Nothing
tests/testthat/_snaps/train_sdm.md
In caretSDM: Build Species Distribution Modeling using 'caret'
train_sdm
Code
i2
Output
caretSDM
...............................
Class : input_sdm
-------- Occurrences --------
Species Names : Araucaria angustifolia
Number of presences : 420
Pseudoabsence methods :
Method to obtain PAs : random
Number of PA sets : 3
Number of PAs in each set : 420
-------- Predictors ---------
Number of Predictors : 2
Predictors Names : bio1, bio12
--------- Scenarios ---------
Number of Scenarios : 1
Scenarios Names : current
----------- Models ----------
Algorithms Names : kknn naive_bayes
Variables Names : bio1 bio12
Model Validation :
Method : cv
Number : 2
Metrics :
$`Araucaria angustifolia`
algo ROC Sens Spec ROCSD
1 kknn 0.6605192 0.9920635 0.1097222 0.08681466
2 naive_bayes 0.8402910 0.9706349 0.3631944 0.11121967
train_sdm - pca
Code
i2
Output
caretSDM
...............................
Class : input_sdm
-------- Occurrences --------
Species Names : Araucaria angustifolia
Number of presences : 420
Pseudoabsence methods :
Method to obtain PAs : random
Number of PA sets : 3
Number of PAs in each set : 420
-------- Predictors ---------
Number of Predictors : 4
Predictors Names : bio1, bio12, PC1, PC2
PCA-transformed variables : DONE
Cummulative proportion ( 0.99 ) : PC1
--------- Scenarios ---------
Number of Scenarios : 1
Scenarios Names : current
----------- Models ----------
Algorithms Names : kknn naive_bayes
Variables Names : PC1
Model Validation :
Method : cv
Number : 2
Metrics :
$`Araucaria angustifolia`
algo ROC Sens Spec ROCSD
1 kknn 0.5741815 0.9801587 0.08819444 0.0710965
2 naive_bayes 0.8392427 0.9857143 0.15208333 0.1099336
train_sdm - vif
Code
i2
Output
caretSDM
...............................
Class : input_sdm
-------- Occurrences --------
Species Names : Araucaria angustifolia
Number of presences : 420
Pseudoabsence methods :
Method to obtain PAs : random
Number of PA sets : 3
Number of PAs in each set : 420
-------- Predictors ---------
Number of Predictors : 2
Predictors Names : bio1, bio12
Area (VIF) : all
Threshold : 0.5
Selected Variables (VIF) : bio1, bio12
--------- Scenarios ---------
Number of Scenarios : 1
Scenarios Names : current
----------- Models ----------
Algorithms Names : naive_bayes kknn
Variables Names : bio1 bio12
Model Validation :
Method : cv
Number : 2
Metrics :
$`Araucaria angustifolia`
algo ROC Sens Spec ROCSD
1 kknn 0.6314964 0.9865079 0.1381944 0.06344787
2 naive_bayes 0.8625942 0.9666667 0.3645833 0.04378965
train_sdm - change ctrl
Code
i2
Output
caretSDM
...............................
Class : input_sdm
-------- Occurrences --------
Species Names : Araucaria angustifolia
Number of presences : 420
Pseudoabsence methods :
Method to obtain PAs : random
Number of PA sets : 3
Number of PAs in each set : 420
-------- Predictors ---------
Number of Predictors : 4
Predictors Names : bio1, bio12, PC1, PC2
PCA-transformed variables : DONE
Cummulative proportion ( 0.99 ) : PC1
--------- Scenarios ---------
Number of Scenarios : 1
Scenarios Names : current
----------- Models ----------
Algorithms Names : kknn naive_bayes
Variables Names : PC1
Model Validation :
Method : boot
Number : 10
Metrics :
$`Araucaria angustifolia`
algo ROC Sens Spec ROCSD
1 kknn 0.5646017 0.9898871 0.07271169 0.06067877
2 naive_bayes 0.8402030 0.9966436 0.14952596 0.10851423
train_sdm - selecting vars
Code
i2
Output
caretSDM
...............................
Class : input_sdm
-------- Occurrences --------
Species Names : Araucaria angustifolia
Number of presences : 420
Pseudoabsence methods :
Method to obtain PAs : random
Number of PA sets : 3
Number of PAs in each set : 420
-------- Predictors ---------
Number of Predictors : 2
Predictors Names : bio1, bio12
--------- Scenarios ---------
Number of Scenarios : 1
Scenarios Names : current
----------- Models ----------
Algorithms Names : naive_bayes kknn
Variables Names : bio1 bio12
Model Validation :
Method : cv
Number : 2
Metrics :
$`Araucaria angustifolia`
algo ROC Sens Spec ROCSD
1 kknn 0.5642758 0.9785714 0.05347222 0.08865492
2 naive_bayes 0.8495552 0.9690476 0.40902778 0.04172257
train_sdm - ESM
Code
i2
Output
caretSDM
...............................
Class : input_sdm
-------- Occurrences --------
Species Names : Araucaria angustifolia
Number of presences : 420
Pseudoabsence methods :
Method to obtain PAs : random
Number of PA sets : 3
Number of PAs in each set : 420
Ensemble of Small Models (ESM): TRUE
Number of Records : 20
-------- Predictors ---------
Number of Predictors : 3
Predictors Names : bio1, bio4, bio12
--------- Scenarios ---------
Number of Scenarios : 1
Scenarios Names : current
----------- Models ----------
Algorithms Names : naive_bayes kknn
Variables Names : bio1 bio4 bio12
Model Validation :
Method : cv
Number : 2
Metrics :
$`Araucaria angustifolia`
algo ROC Sens Spec ROCSD
1 kknn 0.5662136 0.9883598 0.05787037 0.06801226
2 naive_bayes 0.8406983 0.9748677 0.43356481 0.08100005
mahal.dist train
Code
i2
Output
caretSDM
...............................
Class : input_sdm
-------- Occurrences --------
Species Names : Araucaria angustifolia
Number of presences : 420
Pseudoabsence methods :
Method to obtain PAs : bioclim
Number of PA sets : 3
Number of PAs in each set : 420
-------- Predictors ---------
Number of Predictors : 7
Predictors Names : GID0, CODIGOIB1, NOMEUF2, SIGLAUF3, bio1, bio4, bio12
----------- Models ----------
Algorithms Names : mahal.custom
Variables Names : bio1 bio4 bio12
Model Validation :
Method : cv
Number : 3
Metrics :
$`Araucaria angustifolia`
algo ROC TSS Sensitivity Specificity
1 mahal.custom 0.9886484 0.8152958 0.8254444 1
train_sdm - one species ESM
Code
i1
Output
caretSDM
...............................
Class : input_sdm
-------- Occurrences --------
Species Names : Salminus brasiliensis Araucaria angustifolia
Number of presences : 82 21
Pseudoabsence methods :
Method to obtain PAs : bioclim
Number of PA sets : 10
Number of PAs in each set : 82 21
Data Cleaning : NAs, Capitals, Centroids, Geographically Duplicated, Identical Lat/Long, Institutions, Invalid, Non-terrestrial, Duplicated Cell (grid)
Ensemble of Small Models (ESM): TRUE
Number of Records : 30
-------- Predictors ---------
Number of Predictors : 3
Predictors Names : bio1, bio4, bio12
--------- Scenarios ---------
Number of Scenarios : 1
Scenarios Names : current
----------- Models ----------
Algorithms Names : mahal.custom
Variables Names : bio1 bio4 bio12
Model Validation :
Method : repeatedcv
Number : 4
Metrics :
$`Salminus brasiliensis`
algo ROC TSS Sensitivity Specificity
1 mahal.custom 0.6988333 0.3477778 0.8586 0.9541667
$`Araucaria angustifolia`
algo ROC TSS Sensitivity Specificity
1 mahal.custom 0.9612472 0.7656548 0.95015 1
Code
i2
Output
caretSDM
...............................
Class : input_sdm
-------- Occurrences --------
Species Names : Salminus brasiliensis Araucaria angustifolia
Number of presences : 82 21
Pseudoabsence methods :
Method to obtain PAs : bioclim
Number of PA sets : 10
Number of PAs in each set : 82 21
Data Cleaning : NAs, Capitals, Centroids, Geographically Duplicated, Identical Lat/Long, Institutions, Invalid, Non-terrestrial, Duplicated Cell (grid)
Ensemble of Small Models (ESM): TRUE
Number of Records : 30
-------- Predictors ---------
Number of Predictors : 3
Predictors Names : bio1, bio4, bio12
--------- Scenarios ---------
Number of Scenarios : 1
Scenarios Names : current
----------- Models ----------
Algorithms Names : kknn
Variables Names : bio1 bio4 bio12
Model Validation :
Method : repeatedcv
Number : 4
Metrics :
$`Salminus brasiliensis`
algo ROC TSS Sensitivity Specificity
1 kknn 0.9059954 0.7541667 0.8386083 0.9205417
$`Araucaria angustifolia`
algo ROC TSS Sensitivity Specificity
1 kknn 0.9562599 0.8770238 0.977 0.899875
Code
i1$models
Output
caretSDM
.........................
Class : Models
Algorithms Names : mahal.custom
Variables Names : bio1 bio4 bio12
Model Validation :
Method : repeatedcv
Number : 4
Metrics :
$`Salminus brasiliensis`
algo ROC TSS Sensitivity Specificity Pos Pred Value
m1.11 mahal.custom 0.6950000 0.4166667 0.85000 1.00000 1.0000000
m1.21 mahal.custom 0.7083333 0.3750000 0.85000 1.00000 1.0000000
m1.31 mahal.custom 0.7166667 0.2750000 0.90000 0.90000 0.8750000
m2.11 mahal.custom 0.6072222 0.3916667 0.85825 1.00000 1.0000000
m2.21 mahal.custom 0.7058333 0.3333333 0.90000 0.95000 0.9333333
m2.31 mahal.custom 0.6833333 0.2833333 0.90825 0.90000 0.8750000
m3.11 mahal.custom 0.7966667 0.3166667 0.90000 0.85000 0.8750000
m3.21 mahal.custom 0.7713889 0.4333333 0.86675 1.00000 1.0000000
m3.31 mahal.custom 0.7666667 0.3916667 0.86675 1.00000 1.0000000
m4.11 mahal.custom 0.6844444 0.2833333 0.90000 0.95000 0.8750000
m4.21 mahal.custom 0.7100000 0.3333333 0.90000 1.00000 1.0000000
m4.31 mahal.custom 0.7483333 0.3750000 0.90000 0.95000 0.9375000
m5.11 mahal.custom 0.7866667 0.4250000 0.90825 1.00000 1.0000000
m5.21 mahal.custom 0.7836111 0.4333333 0.90000 1.00000 1.0000000
m5.31 mahal.custom 0.7250000 0.2916667 0.75825 0.95000 0.9167500
m6.11 mahal.custom 0.6633333 0.2500000 0.90825 0.90000 0.7917500
m6.21 mahal.custom 0.6158333 0.1916667 0.75825 0.90000 0.8666667
m6.31 mahal.custom 0.7033333 0.4166667 0.80000 0.95000 0.8750000
m7.11 mahal.custom 0.6800000 0.3000000 0.73325 0.95825 0.9167500
m7.21 mahal.custom 0.7150000 0.4416667 0.86675 1.00000 1.0000000
m7.31 mahal.custom 0.6266667 0.3583333 0.90825 1.00000 1.0000000
m8.11 mahal.custom 0.6450000 0.3750000 0.90000 1.00000 1.0000000
m8.21 mahal.custom 0.6650000 0.2333333 0.80825 0.90000 0.9000000
m8.31 mahal.custom 0.5916667 0.4500000 0.81675 1.00000 1.0000000
m9.11 mahal.custom 0.7283333 0.2916667 0.90825 0.95000 0.9167500
m9.21 mahal.custom 0.6800000 0.3833333 0.76675 0.95000 0.9375000
m9.31 mahal.custom 0.6933333 0.4083333 0.81675 0.91675 0.8332500
m10.11 mahal.custom 0.6483333 0.3916667 0.85000 1.00000 1.0000000
m10.21 mahal.custom 0.7533333 0.2750000 0.90000 0.85000 0.7556667
m10.31 mahal.custom 0.6666667 0.3083333 0.85000 0.90000 0.7776667
Neg Pred Value Precision Recall F1 Prevalence Detection Rate
m1.11 0.7082500 1.0000000 0.85000 0.7070000 0.5 0.42950
m1.21 0.7917500 1.0000000 0.85000 0.6690000 0.5 0.42500
m1.31 0.6250000 0.8750000 0.90000 0.7030000 0.5 0.45000
