.09e+04 0.0000
-## test_error     1.02e-01 0.00266 1.01e-01 0.0024
-## test_accuracy  8.98e-01 0.00266 8.99e-01 0.0024
-## test_events    1.17e+03 0.00000 1.17e+03 0.0000
-## test_count     7.71e+03 0.00000 7.71e+03 0.0000
+
##                    mean     sd   median     IQR
+## train_error    0.00e+00 0.0000 0.00e+00 0.00000
+## train_accuracy 1.00e+00 0.0000 1.00e+00 0.00000
+## train_events   4.69e+03 0.0000 4.69e+03 0.00000
+## train_count    3.09e+04 0.0000 3.09e+04 0.00000
+## test_error     8.76e-02 0.0067 8.70e-02 0.00668
+## test_accuracy  9.12e-01 0.0067 9.13e-01 0.00668
+## test_events    1.17e+03 0.0000 1.17e+03 0.00000
+## test_count     7.71e+03 0.0000 7.71e+03 0.00000
 summary(res_rf_sp$error_rep)["test_accuracy",]
-
##                mean      sd median    IQR
-## test_accuracy 0.898 0.00266  0.899 0.0024
+
##                mean     sd median     IQR
+## test_accuracy 0.912 0.0067  0.913 0.00668

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 462c2c99..bd883eca 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 5647dd3e..7d7f0b0c 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -4,7 +4,7 @@ pkgdown_sha: ~ articles: custom-pred-and-model-functions: custom-pred-and-model-functions.html spatial-modeling-use-case: spatial-modeling-use-case.html -last_built: 2023-09-16T04:26Z +last_built: 2023-09-17T04:25Z urls: reference: https://giscience-fsu.github.io/sperrorest/reference article: https://giscience-fsu.github.io/sperrorest/articles diff --git a/reference/add.distance.html b/reference/add.distance.html index 0fc1bed5..2030dc7f 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]]$distance -#> [1] 39.88192 +#> [1] 48.93997 # spatial partitioning: more substantial distance, depending on number of # folds etc. sp.parti[[1]][[1]]$distance -#> [1] 400.8701 +#> [1] 407.12 diff --git a/reference/as.resampling.html b/reference/as.resampling.html index eb5bd45b..fdd1bffd 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 714752 715302 714612 713552 714292 ... -#> $ y : num 9561022 9557472 9560552 9560652 9558982 ... -#> $ dem : num 1848 2857 2032 1852 2459 ... -#> $ slope : num 33.4 39.5 26.8 46 43.6 ... -#> $ hcurv : num -0.00347 -0.01021 0.00046 -0.00891 0.00214 ... -#> $ vcurv : num 0.02357 -0.01579 0.00324 -0.00949 0.00466 ... -#> $ carea : num 1752 1319 861 6218 724 ... -#> $ cslope : num 23.8 36.3 24.4 32.8 38.8 ... -#> $ distroad : num 159 300 300 280 300 ... +#> $ x : num 713832 712842 714612 714792 714462 ... +#> $ y : num 9559662 9560152 9560552 9561002 9560452 ... +#> $ dem : num 2288 1930 2032 1857 2105 ... +#> $ slope : num 18.3 28.1 26.8 23.1 19.5 ... +#> $ hcurv : num 0.01917 0.00753 0.00046 0.0009 0.00453 ... +#> $ vcurv : num 0.00333 0.01487 0.00324 0.0089 0.00257 ... +#> $ carea : num 259 1078 861 2301 357 ... +#> $ cslope : num 20.2 28.3 24.4 24.8 16 ... +#> $ distroad : num 300 300 300 181 300 ... #> $ slides : Factor w/ 2 levels "FALSE","TRUE": 2 2 2 2 2 2 2 2 2 2 ... -#> $ distdeforest : num 0 300 300 0 300 ... -#> $ distslidespast: num 5 100 89 100 100 39 5 46 100 0 ... -#> $ log.carea : num 3.24 3.12 2.94 3.79 2.86 ... +#> $ distdeforest : num 300 0.56 300 0 300 ... +#> $ distslidespast: num 41 100 89 16 2 4 34 56 12 100 ... +#> $ log.carea : num 2.41 3.03 2.94 3.36 2.55 ... # the corresponding training sample - larger: str(ecuador[parti[[1]]$train, ]) #> 'data.frame': 676 obs. of 13 variables: @@ -188,10 +188,10 @@

Examples

#> $ vcurv : num -0.00029 -0.00649 -0.04051 -0.04634 0.02642 ... #> $ carea : num 5577 1399 351155 501 671 ... #> $ cslope : num 34.4 30.7 32.8 33.9 41.6 ... -#> $ distroad : num 300 300 300 300 300 300 300 300 300 30 ... +#> $ 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 41 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': 278 obs. of 13 variables: -#> $ x : num 712882 715232 715042 714842 714922 ... -#> $ y : num 9560002 9559582 9559312 9558892 9558862 ... -#> $ dem : num 1912 2199 2320 2483 2536 ... -#> $ slope : num 25.6 23.2 42.9 68.8 54.9 ... -#> $ hcurv : num -0.00681 -0.00501 -0.01106 -0.04921 0.01305 ... -#> $ vcurv : num -0.00029 -0.00649 -0.04634 -0.12438 -0.00555 ... -#> $ carea : num 5577 1399 501 754 430 ... -#> $ cslope : num 34.4 30.7 33.9 53.7 46.3 ... -#> $ distroad : num 300 300 300 300 300 ... +#> 'data.frame': 263 obs. of 13 variables: +#> $ x : num 712882 715272 714842 712992 714932 ... +#> $ y : num 9560002 9557702 9558892 9560672 9557982 ... +#> $ dem : num 1912 2813 2483 1926 2650 ... +#> $ slope : num 25.6 31 68.8 27.2 37.3 ... +#> $ hcurv : num -0.00681 -0.00123 -0.04921 -0.00199 0.01633 ... +#> $ vcurv : num -0.00029 0.00393 -0.12438 0.00659 -0.01813 ... +#> $ carea : num 5577 2081 754 3554 1131 ... +#> $ cslope : num 34.4 37.6 53.7 27.8 35.1 ... +#> $ distroad : num 300 300 300 30 300 300 300 300 300 300 ... #> $ slides : Factor w/ 2 levels "FALSE","TRUE": 2 2 2 2 2 2 2 2 2 2 ... -#> $ distdeforest : num 15 300 300 300 300 ... -#> $ distslidespast: num 9 21 100 100 100 35 26 100 10 100 ... -#> $ log.carea : num 3.75 3.15 2.7 2.88 2.63 ... +#> $ distdeforest : num 15 300 300 183 300 ... +#> $ distslidespast: num 9 100 100 20 100 100 100 100 31 89 ... +#> $ log.carea : num 3.75 3.32 2.88 3.55 3.05 ... # bootstrap training sample: same size as nrow(ecuador): str(ecuador[parti[[1]]$train, ]) #> 'data.frame': 751 obs. of 13 variables: -#> $ x : num 713982 714902 712722 714402 714332 ... -#> $ y : num 9560462 9559262 9560272 9559252 9560262 ... -#> $ dem : num 2133 2363 1827 2326 2138 ... -#> $ slope : num 5.23 50.7 29.29 49.62 25.72 ... -#> $ hcurv : num 0.00366 -0.01407 0.00009 -0.00241 -0.00491 ... -#> $ vcurv : num 0.00525 0.00547 -0.00599 0.0002 0.00581 ... -#> $ carea : num 162 519 12874 673 1855 ... -#> $ cslope : num 4.44 35.25 22.88 36.82 23.83 ... -#> $ distroad : num 300 300 235 300 300 ... -#> $ slides : Factor w/ 2 levels "FALSE","TRUE": 1 2 1 1 2 2 2 2 1 2 ... -#> $ distdeforest : num 10 300 0 300 300 300 300 300 93.5 0 ... -#> $ distslidespast: num 100 59 43 50 10 100 100 100 70 25 ... -#> $ log.carea : num 2.21 2.72 4.11 2.83 3.27 ... +#> $ x : num 714882 714742 713712 714272 713132 ... +#> $ y : num 9558512 9560142 9561182 9559012 