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Examples env = predictors, categorical = "biome") #> Extracting predictor information for presence locations -#> Extracting predictor information for presence locations [66ms] +#> Extracting predictor information for presence locations [62ms] #> #> Extracting predictor information for absence/background locations -#> Extracting predictor information for absence/background locations [127ms] +#> Extracting predictor information for absence/background locations [116ms] #> # Train a Maxent model diff --git a/dev/reference/SDMtune-class-1.png b/dev/reference/SDMtune-class-1.png index d9a87e595ede309a6bca0e8a377a57a4a88a31ce..ce3d58e8ce0751becafe91182c8ca456b071b572 100644 GIT binary patch literal 36882 zcmd?RcUV(d)HfPM#TgZJbZiJXV*n8;ihxMd!4|5*C<0Ox4M-@`5?T_JQEY&QUIG>Z zB8Er_EeWV77^FxGB_yacA&}4`gd}$#bl&%t?|$z;@AusMJl}bq8RYDJ_St9cwbxp| z-&&iyXKYSw_;u&6Fc@qD;`DJl7;JSY3?_4V?HX{0o&01H{8)3@^3-wg6I=`CG(7n6 z%k|TaK`@wN4D{cM_@Sw*FxYMw;`p(@Lo>%Y5t&2>7i~#PuWmw)(Ah3rX6anz>G-`D zj#kdy+}e(`d%j_xY|f|PybXD41CO5VFdTWj`?1LdpA3Zh?^{}T-572=eM|q=&9z%L z4&pXxBG}`sm5iZ-C0&B}arJf`1hNetG2bW>hfj1Fr%+ilSTsg0!It$>ZN>5o|9k$2 zOlg}~34=v(Kl$iuTTVB?)k*s=7F~XSigxig*z1>F{ZMN+16fjei23_Ke5aEXJ=Bn- zozZakW3r~G$%)rF5T9gcT51TAo!w{ar#xGyKI9EB!!#*jjqQhW}Aq_-Us$$LExkamkpA{7q8SZ`jZ3)I< z*$#CN$^(ZQ98|*2mGe^HES7kn87exkC`8F|8Q4+(1t&s{1;MVNJ)>!ODq`%_6(*q? 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Examplesdata <- prepareSWD(species = "Virtual species", p = p_coords, a = bg_coords, env = predictors, categorical = "biome") #> Extracting predictor information for presence locations -#> Extracting predictor information for presence locations [32ms] +#> Extracting predictor information for presence locations [33ms] #> #> Extracting predictor information for absence/background locations -#> Extracting predictor information for absence/background locations [61ms] +#> Extracting predictor information for absence/background locations [56ms] #> # Split presence locations in training (80%) and testing (20%) datasets @@ -169,12 +169,13 @@

Examples # Run the gridSearch function using as metric the AUC output <- gridSearch(model, hypers = h, metric = "auc", test = test) -#> Grid Search ■■■■■■ 18% | ETA: 22s - 00:00:05 -#> Grid Search ■■■■■■■■■■■■ 36% | ETA: 14s - 00:00:7.8 -#> Grid Search ■■■■■■■■■■■■■■■■■ 55% | ETA: 9s - 00:00:10.8 -#> Grid Search ■■■■■■■■■■■■■■■■■■■■■■■ 73% | ETA: 5s - 00:00:13.5 -#> Grid Search ■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 91% | ETA: 2s - 00:00:16.5 -#> Grid Search ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 100% | ETA: 0s - 00:00:16.5 +#> Grid Search ■■■■ 9% | ETA: 10s - 00:00:01 +#> Grid Search ■■■■■■ 18% | ETA: 30s - 00:00:6.8 +#> Grid Search ■■■■■■■■■■■■ 36% | ETA: 18s - 00:00:10.4 +#> Grid Search ■■■■■■■■■■■■■■■■■ 55% | ETA: 11s - 00:00:13.5 +#> Grid Search ■■■■■■■■■■■■■■■■■■■■■■■ 73% | ETA: 6s - 00:00:16.9 +#> Grid Search ■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 91% | ETA: 2s - 00:00:19.8 +#> Grid Search ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 100% | ETA: 0s - 00:00:19.8 #> output #> @@ -194,7 +195,7 @@

Examplesp <- plot(output, title = "My experiment", interactive = TRUE) # Print the temporary folder that stores the files used to create the chart str(p) -#> 'SDMtuneChart' chr "/tmp/RtmphY93Px/SDMtune241216815a23" +#> 'SDMtuneChart' chr "/tmp/Rtmpt4YEzY/SDMtune2529432a41e5" # } diff --git a/dev/reference/addSamplesToBg.html b/dev/reference/addSamplesToBg.html index bdfc643d..892ed34e 100644 --- a/dev/reference/addSamplesToBg.html +++ b/dev/reference/addSamplesToBg.html @@ -120,10 +120,10 @@

Examples env = predictors, categorical = "biome") #> Extracting predictor information for presence locations -#> Extracting predictor information for presence locations [35ms] +#> Extracting predictor information for presence locations [29ms] #> #> Extracting predictor information for absence/background locations -#> Extracting predictor information for absence/background locations [61ms] +#> Extracting predictor information for absence/background locations [50ms] #> data #> diff --git a/dev/reference/aicc.html b/dev/reference/aicc.html index a7fc403e..ac155771 100644 --- a/dev/reference/aicc.html +++ b/dev/reference/aicc.html @@ -136,7 +136,7 @@

Examples#> Extracting predictor information for presence locations [28ms] #> #> Extracting predictor information for absence/background locations -#> Extracting predictor information for absence/background locations [56ms] +#> Extracting predictor information for absence/background locations [52ms] #> # Train a model diff --git a/dev/reference/auc.html b/dev/reference/auc.html index 85c12699..561dab9c 100644 --- a/dev/reference/auc.html +++ b/dev/reference/auc.html @@ -140,7 +140,7 @@

Examples#> Extracting predictor information for presence locations [29ms] #> #> Extracting predictor information for absence/background locations -#> Extracting predictor information for absence/background locations [56ms] +#> Extracting predictor information for absence/background locations [50ms] #> # Split presence locations in training (80%) and testing (20%) datasets @@ -157,12 +157,12 @@

Examples # Compute the training AUC auc(model) -#> [1] 0.8384491 +#> [1] 0.8384109 # Compute the testing AUC auc(model, test = test) -#> [1] 0.839615 +#> [1] 0.8523 # \donttest{ # Same example but using cross validation instead of training and testing # datasets @@ -177,17 +177,17 @@

Examples # Compute the training AUC auc(model) -#> [1] 0.8414128 +#> [1] 0.8412579 # Compute the testing AUC auc(model, test = TRUE) -#> [1] 0.8348233 +#> [1] 0.8361298 # Compute the AUC for the held apart testing dataset auc(model, test = test)# } -#> [1] 0.8446613 +#> [1] 0.8556938