m2.11 0.6480000 1.0000000 0.85825 0.6775000 0.5 0.42925
m2.21 0.8666667 0.9333333 0.90000 0.6827500 0.5 0.45000
m2.31 0.5915000 0.8750000 0.90825 0.6995000 0.5 0.45250
m3.11 0.8667500 0.8750000 0.90000 0.7270000 0.5 0.45225
m3.21 0.8335000 1.0000000 0.86675 0.7180000 0.5 0.43325
m3.31 0.8250000 1.0000000 0.86675 0.7245000 0.5 0.42975
m4.11 0.7000000 0.8750000 0.90000 0.7087500 0.5 0.45000
m4.21 0.6667500 1.0000000 0.90000 0.7082500 0.5 0.45225
m4.31 0.7917500 0.9375000 0.90000 0.7002500 0.5 0.45000
m5.11 0.8667500 1.0000000 0.90825 0.7355000 0.5 0.45250
m5.21 0.8750000 1.0000000 0.90000 0.7322500 0.5 0.45000
m5.31 0.6832500 0.9167500 0.75825 0.6617500 0.5 0.37750
m6.11 0.8335000 0.7917500 0.90825 0.7042500 0.5 0.45250
m6.21 0.7000000 0.8666667 0.75825 0.6480000 0.5 0.37925
m6.31 0.7500000 0.8750000 0.80000 0.6510000 0.5 0.40000
m7.11 0.7750000 0.9167500 0.73325 0.6180000 0.5 0.36375
m7.21 0.7750000 1.0000000 0.86675 0.6912500 0.5 0.42975
m7.31 0.8542500 1.0000000 0.90825 0.8295000 0.5 0.45475
m8.11 0.7917500 1.0000000 0.90000 0.6880000 0.5 0.45225
m8.21 0.7085000 0.9000000 0.80825 0.6745000 0.5 0.40700
m8.31 0.6657500 1.0000000 0.81675 0.7366667 0.5 0.40475
m9.11 0.8250000 0.9167500 0.90825 0.7030000 0.5 0.45425
m9.21 0.7260000 0.9375000 0.76675 0.6107500 0.5 0.38650
m9.31 0.7500000 0.8332500 0.81675 0.6495000 0.5 0.40925
m10.11 0.8125000 1.0000000 0.85000 0.6755000 0.5 0.42500
m10.21 0.8542500 0.7556667 0.90000 0.7120000 0.5 0.45225
m10.31 0.8750000 0.7776667 0.85000 0.6877500 0.5 0.43175
Detection Prevalence Balanced Accuracy Accuracy Kappa AccuracyLower
m1.11 0.76125 0.70825 0.71125 0.41650 0.37575
m1.21 0.75675 0.68750 0.69075 0.37250 0.35100
m1.31 0.78850 0.63750 0.64075 0.27250 0.30825
m2.11 0.78325 0.69575 0.69575 0.39175 0.36575
m2.21 0.80825 0.66675 0.66675 0.33325 0.33200
m2.31 0.80900 0.64175 0.64550 0.28900 0.31575
m3.11 0.74075 0.65825 0.66350 0.32000 0.32600
m3.21 0.70000 0.71675 0.71675 0.43325 0.37850
m3.31 0.69075 0.69575 0.69525 0.39200 0.36125
m4.11 0.78325 0.64175 0.64175 0.28325 0.30800
m4.21 0.78175 0.66675 0.66575 0.33325 0.32700
m4.31 0.78325 0.68750 0.68750 0.37500 0.34650
m5.11 0.73625 0.71250 0.70900 0.42250 0.37525
m5.21 0.72925 0.71675 0.71675 0.43325 0.37225
m5.31 0.64075 0.64575 0.64075 0.29375 0.31000
m6.11 0.78400 0.62500 0.62050 0.24875 0.29175
m6.21 0.66675 0.59575 0.59575 0.19175 0.28050
m6.31 0.70900 0.70825 0.70675 0.41475 0.37950
m7.11 0.62950 0.65000 0.64525 0.29875 0.31200
m7.21 0.74300 0.72075 0.71350 0.44200 0.37925
m7.31 0.76575 0.67925 0.68175 0.36025 0.36075
m8.11 0.80900 0.68750 0.68625 0.37425 0.34900
m8.21 0.69550 0.61675 0.61800 0.23150 0.28775
m8.31 0.73850 0.72500 0.71825 0.45525 0.38800
m9.11 0.79575 0.64575 0.64575 0.29175 0.31125
m9.21 0.72275 0.69175 0.69075 0.38325 0.34850
m9.31 0.74300 0.70400 0.70000 0.40400 0.38425
m10.11 0.73625 0.69575 0.68625 0.38850 0.37100
m10.21 0.76350 0.63750 0.64075 0.27775 0.30550
m10.31 0.72275 0.65425 0.66350 0.30500 0.34675
AccuracyUpper AccuracyNull AccuracyPValue McnemarPValue Positive
m1.11 0.92625 0.5225 0.44900 0.38725 5.25
m1.21 0.92100 0.5225 0.44250 0.47125 5.25
m1.31 0.89425 0.5225 0.42375 0.40750 5.25
m2.11 0.90825 0.5000 0.48650 0.45125 5.25
m2.21 0.90550 0.5000 0.39275 0.36400 5.25
m2.31 0.88975 0.5225 0.47100 0.38625 5.25
m3.11 0.90900 0.5225 0.39200 0.49975 5.25
m3.21 0.92725 0.5000 0.22875 0.52825 5.25
m3.31 0.91350 0.5225 0.30900 0.59550 5.25
m4.11 0.89500 0.5000 0.36350 0.37225 5.25
m4.21 0.91050 0.5225 0.38750 0.36900 5.25
m4.31 0.92225 0.5000 0.34150 0.40750 5.25
m5.11 0.92350 0.5225 0.30050 0.47125 5.25
m5.21 0.93550 0.5000 0.29825 0.58050 5.25
m5.31 0.89150 0.5225 0.41275 0.70425 5.25
m6.11 0.88375 0.5225 0.38875 0.38900 5.25
m6.21 0.86800 0.5000 0.39275 0.68775 5.25
m6.31 0.91650 0.5225 0.45350 0.59550 5.25
m7.11 0.89425 0.5225 0.44775 0.43825 5.25
m7.21 0.92425 0.5225 0.39200 0.43825 5.25
m7.31 0.90025 0.5225 0.33000 0.40700 5.25
m8.11 0.91825 0.5225 0.43875 0.30500 5.25
m8.21 0.88450 0.5225 0.39125 0.55900 5.25
m8.31 0.91825 0.5225 0.49900 0.46525 5.25
m9.11 0.89475 0.5000 0.36250 0.40975 5.25
m9.21 0.92425 0.5225 0.54825 0.48775 5.25
m9.31 0.89350 0.5225 0.49700 0.51625 5.25
m10.11 0.89475 0.5225 0.37075 0.43600 5.25
m10.21 0.89675 0.5225 0.44000 0.45625 5.25
m10.31 0.88600 0.5225 0.39625 0.56700 5.25
Negative True Positive False Positive True Negative False Negative
m1.11 5.25 4.50 3.00 5.25 3.50
m1.21 5.25 4.50 3.25 5.25 3.50
m1.31 5.25 4.75 3.25 4.75 3.50
m2.11 5.25 4.50 3.25 5.25 3.75
m2.21 5.25 4.75 3.25 5.00 3.75
m2.31 5.25 4.75 3.25 4.75 3.75
m3.11 5.25 4.75 3.25 4.50 3.00
m3.21 5.25 4.50 3.00 5.25 2.75
m3.31 5.25 4.50 3.25 5.25 2.75
m4.11 5.25 4.75 3.50 5.00 3.50
m4.21 5.25 4.75 3.50 5.25 3.50
m4.31 5.25 4.75 3.00 5.00 3.50
m5.11 5.25 4.75 3.00 5.25 3.00
m5.21 5.25 4.75 3.00 5.25 3.00
m5.31 5.25 4.00 3.50 5.00 2.75
m6.11 5.25 4.75 3.50 4.75 3.50
m6.21 5.25 4.00 3.75 4.75 3.00
m6.31 5.25 4.25 2.75 5.00 3.25
m7.11 5.25 3.75 3.50 5.00 2.75
m7.21 5.25 4.50 3.00 5.25 3.25
m7.31 5.25 4.75 3.25 5.25 3.25
m8.11 5.25 4.75 3.25 5.25 3.75
m8.21 5.25 4.25 3.50 4.75 3.00
m8.31 5.25 4.25 3.00 5.25 3.50
m9.11 5.25 4.75 3.50 5.00 3.50
m9.21 5.25 4.00 3.00 5.00 3.50
m9.31 5.25 4.25 2.75 4.75 3.50
m10.11 5.25 4.50 3.25 5.25 3.25
m10.21 5.25 4.75 3.50 4.50 3.25
m10.31 5.25 4.50 3.00 4.75 3.00
ROCSD TSSSD SensitivitySD SpecificitySD Pos Pred ValueSD
m1.11 0.19077813 0.3774917 0.3000926 0.20059640 0.15591237
m1.21 0.10754844 0.2061553 0.2061553 0.09428105 0.04201091
m1.31 0.25655842 0.3862210 0.1258306 0.27537853 0.25000000
m2.11 0.37074666 0.5000000 0.3714910 0.34996035 0.19341320
m2.21 0.12893797 0.2494438 0.3283122 0.25163913 0.11547005
m2.31 0.09714441 0.4255715 0.1667001 0.32015621 0.25000000
m3.11 0.05347204 0.2061553 0.2061553 0.30000000 0.25000000
m3.21 0.11993954 0.2581989 0.2582420 0.13350000 0.03934463
m3.31 0.18102588 0.2910708 0.2909850 0.25000000 0.11199107
m4.11 0.20646346 0.3333333 0.1154701 0.33993468 0.25000000
m4.21 0.12806248 0.3415650 0.1154701 0.25166115 0.13874437
m4.31 0.06540472 0.1763834 0.1707825 0.10000000 0.12500000
m5.11 0.14636332 0.2872281 0.2872281 0.25824197 0.11518789
m5.21 0.15881603 0.3468109 0.2000000 0.26287941 0.14752034
m5.31 0.08766519 0.3457413 0.2005964 0.26880399 0.16870586
m6.11 0.15818414 0.2061553 0.1165000 0.11650000 0.24994449
m6.21 0.24608603 0.4229526 0.2629428 0.28722813 0.23094011
m6.31 0.20551651 0.3785939 0.3127655 0.10000000 0.25000000
m7.11 0.15748016 0.2018434 0.3267347 0.25000000 0.16650000
m7.21 0.17086924 0.2629956 0.2628794 0.20615528 0.07387828
m7.31 0.24962194 0.4245913 0.4244670 0.12583057 0.03603124
m8.11 0.12897028 0.2061553 0.2061553 0.12572026 0.09136192
m8.21 0.17725270 0.1663887 0.1885619 0.20000000 0.20000000
m8.31 0.42768542 0.4776486 0.3415650 0.28493669 0.13947760
m9.11 0.09758187 0.2061553 0.2005964 0.23350000 0.16650000
m9.21 0.09797959 0.1575272 0.2905551 0.23350000 0.12500000
m9.31 0.26975984 0.4961892 0.3918404 0.16650000 0.33350000
m10.11 0.29471267 0.4374802 0.4374230 0.12583057 0.15427573
m10.21 0.09933110 0.1374369 0.2494439 0.19148542 0.21423430
m10.31 0.28394053 0.4879094 0.3773666 0.28722813 0.38509263
Neg Pred ValueSD PrecisionSD RecallSD F1SD PrevalenceSD
m1.11 0.3437134 0.15591237 0.3000926 0.17275513 0.03674235
m1.21 0.2499445 0.04201091 0.2061553 0.21942709 0.03674235
m1.31 0.4787136 0.25000000 0.1258306 0.14594377 0.03674235
m2.11 0.4787136 0.19341320 0.3714910 0.31575993 0.00000000
m2.21 0.2309401 0.11547005 0.3283122 0.15678116 0.00000000
m2.31 0.5773503 0.25000000 0.1667001 0.18681631 0.03674235
m3.11 0.1631633 0.25000000 0.2061553 0.16529872 0.03674235
m3.21 0.1922576 0.03934463 0.2582420 0.23294402 0.00000000
m3.31 0.2362908 0.11199107 0.2909850 0.27597509 0.03674235
m4.11 0.4760952 0.25000000 0.1154701 0.13488514 0.00000000
m4.21 0.4714046 0.13874437 0.1154701 0.13710671 0.03674235
m4.31 0.2499445 0.12500000 0.1707825 0.15763566 0.00000000
m5.11 0.1631633 0.11518789 0.2872281 0.27243776 0.03674235
m5.21 0.2500000 0.14752034 0.2000000 0.15093348 0.00000000
m5.31 0.2849367 0.16870586 0.2005964 0.20967813 0.03674235
m6.11 0.1922576 0.24994449 0.1165000 0.15238766 0.03674235
m6.21 0.3188521 0.23094011 0.2629428 0.19362507 0.00000000
m6.31 0.2886751 0.25000000 0.3127655 0.31160338 0.03674235
m7.11 0.2629956 0.16650000 0.3267347 0.20967813 0.03674235
m7.21 0.2629956 0.07387828 0.2628794 0.24635797 0.03674235
m7.31 0.1716748 0.03603124 0.4244670 0.11242998 0.03674235
m8.11 0.2499445 0.09136192 0.2061553 0.21942709 0.03674235
m8.21 0.2096736 0.20000000 0.1885619 0.13176874 0.03674235
m8.31 0.5000556 0.13947760 0.3415650 0.16686596 0.03674235
m9.11 0.2362908 0.16650000 0.2005964 0.20967813 0.00000000
m9.21 0.3309713 0.12500000 0.2905551 0.13000865 0.03674235
m9.31 0.2886751 0.33350000 0.3918404 0.36530672 0.03674235
m10.11 0.3750000 0.15427573 0.4374230 0.35941202 0.03674235
m10.21 0.1716748 0.21423430 0.2494439 0.06271098 0.03674235
m10.31 0.2500000 0.38509263 0.3773666 0.34389000 0.03674235
Detection RateSD Detection PrevalenceSD Balanced AccuracySD AccuracySD
m1.11 0.15988642 0.15988642 0.18887099 0.20149835
m1.21 0.11088432 0.13505894 0.10307764 0.09033779
m1.31 0.07502444 0.12972535 0.19311050 0.19420500
m2.11 0.18584649 0.18584649 0.25000000 0.25000000
m2.21 0.16413079 0.21665391 0.12472202 0.12472202
m2.31 0.08230836 0.11816796 0.21269442 0.19754578
m3.11 0.11088432 0.23789774 0.10307764 0.09386648
m3.21 0.12905651 0.14142136 0.12905651 0.12905651
m3.31 0.14569489 0.16781215 0.14562137 0.15058857
m4.11 0.05773503 0.19145648 0.16670008 0.16670008
m4.21 0.08446054 0.10144416 0.17078251 0.19127184
m4.31 0.08539126 0.12500000 0.08825484 0.08825484
m5.11 0.13150760 0.17262363 0.14361407 0.14615745
m5.21 0.10000000 0.15670221 0.17346157 0.17346157
m5.31 0.08948929 0.14595290 0.17302601 0.17135830
m6.11 0.05219435 0.09342733 0.10322588 0.12311648
m6.21 0.13155069 0.21879728 0.21151576 0.21151576
m6.31 0.15242047 0.13012302 0.18922363 0.18922892
m7.11 0.16165472 0.28075672 0.10095874 0.10974326
m7.21 0.11257701 0.14984214 0.13161402 0.14941776
m7.31 0.22290357 0.22290357 0.21241998 0.21312966
m8.11 0.09988160 0.09988160 0.10307764 0.11116504
m8.21 0.09824799 0.19224355 0.08317251 0.08425359
m8.31 0.16732877 0.16732877 0.23880885 0.22341218
m9.11 0.10044360 0.14946209 0.10314512 0.10314512