9560672 ... +#> $ dem : num 2702 2162 1785 2433 1897 ... +#> $ slope : num 26.6 26.6 47.7 37.7 24.7 ... +#> $ hcurv : num -0.0027 -0.00978 -0.0326 -0.01909 0.00306 ... +#> $ vcurv : num -0.00769 0.00367 -0.0331 -0.01181 -0.00436 ... +#> $ carea : num 1033 3003 2834 1286 2603 ... +#> $ cslope : num 21.4 31.72 9.81 38.98 28.95 ... +#> $ distroad : num 300 300 61.2 300 10 ... +#> $ slides : Factor w/ 2 levels "FALSE","TRUE": 2 1 2 1 2 2 2 2 2 1 ... +#> $ distdeforest : num 300 300 57.2 300 166.3 ... +#> $ distslidespast: num 12 100 74 100 2 2 100 1 4 100 ... +#> $ log.carea : num 3.01 3.48 3.45 3.11 3.42 ... diff --git a/reference/err_default.html b/reference/err_default.html index c0d25b84..52a1d0f0 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] 0.5106984 +#> [1] 0.5045112 #> #> $error -#> [1] 0.494 +#> [1] 0.47 #> #> $accuracy -#> [1] 0.506 +#> [1] 0.53 #> #> $sensitivity -#> [1] 0.3259557 +#> [1] 0.3167702 #> #> $specificity -#> [1] 0.6838966 +#> [1] 0.729207 #> #> $fpr70 -#> [1] 0.7017893 +#> [1] 0.729207 #> #> $fpr80 -#> [1] 0.7932406 +#> [1] 0.8220503 #> #> $fpr90 -#> [1] 0.9045726 +#> [1] 0.9323017 #> #> $tpr80 -#> [1] 0.2132797 +#> [1] 0.2318841 #> #> $tpr90 -#> [1] 0.1187123 +#> [1] 0.07867495 #> #> $tpr95 -#> [1] 0.05231388 +#> [1] 0.03933747 #> #> $events -#> [1] 497 +#> [1] 483 #> #> $count #> [1] 1000 #> err_default(obs > 0, obs + rnorm(1000)) # some discrimination #> $auroc -#> [1] 0.8318179 +#> [1] 0.8267717 #> #> $error -#> [1] 0.26 +#> [1] 0.271 #> #> $accuracy -#> [1] 0.74 +#> [1] 0.729 #> #> $sensitivity -#> [1] 0.6096579 +#> [1] 0.5879917 #> #> $specificity -#> [1] 0.8687873 +#> [1] 0.860735 #> #> $fpr70 -#> [1] 0.2067594 +#> [1] 0.2205029 #> #> $fpr80 -#> [1] 0.332008 +#> [1] 0.2901354 #> #> $fpr90 -#> [1] 0.4890656 +#> [1] 0.4874275 #> #> $tpr80 -#> [1] 0.6881288 +#> [1] 0.679089 #> #> $tpr90 -#> [1] 0.5513078 +#> [1] 0.505176 #> #> $tpr95 -#> [1] 0.4285714 +#> [1] 0.3374741 #> #> $events -#> [1] 497 +#> [1] 483 #> #> $count #> [1] 1000 @@ -196,44 +196,44 @@

Examples

# Three mock regression examples: err_default(obs, rnorm(1000)) # just noise, but no bias #> $bias -#> [1] -0.06419451 +#> [1] -0.03145116 #> #> $stddev -#> [1] 1.398668 +#> [1] 1.427456 #> #> $rmse -#> [1] 1.399441 +#> [1] 1.427089 #> #> $mad -#> [1] 1.407359 +#> [1] 1.436252 #> #> $median -#> [1] -0.08687487 +#> [1] 0.01181974 #> #> $iqr -#> [1] 1.89035 +#> [1] 1.925503 #> #> $count #> [1] 1000 #> err_default(obs, obs + rnorm(1000)) # some association, no bias #> $bias -#> [1] 0.01807706 +#> [1] -0.02537983 #> #> $stddev -#> [1] 0.9660815 +#> [1] 1.028248 #> #> $rmse -#> [1] 0.9657676 +#> [1] 1.028047 #> #> $mad -#> [1] 0.9951806 +#> [1] 1.035757 #> #> $median -#> [1] 0.0352395 +#> [1] 0.01510408 #> #> $iqr -#> [1] 1.330368 +#> [1] 1.420337 #> #> $count #> [1] 1000 @@ -243,7 +243,7 @@

Examples

#> [1] -1 #> #> $stddev -#> [1] 6.17457e-17 +#> [1] 6.234229e-17 #> #> $rmse #> [1] 1 diff --git a/reference/partition_cv.html b/reference/partition_cv.html index 48310845..12f3848f 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 -#> 21788 715392.5 9560172 1988.71 40.476603 -0.01919 -0.04051 351155.1250 -#> 37912 712802.5 9559952 1838.40 52.101344 0.00183 -0.09203 634.3320 -#> 27864 712992.5 9560672 1926.38 27.224090 -0.00199 0.00659 3553.7166 -#> 13748 715232.5 9559542 2212.12 26.123438 0.00023 -0.00043 1806.4552 -#> 30059 713832.5 9559662 2288.02 18.277927 0.01917 0.00333 258.6537 -#> 1300 714042.5 9558482 2408.27 24.148452 0.00659 0.01041 772.9746 -#> 8598 714002.5 9558872 2310.02 39.583935 -0.00498 -0.00752 1321.5997 -#> 15843 714042.5 9558902 2332.30 50.097711 -0.00858 -0.00292 1233.9299 -#> 28293 713762.5 9558812 2250.98 33.285410 0.00118 0.00761 928.6858 -#> 39885 715062.5 9561022 1840.94 38.435728 0.00250 -0.01340 604.1032 -#> 20151 714602.5 9559922 2184.66 48.341595 -0.00979 -0.01542 674.0147 -#> 38827 713412.5 9560472 1869.10 16.871315 -0.00156 0.00406 2421.1145 -#> 47255 713552.5 9559962 2191.77 33.578765 -0.00068 0.00588 1295.1610 -#> 21703 713842.5 9559672 2282.58 24.991846 0.01581 0.01189 287.5425 -#> 30800 715182.5 9557582 2772.37 39.204064 -0.02475 0.00665 25318.3555 -#> 45869 714342.5 9560282 2127.11 27.623441 -0.02169 -0.00160 9344.2930 -#> 40307 713302.5 9560702 1845.07 39.004675 0.00129 0.02510 3342.5518 -#> 48055 712862.5 9559972 1902.48 29.016875 -0.00720 0.01250 3686.1240 -#> 18005 714832.5 9557912 2660.76 31.912030 0.00191 -0.00261 554.9243 -#> 22933 712712.5 9560432 1902.17 38.123466 -0.00746 0.00096 3210.5781 -#> 31576 713942.5 9558492 2387.12 46.143156 -0.05634 -0.02626 4316.0977 -#> 4827 714962.5 9559882 2274.42 29.244912 0.00051 0.00889 362.8363 -#> 6676 715092.5 9559912 2217.44 41.707890 -0.00077 -0.00284 2010.4285 -#> 34280 713652.5 9560702 1896.37 41.273588 0.00382 0.00088 2322.1746 -#> 34565 712972.5 9560172 2007.72 27.947162 0.00598 0.01212 561.1948 -#> 6763 714692.5 9561052 1830.60 48.394880 -0.01996 0.00626 1014.7159 -#> 1635 713102.5 9559052 2172.87 31.484031 -0.00064 -0.00916 2009.1686 -#> 23141 715632.5 9558942 2495.99 34.376895 0.01275 0.00605 798.6775 -#> 2950 715042.5 9557712 2740.72 26.525081 -0.00089 -0.00051 779.0555 -#> 46756 713372.5 9559192 2120.47 46.005073 0.03696 0.02514 297.7842 -#> 12868 712532.5 9560332 1913.92 61.937311 -0.03543 -0.05146 1347.6224 -#> 15990 712852.5 9559982 1894.77 37.694320 -0.01545 0.02405 5681.2412 -#> 5728 713502.5 9559662 2140.27 34.307567 -0.00154 0.00124 2114.2253 -#> 47351 714222.5 9558792 2420.94 28.668516 -0.09566 -0.03355 14919.6260 -#> 40738 714972.5 9557642 2745.48 42.542689 -0.00294 -0.00096 520.5570 -#> 17594 714832.5 9561052 1818.04 46.167220 -0.01353 0.00063 5035.0000 -#> 36931 712852.5 9558892 2102.80 30.535786 0.00140 -0.00070 2562.7559 -#> 18564 715552.5 9558592 2648.36 26.291314 0.00316 0.00714 475.3120 -#> 6967 715342.5 9560192 1995.36 44.881503 -0.00666 -0.00504 1020.2766 -#> 43092 715532.5 9558192 2770.31 36.833165 -0.00442 -0.00178 800.1933 -#> 29926 714702.5 9557622 2637.00 44.642580 0.01843 0.04407 344.8395 -#> 41037 715532.5 9558792 2529.58 32.846524 -0.00585 -0.00974 2136.6939 -#> 44550 714842.5 9557642 2696.38 15.173068 0.00359 -0.00279 930.0871 -#> 23029 713502.5 