m9.21 0.15065524 0.25268871 0.07892824 0.06843488
m9.31 0.19619803 0.16741764 0.24827001 0.24768932
m10.11 0.19678838 0.19678838 0.21879728 0.22603005
m10.21 0.12495733 0.20979593 0.06883979 0.05995206
m10.31 0.20352621 0.21760113 0.24407427 0.23264924
KappaSD AccuracyLowerSD AccuracyUpperSD AccuracyNullSD
m1.11 0.3772377 0.16006353 0.12904521 0.02598076
m1.21 0.1977768 0.08152709 0.04481815 0.02598076
m1.31 0.3877563 0.16890505 0.11103453 0.02598076
m2.11 0.5000000 0.20487618 0.14554953 0.00000000
m2.21 0.2494439 0.10663645 0.06870953 0.00000000
m2.31 0.4231488 0.15992368 0.12004825 0.02598076
m3.11 0.1952323 0.08499951 0.05232272 0.02598076
m3.21 0.2582420 0.12453246 0.05168091 0.00000000
m3.31 0.2914653 0.13752909 0.06913513 0.02598076
m4.11 0.3333000 0.13678785 0.09905891 0.00000000
m4.21 0.3382021 0.14916965 0.12605125 0.02598076
m4.31 0.1763205 0.07786527 0.05078304 0.00000000
m5.11 0.2916316 0.15352171 0.05762812 0.02598076
m5.21 0.3467548 0.15668945 0.08316800 0.00000000
m5.31 0.3438879 0.14848906 0.09895117 0.02598076
m6.11 0.2106949 0.09876698 0.08042958 0.02598076
m6.21 0.4229132 0.16057086 0.13928987 0.00000000
m6.31 0.3788363 0.18656992 0.08659677 0.02598076
m7.11 0.2039532 0.09313789 0.05929235 0.02598076
m7.21 0.2737736 0.14856059 0.06811449 0.02598076
m7.31 0.4238399 0.20515746 0.10111174 0.02598076
m8.11 0.2097338 0.10725049 0.07248678 0.02598076
m8.21 0.1665873 0.06976329 0.05007994 0.02598076
m8.31 0.4754706 0.18144122 0.14097990 0.02598076
m9.11 0.2061553 0.10183442 0.05533760 0.00000000
m9.21 0.1596277 0.04482094 0.05293628 0.02598076
m9.31 0.4999653 0.23668034 0.11239958 0.02598076
m10.11 0.4396919 0.23908855 0.14287145 0.02598076
m10.21 0.1298820 0.06036279 0.02947739 0.02598076
m10.31 0.4831494 0.20848881 0.12271648 0.02598076
AccuracyPValueSD McnemarPValueSD PositiveSD NegativeSD True PositiveSD
m1.11 0.4051601 0.1973989 0.5 0.5 1.7078251
m1.21 0.1600708 0.4196406 0.5 0.5 1.2909944
m1.31 0.3617857 0.3986389 0.5 0.5 0.9574271
m2.11 0.3934103 0.4392251 0.5 0.5 1.8257419
m2.21 0.2489244 0.4439020 0.5 0.5 1.6329932
m2.31 0.4098203 0.2446717 0.5 0.5 0.8164966
m3.11 0.1801074 0.4174734 0.5 0.5 1.1547005
m3.21 0.1498232 0.4174734 0.5 0.5 1.2583057
m3.31 0.2646638 0.4693893 0.5 0.5 1.4142136
m4.11 0.3365734 0.4205048 0.5 0.5 0.9574271
m4.21 0.3882624 0.2199318 0.5 0.5 0.9574271
m4.31 0.2089569 0.3986389 0.5 0.5 0.9574271
m5.11 0.1753957 0.4196406 0.5 0.5 1.5000000
m5.21 0.2657008 0.4895777 0.5 0.5 1.2583057
m5.31 0.3152823 0.4084820 0.5 0.5 1.1547005
m6.11 0.3020491 0.2726866 0.5 0.5 0.5000000
m6.21 0.3910689 0.4416831 0.5 0.5 1.4142136
m6.31 0.2862278 0.4693893 0.5 0.5 1.7320508
m7.11 0.2324213 0.3868026 0.5 0.5 1.5000000
m7.21 0.2619523 0.4132227 0.5 0.5 1.2583057
m7.31 0.3388943 0.4392251 0.5 0.5 2.4494897
m8.11 0.2688647 0.2136118 0.5 0.5 1.1547005
m8.21 0.1842614 0.4330000 0.5 0.5 0.9574271
m8.31 0.4461353 0.4128134 0.5 0.5 1.7078251
m9.11 0.2251599 0.4090529 0.5 0.5 0.9574271
m9.21 0.2134719 0.4284860 0.5 0.5 1.4142136
m9.31 0.4078540 0.3999553 0.5 0.5 1.9148542
m10.11 0.3828684 0.3760000 0.5 0.5 2.1602469
m10.21 0.1207256 0.4364496 0.5 0.5 1.2583057
m10.31 0.4078794 0.4999853 0.5 0.5 2.2173558
False PositiveSD True NegativeSD False NegativeSD
m1.11 1.4142136 0.9574271 1.2909944
m1.21 0.9574271 0.5000000 0.5773503
m1.31 0.5773503 1.5000000 1.2909944
m2.11 2.0615528 1.7320508 1.8929694
m2.21 1.7078251 1.2909944 1.2583057
m2.31 0.9574271 1.7320508 1.5000000
m3.11 0.9574271 1.7320508 1.5000000
m3.21 1.4142136 1.0000000 0.5000000
m3.31 1.7078251 1.2909944 1.2583057
m4.11 0.5773503 1.7078251 1.7320508
m4.21 0.5773503 1.2583057 1.7320508
m4.31 0.8164966 0.8164966 0.5773503
m5.11 1.4142136 1.2583057 1.4142136
m5.21 1.0000000 1.2583057 1.6329932
m5.31 1.2909944 1.2909944 1.5000000
m6.11 1.0000000 0.9574271 1.0000000
m6.21 1.5000000 1.5000000 1.4142136
m6.31 1.5000000 0.8164966 0.5000000
m7.11 1.9148542 1.2909944 1.2583057
m7.21 1.6329932 1.1547005 0.9574271
m7.31 2.0615528 0.8164966 0.5000000
m8.11 0.9574271 0.5773503 0.9574271
m8.21 1.0000000 1.2583057 1.0000000
m8.31 2.1602469 1.7078251 1.2909944
m9.11 1.2909944 1.5000000 1.0000000
m9.21 1.5000000 1.5000000 1.0000000
m9.31 2.2173558 0.5000000 1.0000000
m10.11 2.3629078 0.8164966 0.5000000
m10.21 1.2909944 1.2909944 0.9574271
m10.31 1.8257419 1.5000000 1.4142136
$`Araucaria angustifolia`
algo ROC TSS Sensitivity Specificity Pos Pred Value
m1.1 mahal.custom 0.9633929 0.7916667 0.98800 1 1
m2.1 mahal.custom 0.9555556 0.7732143 0.94000 1 1
m3.1 mahal.custom 0.9734127 0.7613095 0.92800 1 1
m4.1 mahal.custom 0.9615079 0.7428571 0.95100 1 1
m5.1 mahal.custom 0.9556264 0.7898810 0.95050 1 1
m6.1 mahal.custom 0.9700397 0.6815476 0.89000 1 1
m7.1 mahal.custom 0.9660714 0.7678571 0.97625 1 1
m8.1 mahal.custom 0.9461310 0.7851190 0.95175 1 1
m9.1 mahal.custom 0.9570437 0.7666667 0.96300 1 1
m10.1 mahal.custom 0.9636905 0.7964286 0.96300 1 1
Neg Pred Value Precision Recall F1 Prevalence Detection Rate
m1.1 0.96425 1 0.98800 0.96475 0.76650 0.75725
m2.1 0.85325 1 0.94000 0.94375 0.77350 0.72700
m3.1 0.83325 1 0.92800 0.93675 0.77350 0.71775
m4.1 0.82250 1 0.95100 0.94575 0.77350 0.73600
m5.1 0.85175 1 0.95050 0.95075 0.76650 0.72875
m6.1 0.77525 1 0.89000 0.90900 0.77350 0.68850
m7.1 0.92850 1 0.97625 0.95900 0.77350 0.75500
m8.1 0.83675 1 0.95175 0.95175 0.78125 0.74400
m9.1 0.86650 1 0.96300 0.95150 0.76650 0.73850
m10.1 0.86650 1 0.96300 0.95775 0.78125 0.75250
Detection Prevalence Balanced Accuracy Accuracy Kappa AccuracyLower
m1.1 0.80400 0.89575 0.94425 0.83200 0.78200
m2.1 0.76475 0.88650 0.91575 0.77100 0.74550
m3.1 0.75500 0.88075 0.90675 0.75525 0.73775
m4.1 0.78325 0.87150 0.91500 0.74875 0.74125
m5.1 0.76625 0.89500 0.92500 0.78925 0.75550
m6.1 0.73550 0.84050 0.86800 0.65950 0.68275
m7.1 0.80175 0.88400 0.93500 0.79775 0.77200
m8.1 0.78125 0.89275 0.92525 0.78450 0.75700
m9.1 0.78550 0.88325 0.92500 0.78450 0.75875
m10.1 0.78975 0.89850 0.93375 0.80275 0.76575
AccuracyUpper AccuracyNull AccuracyPValue McnemarPValue Positive Negative
m1.1 0.99500 0.76650 0.98050 0.7400000 20.5 6.25
m2.1 0.98150 0.77350 0.96250 0.7127500 20.5 6.00
m3.1 0.97400 0.77350 0.96275 0.5670000 20.5 6.00
m4.1 0.98550 0.77350 0.97400 1.0000000 20.5 6.00
m5.1 0.98925 0.76650 0.93150 0.8700000 20.5 6.25
m6.1 0.96325 0.77350 0.88725 0.3512500 20.5 6.00
m7.1 0.98775 0.77350 0.93800 0.7493333 20.5 6.00
m8.1 0.98575 0.78125 0.98975 1.0000000 20.5 5.75
m9.1 0.98575 0.76650 0.97275 1.0000000 20.5 6.25
m10.1 0.99125 0.78125 0.98700 1.0000000 20.5 5.75
True Positive False Positive True Negative False Negative ROCSD
m1.1 20.25 11.00 6.25 1.25 0.03023239
m2.1 19.25 11.25 6.00 1.00 0.04038661
m3.1 19.00 11.50 6.00 1.00 0.03261702
m4.1 19.50 11.25 6.00 1.25 0.03677011
m5.1 19.50 11.00 6.25 1.00 0.04473929
m6.1 18.25 11.50 6.00 1.25 0.03441417
m7.1 20.00 11.00 6.00 1.25 0.03050053
m8.1 19.50 11.75 5.75 1.00 0.05257337
m9.1 19.75 11.25 6.25 1.25 0.03565306
m10.1 19.75 10.75 5.75 1.00 0.02423236
TSSSD SensitivitySD SpecificitySD Pos Pred ValueSD Neg Pred ValueSD
m1.1 0.1280339 0.09085657 0.1484644 0.04236744 0.07150000
m2.1 0.1451584 0.14195158 0.1359470 0.03753221 0.20931854
m3.1 0.1770077 0.15516738 0.1359470 0.03626752 0.23570232
m4.1 0.1599910 0.14670236 0.1593683 0.04427942 0.05641144
m5.1 0.1704613 0.17058991 0.1364609 0.03880722 0.10806904
m6.1 0.2354255 0.23528334 0.1593683 0.04201984 0.25955780
m7.1 0.2088146 0.15220710 0.2096736 0.05162929 0.14300000
m8.1 0.1591309 0.11893240 0.1359470 0.03724133 0.11983704
m9.1 0.1710644 0.12114007 0.1484644 0.04543494 0.09034932
m10.1 0.1373337 0.04383587 0.1359470 0.03722454 0.09034932
PrecisionSD RecallSD F1SD PrevalenceSD Detection RateSD
m1.1 0.04236744 0.09085657 0.08567380 0.017521415 0.07625123
m2.1 0.03753221 0.14195158 0.13703862 0.005196152 0.11034793
m3.1 0.03626752 0.15516738 0.15548097 0.005196152 0.12001493
m4.1 0.04427942 0.14670236 0.15362617 0.005196152 0.11330012
m5.1 0.03880722 0.17058991 0.16469871 0.011789826 0.13359266
m6.1 0.04201984 0.23528334 0.23139721 0.005196152 0.18123557
m7.1 0.05162929 0.15220710 0.13875968 0.005196152 0.11946652
m8.1 0.03724133 0.11893240 0.11978696 0.013200379 0.09628560
m9.1 0.04543494 0.12114007 0.12182604 0.017521415 0.09428680
m10.1 0.03722454 0.04383587 0.04053805 0.013200379 0.03192047
Detection PrevalenceSD Balanced AccuracySD AccuracySD KappaSD
m1.1 0.07625123 0.06411123 0.06094807 0.07058801
m2.1 0.11034793 0.07283543 0.11071096 0.16399797
m3.1 0.12001493 0.08845856 0.11920570 0.21282916
m4.1 0.11330012 0.07990620 0.11530250 0.11658009
m5.1 0.13359266 0.08509358 0.13052714 0.13895443
m6.1 0.18123557 0.11773523 0.18170925 0.19442115
m7.1 0.11946652 0.10444456 0.11599713 0.17728955
m8.1 0.09628560 0.07940351 0.09026073 0.15286268
m9.1 0.09428680 0.08566359 0.09567088 0.15369776
m10.1 0.03192047 0.06848114 0.03777455 0.10781891
AccuracyLowerSD AccuracyUpperSD AccuracyNullSD AccuracyPValueSD
m1.1 0.05842944 0.05000333 0.017521415 0.02573584
m2.1 0.10353542 0.09182002 0.005196152 0.20286038
m3.1 0.12014262 0.10017110 0.005196152 0.29012914
m4.1 0.10318915 0.10068888 0.005196152 0.05516641
m5.1 0.12462343 0.10599174 0.011789826 0.11299705
m6.1 0.17047458 0.15024535 0.005196152 0.22112591
m7.1 0.11326775 0.09232010 0.005196152 0.11671332
m8.1 0.07903744 0.07970989 0.013200379 0.05772564
m9.1 0.08705314 0.08171087 0.017521415 0.04847250
m10.1 0.04650717 0.03024759 0.013200379 0.04819665
McnemarPValueSD PositiveSD NegativeSD True PositiveSD False PositiveSD
m1.1 0.300222140 0.5773503 0.5 2.0816660 1.6329932
m2.1 0.334659404 0.5773503 0.0 2.9860788 2.9860788
m3.1 0.396720304 0.5773503 0.0 3.1622777 3.1091264
m4.1 0.003265986 0.5773503 0.0 2.9860788 3.2015621
m5.1 0.260000000 0.5773503 0.5 3.6968455 3.2659863
m6.1 0.191842600 0.5773503 0.0 4.7609523 4.7958315
m7.1 0.434167402 0.5773503 0.0 3.3166248 2.9439203
m8.1 0.003593976 0.5773503 0.5 2.2173558 2.7537853
m9.1 0.003304038 0.5773503 0.5 2.5000000 2.6299556
m10.1 0.001414214 0.5773503 0.5 0.9574271 0.9574271
True NegativeSD False NegativeSD
m1.1 0.8164966 0.9574271
m2.1 0.8164966 0.8164966
m3.1 0.8164966 0.8164966
m4.1 0.9574271 0.9574271
m5.1 0.9574271 0.8164966
m6.1 0.9574271 0.9574271
m7.1 1.2583057 1.2583057
m8.1 0.5000000 0.8164966
m9.1 0.8164966 0.9574271
m10.1 0.5000000 0.8164966
Code
i2$models
Output
caretSDM
.........................