9559102 2174.07 56.457542 0.03395 0.01495 266.5888 -#> 39599 714112.5 9558562 2374.25 29.168135 0.00283 0.00117 1018.2141 -#> 20220 714052.5 9558522 2378.67 39.656128 -0.00855 -0.02175 1122.9120 -#> 22299 713532.5 9560362 1975.58 27.709958 0.00478 0.01092 412.5849 -#> 2718 715062.5 9558222 2719.56 44.753160 -0.00119 -0.01560 977.4719 -#> 48808 715032.5 9557792 2707.02 25.437607 -0.02329 -0.01181 11655.4316 -#> 42941 713252.5 9559742 1990.10 28.954422 -0.04708 0.00408 76318.0156 -#> 30406 715312.5 9560152 2029.10 34.137398 0.00953 -0.02283 590.1536 -#> 34005 714922.5 9558842 2559.23 52.831292 0.03658 0.04602 345.8933 -#> 2595 714912.5 9557662 2700.72 24.194862 -0.02681 -0.01319 3457.7519 -#> 33350 713132.5 9560672 1896.89 24.695054 0.00306 -0.00436 2602.7390 -#> 28845 714892.5 9559272 2374.42 45.750107 -0.00209 0.02739 365.7626 -#> 44650 715612.5 9558142 2764.88 39.360482 -0.00550 -0.00941 1290.7709 -#> 31561 713302.5 9559192 2092.11 36.527778 0.00406 -0.00055 773.0359 -#> 16355 712962.5 9560212 1992.88 39.526066 0.01927 -0.00316 454.9674 -#> 13146 713792.5 9558552 2341.40 29.825318 -0.00104 0.01664 1898.5588 -#> 2552 714942.5 9557652 2719.32 34.873649 -0.01238 -0.00851 1012.5963 -#> 41783 715572.5 9558652 2609.51 32.408212 0.00291 -0.00081 940.6002 -#> 12137 712842.5 9558902 2097.88 30.098619 0.00024 -0.00474 2343.2100 -#> 39722 715022.5 9557712 2735.53 30.260766 0.00133 0.00367 806.0966 -#> 39166 712832.5 9560142 1922.46 39.660139 0.01469 0.02940 987.8085 -#> 32879 713912.5 9559672 2238.00 31.976775 -0.00278 -0.00412 1350.3611 -#> 44767 714452.5 9560402 2092.27 12.462978 -0.00195 -0.00205 1112.2626 -#> 10073 715572.5 9558612 2632.78 32.161267 0.00129 0.00171 788.0004 -#> 47718 712812.5 9559892 1867.55 54.938631 -0.01491 0.01061 5744.7583 -#> 17345 713922.5 9559682 2227.44 34.490913 -0.06509 -0.01211 49347.5547 -#> 6217 714442.5 9559932 2151.30 36.670445 0.01489 0.00241 1752.9242 -#> 30287 713992.5 9557822 2403.92 18.215474 0.03890 -0.00911 173.2806 -#> 43201 715712.5 9557922 2852.82 31.462258 -0.00949 0.00520 3390.5708 -#> 42201 714642.5 9557452 2592.29 13.225585 -0.02084 -0.02236 9755.3027 -#> 34887 714662.5 9557392 2609.02 51.020746 -0.01878 -0.04351 1442.9247 -#> 20797 713012.5 9560392 1842.25 36.649818 0.01299 0.00141 4611.0718 -#> 46138 715202.5 9559582 2194.40 18.860052 -0.02143 -0.05078 622888.2500 -#> 33875 712832.5 9559852 1892.81 37.554519 -0.01175 0.04195 1901.6743 -#> 15432 715632.5 9558922 2509.67 35.827624 -0.00444 -0.00017 759.9937 -#> 22907 713172.5 9559612 2056.81 22.272588 -0.00166 0.01476 1404.4454 -#> 5911 713952.5 9561282 1801.16 33.789613 0.00466 -0.01776 1021.8267 -#> 28009 715002.5 9559922 2237.68 34.603786 -0.01109 0.00298 1533.7925 -#> 16071 714562.5 9560062 2166.69 28.696018 0.02281 0.04759 158.9370 -#> 48574 712692.5 9560412 1901.97 41.978326 0.01364 -0.00094 1392.6008 -#> 14494 715332.5 9558792 2557.22 20.720446 0.01267 0.00483 343.1384 -#> 44612 714932.5 9558822 2578.80 38.255246 0.01257 0.00044 288.5468 -#> 9606 714392.5 9559172 2353.05 30.783303 -0.00185 -0.01185 953.8614 -#> 24723 712942.5 9560202 1983.18 33.071697 0.00285 -0.01325 595.0426 -#> 42617 714092.5 9558392 2469.21 42.799947 -0.01397 0.00526 706.1463 -#> 7771 715232.5 9560922 1816.88 37.180377 0.00124 0.01166 1813.7841 -#> 37115 715612.5 9559202 2358.15 9.727677 -0.01020 0.00279 39495.1797 -#> 24532 715202.5 9560922 1799.62 4.488551 -0.02312 -0.01848 1890804.2500 -#> 47722 712732.5 9561022 1999.03 37.492639 0.00004 -0.02384 947.2075 -#> 10213 715542.5 9558782 2532.10 35.413375 -0.00893 -0.00867 2247.8748 -#> 48461 714672.5 9559272 2316.84 40.381492 -0.01966 -0.02234 849.0547 -#> 21490 714672.5 9560382 2021.86 32.804126 -0.00783 -0.00367 1966.2112 -#> 9984 712742.5 9560092 1848.56 23.612737 0.00614 -0.00093 118.1402 -#> 13072 714862.5 9558932 2448.29 41.052426 -0.04989 -0.02781 29995.1543 -#> 32405 713022.5 9558982 2208.85 15.004619 0.00599 0.00301 338.9676 -#> 7317 713682.5 9558582 2347.46 28.347660 -0.02196 0.00336 2806.2644 -#> 29431 714882.5 9559892 2258.03 21.917927 -0.00849 0.00150 503.5590 -#> 25584 715062.5 9558532 2675.93 37.688591 0.00534 0.01476 1275.9446 -#> 11288 712822.5 9560442 1873.42 39.609718 -0.00465 -0.00105 4432.8760 -#> 15186 713322.5 9561212 1936.88 36.079725 0.00165 0.00565 1410.8372 -#> 16464 715252.5 9559982 2143.24 23.185883 -0.00298 -0.00712 1010.5097 -#> 15558 714732.5 9559502 2283.66 53.431752 0.00390 -0.02831 454.9113 -#> 8635 713832.5 9557902 2397.71 47.119476 0.02295 0.02715 520.7355 -#> 30267 714442.5 9557512 2612.30 28.765919 -0.00458 0.00479 604.0975 -#> 441 714312.5 9560972 1916.59 31.302976 0.00540 0.00650 1133.1501 -#> 42329 715852.5 9558702 2651.92 22.870756 0.00321 0.00548 532.8210 -#> 16542 713772.5 9559622 2304.85 30.810232 -0.00712 0.00923 811.5580 -#> 49835 713272.5 9560372 1898.68 32.825898 0.00846 0.00794 1363.5321 -#> 16878 714232.5 9558172 2485.33 22.808304 0.00818 0.00502 1428.8546 -#> 13286 713662.5 9559702 2268.14 41.996660 -0.01341 0.04431 315.9084 -#> 4007 714132.5 9558412 2464.44 43.720118 0.00174 -0.03065 704.4843 -#> 31497 714032.5 9560672 2060.77 16.216997 0.00973 -0.00943 371.5514 -#> 12974 714662.5 9557332 2667.61 41.941657 -0.00065 0.00475 515.1661 -#> 8831 714372.5 9559852 2200.77 48.760427 -0.00457 0.01697 412.1185 -#> 31201 714672.5 9560022 2224.47 38.863727 0.01180 0.01170 253.5129 -#> 36245 713142.5 9560572 1827.82 43.676000 -0.00410 0.00400 4126.7812 -#> 48869 712562.5 9560122 1972.89 24.946582 0.00185 0.01655 560.8554 -#> 20135 714772.5 9560232 2107.52 37.850738 -0.00782 -0.00048 1550.6704 -#> 3733 713762.5 9559632 2297.63 34.214748 -0.01338 0.00518 1137.0179 -#> 29107 713172.5 9559272 2067.58 28.264008 0.02413 0.00528 355.3475 -#> 14148 714162.5 9560352 2149.57 37.133395 0.00424 0.00196 576.1450 -#> 44715 714932.5 9560132 2195.78 30.393692 0.03479 0.02251 168.1733 -#> 42424 714692.5 9560902 1921.34 25.331037 0.01154 -0.00204 618.2027 -#> 43222 714472.5 9560712 2039.25 22.190655 0.01858 0.00312 254.0656 -#> 1281 715102.5 9560032 2120.64 34.649050 -0.00930 -0.00360 4288.7329 -#> 26768 715092.5 9560582 1960.72 36.656121 0.00980 -0.00710 472.0236 -#> 14671 712672.5 9560372 1872.21 39.197189 -0.00873 -0.01417 1520.6630 -#> 17628 714032.5 9560252 2160.03 30.294570 0.00537 -0.00577 423.5598 -#> 484 713812.5 9560592 2016.41 44.301097 -0.00719 0.00079 1647.6451 -#> 31098 714352.5 9559042 2410.15 30.336396 0.00537 -0.00347 1893.3645 -#> 2999 714592.5 9558492 2515.83 47.031814 0.01600 -0.01849 764.8193 -#> 18308 714842.5 9558742 2568.20 26.017441 -0.02552 0.00532 16771.3066 -#> 18246 713902.5 9561342 1818.52 19.110434 -0.01502 0.00282 1659.6211 -#> 15138 713702.5 9561122 1780.68 49.221658 0.00607 -0.04566 12862.4922 -#> 24446 714742.5 9560082 2200.60 28.721801 -0.00598 0.02208 842.4825 -#> 18504 713372.5 9560672 1786.89 6.473850 -0.00278 -0.01172 111973.8906 -#> 16443 714472.5 9559532 2222.55 16.744119 0.03263 -0.02012 305.3624 -#> 40653 714262.5 9561262 1791.94 12.222336 0.00161 0.00709 1181.2555 -#> 9320 713872.5 9559262 2378.76 29.858550 0.00309 0.01011 501.7867 -#> 9526 714432.5 9560582 2076.13 31.080096 -0.00172 -0.00119 792.3544 -#> 35257 713822.5 9557982 2350.80 29.307937 0.01549 -0.00169 649.9975 -#> 31440 714842.5 9558392 2704.41 25.801435 0.00992 -0.01052 451.7209 -#> 6577 714142.5 9559902 2244.30 57.890510 -0.06986 -0.05013 616.4128 -#> 41373 715332.5 9560862 1873.10 29.124591 -0.00166 0.00156 758.4840 -#> 39234 712902.5 9559652 1953.39 29.731926 0.07379 -0.00249 148.1857 -#> 46567 714472.5 9560352 2080.13 21.381639 0.01336 0.01403 609.8665 -#> 24516 713802.5 9560862 1873.21 23.349749 -0.00771 0.00661 5642.7544 -#> cslope distroad slides distdeforest distslidespast log.carea -#> 21788 32.8144388 300.00 TRUE 300.00 40 5.545499 -#> 37912 30.2945705 300.00 TRUE 9.15 2 2.802317 -#> 27864 27.8153821 30.00 TRUE 183.39 20 3.550683 -#> 13748 34.1190637 300.00 TRUE 300.00 2 3.256827 -#> 30059 20.1921786 300.00 TRUE 300.00 41 2.412719 -#> 1300 27.5306221 300.00 TRUE 300.00 6 2.888165 -#> 8598 39.5982591 300.00 TRUE 300.00 100 3.121100 -#> 15843 42.2218329 300.00 TRUE 300.00 100 3.091290 -#> 28293 28.9022830 300.00 TRUE 300.00 4 2.967869 -#> 39885 6.0848118 210.57 TRUE 0.00 100 2.781111 -#> 20151 44.7955593 300.00 TRUE 300.00 100 2.828669 -#> 38827 31.0359778 300.00 TRUE 52.52 39 3.384015 -#> 47255 19.6776625 300.00 TRUE 260.03 100 3.112324 -#> 21703 22.2691506 300.00 TRUE 300.00 34 2.458702 -#> 30800 33.2481679 300.00 TRUE 300.00 100 4.403435 -#> 45869 23.4546003 300.00 TRUE 300.00 12 3.970546 -#> 40307 29.5136290 77.82 TRUE 76.41 54 3.524078 -#> 48055 33.8257094 300.00 TRUE 1.67 0 3.566570 -#> 18005 24.9059024 300.00 TRUE 300.00 0 2.744234 -#> 22933 33.0631025 87.56 TRUE 115.40 2 3.506583 -#> 31576 26.9353189 300.00 TRUE 300.00 4 3.635091 -#> 4827 21.0739607 300.00 TRUE 300.00 0 2.559711 -#> 6676 34.6215477 300.00 TRUE 300.00 100 3.303289 -#> 34280 33.6211634 300.00 TRUE 0.00 100 3.365895 -#> 34565 20.3801088 300.00 TRUE 68.13 100 2.749114 -#> 6763 16.2181433 125.47 TRUE 0.00 6 3.006344 -#> 1635 27.8268412 300.00 TRUE 300.00 100 3.303016 -#> 23141 30.2917057 300.00 TRUE 300.00 100 2.902371 -#> 2950 28.2038475 300.00 TRUE 300.00 100 2.891568 -#> 46756 38.9617030 300.00 TRUE 300.00 37 2.473902 -#> 12868 37.5883232 119.34 TRUE 79.16 61 3.129568 -#> 15990 34.1488575 300.00 TRUE 0.00 0 3.754443 -#> 5728 40.4084215 300.00 TRUE 300.00 2 3.325151 -#> 47351 34.8788058 300.00 TRUE 300.00 100 4.173758 -#> 40738 28.9601518 300.00 TRUE 300.00 100 2.716468 -#> 17594 19.2319014 138.35 TRUE 0.00 60 3.701999 -#> 36931 27.2091927 300.00 TRUE 300.00 100 3.408707 -#> 18564 21.7002035 300.00 TRUE 300.00 100 2.676979 -#> 6967 41.1194621 300.00 TRUE 300.00 85 3.008718 -#> 43092 39.4785110 300.00 TRUE 300.00 100 2.903195 -#> 29926 32.0948039 300.00 TRUE 300.00 100 2.537617 -#> 41037 31.5705475 300.00 TRUE 300.00 100 3.329742 -#> 44550 24.8262613 300.00 TRUE 300.00 100 2.968524 -#> 23029 46.6015223 300.00 TRUE 300.00 34 2.425842 -#> 39599 33.1347223 300.00 TRUE 300.00 25 3.007839 -#> 20220 32.1755909 300.00 TRUE 300.00 8 3.050346 -#> 22299 21.7191111 300.00 TRUE 127.54 2 2.615513 -#> 2718 36.7328972 300.00 TRUE 300.00 100 2.990104 -#> 48808 33.7735702 300.00 TRUE 300.00 100 4.066528 -#> 42941 39.0631166 300.00 TRUE 300.00 17 4.882627 -#> 30406 43.1007501 300.00 TRUE 300.00 85 2.770965 -#> 34005 41.7336728 300.00 TRUE 300.00 100 2.538942 -#> 2595 30.4395288 300.00 TRUE 300.00 100 3.538794 -#> 33350 28.9527033 10.00 TRUE 166.26 2 3.415431 -#> 28845 29.8425068 300.00 TRUE 300.00 54 2.563199 -#> 44650 37.3625778 300.00 TRUE 300.00 100 3.110849 -#> 31561 31.4427779 300.00 TRUE 300.00 0 2.888200 -#> 16355 28.2468193 300.00 TRUE 58.52 100 2.657980 -#> 13146 33.5764727 300.00 TRUE 300.00 2 3.278424 -#> 2552 32.2156979 300.00 TRUE 300.00 100 3.005436 -#> 41783 28.1860858 300.00 TRUE 300.00 100 2.973405 -#> 12137 27.5884908 300.00 TRUE 300.00 100 3.369811 -#> 39722 26.5416969 300.00 TRUE 300.00 100 2.906387 -#> 39166 29.8184425 300.00 TRUE 1.90 90 2.994673 -#> 32879 29.7382284 300.00 TRUE 300.00 6 3.130450 -#> 44767 17.2953040 300.00 TRUE 300.00 25 3.046207 -#> 10073 25.7613284 300.00 TRUE 300.00 100 2.896526 -#> 47718 32.3234140 300.00 TRUE 10.00 1 3.759272 -#> 17345 29.8213073 300.00 TRUE 300.00 2 4.693266 -#> 6217 30.9718066 300.00 TRUE 300.00 1 3.243763 -#> 30287 19.8484039 300.00 TRUE 300.00 65 2.238750 -#> 43201 41.8282109 300.00 TRUE 300.00 100 3.530273 -#> 42201 39.5587250 300.00 TRUE 300.00 100 3.989241 -#> 34887 42.9357383 300.00 TRUE 300.00 100 3.159244 -#> 20797 32.0569886 285.07 TRUE 0.00 96 3.663802 -#> 46138 32.6946907 300.00 TRUE 300.00 8 5.794410 -#> 33875 27.5695832 300.00 TRUE 4.67 0 3.279136 -#> 15432 28.7676379 300.00 TRUE 300.00 100 2.880810 -#> 22907 33.8136772 300.00 TRUE 205.28 1 3.147505 -#> 5911 4.1384742 42.88 TRUE 0.00 13 3.009377 -#> 28009 27.0178248 300.00 TRUE 300.00 29 3.185767 -#> 16071 29.6213451 300.00 TRUE 300.00 100 2.201225 -#> 48574 34.4244502 96.21 TRUE 101.32 10 3.143827 -#> 14494 21.1851145 300.00 TRUE 300.00 28 2.535469 -#> 44612 34.6914486 300.00 TRUE 300.00 100 2.460216 -#> 9606 33.8239905 300.00 TRUE 300.00 0 2.979485 -#> 24723 28.1734807 300.00 TRUE 36.63 100 2.774548 -#> 42617 31.2319294 300.00 TRUE 300.00 100 2.848895 -#> 7771 16.5791068 300.00 TRUE 35.59 100 3.258586 -#> 37115 32.1870501 300.00 TRUE 300.00 100 4.596544 -#> 24532 