Class : Models
Algorithms Names : kknn
Variables Names : bio1 bio4 bio12
Model Validation :
Method : repeatedcv
Number : 4
Metrics :
$`Salminus brasiliensis`
algo ROC TSS Sensitivity Specificity Pos Pred Value
m1.11 kknn 0.9500000 0.9000000 0.95000 0.95000 0.95825
m1.21 kknn 0.8000000 0.5250000 0.71675 0.85825 0.81675
m1.31 kknn 0.9750000 0.8500000 0.90000 0.95000 0.95825
m2.11 kknn 0.8891667 0.6583333 0.80000 0.85825 0.86425
m2.21 kknn 0.8259722 0.4416667 0.71675 0.77500 0.79275
m2.31 kknn 0.9583333 0.7416667 0.86675 0.87500 0.89275
m3.11 kknn 0.9791667 0.9583333 0.95825 1.00000 1.00000
m3.21 kknn 0.8433333 0.6333333 0.67500 0.95825 0.95825
m3.31 kknn 0.9291667 0.8583333 0.90825 0.95000 0.95825
m4.11 kknn 0.9291667 0.8583333 0.90000 0.95825 0.95000
m4.21 kknn 0.7958333 0.5916667 0.68325 0.90825 0.88750
m4.31 kknn 0.9650000 0.8583333 0.90825 0.95000 0.95825
m5.11 kknn 0.9541667 0.9083333 0.95825 0.95000 0.95825
m5.21 kknn 0.8391667 0.5166667 0.66675 0.90000 0.88750
m5.31 kknn 0.9375000 0.8750000 0.91675 0.95825 0.95825
m6.11 kknn 0.9041667 0.8083333 0.85825 0.95000 0.93750
m6.21 kknn 0.8941667 0.6666667 0.80000 0.86675 0.88100
m6.31 kknn 0.9750000 0.9500000 0.95000 1.00000 1.00000
m7.11 kknn 0.9000000 0.8000000 0.85000 0.95000 0.96425
m7.21 kknn 0.7466667 0.4083333 0.55825 0.85000 0.81250
m7.31 kknn 0.9900000 0.9000000 0.95000 0.95000 0.95825
m8.11 kknn 0.9291667 0.8583333 0.90825 0.95000 0.95000
m8.21 kknn 0.8958333 0.5083333 0.76675 0.74175 0.82500
m8.31 kknn 0.9250000 0.8500000 0.90000 0.95000 0.95000
m9.11 kknn 0.8833333 0.7666667 0.81675 0.95000 0.95825
m9.21 kknn 0.9030556 0.7083333 0.80000 0.90825 0.91425
m9.31 kknn 0.9250000 0.8500000 0.95000 0.90000 0.92250
m10.11 kknn 0.9750000 0.9500000 0.95000 1.00000 1.00000
m10.21 kknn 0.7875000 0.4750000 0.62500 0.85000 0.82500
m10.31 kknn 0.9750000 0.9500000 0.95000 1.00000 1.00000
Neg Pred Value Precision Recall F1 Prevalence Detection Rate
m1.11 0.95825 0.95825 0.95000 0.94950 0.5 0.47500
m1.21 0.73675 0.81675 0.71675 0.75425 0.5 0.35700
m1.31 0.92850 0.95825 0.90000 0.91475 0.5 0.45000
m2.11 0.81650 0.86425 0.80000 0.82525 0.5 0.40000
m2.21 0.71675 0.79275 0.71675 0.72050 0.5 0.35825
m2.31 0.86600 0.89275 0.86675 0.86675 0.5 0.43200
m3.11 0.95825 1.00000 0.95825 0.97725 0.5 0.47750
m3.21 0.76325 0.95825 0.67500 0.76900 0.5 0.33200
m3.31 0.90825 0.95825 0.90825 0.93050 0.5 0.45250
m4.11 0.91650 0.95000 0.90000 0.92225 0.5 0.45225
m4.21 0.76400 0.88750 0.68325 0.74175 0.5 0.33650
m4.31 0.92250 0.95825 0.90825 0.92675 0.5 0.45425
m5.11 0.95825 0.95825 0.95825 0.95450 0.5 0.47750
m5.21 0.71200 0.88750 0.66675 0.71675 0.5 0.33425
m5.31 0.92850 0.95825 0.91675 0.92725 0.5 0.45475
m6.11 0.87500 0.93750 0.85825 0.89400 0.5 0.42750
m6.21 0.83675 0.88100 0.80000 0.82225 0.5 0.40225
m6.31 0.95825 1.00000 0.95000 0.97225 0.5 0.47500
m7.11 0.89575 0.96425 0.85000 0.89050 0.5 0.42950
m7.21 0.67625 0.81250 0.55825 0.64275 0.5 0.28425
m7.31 0.95825 0.95825 0.95000 0.94950 0.5 0.47500
m8.11 0.90825 0.95000 0.90825 0.92725 0.5 0.45250
m8.21 0.80350 0.82500 0.76675 0.75825 0.5 0.37975
m8.31 0.91425 0.95000 0.90000 0.92225 0.5 0.45225
m9.11 0.85700 0.95825 0.81675 0.86475 0.5 0.40475
m9.21 0.83675 0.91425 0.80000 0.84050 0.5 0.40000
m9.31 0.95825 0.92250 0.95000 0.93025 0.5 0.47500
m10.11 0.95825 1.00000 0.95000 0.97225 0.5 0.47500
m10.21 0.69700 0.82500 0.62500 0.70425 0.5 0.31150
m10.31 0.95825 1.00000 0.95000 0.97225 0.5 0.47500
Detection Prevalence Balanced Accuracy Accuracy Kappa AccuracyLower
m1.11 0.50000 0.95000 0.95000 0.90000 0.63425
m1.21 0.45000 0.76250 0.76350 0.52500 0.41950
m1.31 0.47500 0.92500 0.92500 0.85000 0.60650
m2.11 0.47075 0.82925 0.82925 0.65825 0.49675
m2.21 0.49575 0.72075 0.72075 0.44175 0.37825
m2.31 0.50000 0.87075 0.86350 0.73375 0.54350
m3.11 0.47750 0.97925 0.97725 0.95500 0.67150
m3.21 0.35450 0.81675 0.80900 0.62400 0.46625
m3.31 0.47500 0.92925 0.92950 0.85825 0.61100
m4.11 0.47500 0.92925 0.92950 0.85825 0.61100
m4.21 0.38425 0.79600 0.78850 0.58625 0.45750
m4.31 0.47925 0.92925 0.92925 0.85825 0.60425
m5.11 0.50250 0.95425 0.95225 0.90500 0.63725
m5.21 0.42975 0.75825 0.76125 0.51650 0.41600
m5.31 0.47725 0.93750 0.93175 0.86625 0.61325
m6.11 0.45250 0.90425 0.90225 0.80500 0.58550
m6.21 0.47025 0.83325 0.83400 0.66825 0.49400
m6.31 0.47500 0.97500 0.97500 0.95000 0.66925
m7.11 0.45225 0.90000 0.90675 0.80875 0.57900
m7.21 0.35675 0.70425 0.71125 0.41350 0.37050
m7.31 0.50000 0.95000 0.95000 0.90000 0.63425
m8.11 0.47750 0.92925 0.92725 0.85500 0.60950
m8.21 0.51600 0.75400 0.74300 0.50325 0.40575
m8.31 0.47725 0.92500 0.92725 0.85350 0.60950
m9.11 0.42975 0.88325 0.87950 0.76125 0.54900
m9.21 0.44575 0.85425 0.85425 0.70825 0.51450
m9.31 0.52275 0.92500 0.92725 0.85350 0.60300
m10.11 0.47500 0.97500 0.97500 0.95000 0.66925
m10.21 0.40925 0.73750 0.73850 0.47775 0.39550
m10.31 0.47500 0.97500 0.97500 0.95000 0.66850
AccuracyUpper AccuracyNull AccuracyPValue McnemarPValue Positive
m1.11 0.99850 0.5000 0.00575 1.0000000 5.25
m1.21 0.95450 0.5225 0.19800 0.8700000 5.25
m1.31 0.99300 0.5000 0.01675 0.7400000 5.25
m2.11 0.96750 0.5000 0.10050 1.0000000 5.25
m2.21 0.93275 0.5000 0.16725 1.0000000 5.25
m2.31 0.96700 0.5225 0.11200 0.5485000 5.25
m3.11 0.99950 0.5225 0.00400 1.0000000 5.25
m3.21 0.97200 0.5225 0.07650 0.5520000 5.25
m3.31 0.99350 0.5225 0.01850 1.0000000 5.25
m4.11 0.99350 0.5225 0.01850 1.0000000 5.25
m4.21 0.95025 0.5225 0.15475 0.7113333 5.25
m4.31 0.99800 0.5000 0.00650 1.0000000 5.25
m5.11 0.99875 0.5225 0.00650 1.0000000 5.25
m5.21 0.95625 0.5225 0.16825 0.8700000 5.25
m5.31 0.99375 0.5225 0.01900 0.7400000 5.25
m6.11 0.98275 0.5225 0.04675 1.0000000 5.25
m6.21 0.97750 0.5225 0.06550 0.8700000 5.25
m6.31 0.99925 0.5225 0.00350 1.0000000 5.25
m7.11 0.99300 0.5225 0.02150 0.8266667 5.25
m7.21 0.93200 0.5225 0.19825 0.7162500 5.25
m7.31 0.99850 0.5000 0.00575 1.0000000 5.25
m8.11 0.99325 0.5225 0.01750 1.0000000 5.25
m8.21 0.93625 0.5225 0.24675 0.6385000 5.25
m8.31 0.99325 0.5225 0.01750 1.0000000 5.25
m9.11 0.98725 0.5225 0.03200 0.6533333 5.25
m9.21 0.98625 0.5000 0.07125 0.8700000 5.25
m9.31 0.99800 0.5225 0.00900 1.0000000 5.25
m10.11 0.99925 0.5225 0.00350 1.0000000 5.25
m10.21 0.94400 0.5225 0.21950 0.7742500 5.25
m10.31 0.99925 0.5000 0.00325 1.0000000 5.25
Negative True Positive False Positive True Negative False Negative
m1.11 5.25 5.00 0.25 5.00 0.25
m1.21 5.25 3.75 2.00 4.50 1.00
m1.31 5.25 4.75 0.50 5.00 0.25
m2.11 5.25 4.25 1.00 4.50 0.75
m2.21 5.25 3.75 1.75 4.00 1.50
m2.31 5.25 4.50 0.75 4.50 0.75
m3.11 5.25 5.00 0.25 5.25 0.00
m3.21 5.25 3.50 1.75 5.00 0.25
m3.31 5.25 4.75 0.50 5.00 0.25
m4.11 5.25 4.75 0.50 5.00 0.25
m4.21 5.25 3.50 1.75 4.75 0.50
m4.31 5.25 4.75 0.50 5.00 0.25
m5.11 5.25 5.00 0.25 5.00 0.25
m5.21 5.25 3.50 2.00 4.75 1.00
m5.31 5.25 4.75 0.50 5.00 0.25
m6.11 5.25 4.50 0.75 5.00 0.25
m6.21 5.25 4.25 1.00 4.50 0.75
m6.31 5.25 5.00 0.25 5.25 0.00
m7.11 5.25 4.50 0.75 5.00 0.25
m7.21 5.25 3.00 2.25 4.50 0.75
m7.31 5.25 5.00 0.25 5.00 0.25
m8.11 5.25 4.75 0.50 5.00 0.25
m8.21 5.25 4.00 1.50 3.75 1.50
m8.31 5.25 4.75 0.50 5.00 0.25
m9.11 5.25 4.25 1.00 5.00 0.25
m9.21 5.25 4.25 1.25 4.75 0.75
m9.31 5.25 5.00 0.25 4.75 0.50
m10.11 5.25 5.00 0.25 5.25 0.00
m10.21 5.25 3.25 2.00 4.50 1.00
m10.31 5.25 5.00 0.25 5.25 0.00
ROCSD TSSSD SensitivitySD SpecificitySD Pos Pred ValueSD
m1.11 0.05773503 0.11547005 0.1000000 0.1000000 0.08350000
m1.21 0.11970256 0.23940513 0.2212591 0.1641308 0.13731563
m1.31 0.05000000 0.19148542 0.2000000 0.1000000 0.08350000
m2.11 0.03370625 0.30595933 0.1632993 0.1893117 0.18862374
m2.21 0.11069680 0.20615528 0.1885619 0.2061553 0.17533848
m2.31 0.08333333 0.33374974 0.1631633 0.2500000 0.21450000
m3.11 0.04166667 0.08333333 0.0835000 0.0000000 0.00000000
m3.21 0.08814970 0.14142136 0.2217356 0.0835000 0.08350000
m3.31 0.08858455 0.17716910 0.1067969 0.1000000 0.08350000
m4.11 0.08858455 0.17716910 0.1154701 0.0835000 0.10000000
m4.21 0.15053915 0.30107831 0.2849367 0.1067969 0.13149778