32.5462946 300.00 TRUE 7.49 100 6.276647 -#> 47722 33.1158146 300.00 TRUE 65.02 1 2.976445 -#> 10213 31.5986224 300.00 TRUE 300.00 100 3.351772 -#> 48461 35.7800684 300.00 TRUE 300.00 61 2.928936 -#> 21490 34.0600491 300.00 TRUE 300.00 1 3.293630 -#> 9984 3.9465333 300.00 TRUE 36.46 76 2.072398 -#> 13072 34.9446959 300.00 TRUE 300.00 100 4.477051 -#> 32405 15.7620689 300.00 FALSE 300.00 100 2.530158 -#> 7317 27.2258085 300.00 FALSE 300.00 61 3.448129 -#> 29431 20.8917601 300.00 FALSE 300.00 42 2.702050 -#> 25584 27.1467403 300.00 FALSE 300.00 100 3.105832 -#> 11288 28.5373089 132.07 FALSE 85.50 1 3.646686 -#> 15186 38.5486005 60.00 FALSE 0.00 100 3.149477 -#> 16464 35.0461095 300.00 FALSE 300.00 100 3.004540 -#> 15558 50.3440826 300.00 FALSE 300.00 45 2.657927 -#> 8635 27.6818829 300.00 FALSE 300.00 23 2.716617 -#> 30267 25.2703036 300.00 FALSE 300.00 100 2.781107 -#> 441 25.1133131 294.31 FALSE 152.09 100 3.054287 -#> 42329 19.2278906 300.00 FALSE 300.00 100 2.726581 -#> 16542 21.1123488 300.00 FALSE 300.00 100 2.909320 -#> 49835 32.3612292 300.00 FALSE 0.00 100 3.134665 -#> 16878 25.3889058 300.00 FALSE 300.00 18 3.154988 -#> 13286 25.8386777 300.00 FALSE 300.00 76 2.499561 -#> 4007 32.0925120 300.00 FALSE 300.00 68 2.847871 -#> 31497 21.2704852 300.00 FALSE 154.49 100 2.570019 -#> 12974 37.3385136 300.00 FALSE 300.00 100 2.711947 -#> 8831 32.5554619 300.00 FALSE 300.00 24 2.615022 -#> 31201 33.7225769 300.00 FALSE 300.00 100 2.404000 -#> 36245 25.8535746 110.19 FALSE 81.50 20 3.615611 -#> 48869 24.8755356 138.98 FALSE 0.00 63 2.748851 -#> 20135 32.3325813 300.00 FALSE 300.00 100 3.190519 -#> 3733 22.1001281 300.00 FALSE 300.00 100 3.055767 -#> 29107 25.2307695 300.00 FALSE 300.00 100 2.550653 -#> 14148 21.8778204 300.00 FALSE 214.75 100 2.760532 -#> 44715 28.4502193 300.00 FALSE 300.00 100 2.225757 -#> 42424 22.6071957 275.21 FALSE 93.50 70 2.791131 -#> 43222 21.2653286 300.00 FALSE 300.00 100 2.404946 -#> 1281 35.3938948 300.00 FALSE 300.00 100 3.632329 -#> 26768 32.2821611 300.00 FALSE 207.22 100 2.673964 -#> 14671 37.8822505 123.04 FALSE 75.11 0 3.182033 -#> 17628 26.9920417 300.00 FALSE 205.59 100 2.626915 -#> 484 32.1457971 300.00 FALSE 55.63 100 3.216864 -#> 31098 34.6576441 300.00 FALSE 300.00 45 3.277234 -#> 2999 45.5839492 300.00 FALSE 300.00 100 2.883559 -#> 18308 30.0206966 300.00 FALSE 300.00 55 4.224567 -#> 18246 7.9876046 18.49 FALSE 0.00 29 3.220009 -#> 15138 20.6459612 116.69 FALSE 19.61 40 4.109325 -#> 24446 31.3631367 300.00 FALSE 300.00 100 2.925561 -#> 18504 23.0959287 144.96 FALSE 4.48 89 5.049117 -#> 16443 26.5382592 300.00 FALSE 300.00 75 2.484816 -#> 40653 0.1420935 18.02 FALSE 140.15 100 3.072344 -#> 9320 18.1392708 300.00 FALSE 300.00 93 2.700519 -#> 9526 27.5695832 300.00 FALSE 300.00 100 2.898919 -#> 35257 30.8818522 300.00 FALSE 300.00 79 2.812912 -#> 31440 24.0350065 300.00 FALSE 300.00 96 2.654870 -#> 6577 42.4074712 300.00 FALSE 300.00 70 2.789872 -#> 41373 31.1287970 300.00 FALSE 120.90 100 2.879946 -#> 39234 33.4859454 300.00 FALSE 0.00 100 2.170806 -#> 46567 16.9480916 300.00 FALSE 300.00 78 2.785235 -#> 24516 36.2544774 300.00 FALSE 122.13 100 3.751491 +#> x y dem slope hcurv vcurv carea +#> 15412 715272.5 9557702 2813.17 30.952326 -0.00123 0.00393 2081.0056 +#> 25090 715362.5 9560102 2059.29 49.119672 0.02059 -0.00628 556.0121 +#> 42733 715112.5 9559062 2399.70 36.054515 -0.00041 0.01741 746.4155 +#> 5999 715552.5 9558812 2518.03 7.984167 -0.01863 -0.00507 159065.0781 +#> 1300 714042.5 9558482 2408.27 24.148452 0.00659 0.01041 772.9746 +#> 8059 713292.5 9559122 2136.06 29.662025 0.02240 -0.00250 1413.3312 +#> 37265 712812.5 9559942 1860.77 63.337047 0.00514 -0.00644 1179.0695 +#> 8869 715062.5 9559392 2278.56 41.401930 0.00310 0.01440 1088.8639 +#> 32622 714972.5 9557762 2687.38 30.556412 -0.00730 -0.01491 1844.4353 +#> 38827 713412.5 9560472 1869.10 16.871315 -0.00156 0.00406 2421.1145 +#> 47255 713552.5 9559962 2191.77 33.578765 -0.00068 0.00588 1295.1610 +#> 37270 714852.5 9561032 1838.96 35.158982 -0.00854 0.01264 5057.1431 +#> 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 +#> 1393 714832.5 9557902 2664.72 31.295528 0.00550 -0.00120 512.9740 +#> 38993 714202.5 9558402 2475.75 33.159359 -0.01607 0.00677 1343.5006 +#> 39629 714602.5 9558912 2474.89 40.258307 -0.06175 -0.08226 2325.2053 +#> 27375 714292.5 9558982 2458.96 43.600942 0.00214 0.00466 724.0713 +#> 44328 713222.5 9558932 2227.94 29.155530 -0.00769 -0.00281 1325.6138 +#> 3835 714142.5 9558552 2388.42 32.900383 0.00979 -0.00269 491.8017 +#> 39061 712922.5 9559912 1931.50 36.315211 0.00414 -0.00104 1641.4587 +#> 8019 714672.5 9559272 2316.84 40.381492 -0.01966 -0.02234 849.0547 +#> 2990 713542.5 9560372 1970.07 31.887966 0.01903 0.01447 331.4833 +#> 18843 715192.5 9559332 2265.75 41.153267 -0.07664 -0.05766 140591.4062 +#> 30125 715162.5 9558992 2438.18 39.856663 0.00471 -0.01382 560.8309 +#> 15340 715452.5 9558852 2523.95 34.165473 0.00151 0.00899 1394.2274 +#> 47210 714952.5 9557612 2744.35 40.884549 -0.00493 0.00733 598.7983 +#> 8288 714842.5 9557712 2679.04 28.939525 0.00902 0.01358 802.7622 +#> 45575 715432.5 9559452 2305.28 24.234396 0.00993 -0.00413 364.9788 +#> 36230 713322.5 9560662 1798.99 40.128245 -0.00033 -0.03917 116.1986 +#> 7879 714722.5 9559232 2356.19 36.803944 -0.01097 0.00156 816.6036 +#> 27013 713062.5 9559782 1976.81 41.632259 0.01414 0.03416 1204.8629 +#> 34642 714962.5 9559862 2282.08 22.160862 -0.00028 0.01318 289.0063 +#> 45104 715062.5 9559242 2345.02 24.156474 -0.01743 0.01313 783.4548 +#> 29667 713892.5 9559712 2239.25 41.592725 0.00804 0.00346 543.0749 +#> 12460 712762.5 9559622 1906.41 24.261898 0.01047 -0.00887 366.8537 +#> 6557 713312.5 9559022 2181.69 27.634327 -0.00669 -0.00991 1403.0193 +#> 11687 714552.5 9560382 2052.66 29.575508 -0.00255 -0.00365 1536.2647 +#> 37828 713482.5 9558652 2330.58 20.445426 0.00606 -0.00556 660.3690 +#> 48434 713352.5 9559042 2176.63 35.702146 -0.01771 -0.01939 703.7817 +#> 36931 712852.5 9558892 2102.80 30.535786 0.00140 -0.00070 2562.7559 +#> 6310 713802.5 9559752 2238.38 19.651306 -0.02379 0.01338 10787.6299 +#> 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 +#> 4605 714852.5 