m4.31 0.04725816 0.09574271 0.1067969 0.1000000 0.08350000
m5.11 0.05335937 0.10671874 0.0835000 0.1000000 0.08350000
m5.21 0.12583057 0.18358568 0.1667001 0.1373156 0.16520190
m5.31 0.07978559 0.15957118 0.1665000 0.0835000 0.08350000
m6.11 0.14166667 0.28333333 0.1893117 0.1000000 0.12500000
m6.21 0.08247895 0.16329932 0.1632993 0.1631633 0.15779100
m6.31 0.05000000 0.10000000 0.1000000 0.0000000 0.00000000
m7.11 0.08164966 0.16329932 0.1914854 0.1000000 0.07150000
m7.21 0.15930404 0.17716910 0.2060071 0.1000000 0.14218884
m7.31 0.02000000 0.11547005 0.1000000 0.1000000 0.08350000
m8.11 0.09464847 0.18929694 0.1067969 0.1000000 0.10000000
m8.21 0.14101156 0.26579719 0.2516391 0.3945955 0.23629078
m8.31 0.09574271 0.19148542 0.1154701 0.1000000 0.10000000
m9.11 0.08819171 0.17638342 0.2133594 0.1000000 0.08350000
m9.21 0.11665212 0.17716910 0.1914854 0.1067969 0.10806904
m9.31 0.05000000 0.10000000 0.1000000 0.1154701 0.09002407
m10.11 0.05000000 0.10000000 0.1000000 0.0000000 0.00000000
m10.21 0.15478480 0.20615528 0.1258306 0.1632993 0.16520190
m10.31 0.05000000 0.10000000 0.1000000 0.0000000 0.00000000
Neg Pred ValueSD PrecisionSD RecallSD F1SD PrevalenceSD
m1.11 0.08350000 0.08350000 0.1000000 0.05888124 0.00000000
m1.21 0.14981405 0.13731563 0.2212591 0.17129604 0.03674235
m1.31 0.14300000 0.08350000 0.2000000 0.11791346 0.00000000
m2.11 0.16440702 0.18862374 0.1632993 0.15101959 0.00000000
m2.21 0.10873324 0.17533848 0.1885619 0.12085391 0.00000000
m2.31 0.15542630 0.21450000 0.1631633 0.16316326 0.03674235
m3.11 0.08350000 0.00000000 0.0835000 0.04550000 0.03674235
m3.21 0.16331437 0.08350000 0.2217356 0.10120277 0.03674235
m3.31 0.10679693 0.08350000 0.1067969 0.08344459 0.03674235
m4.11 0.09641749 0.10000000 0.1154701 0.09685169 0.03674235
m4.21 0.19419063 0.13149778 0.2849367 0.21143695 0.03674235
m4.31 0.09002407 0.08350000 0.1067969 0.04973513 0.00000000
m5.11 0.08350000 0.08350000 0.0835000 0.05253887 0.03674235
m5.21 0.06271098 0.16520190 0.1667001 0.11160794 0.03674235
m5.31 0.14300000 0.08350000 0.1665000 0.09506270 0.03674235
m6.11 0.15945532 0.12500000 0.1893117 0.15729590 0.03674235
m6.21 0.11983704 0.15779100 0.1632993 0.09814742 0.03674235
m6.31 0.08350000 0.00000000 0.1000000 0.05550000 0.03674235
m7.11 0.12505565 0.07150000 0.1914854 0.10454505 0.03674235
m7.21 0.09409702 0.14218884 0.2060071 0.14411656 0.03674235
m7.31 0.08350000 0.08350000 0.1000000 0.05888124 0.00000000
m8.11 0.10679693 0.10000000 0.1067969 0.09506270 0.03674235
m8.21 0.17126661 0.23629078 0.2516391 0.13348034 0.03674235
m8.31 0.10171324 0.10000000 0.1154701 0.09685169 0.03674235
m9.11 0.16512218 0.08350000 0.2133594 0.11196837 0.03674235
m9.21 0.14719007 0.10806904 0.1914854 0.10711053 0.00000000
m9.31 0.08350000 0.09002407 0.1000000 0.04854809 0.03674235
m10.11 0.08350000 0.00000000 0.1000000 0.05550000 0.03674235
m10.21 0.11192371 0.16520190 0.1258306 0.10307764 0.03674235
m10.31 0.08350000 0.00000000 0.1000000 0.05550000 0.00000000
Detection RateSD Detection PrevalenceSD Balanced AccuracySD AccuracySD
m1.11 0.05000000 0.08164966 0.05773503 0.05773503
m1.21 0.09572704 0.11233581 0.11960212 0.13077557
m1.31 0.10000000 0.12583057 0.09574271 0.09574271
m2.11 0.08164966 0.08844348 0.15304329 0.15304329
m2.21 0.09428105 0.16844064 0.10322588 0.10322588
m2.31 0.07851964 0.11104354 0.16689992 0.17425173
m3.11 0.02598076 0.02598076 0.04150000 0.04550000
m3.21 0.08298193 0.12763620 0.07086783 0.07502444
m3.31 0.04092676 0.06204837 0.08844348 0.08802083
m4.11 0.08446054 0.06204837 0.08844348 0.08802083
m4.21 0.13252798 0.14089801 0.15045930 0.15988642
m4.31 0.05328149 0.09159103 0.04784262 0.04784262
m5.11 0.02598076 0.06837397 0.05328149 0.05525924
m5.21 0.09572704 0.13277927 0.09181322 0.08581181
m5.31 0.06411123 0.07842353 0.07990202 0.08712587
m6.11 0.08760708 0.04092676 0.14167657 0.14149293
m6.21 0.10376375 0.14942194 0.08178580 0.08676789
m6.31 0.06204837 0.06204837 0.05000000 0.05000000
m7.11 0.12065516 0.15373652 0.08164966 0.07443733
m7.21 0.12510895 0.15391204 0.08872570 0.08971575
m7.31 0.05000000 0.08164966 0.05773503 0.05773503
m8.11 0.04092676 0.02598076 0.09463394 0.09506270
m8.21 0.10974326 0.29943391 0.13305888 0.14984214
m8.31 0.08446054 0.07842353 0.09574271 0.09506270
m9.11 0.08987167 0.13010348 0.08825484 0.09136557
m9.21 0.09574271 0.12263326 0.08872570 0.08872570
m9.31 0.06204837 0.11321182 0.05000000 0.04868521
m10.11 0.06204837 0.06204837 0.05000000 0.05000000
m10.21 0.06035727 0.11117966 0.10307764 0.11149140
m10.31 0.05000000 0.05000000 0.05000000 0.05000000
KappaSD AccuracyLowerSD AccuracyUpperSD AccuracyNullSD
m1.11 0.11547005 0.09317859 0.001732051 0.00000000
m1.21 0.24249948 0.11276524 0.071339564 0.02598076
m1.31 0.19148542 0.13276671 0.012083046 0.00000000
m2.11 0.30589582 0.15903537 0.059668529 0.00000000
m2.21 0.20600708 0.08126654 0.059868606 0.00000000
m2.31 0.33988662 0.18560442 0.051813769 0.02598076
m3.11 0.09000000 0.05736724 0.001000000 0.02598076
m3.21 0.14314561 0.08442896 0.023930455 0.02598076
m3.31 0.17731023 0.11128642 0.011090537 0.02598076
m4.11 0.17731023 0.11128642 0.011090537 0.02598076
m4.21 0.30728746 0.17313867 0.048300276 0.02598076
m4.31 0.09577186 0.06497884 0.001414214 0.00000000
m5.11 0.11000000 0.07817235 0.001500000 0.02598076
m5.21 0.17642562 0.08113158 0.040743098 0.02598076
m5.31 0.17016536 0.10052985 0.011206397 0.02598076
m6.11 0.28301943 0.16785013 0.033180064 0.02598076
m6.21 0.17033765 0.09253108 0.027160020 0.02598076
m6.31 0.10000000 0.07693450 0.001500000 0.02598076
m7.11 0.15484481 0.08721238 0.010739336 0.02598076
m7.21 0.17701318 0.09145308 0.040849317 0.02598076
m7.31 0.11547005 0.09317859 0.001732051 0.00000000
m8.11 0.19000000 0.12360286 0.012203142 0.02598076
m8.21 0.27163379 0.13352247 0.072724938 0.02598076
m8.31 0.19039170 0.12360286 0.012203142 0.02598076
m9.11 0.18075651 0.11986937 0.013073510 0.02598076
m9.21 0.17702801 0.11113168 0.027097048 0.00000000
m9.31 0.09788939 0.07617524 0.001414214 0.02598076
m10.11 0.10000000 0.07693450 0.001500000 0.02598076
m10.21 0.21436787 0.10561566 0.053099749 0.02598076
m10.31 0.10000000 0.07833475 0.001500000 0.00000000
AccuracyPValueSD McnemarPValueSD PositiveSD NegativeSD True PositiveSD
m1.11 0.006075909 0.00000000 0.5 0.5 0.8164966
m1.21 0.282014184 0.33465940 0.5 0.5 0.9574271
m1.31 0.025979158 0.36769553 0.5 0.5 1.2583057
m2.11 0.184371907 0.00000000 0.5 0.5 1.2583057
m2.21 0.156538334 0.37600000 0.5 0.5 1.0000000
m2.31 0.184184690 0.09687363 0.5 0.5 0.5773503
m3.11 0.006000000 NA 0.5 0.5 0.0000000
m3.21 0.073708887 0.31806079 0.5 0.5 1.0000000
m3.31 0.028722813 0.00000000 0.5 0.5 0.5000000
m4.11 0.028722813 0.00000000 0.5 0.5 0.9574271
m4.21 0.169056943 0.49998533 0.5 0.5 1.2909944
m4.31 0.005259911 0.00000000 0.5 0.5 0.5000000
m5.11 0.006403124 0.00000000 0.5 0.5 0.0000000
m5.21 0.154189007 0.37921322 0.5 0.5 0.9574271
m5.31 0.028565714 0.36769553 0.5 0.5 0.5000000
m6.11 0.083691397 0.00000000 0.5 0.5 1.0000000
m6.21 0.080917654 0.26000000 0.5 0.5 1.2583057
m6.31 0.005000000 NA 0.5 0.5 0.8164966
m7.11 0.026851443 0.30022214 0.5 0.5 1.2909944
m7.21 0.131289439 0.36061833 0.5 0.5 1.4142136
m7.31 0.006075909 0.00000000 0.5 0.5 0.8164966
m8.11 0.025632011 0.00000000 0.5 0.5 0.5000000
m8.21 0.278717898 0.48957771 0.5 0.5 1.2583057
m8.31 0.025632011 0.00000000 0.5 0.5 0.9574271
m9.11 0.030397368 0.30022214 0.5 0.5 0.9574271
m9.21 0.071500000 0.26000000 0.5 0.5 1.4142136
m9.31 0.005416026 0.00000000 0.5 0.5 0.8164966
m10.11 0.005000000 NA 0.5 0.5 0.8164966
m10.21 0.186528818 0.26660630 0.5 0.5 0.5000000
m10.31 0.005188127 NA 0.5 0.5 0.8164966
False PositiveSD True NegativeSD False NegativeSD
m1.11 0.5000000 0.8164966 0.5000000
m1.21 1.4142136 0.9574271 0.8164966
m1.31 1.0000000 0.8164966 0.5000000
m2.11 0.8164966 1.0000000 0.9574271
m2.21 0.9574271 0.8164966 1.2583057
m2.31 0.9574271 1.0000000 1.5000000
m3.11 0.5000000 0.5000000 0.0000000
m3.21 1.2583057 0.0000000 0.5000000
m3.31 0.5773503 0.8164966 0.5000000
m4.11 0.5773503 0.0000000 0.5000000
m4.21 1.7078251 0.5000000 0.5773503
m4.31 0.5773503 0.8164966 0.5000000
m5.11 0.5000000 0.8164966 0.5000000
m5.21 0.8164966 0.9574271 0.8164966
m5.31 1.0000000 0.0000000 0.5000000
m6.11 0.9574271 0.8164966 0.5000000
m6.21 0.8164966 0.5773503 0.9574271
m6.31 0.5000000 0.5000000 0.0000000
m7.11 0.9574271 0.8164966 0.5000000
m7.21 0.9574271 1.0000000 0.5000000
m7.31 0.5000000 0.8164966 0.5000000
m8.11 0.5773503 0.8164966 0.5000000
m8.21 1.2909944 1.8929694 2.3804761
m8.31 0.5773503 0.8164966 0.5000000
m9.11 1.1547005 0.8164966 0.5000000
m9.21 0.9574271 0.5773503 0.5773503
m9.31 0.5000000 0.9574271 0.5773503
m10.11 0.5000000 0.5000000 0.0000000
m10.21 0.8164966 1.2583057 0.8164966
m10.31 0.5000000 0.5000000 0.0000000
$`Araucaria angustifolia`
algo ROC TSS Sensitivity Specificity Pos Pred Value
m1.1 kknn 0.9729167 0.9458333 0.98750 0.95825 0.98750
m2.1 kknn 0.9520833 0.8922619 0.97550 0.91650 0.97550
m3.1 kknn 0.9464286 0.8928571 0.97625 0.91650 0.97675
m4.1 kknn 0.9464286 0.8678571 0.95125 0.91650 0.97625
m5.1 kknn 0.9464286 0.7797619 0.98800 0.79150 0.94800
m6.1 kknn 0.9732143 0.9464286 0.98800 0.95825 0.98800
m7.1 kknn 0.9464286 0.8928571 0.97625 0.91650 0.97675
m8.1 kknn 0.9692460 0.8922619 0.97550 0.91650 0.97675
m9.1 kknn 0.9464286 0.8511905 0.97625 0.87500 0.96625
m10.1 kknn 0.9629960 0.8089286 0.97550 0.83325 0.95625
Neg Pred Value Precision Recall F1 Prevalence Detection Rate
m1.1 0.95825 0.98750 0.98750 0.98750 0.76650 0.75700
m2.1 0.92250 0.97550 0.97550 0.97550 0.77350 0.75475
m3.1 0.93750 0.97675 0.97625 0.97575 0.77350 0.75500
m4.1 0.86600 0.97625 0.95125 0.96275 0.77350 0.73575
m5.1 0.95825 0.94800 0.98800 0.96625 0.76650 0.75725
m6.1 0.96425 0.98800 0.98800 0.98800 0.77350 0.76425
m7.1 0.93750 0.97675 0.97625 0.97575 0.77350 0.75500
m8.1 0.91650 0.97675 0.97550 0.97575 0.78125 0.76200
m9.1 0.94450 0.96625 0.97625 0.97025 0.76650 0.74875
m10.1 0.91650 0.95625 0.97550 0.96475 0.78100 0.76175
Detection Prevalence Balanced Accuracy Accuracy Kappa AccuracyLower
m1.1 0.76650 0.97300 0.98075 0.94575 0.84150
m2.1 0.77400 0.94625 0.96200 0.89175 0.80875
m3.1 0.77400 0.94650 0.96275 0.89500 0.80975
m4.1 0.75425 0.93400 0.94375 0.84725 0.78375
m5.1 0.80350 0.89000 0.94450 0.80575 0.79000
m6.1 0.77400 0.97325 0.98125 0.94600 0.83850
m7.1 0.77400 0.94650 0.96275 0.89500 0.80975
m8.1 0.78050 0.94625 0.96225 0.88900 0.80775
m9.1 0.78650 0.92550 0.95425 0.86700 0.79850
m10.1 0.79900 0.90450 0.94350 0.81925 0.78225
AccuracyUpper AccuracyNull AccuracyPValue McnemarPValue Positive Negative
m1.1 0.99775 0.76650 0.01075 1.0000000 20.5 6.25
m2.1 0.99725 0.77350 0.01550 1.0000000 20.5 6.00
m3.1 0.99725 0.77350 0.01575 0.8266667 20.5 6.00
m4.1 0.99150 0.77350 0.04275 0.8266667 20.5 6.00
m5.1 0.98725 0.76650 0.07350 0.5670000 20.5 6.25
m6.1 0.99950 0.77350 0.00550 1.0000000 20.5 6.00
m7.1 0.99725 0.77350 0.01575 0.8266667 20.5 6.00
m8.1 0.99725 0.78125 0.02000 1.0000000 20.5 5.75
m9.1 0.99525 0.76650 0.02525 0.6533333 20.5 6.25
m10.1 0.99150 0.78100 0.04725 0.7493333 20.5 5.75
True Positive False Positive True Negative False Negative ROCSD
m1.1 20.25 0.25 6.00 0.25 0.05416667
m2.1 20.00 0.50 5.50 0.50 0.05626286
m3.1 20.00 0.50 5.50 0.50 0.03948363
m4.1 19.50 1.00 5.50 0.50 0.06484180
m5.1 20.25 0.25 5.00 1.25 0.06219745
m6.1 20.25 0.25 5.75 0.25 0.03933378
m7.1 20.00 0.50 5.50 0.50 0.03948363
m8.1 20.00 0.50 5.25 0.50 0.05147271
m9.1 20.00 0.50 5.50 1.00 0.07866757
m10.1 20.00 0.50 4.75 1.00 0.04857222
TSSSD SensitivitySD SpecificitySD Pos Pred ValueSD Neg Pred ValueSD
m1.1 0.10833333 0.02500000 0.08350000 0.02500000 0.08350000
m2.1 0.10092870 0.02830194 0.09641749 0.02830194 0.09002407
m3.1 0.07896726 0.04750000 0.09641749 0.02687471 0.12500000
m4.1 0.11034030 0.05632865 0.09641749 0.02750000 0.15542630
m5.1 0.31429473 0.02400000 0.31564167 0.07547185 0.08350000
m6.1 0.07866757 0.02400000 0.08350000 0.02400000 0.07150000
m7.1 0.07896726 0.04750000 0.09641749 0.02687471 0.12500000
m8.1 0.09960950 0.02830194 0.09641749 0.02687471 0.09641749
m9.1 0.16947743 0.04750000 0.19225764 0.05141012 0.11100000
m10.1 0.22596375 0.02830194 0.23570232 0.05907270 0.09641749
PrecisionSD RecallSD F1SD PrevalenceSD Detection RateSD
m1.1 0.02500000 0.02500000 0.02500000 0.011789826 0.02089657
m2.1 0.02830194 0.02830194 0.02042058 0.005196152 0.02233644
m3.1 0.02687471 0.04750000 0.02043486 0.005196152 0.03426368
m4.1 0.02750000 0.05632865 0.03223223 0.005196152 0.04401799
m5.1 0.07547185 0.02400000 0.04209810 0.017521415 0.01912023
m6.1 0.02400000 0.02400000 0.01385641 0.005196152 0.01607016
m7.1 0.02687471 0.04750000 0.02043486 0.005196152 0.03426368
m8.1 0.02687471 0.02830194 0.01963628 0.013200379 0.01581139
m9.1 0.05141012 0.04750000 0.02392175 0.011789826 0.04669315
m10.1 0.05907270 0.02830194 0.02940947 0.018493242 0.02093442
Detection PrevalenceSD Balanced AccuracySD AccuracySD KappaSD
m1.1 0.01178983 0.05400000 0.03850000 0.10850000
m2.1 0.02760435 0.05024191 0.03144307 0.08877453
m3.1 0.05086584 0.03929801 0.03021451 0.07874431
m4.1 0.05150647 0.05515433 0.04817589 0.12794628
m5.1 0.08317251 0.15699045 0.07084961 0.26572715
m6.1 0.02760435 0.03916950 0.02165448 0.06261523
m7.1 0.05086584 0.03929801 0.03021451 0.07874431
m8.1 0.02414539 0.04968819 0.03024759 0.08749095
m9.1 0.08102057 0.08485478 0.03708099 0.11516510
m10.1 0.06000000 0.11292918 0.04804512 0.16584405
AccuracyLowerSD AccuracyUpperSD AccuracyNullSD AccuracyPValueSD
m1.1 0.06177648 0.0045000000 0.011789826 0.020172176
m2.1 0.05031484 0.0041932485 0.005196152 0.017521415
m3.1 0.04549267 0.0041932485 0.005196152 0.018006943
m4.1 0.07007793 0.0110905365 0.005196152 0.053643111
m5.1 0.10004332 0.0199562020 0.017521415 0.119940263
m6.1 0.03649201 0.0005773503 0.005196152 0.005196152
m7.1 0.04549267 0.0041932485 0.005196152 0.018006943
m8.1 0.04600272 0.0041932485 0.013200379 0.018202564
m9.1 0.05588306 0.0049244289 0.011789826 0.019872510
m10.1 0.06938960 0.0110905365 0.018493242 0.050651588
McnemarPValueSD PositiveSD NegativeSD True PositiveSD False PositiveSD
m1.1 NA 0.5773503 0.5 0.9574271 0.5000000
m2.1 0.0000000 0.5773503 0.0 0.8164966 0.5773503
m3.1 0.3002221 0.5773503 0.0 0.8164966 1.0000000
m4.1 0.3002221 0.5773503 0.0 1.2909944 1.1547005
m5.1 0.6123545 0.5773503 0.5 0.5000000 0.5000000
m6.1 0.0000000 0.5773503 0.0 0.5000000 0.5000000
m7.1 0.3002221 0.5773503 0.0 0.8164966 1.0000000
m8.1 0.0000000 0.5773503 0.5 0.8164966 0.5773503
m9.1 0.3002221 0.5773503 0.5 0.8164966 1.0000000
m10.1 0.4341674 0.5773503 0.5 0.8164966 0.5773503
True NegativeSD False NegativeSD
m1.1 0.8164966 0.5000000
m2.1 0.5773503 0.5773503
m3.1 0.5773503 0.5773503
m4.1 0.5773503 0.5773503
m5.1 2.1602469 1.8929694
m6.1 0.5000000 0.5000000
m7.1 0.5773503 0.5773503
m8.1 0.5000000 0.5773503
m9.1 1.5000000 1.1547005
m10.1 1.2583057 1.4142136
train_sdm - independent data
Code
i1
Output
caretSDM
...............................
Class : input_sdm
-------- Occurrences --------
Species Names : Salminus brasiliensis Araucaria angustifolia
Number of presences : 76 21
Pseudoabsence methods :
Method to obtain PAs : bioclim
Number of PA sets : 10
Number of PAs in each set : 76 21
Independent Test : TRUE (number of records = 26 )
Data Cleaning : NAs, Capitals, Centroids, Geographically Duplicated, Identical Lat/Long, Institutions, Invalid, Non-terrestrial, Duplicated Cell (grid), Methods also applied in independent_test
-------- Predictors ---------
Number of Predictors : 3
Predictors Names : bio1, bio4, bio12
--------- Scenarios ---------
Number of Scenarios : 1
Scenarios Names : current
----------- Models ----------
Algorithms Names : kknn
Variables Names : bio1 bio4 bio12
Model Validation :
Method : repeatedcv
Number : 4
Metrics :
$`Salminus brasiliensis`
algo ROC TSS Sensitivity Specificity
1 kknn 0.90775 0.7841667 0.848325 0.940825
$`Araucaria angustifolia`
algo ROC TSS Sensitivity Specificity
1 kknn 0.9652412 0.8711842 0.97355 0.897375
Independent Validation :
ROC (mean +- sd) : NA +- NA
Code
i1$models
Output
caretSDM
.........................