9557892 2677.78 26.933600 0.00321 0.01169 350.1799 +#> 47091 713382.5 9559182 2122.71 48.037927 0.04527 0.04613 289.6113 +#> 5578 714962.5 9557612 2751.95 39.191459 0.00288 0.01452 437.3965 +#> 29926 714702.5 9557622 2637.00 44.642580 0.01843 0.04407 344.8395 +#> 16015 712582.5 9560312 1899.18 56.546924 0.03480 0.02530 937.1306 +#> 20220 714052.5 9558522 2378.67 39.656128 -0.00855 -0.02175 1122.9120 +#> 35194 713692.5 9559952 2185.68 28.796859 -0.00449 -0.00181 2406.1252 +#> 421 714892.5 9561022 1849.01 26.132032 0.00002 0.00588 749.2502 +#> 18892 712682.5 9560202 1839.08 40.923511 -0.01364 -0.03357 473.4709 +#> 39520 715342.5 9557622 2889.48 41.648875 -0.01282 0.00542 860.8840 +#> 27875 714832.5 9561032 1838.22 41.212281 -0.01160 0.01690 5121.6924 +#> 10507 714892.5 9559282 2374.45 42.842346 -0.01057 0.02427 380.9472 +#> 20065 713772.5 9558852 2221.21 28.019928 -0.00568 -0.02553 1750.4175 +#> 46065 713072.5 9559162 2109.39 16.264553 -0.00195 -0.00656 3036.7383 +#> 35014 713502.5 9559772 2190.48 44.235780 0.00927 0.01643 670.2289 +#> 17728 713092.5 9560652 1900.14 30.279101 0.00645 -0.00395 2011.3270 +#> 28046 714882.5 9558492 2711.77 28.305834 -0.00131 0.00201 788.0621 +#> 40270 713312.5 9559012 2186.56 30.935710 -0.00771 -0.00599 1378.5760 +#> 7385 713752.5 9560122 2064.61 31.771083 -0.02605 -0.03594 16587.9980 +#> 40216 714602.5 9560592 2015.55 34.368873 -0.00093 0.00664 1807.2659 +#> 1973 715252.5 9557822 2861.00 15.570701 0.01865 0.00335 195.0293 +#> 8125 713452.5 9559132 2120.44 42.140473 -0.05458 -0.14813 287033.4688 +#> 44767 714452.5 9560402 2092.27 12.462978 -0.00195 -0.00205 1112.2626 +#> 33910 715202.5 9559572 2195.66 13.791667 -0.04376 -0.01644 1057765.6250 +#> 30287 713992.5 9557822 2403.92 18.215474 0.03890 -0.00911 173.2806 +#> 34887 714662.5 9557392 2609.02 51.020746 -0.01878 -0.04351 1442.9247 +#> 27915 715232.5 9559552 2208.93 25.480579 0.00171 -0.00261 1627.8989 +#> 34615 713242.5 9560732 1879.60 27.593647 0.00384 0.00175 1026.6818 +#> 29228 712782.5 9560422 1870.56 32.906112 -0.00218 -0.00372 4154.5786 +#> 43074 713172.5 9559622 2052.40 31.527576 -0.00193 0.02812 1423.7578 +#> 14545 713562.5 9559602 2222.17 54.239050 0.01011 0.00739 789.7573 +#> 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 +#> 10617 713232.5 9560722 1879.95 26.497006 0.00258 0.00422 1159.2100 +#> 49133 714162.5 9558462 2451.13 21.890426 0.00042 0.00957 495.4453 +#> 23783 713062.5 9560252 1983.09 36.429803 0.00774 -0.00224 1137.3363 +#> 1916 715202.5 9559512 2213.91 26.327984 -0.00292 -0.00109 2047.9031 +#> 750 712812.5 9560032 1847.09 54.959258 -0.00767 -0.03713 6379.7974 +#> 1997 714752.5 9561012 1853.19 26.566334 -0.00693 0.01133 2397.2681 +#> 7409 714772.5 9559122 2404.90 30.215502 -0.01016 -0.00294 734.9908 +#> 34984 715042.5 9559312 2320.49 42.857816 -0.01106 -0.04634 500.5027 +#> 24723 712942.5 9560202 1983.18 33.071697 0.00285 -0.01325 595.0426 +#> 38103 715392.5 9560162 1998.16 46.285823 0.00708 -0.02538 807.8237 +#> 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 +#> 7771 715232.5 9560922 1816.88 37.180377 0.00124 0.01166 1813.7841 +#> 42640 714332.5 9558522 2403.10 38.871176 -0.00212 0.02732 4237.6934 +#> 43834 713092.5 9560662 1905.55 31.323603 0.00758 -0.00288 2144.8164 +#> 9864 714952.5 9557592 2752.40 31.989380 -0.00983 0.01253 528.1924 +#> 33142 715652.5 9558212 2740.94 14.381241 -0.00465 -0.01036 2505.8369 +#> 17322 712612.5 9560402 1924.48 43.807780 -0.00486 0.02306 1718.3652 +#> 34718 714802.5 9558802 2599.22 44.650028 0.01996 0.04684 209.1778 +#> 10785 714052.5 9557852 2377.76 20.984006 -0.00025 -0.05345 2417.1389 +#> 36744 714922.5 9557722 2692.39 25.980771 0.00316 -0.00427 866.9398 +#> 31983 714302.5 9559912 2204.77 23.464914 -0.01332 0.01842 246.5161 +#> 40459 712542.5 9559192 1992.60 39.817129 -0.00162 -0.00517 1060.5494 +#> 18286 714792.5 9557882 2650.57 28.253695 -0.00124 0.00204 853.2902 +#> 14694 715032.5 9559482 2307.44 44.173327 -0.00007 -0.00463 1050.7473 +#> 11111 715142.5 9558482 2650.65 42.343300 -0.00533 0.01493 2212.0525 +#> 20458 713612.5 9559562 2288.02 33.440109 0.00112 0.03538 265.4677 +#> 20067 714242.5 9560442 2125.23 38.640274 0.00363 -0.00363 389.2691 +#> 49363 712772.5 9559552 1935.91 29.036928 -0.03220 -0.03290 2481.5632 +#> 43426 713782.5 9560822 1876.35 23.823012 -0.00345 -0.00075 8141.7549 +#> 441 714312.5 9560972 1916.59 31.302976 0.00540 0.00650 1133.1501 +#> 9665 713362.5 9558812 2275.28 31.494917 0.01306 -0.00626 450.7346 +#> 28162 714062.5 9561152 1785.47 48.444727 0.02309 -0.03409 1575.9425 +#> 25140 713332.5 9560692 1809.95 52.257189 0.00075 -0.01945 259.7421 +#> 42329 715852.5 9558702 2651.92 22.870756 0.00321 0.00548 532.8210 +#> 39193 715332.5 9560852 1872.90 30.290560 -0.00161 0.00171 756.8818 +#> 44396 715512.5 9558102 2845.31 26.598420 0.01263 0.03598 186.3527 +#> 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 +#> 24009 714042.5 9558962 2357.58 34.361998 0.05041 -0.00011 201.1272 +#> 48869 712562.5 9560122 1972.89 24.946582 0.00185 0.01655 560.8554 +#> 33370 714202.5 9558962 2472.93 32.641406 -0.00307 0.02997 230.8641 +#> 3733 713762.5 9559632 2297.63 34.214748 -0.01338 0.00518 1137.0179 +#> 29835 715402.5 9558512 2610.14 27.118092 -0.01322 -0.00759 4131.5356 +#> 17746 714752.5 9558322 2648.52 47.736552 -0.00068 -0.00312 593.7773 +#> 44267 712972.5 9559042 2191.01 22.269151 0.01462 0.00268 252.0072 +#> 7553 714642.5 9559132 2391.59 63.967873 -0.00066 0.05257 549.3252 +#> 41855 713182.5 9560452 1851.53 45.178295 0.00667 0.00863 1098.3462 +#> 46509 712512.5 9559532 1860.09 89.988751 -0.00270 1018.51947 842.1514 +#> 43222 714472.5 9560712 2039.25 22.190655 0.01858 0.00312 254.0656 +#> 48845 713232.5 9561322 2041.83 29.912408 -100.76649 100.79199 107.2986 +#> 5861 713882.5 9559182 2350.43 27.393685 -0.02608 0.00148 10771.1963 +#> 14671 712672.5 9560372 1872.21 39.197189 -0.00873 -0.01417 1520.6630 +#> 29716 715272.5 9559992 2144.94 25.717784 0.00262 -0.00502 520.5218 +#> 3868 714542.5 9560622 2024.15 29.447166 0.00360 0.00750 1892.8978 +#> 14763 713462.5 9560852 1830.40 47.739989 0.01354 0.00907 1281.5044 +#> 31098 714352.5 9559042 2410.15 30.336396 0.00537 -0.00347 1893.3645 +#> 40750 