Class : Models
Algorithms Names : kknn
Variables Names : bio1 bio4 bio12
Model Validation :
Method : repeatedcv
Number : 4
Metrics :
$`Salminus brasiliensis`
algo ROC TSS Sensitivity Specificity Pos Pred Value
m1.1 kknn 0.9000000 0.8000000 0.90000 0.90000 0.92250
m2.1 kknn 0.9250000 0.8500000 0.85000 1.00000 1.00000
m3.1 kknn 0.8791667 0.7583333 0.80825 0.95000 0.95825
m4.1 kknn 0.9458333 0.8166667 0.86675 0.95000 0.95825
m5.1 kknn 0.9250000 0.8500000 0.85000 1.00000 1.00000
m6.1 kknn 0.9583333 0.8583333 0.90825 0.95000 0.95825
m7.1 kknn 0.9233333 0.7666667 0.81675 0.95000 0.95000
m8.1 kknn 0.8791667 0.7583333 0.80825 0.95000 0.95825
m9.1 kknn 0.8125000 0.5250000 0.81675 0.75825 0.82500
m10.1 kknn 0.9291667 0.8583333 0.85825 1.00000 1.00000
Neg Pred Value Precision Recall F1 Prevalence Detection Rate
m1.1 0.92850 0.92250 0.90000 0.89550 0.5 0.45000
m2.1 0.89275 1.00000 0.85000 0.90975 0.5 0.42725
m3.1 0.87050 0.95825 0.80825 0.84725 0.5 0.40425
m4.1 0.88675 0.95825 0.86675 0.89950 0.5 0.42975
m5.1 0.89575 1.00000 0.85000 0.90975 0.5 0.42950
m6.1 0.90825 0.95825 0.90825 0.93050 0.5 0.45250
m7.1 0.83675 0.95000 0.81675 0.87225 0.5 0.40475
m8.1 0.86450 0.95825 0.80825 0.84725 0.5 0.40250
m9.1 0.74975 0.82500 0.81675 0.79225 0.5 0.40700
m10.1 0.87475 1.00000 0.85825 0.92175 0.5 0.42750
Detection Prevalence Balanced Accuracy Accuracy Kappa AccuracyLower
m1.1 0.52025 0.90000 0.90225 0.80350 0.57525
m2.1 0.42725 0.92500 0.92725 0.85350 0.60950
m3.1 0.42925 0.87925 0.87925 0.75825 0.55250
m4.1 0.45475 0.90825 0.90450 0.81125 0.57675
m5.1 0.42950 0.92500 0.92950 0.85525 0.61100
m6.1 0.47500 0.92925 0.92950 0.85825 0.61100
m7.1 0.42975 0.88325 0.87950 0.76125 0.54900
m8.1 0.42750 0.87925 0.87725 0.75500 0.55125
m9.1 0.54550 0.76250 0.76825 0.52375 0.43975
m10.1 0.42750 0.92925 0.92725 0.85500 0.60300
AccuracyUpper AccuracyNull AccuracyPValue McnemarPValue Positive Negative
m1.1 0.99250 0.5225 0.02300 0.8700000 5.25 5.25
m2.1 0.99325 0.5225 0.01750 0.7400000 5.25 5.25
m3.1 0.98200 0.5000 0.04675 0.7493333 5.25 5.25
m4.1 0.99275 0.5225 0.02400 0.8700000 5.25 5.25
m5.1 0.99350 0.5225 0.01850 0.7400000 5.25 5.25
m6.1 0.99350 0.5225 0.01850 1.0000000 5.25 5.25
m7.1 0.98725 0.5225 0.03200 0.8266667 5.25 5.25
m8.1 0.98200 0.5225 0.04925 0.7493333 5.25 5.25
m9.1 0.93425 0.5225 0.22525 0.9207500 5.25 5.25
m10.1 0.99800 0.5225 0.00900 1.0000000 5.25 5.25
True Positive False Positive True Negative False Negative ROCSD
m1.1 4.75 0.50 4.75 0.75 0.08164966
m2.1 4.50 0.75 5.25 0.00 0.09574271
m3.1 4.25 1.00 5.00 0.25 0.12720281
m4.1 4.50 1.00 5.00 0.25 0.05335937
m5.1 4.50 0.75 5.25 0.00 0.09574271
m6.1 4.75 0.50 5.00 0.25 0.08858455
m7.1 4.25 1.00 5.00 0.25 0.07272475
m8.1 4.25 1.00 5.00 0.25 0.12720281
m9.1 4.25 1.25 4.00 1.50 0.26714367
m10.1 4.50 0.75 5.25 0.00 0.04787136
TSSSD SensitivitySD SpecificitySD Pos Pred ValueSD Neg Pred ValueSD
m1.1 0.16329932 0.20000000 0.1154701 0.09002407 0.1430000
m2.1 0.19148542 0.19148542 0.0000000 0.00000000 0.1369121
m3.1 0.25440563 0.28332358 0.1000000 0.08350000 0.1770056
m4.1 0.13743685 0.16316326 0.1000000 0.08350000 0.1395024
m5.1 0.19148542 0.19148542 0.0000000 0.00000000 0.1250557
m6.1 0.17716910 0.10679693 0.1000000 0.08350000 0.1067969
m7.1 0.17638342 0.13731563 0.1000000 0.10000000 0.1198370
m8.1 0.25440563 0.28332358 0.1000000 0.08350000 0.1780197
m9.1 0.44586578 0.13731563 0.3804019 0.23629078 0.2887714
m10.1 0.09574271 0.09577186 0.0000000 0.00000000 0.0835000
PrecisionSD RecallSD F1SD PrevalenceSD Detection RateSD
m1.1 0.09002407 0.20000000 0.10492696 0.03674235 0.10653638
m2.1 0.00000000 0.19148542 0.11866023 0.03674235 0.11452911
m3.1 0.08350000 0.28332358 0.18909676 0.00000000 0.14167657
m4.1 0.08350000 0.16316326 0.08205892 0.03674235 0.06428063
m5.1 0.00000000 0.19148542 0.11866023 0.03674235 0.12065516
m6.1 0.08350000 0.10679693 0.08344459 0.03674235 0.04092676
m7.1 0.10000000 0.13731563 0.09493989 0.03674235 0.03755330
m8.1 0.08350000 0.28332358 0.18909676 0.03674235 0.13665650
m9.1 0.23629078 0.13731563 0.15492229 0.03674235 0.06428063
m10.1 0.00000000 0.09577186 0.05301179 0.03674235 0.03175426
Detection PrevalenceSD Balanced AccuracySD AccuracySD KappaSD
m1.1 0.15329791 0.08164966 0.08177357 0.16344928
m2.1 0.11452911 0.09574271 0.09506270 0.19039170
m3.1 0.17016340 0.12723567 0.12723567 0.25437292
m4.1 0.11327842 0.06883979 0.07448266 0.14550916
m5.1 0.12065516 0.09574271 0.08802083 0.18241962
m6.1 0.06204837 0.08844348 0.08802083 0.17731023
m7.1 0.05994650 0.08825484 0.09136557 0.18075651
m8.1 0.16635805 0.12723567 0.12650527 0.25317978
m9.1 0.15649601 0.22312403 0.21238075 0.44813568
m10.1 0.03175426 0.04784262 0.04868521 0.09712535
AccuracyLowerSD AccuracyUpperSD AccuracyNullSD AccuracyPValueSD
m1.1 0.11151196 0.011733144 0.02598076 0.023916521
m2.1 0.12360286 0.012203142 0.02598076 0.025632011
m3.1 0.14741438 0.032690468 0.00000000 0.083611702
m4.1 0.09838149 0.010594810 0.02598076 0.027080128
m5.1 0.11128642 0.011090537 0.02598076 0.028722813
m6.1 0.11128642 0.011090537 0.02598076 0.028722813
m7.1 0.11986937 0.013073510 0.02598076 0.030397368
m8.1 0.15211481 0.032690468 0.02598076 0.082001524
m9.1 0.18506103 0.112562205 0.02598076 0.395868981
m10.1 0.07617524 0.001414214 0.02598076 0.005416026
McnemarPValueSD PositiveSD NegativeSD True PositiveSD False PositiveSD
m1.1 0.3002221 0.5 0.5 1.2583057 1.0000000
m2.1 0.3676955 0.5 0.5 1.2909944 0.9574271
m3.1 0.4341674 0.5 0.5 1.5000000 1.4142136
m4.1 0.3002221 0.5 0.5 0.5773503 0.9574271
m5.1 0.3676955 0.5 0.5 1.2909944 0.9574271
m6.1 0.0000000 0.5 0.5 0.5000000 0.5773503
m7.1 0.3002221 0.5 0.5 0.5000000 0.8164966
m8.1 0.4341674 0.5 0.5 1.5000000 1.4142136
m9.1 0.1585000 0.5 0.5 0.5000000 0.8164966
m10.1 0.0000000 0.5 0.5 0.5773503 0.5000000
True NegativeSD False NegativeSD
m1.1 0.9574271 0.5773503
m2.1 0.5000000 0.0000000
m3.1 0.8164966 0.5000000
m4.1 0.8164966 0.5000000
m5.1 0.5000000 0.0000000
m6.1 0.8164966 0.5000000
m7.1 0.8164966 0.5000000
m8.1 0.8164966 0.5000000
m9.1 2.0000000 1.8929694
m10.1 0.5000000 0.0000000
$`Araucaria angustifolia`
algo ROC TSS Sensitivity Specificity Pos Pred Value
m1.1 kknn 0.9682018 0.8486842 0.97375 0.87500 0.96375
m2.1 kknn 0.9747807 0.8903509 0.97350 0.91650 0.97425
m3.1 kknn 0.9714912 0.7653509 0.97350 0.79150 0.94325
m4.1 kknn 0.9791667 0.9451754 0.98675 0.95825 0.98750
m5.1 kknn 0.9802632 0.8903509 0.97350 0.91650 0.97500
m6.1 kknn 0.9451754 0.8903509 0.97350 0.91650 0.97425
m7.1 kknn 0.9353070 0.8640351 0.94725 0.91650 0.97275
m8.1 kknn 0.9475877 0.8951754 0.98675 0.90825 0.97425
m9.1 kknn 0.9660088 0.8903509 0.97350 0.91650 0.97425
m10.1 kknn 0.9844298 0.8320175 0.97350 0.85825 0.96175
Neg Pred Value Precision Recall F1 Prevalence Detection Rate
m1.1 0.93750 0.96375 0.97375 0.96700 0.76000 0.74000
m2.1 0.92250 0.97425 0.97350 0.97350 0.76000 0.74000
m3.1 0.92250 0.94325 0.97350 0.95625 0.75275 0.73275
m4.1 0.95825 0.98750 0.98675 0.98675 0.76800 0.75750
m5.1 0.92850 0.97500 0.97350 0.97350 0.76000 0.74000
m6.1 0.92700 0.97425 0.97350 0.97350 0.75275 0.73300
m7.1 0.85100 0.97275 0.94725 0.95975 0.76000 0.72000
m8.1 0.95000 0.97425 0.98675 0.98025 0.76800 0.75750
m9.1 0.91650 0.97425 0.97350 0.97350 0.76800 0.74750
m10.1 0.91425 0.96175 0.97350 0.96700 0.77600 0.75550
Detection Prevalence Balanced Accuracy Accuracy Kappa AccuracyLower
m1.1 0.77000 0.92425 0.95000 0.85975 0.78475
m2.1 0.76000 0.94525 0.96000 0.89025 0.79875
m3.1 0.78275 0.88275 0.93000 0.77725 0.76075
m4.1 0.76750 0.97275 0.97950 0.94150 0.82775
m5.1 0.76000 0.94550 0.96000 0.89000 0.79600
m6.1 0.75300 0.94525 0.96050 0.89275 0.80075
m7.1 0.74000 0.93200 0.94000 0.84150 0.77175
m8.1 0.77800 0.94775 0.96925 0.90775 0.81300
m9.1 0.76750 0.94525 0.95950 0.88675 0.79700
m10.1 0.78625 0.91625 0.94875 0.84775 0.77775
AccuracyUpper AccuracyNull AccuracyPValue McnemarPValue Positive Negative
m1.1 0.99475 0.76000 0.0230 0.6533333 19 6.00
m2.1 0.99700 0.76000 0.0150 1.0000000 19 6.00
m3.1 0.98600 0.75275 0.0745 0.7113333 19 6.25
m4.1 0.99950 0.76800 0.0270 1.0000000 19 5.75
m5.1 0.99900 0.76000 0.0090 1.0000000 19 6.00
m6.1 0.99700 0.75275 0.0135 1.0000000 19 6.25
m7.1 0.99100 0.76000 0.0420 1.0000000 19 6.00
m8.1 0.99725 0.76800 0.0270 1.0000000 19 5.75
m9.1 0.99700 0.76800 0.0195 1.0000000 19 5.75
m10.1 0.99675 0.77600 0.0355 1.0000000 19 5.50
True Positive False Positive True Negative False Negative ROCSD
m1.1 18.50 0.50 5.25 0.75 0.03623402
m2.1 18.50 0.50 5.50 0.50 0.05517432
m3.1 18.50 0.50 5.00 1.25 0.05413307
m4.1 18.75 0.50 5.50 0.25 0.04166667
m5.1 18.50 0.50 5.50 0.50 0.03250254
m6.1 18.50 0.50 5.75 0.50 0.05045449
m7.1 18.00 1.00 5.50 0.50 0.06258010
m8.1 18.75 0.25 5.25 0.50 0.06301305
m9.1 18.50 0.50 5.25 0.50 0.05194175
m10.1 18.50 0.50 4.75 0.75 0.01569783
TSSSD SensitivitySD SpecificitySD Pos Pred ValueSD Neg Pred ValueSD
m1.1 0.13950310 0.05250000 0.15945532 0.04571196 0.12500000
m2.1 0.10090897 0.03059956 0.09641749 0.02975875 0.09002407
m3.1 0.30280104 0.03059956 0.31564167 0.08206248 0.09002407
m4.1 0.08240480 0.05250000 0.08350000 0.02500000 0.14300000
m5.1 0.06583819 0.03059956 0.09641749 0.02886751 0.08256109
m6.1 0.10090897 0.03059956 0.09641749 0.02975875 0.08601938
m7.1 0.13020728 0.04286704 0.09641749 0.03148942 0.11737405
m8.1 0.12602611 0.02650000 0.10679693 0.02975875 0.10000000
m9.1 0.10090897 0.03059956 0.09641749 0.02975875 0.09641749
m10.1 0.08465253 0.03059956 0.09577186 0.02553919 0.10171324
PrecisionSD RecallSD F1SD PrevalenceSD Detection RateSD
m1.1 0.04571196 0.05250000 0.0255342907 0.00000000 0.04000000
m2.1 0.02975875 0.03059956 0.0216410105 0.00000000 0.02309401
m3.1 0.08206248 0.03059956 0.0404423458 0.01450000 0.01889224
m4.1 0.02500000 0.05250000 0.0266520793 0.01600000 0.02600000
m5.1 0.02886751 0.03059956 0.0005773503 0.00000000 0.02309401
m6.1 0.02975875 0.03059956 0.0216410105 0.01450000 0.03320643
m7.1 0.03148942 0.04286704 0.0347311100 0.00000000 0.03265986
m8.1 0.02975875 0.02650000 0.0253294953 0.01600000 0.00500000
m9.1 0.02975875 0.03059956 0.0216410105 0.01600000 0.01892969
m10.1 0.02553919 0.03059956 0.0133416641 0.01847521 0.02968164