712962.5 9558702 2126.06 34.704054 -0.00264 0.00624 1870.8307 +#> 4943 712452.5 9559172 1927.46 34.465130 0.00105 -0.01576 1045.1074 +#> 10127 713722.5 9558712 2311.10 19.481138 0.01129 0.00491 261.7371 +#> 9526 714432.5 9560582 2076.13 31.080096 -0.00172 -0.00119 792.3544 +#> 35257 713822.5 9557982 2350.80 29.307937 0.01549 -0.00169 649.9975 +#> 6577 714142.5 9559902 2244.30 57.890510 -0.06986 -0.05013 616.4128 +#> 36141 714222.5 9559422 2316.64 16.277158 -0.07627 -0.00124 25826.6914 +#> 11570 715572.5 9558482 2685.72 16.814019 0.00282 0.00458 485.7418 +#> 23808 715172.5 9558702 2533.50 22.345927 0.00811 -0.02281 492.4785 +#> 42990 712432.5 9560692 2102.11 32.450038 -0.00087 -0.00573 1490.0312 +#> 26635 715092.5 9560412 2010.59 30.571882 -0.00463 0.00253 1237.0536 +#> 11025 714402.5 9559252 2325.64 49.620437 -0.00241 0.00020 673.2861 +#> 39234 712902.5 9559652 1953.39 29.731926 0.07379 -0.00249 148.1857 +#> 15363 714742.5 9559212 2373.13 33.583348 -0.00247 0.01057 722.3952 +#> 48157 713142.5 9560992 1978.48 44.621380 0.00208 0.00492 772.2691 +#> 40043 713322.5 9560562 1802.09 39.852079 0.04456 -0.01586 941.0288 +#> cslope distroad slides distdeforest distslidespast log.carea +#> 15412 37.59290685 300.00 TRUE 300.00 100 3.318273 +#> 25090 43.53161440 300.00 TRUE 300.00 26 2.745084 +#> 42733 31.28292266 300.00 TRUE 300.00 100 2.872981 +#> 5999 27.14215667 300.00 TRUE 300.00 100 5.201575 +#> 1300 27.53062206 300.00 TRUE 300.00 6 2.888165 +#> 8059 26.97714483 300.00 TRUE 300.00 4 3.150244 +#> 37265 36.51746507 300.00 TRUE 2.45 0 3.071539 +#> 8869 29.11026670 300.00 TRUE 300.00 100 3.036974 +#> 32622 30.17138453 300.00 TRUE 300.00 100 3.265863 +#> 38827 31.03597785 300.00 TRUE 52.52 39 3.384015 +#> 47255 19.67766252 300.00 TRUE 260.03 100 3.112324 +#> 37270 18.94656837 162.65 TRUE 0.00 77 3.703905 +#> 26447 33.03674647 300.00 TRUE 300.00 100 2.676242 +#> 23817 21.61139507 300.00 TRUE 300.00 4 2.425592 +#> 1393 23.41220142 300.00 TRUE 300.00 1 2.710095 +#> 38993 29.54857941 300.00 TRUE 300.00 2 3.128238 +#> 39629 47.54862150 300.00 TRUE 300.00 85 3.366461 +#> 27375 38.77033512 300.00 TRUE 300.00 100 2.859781 +#> 44328 25.50407033 300.00 TRUE 300.00 100 3.122417 +#> 3835 35.13892862 300.00 TRUE 300.00 39 2.691790 +#> 39061 35.24091511 300.00 TRUE 70.65 56 3.215230 +#> 8019 35.78006839 300.00 TRUE 300.00 61 2.928936 +#> 2990 25.32129680 300.00 TRUE 123.56 5 2.520462 +#> 18843 28.52642270 300.00 TRUE 300.00 100 5.147959 +#> 30125 40.73558036 300.00 TRUE 300.00 100 2.748832 +#> 15340 27.97523731 300.00 TRUE 300.00 100 3.144334 +#> 47210 26.91526538 300.00 TRUE 300.00 100 2.777281 +#> 8288 22.71777658 300.00 TRUE 300.00 83 2.904587 +#> 45575 25.72809683 300.00 TRUE 300.00 59 2.562268 +#> 36230 0.05614986 120.49 TRUE 38.30 46 2.065201 +#> 7879 28.58085369 300.00 TRUE 300.00 100 2.912011 +#> 27013 30.91852150 300.00 TRUE 168.05 18 3.080938 +#> 34642 15.64862330 300.00 TRUE 300.00 2 2.460907 +#> 45104 17.99259364 300.00 TRUE 300.00 100 2.894014 +#> 29667 34.25198995 300.00 TRUE 300.00 27 2.734860 +#> 12460 36.25676928 300.00 TRUE 0.00 100 2.564493 +#> 6557 27.41946824 300.00 TRUE 300.00 5 3.147064 +#> 11687 23.19390450 300.00 TRUE 300.00 68 3.186466 +#> 37828 18.76379483 300.00 TRUE 300.00 2 2.819787 +#> 48434 29.93589888 300.00 TRUE 300.00 6 2.847438 +#> 36931 27.20919273 300.00 TRUE 300.00 100 3.408707 +#> 6310 23.82644991 300.00 TRUE 300.00 100 4.032926 +#> 46235 30.10835918 300.00 TRUE 41.37 0 2.752133 +#> 18564 21.70020353 300.00 TRUE 300.00 100 2.676979 +#> 4605 17.63564093 300.00 TRUE 300.00 10 2.544291 +#> 47091 40.15173637 300.00 TRUE 300.00 25 2.461815 +#> 5578 25.74184782 300.00 TRUE 300.00 100 2.640875 +#> 29926 32.09480385 300.00 TRUE 300.00 100 2.537617 +#> 16015 38.83278752 152.09 TRUE 90.23 45 2.971800 +#> 20220 32.17559090 300.00 TRUE 300.00 8 3.050346 +#> 35194 23.44944368 300.00 TRUE 300.00 100 3.381318 +#> 421 10.71373781 182.02 TRUE 0.00 83 2.874627 +#> 18892 27.06308850 235.02 TRUE 27.24 0 2.675293 +#> 39520 35.89408699 300.00 TRUE 300.00 100 2.934945 +#> 27875 21.26704744 157.93 TRUE 0.00 57 3.709413 +#> 10507 27.16221019 300.00 TRUE 300.00 46 2.580865 +#> 20065 30.38968145 300.00 TRUE 300.00 5 3.243142 +#> 46065 29.77088703 300.00 TRUE 251.11 100 3.482407 +#> 35014 32.53025178 300.00 TRUE 300.00 57 2.826223 +#> 17728 28.55220580 30.14 TRUE 140.15 2 3.303483 +#> 28046 19.59114589 300.00 TRUE 300.00 15 2.896560 +#> 40270 27.59765812 300.00 TRUE 300.00 5 3.139431 +#> 7385 28.71034216 300.00 TRUE 300.00 100 4.219794 +#> 40216 26.36407999 300.00 TRUE 300.00 100 3.257022 +#> 1973 19.32586643 300.00 TRUE 300.00 100 2.290100 +#> 8125 33.18743437 300.00 TRUE 300.00 48 5.457933 +#> 44767 17.29530400 300.00 TRUE 300.00 25 3.046207 +#> 33910 32.67349123 300.00 TRUE 300.00 6 6.024389 +#> 30287 19.84840394 300.00 TRUE 300.00 65 2.238750 +#> 34887 42.93573829 300.00 TRUE 300.00 100 3.159244 +#> 27915 33.57475384 300.00 TRUE 300.00 6 3.211627 +#> 34615 34.18953755 17.57 TRUE 142.95 16 3.011436 +#> 29228 30.39942174 131.25 TRUE 80.55 2 3.618527 +#> 43074 33.59480736 300.00 TRUE 205.02 1 3.153436 +#> 14545 39.31407207 300.00 TRUE 300.00 37 2.897494 +#> 47244 28.03883562 300.00 TRUE 300.00 2 2.752448 +#> 39325 37.44852149 300.00 TRUE 300.00 60 2.848009 +#> 10617 33.79820738 17.89 TRUE 145.83 4 3.064162 +#> 49133 23.51934453 300.00 TRUE 300.00 38 2.694996 +#> 23783 27.60052291 300.00 TRUE 0.00 100 3.055889 +#> 1916 29.27355967 300.00 TRUE 300.00 21 3.311309 +#> 750 34.34710094 300.00 TRUE 0.00 0 3.804807 +#> 1997 25.23592609 168.72 TRUE 0.00 5 3.379717 +#> 7409 25.37057117 300.00 TRUE 300.00 100 2.866282 +#> 34984 33.90592344 300.00 TRUE 300.00 100 2.699406 +#> 24723 28.17348070 300.00 TRUE 36.63 100 2.774548 +#> 38103 42.76155912 300.00 TRUE 300.00 31 2.907317 +#> 30363 29.80297267 300.00 TRUE 300.00 100 2.929656 +#> 46792 27.60109586 300.00 TRUE 300.00 18 2.657830 +#> 7771 16.57910676 300.00 TRUE 35.59 100 3.258586 +#> 42640 34.90974550 300.00 TRUE 300.00 100 3.627130 +#> 43834 28.51209876 20.21 TRUE 150.14 2 3.331390 +#> 9864 20.82071332 300.00 TRUE 300.00 100 2.722792 +#> 33142 26.90609806 300.00 TRUE 300.00 100 3.398953 +#> 17322 31.66508551 75.77 TRUE 138.35 1 3.235115 +#> 34718 34.40955334 300.00 TRUE 300.00 100 2.320516 +#> 10785 33.24530310 300.00 TRUE 300.00 100 3.383302 +#> 36744 29.40476700 300.00 TRUE 300.00 100 2.937989 +#> 31983 19.77563830 300.00 FALSE 300.00 65 2.391845 +#> 40459 36.18686842 300.00 FALSE 216.66 10 3.025531 +#> 18286 24.35070629 300.00 FALSE 300.00 42 2.931097 +#> 14694 43.51156088 300.00 FALSE 300.00 100 3.021498 +#> 11111 33.38395889 300.00 FALSE 300.00 100 3.344795 +#> 20458 21.42976745 300.00 FALSE 300.00 28 2.424012 +#> 20067 29.82359915 300.00 FALSE 268.03 100 2.590250 +#> 49363 32.95767829 300.00 FALSE 0.00 100 3.394725 +#> 43426 35.46264977 300.00 FALSE 82.11 100 3.910718 +#> 441 25.11331312 294.31 FALSE 152.09 100 3.054287 +#> 9665 34.08124853 300.00 FALSE 300.00 64 2.653921 +#> 28162 18.66982975 130.00 FALSE 75.00 100 3.197540 +#> 25140 14.48380010 104.12 FALSE 46.28 74 2.414542 +#> 42329 19.22789065 300.00 FALSE 300.00 100 2.726581 +#> 39193 31.48002014 300.00 FALSE 117.12 100 2.879028 +#> 44396 21.44237252 300.00 FALSE 300.00 100 2.270336 +#> 43403 31.06176095 300.00 FALSE 111.88 86 2.788585 +#> 29944 18.62800384 300.00 FALSE 300.00 48 2.452873 +#> 24009 38.49703425 300.00 FALSE 300.00 100 2.303471 +#> 48869 24.87553563 138.98 FALSE 0.00 63 2.748851 +#> 33370 26.41908393 300.00 FALSE 300.00 100 2.363356 +#> 3733 22.10012807 300.00 FALSE 300.00 100 3.055767 +#> 29835 28.85300865 300.00 FALSE 300.00 45 3.616112 +#> 17746 36.39141436 300.00 FALSE 300.00 87 2.773624 +#> 44267 19.73782308 300.00 FALSE 226.30 100 2.401413 +#> 7553 39.77129239 300.00 FALSE 300.00 100 2.739830 +#> 41855 28.70174779 234.40 FALSE 47.05 100 3.040739 +#> 46509 45.57879260 300.00 FALSE 162.53 100 2.925390 +#> 43222 21.26532857 300.00 FALSE 300.00 100 2.404946 +#> 48845 31.55908831 169.72 FALSE 0.00 100 2.030594 +#> 5861 27.61255502 300.00 FALSE 300.00 100 4.032264 +#> 14671 37.88225054 123.04 FALSE 75.11 0 3.182033 +#> 29716 32.69354475 300.00 FALSE 300.00 100 2.716439 +#> 3868 26.14750194 300.00 FALSE 300.00 100 3.277127 +#> 14763 10.14479072 110.04 FALSE 60.35 100 3.107720 +#> 31098 34.65764407 300.00 FALSE 300.00 45 3.277234 +#> 40750 32.09996047 300.00 FALSE 300.00 100 3.272034 +#> 4943 37.77166969 300.00 FALSE 300.00 100 3.019161 +#> 10127 18.61940947 300.00 FALSE 300.00 49 2.417865 +#> 9526 27.56958319 300.00 FALSE 300.00 100 2.898919 +#> 35257 30.88185220 300.00 FALSE 300.00 79 2.812912 +#> 6577 42.40747121 300.00 FALSE 300.00 70 2.789872 +#> 36141 27.30602260 300.00 FALSE 300.00 85 4.412069 +#> 11570 16.60718169 300.00 FALSE 300.00 100 2.686405 +#> 23808 37.53389220 300.00 FALSE 300.00 72 2.692387 +#> 42990 22.23706499 205.00 FALSE 0.00 100 3.173195 +#> 26635 31.39464943 300.00 FALSE 300.00 100 3.092389 +#> 11025 36.81998679 300.00 FALSE 300.00 50 2.828200 +#> 39234 33.48594538 300.00 FALSE 0.00 100 2.170806 +#> 15363 26.94219440 300.00 FALSE 300.00 100 2.858775 +#> 48157 33.52662538 57.15 FALSE 157.06 45 2.887769 +#> 40043 23.99661838 194.79 FALSE 24.89 54 2.973603 diff --git a/reference/partition_cv_strat.html b/reference/partition_cv_strat.html index 39d5012d..58e8e0f5 100644 --- a/reference/partition_cv_strat.html +++ b/reference/partition_cv_strat.html @@ -152,7 +152,7 @@

Examples

parti <- partition_cv(ecuador, nfold = 5, repetition = 1) idx <- parti[["1"]][[1]]$train mean(ecuador$slides[idx] == "TRUE") / mean(ecuador$slides == "TRUE") -#> [1] 1.012163 +#> [1] 1.004666 # 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 45fd97fd..897701ac 100644 --- a/reference/partition_disc.html +++ b/reference/partition_disc.html @@ -192,19 +192,19 @@

Examples

summary(parti) #> $`1` #> n.train n.test -#> 262 692 32 -#> 342 693 31 -#> 363 738 6 -#> 278 722 13 -#> 349 735 8 +#> 213 690 35 +#> 277 724 4 +#> 446 714 6 +#> 581 738 4 +#> 10 723 24 #> #> $`2` #> n.train n.test -#> 542 738 3 -#> 731 737 1 -#> 121 711 20 -#> 163 689 36 -#> 633 711 9 +#> 534 715 6 +#> 692 730 5 +#> 30 711 23 +#> 644 724 9 +#> 748 724 3 #> # leave-one-out with buffer: @@ -212,19 +212,19 @@

Examples

summary(parti) #> $`1` #> n.train n.test -#> 262 692 32 -#> 342 693 31 -#> 363 738 6 -#> 278 722 13 -#> 349 735 8 +#> 213 690 35 +#> 277 724 4 +#> 446 714 6 +#> 581 738 4 +#> 10 723 24 #> #> $`2` #> n.train n.test -#> 542 738 3 -#> 731 737 1 -#> 121 711 20 -#> 163 689 36 -#> 633 711 9 +#> 534 715 6 +#> 692 730 5 +#> 30 711 23 +#> 644 724 9 +#> 748 724 3 #> diff --git a/reference/sperrorest.html b/reference/sperrorest.html index 2de4e897..d4571e45 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) ) -#> Sat Sep 16 04:26:54 2023 Repetition 1 -#> Sat Sep 16 04:26:54 2023 Repetition - Fold 1 -#> Sat Sep 16 04:26:55 2023 Repetition - Fold 2 -#> Sat Sep 16 04:26:55 2023 Repetition - Fold 3 -#> Sat Sep 16 04:26:55 2023 Repetition 2 -#> Sat Sep 16 04:26:55 2023 Repetition - Fold 1 -#> Sat Sep 16 04:26:55 2023 Repetition - Fold 2 -#> Sat Sep 16 04:26:55 2023 Repetition - Fold 3 +#> Sun Sep 17 04:25:29 2023 Repetition 1 +#> Sun Sep 17 04:25:29 2023 Repetition - Fold 1 +#> Sun Sep 17 04:25:29 2023 Repetition - Fold 2 +#> Sun Sep 17 04:25:30 2023 Repetition - Fold 3 +#> Sun Sep 17 04:25:30 2023 Repetition 2 +#> Sun Sep 17 04:25:30 2023 Repetition - Fold 1 +#> Sun Sep 17 04:25:30 2023 Repetition - Fold 2 +#> Sun Sep 17 04:25:30 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) ) -#> Sat Sep 16 04:26:56 2023 Repetition 1 -#> Sat Sep 16 04:26:56 2023 Repetition - Fold 1 -#> Sat Sep 16 04:26:56 2023 Repetition - Fold 2 -#> Sat Sep 16 04:26:56 2023 Repetition - Fold 3 -#> Sat Sep 16 04:26:56 2023 Repetition 2 -#> Sat Sep 16 04:26:56 2023 Repetition - Fold 1 -#> Sat Sep 16 04:26:56 2023 Repetition - Fold 2 -#> Sat Sep 16 04:26:56 2023 Repetition - Fold 3 +#> Sun Sep 17 04:25:30 2023 Repetition 1 +#> Sun Sep 17 04:25:30 2023 Repetition - Fold 1 +#> Sun Sep 17 04:25:30 2023 Repetition - Fold 2 +#> Sun Sep 17 04:25:30 2023 Repetition - Fold 3 +#> Sun Sep 17 04:25:30 2023 Repetition 2 +#> Sun Sep 17 04:25:30 2023 Repetition - Fold 1 +#> Sun Sep 17 04:25:30 2023 Repetition - Fold 2 +#> Sun Sep 17 04:25:31 2023 Repetition - Fold 3 summary(sp_res$error_rep) #> mean sd median IQR #> train_auroc 0.8472530 0.017474834 0.8472530 0.012356574