Detection PrevalenceSD Balanced AccuracySD AccuracySD KappaSD
m1.1 0.06831301 0.06984447 0.03829708 0.108149202
m2.1 0.03265986 0.05057915 0.03265986 0.089540959
m3.1 0.09304255 0.15129965 0.06831301 0.246935045
m4.1 0.03771825 0.04112582 0.03960955 0.105880436
m5.1 0.04618802 0.03290897 0.00000000 0.006928203
m6.1 0.04482559 0.05057915 0.03267517 0.089893919
m7.1 0.02309401 0.06525846 0.05163978 0.135411718
m8.1 0.02103965 0.06283510 0.03960955 0.120311748
m9.1 0.02217356 0.05057915 0.03267517 0.089514896
m10.1 0.04759814 0.04236646 0.02118765 0.068451321
AccuracyLowerSD AccuracyUpperSD AccuracyNullSD AccuracyPValueSD
m1.1 0.05846580 0.005500000 0.00000000 0.02103965
m2.1 0.05031484 0.004690416 0.00000000 0.01773885
m3.1 0.09660702 0.021150256 0.01450000 0.11699715
m4.1 0.06371290 0.004856267 0.01600000 0.04681880
m5.1 0.00000000 0.000000000 0.00000000 0.00000000
m6.1 0.05032809 0.004690416 0.01450000 0.01864582
m7.1 0.07513710 0.011575837 0.00000000 0.05290243
m8.1 0.06371290 0.004856267 0.01600000 0.04681880
m9.1 0.05056349 0.004690416 0.01600000 0.01799074
m10.1 0.03200391 0.004500000 0.01847521 0.04186884
McnemarPValueSD PositiveSD NegativeSD True PositiveSD False PositiveSD
m1.1 0.3002221 0 0.0000000 1.0000000 1.0000000
m2.1 0.0000000 0 0.0000000 0.5773503 0.5773503
m3.1 0.4999853 0 0.5000000 0.5773503 0.5773503
m4.1 0.3676955 0 0.5000000 1.0000000 1.0000000
m5.1 0.0000000 0 0.0000000 0.5773503 0.5773503
m6.1 0.0000000 0 0.5000000 0.5773503 0.5773503
m7.1 0.0000000 0 0.0000000 0.8164966 0.8164966
m8.1 0.0000000 0 0.5000000 0.5000000 0.5000000
m9.1 0.0000000 0 0.5000000 0.5773503 0.5773503
m10.1 0.0000000 0 0.5773503 0.5773503 0.5773503
True NegativeSD False NegativeSD
m1.1 0.9574271 0.9574271
m2.1 0.5773503 0.5773503
m3.1 2.1602469 1.8929694
m4.1 0.5773503 0.5000000
m5.1 0.5773503 0.5773503
m6.1 0.9574271 0.5773503
m7.1 0.5773503 0.5773503
m8.1 0.9574271 0.5773503
m9.1 0.5000000 0.5773503
m10.1 0.9574271 0.5000000
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train_sdm
Code
i2
Output
caretSDM
...............................
Class : input_sdm
-------- Occurrences --------
Species Names : Araucaria angustifolia
Number of presences : 420
Pseudoabsence methods :
Method to obtain PAs : random
Number of PA sets : 3
Number of PAs in each set : 420
-------- Predictors ---------
Number of Predictors : 2
Predictors Names : bio1, bio12
--------- Scenarios ---------
Number of Scenarios : 1
Scenarios Names : current
----------- Models ----------
Algorithms Names : kknn naive_bayes
Variables Names : bio1 bio12
Model Validation :
Method : cv
Number : 2
Metrics :
$`Araucaria angustifolia`
algo ROC Sens Spec ROCSD
1 kknn 0.6605192 0.9920635 0.1097222 0.08681466
2 naive_bayes 0.8402910 0.9706349 0.3631944 0.11121967
train_sdm - pca
Code
i2
Output
caretSDM
...............................
Class : input_sdm
-------- Occurrences --------
Species Names : Araucaria angustifolia
Number of presences : 420
Pseudoabsence methods :
Method to obtain PAs : random
Number of PA sets : 3
Number of PAs in each set : 420
-------- Predictors ---------
Number of Predictors : 4
Predictors Names : bio1, bio12, PC1, PC2
PCA-transformed variables : DONE
Cummulative proportion ( 0.99 ) : PC1
--------- Scenarios ---------
Number of Scenarios : 1
Scenarios Names : current
----------- Models ----------
Algorithms Names : kknn naive_bayes
Variables Names : PC1
Model Validation :
Method : cv
Number : 2
Metrics :
$`Araucaria angustifolia`
algo ROC Sens Spec ROCSD
1 kknn 0.5741815 0.9801587 0.08819444 0.0710965
2 naive_bayes 0.8392427 0.9857143 0.15208333 0.1099336
train_sdm - vif
Code
i2
Output
caretSDM
...............................
Class : input_sdm
-------- Occurrences --------
Species Names : Araucaria angustifolia
Number of presences : 420
Pseudoabsence methods :
Method to obtain PAs : random
Number of PA sets : 3
Number of PAs in each set : 420
-------- Predictors ---------
Number of Predictors : 2
Predictors Names : bio1, bio12
Area (VIF) : all
Threshold : 0.5
Selected Variables (VIF) : bio1, bio12
--------- Scenarios ---------
Number of Scenarios : 1
Scenarios Names : current
----------- Models ----------
Algorithms Names : naive_bayes kknn
Variables Names : bio1 bio12
Model Validation :
Method : cv
Number : 2
Metrics :
$`Araucaria angustifolia`
algo ROC Sens Spec ROCSD
1 kknn 0.6314964 0.9865079 0.1381944 0.06344787
2 naive_bayes 0.8625942 0.9666667 0.3645833 0.04378965
train_sdm - change ctrl
Code
i2
Output
caretSDM
...............................
Class : input_sdm
-------- Occurrences --------
Species Names : Araucaria angustifolia
Number of presences : 420
Pseudoabsence methods :
Method to obtain PAs : random
Number of PA sets : 3
Number of PAs in each set : 420
-------- Predictors ---------
Number of Predictors : 4
Predictors Names : bio1, bio12, PC1, PC2
PCA-transformed variables : DONE
Cummulative proportion ( 0.99 ) : PC1
--------- Scenarios ---------
Number of Scenarios : 1
Scenarios Names : current
----------- Models ----------
Algorithms Names : kknn naive_bayes
Variables Names : PC1
Model Validation :
Method : boot
Number : 10
Metrics :
$`Araucaria angustifolia`
algo ROC Sens Spec ROCSD
1 kknn 0.5646017 0.9898871 0.07271169 0.06067877
2 naive_bayes 0.8402030 0.9966436 0.14952596 0.10851423
train_sdm - selecting vars
Code
i2
Output
caretSDM
...............................
Class : input_sdm
-------- Occurrences --------
Species Names : Araucaria angustifolia
Number of presences : 420
Pseudoabsence methods :
Method to obtain PAs : random
Number of PA sets : 3
Number of PAs in each set : 420
-------- Predictors ---------
Number of Predictors : 2
Predictors Names : bio1, bio12
--------- Scenarios ---------
Number of Scenarios : 1
Scenarios Names : current
----------- Models ----------
Algorithms Names : naive_bayes kknn
Variables Names : bio1 bio12
Model Validation :
Method : cv
Number : 2
Metrics :
$`Araucaria angustifolia`
algo ROC Sens Spec ROCSD
1 kknn 0.5642758 0.9785714 0.05347222 0.08865492
2 naive_bayes 0.8495552 0.9690476 0.40902778 0.04172257
train_sdm - ESM
Code
i2
Output
caretSDM
...............................
Class : input_sdm
-------- Occurrences --------
Species Names : Araucaria angustifolia
Number of presences : 420
Pseudoabsence methods :
Method to obtain PAs : random
Number of PA sets : 3
Number of PAs in each set : 420
Ensemble of Small Models (ESM): TRUE
Number of Records : 20
-------- Predictors ---------
Number of Predictors : 3
Predictors Names : bio1, bio4, bio12
--------- Scenarios ---------
Number of Scenarios : 1
Scenarios Names : current
----------- Models ----------
Algorithms Names : naive_bayes kknn
Variables Names : bio1 bio4 bio12
Model Validation :
Method : cv
Number : 2
Metrics :
$`Araucaria angustifolia`
algo ROC Sens Spec ROCSD
1 kknn 0.5662136 0.9883598 0.05787037 0.06801226
2 naive_bayes 0.8406983 0.9748677 0.43356481 0.08100005
mahal.dist train
Code
i2
Output
caretSDM
...............................
Class : input_sdm
-------- Occurrences --------
Species Names : Araucaria angustifolia
Number of presences : 420
Pseudoabsence methods :
Method to obtain PAs : bioclim
Number of PA sets : 3
Number of PAs in each set : 420
-------- Predictors ---------
Number of Predictors : 7
Predictors Names : GID0, CODIGOIB1, NOMEUF2, SIGLAUF3, bio1, bio4, bio12
----------- Models ----------
Algorithms Names : mahal.custom
Variables Names : bio1 bio4 bio12
Model Validation :
Method : cv
Number : 3
Metrics :
$`Araucaria angustifolia`
algo ROC TSS Sensitivity Specificity
1 mahal.custom 0.9886484 0.8152958 0.8254444 1
train_sdm - one species ESM
Code
i1
Output
caretSDM
...............................
Class : input_sdm
-------- Occurrences --------
Species Names : Salminus brasiliensis Araucaria angustifolia
Number of presences : 82 21
Pseudoabsence methods :
Method to obtain PAs : bioclim
Number of PA sets : 10
Number of PAs in each set : 82 21
Data Cleaning : NAs, Capitals, Centroids, Geographically Duplicated, Identical Lat/Long, Institutions, Invalid, Non-terrestrial, Duplicated Cell (grid)
Ensemble of Small Models (ESM): TRUE
Number of Records : 30
-------- Predictors ---------
Number of Predictors : 3
Predictors Names : bio1, bio4, bio12
--------- Scenarios ---------
Number of Scenarios : 1
Scenarios Names : current
----------- Models ----------
Algorithms Names : mahal.custom
Variables Names : bio1 bio4 bio12
Model Validation :
Method : repeatedcv
Number : 4
Metrics :
$`Salminus brasiliensis`
algo ROC TSS Sensitivity Specificity
1 mahal.custom 0.6988333 0.3477778 0.8586 0.9541667
$`Araucaria angustifolia`
algo ROC TSS Sensitivity Specificity
1 mahal.custom 0.9612472 0.7656548 0.95015 1
Code
i2
Output
caretSDM
...............................
Class : input_sdm
-------- Occurrences --------
Species Names : Salminus brasiliensis Araucaria angustifolia
Number of presences : 82 21
Pseudoabsence methods :
Method to obtain PAs : bioclim
Number of PA sets : 10
Number of PAs in each set : 82 21
Data Cleaning : NAs, Capitals, Centroids, Geographically Duplicated, Identical Lat/Long, Institutions, Invalid, Non-terrestrial, Duplicated Cell (grid)
Ensemble of Small Models (ESM): TRUE
Number of Records : 30
-------- Predictors ---------
Number of Predictors : 3
Predictors Names : bio1, bio4, bio12
--------- Scenarios ---------
Number of Scenarios : 1
Scenarios Names : current
----------- Models ----------
Algorithms Names : kknn
Variables Names : bio1 bio4 bio12
Model Validation :
Method : repeatedcv
Number : 4
Metrics :
$`Salminus brasiliensis`
algo ROC TSS Sensitivity Specificity
1 kknn 0.9059954 0.7541667 0.8386083 0.9205417
$`Araucaria angustifolia`
algo ROC TSS Sensitivity Specificity
1 kknn 0.9562599 0.8770238 0.977 0.899875
Code
i1$models
Output
caretSDM
.........................
Class : Models
Algorithms Names : mahal.custom
Variables Names : bio1 bio4 bio12
Model Validation :
Method : repeatedcv
Number : 4
Metrics :
$`Salminus brasiliensis`
algo ROC TSS Sensitivity Specificity Pos Pred Value
m1.11 mahal.custom 0.6950000 0.4166667 0.85000 1.00000 1.0000000
m1.21 mahal.custom 0.7083333 0.3750000 0.85000 1.00000 1.0000000
m1.31 mahal.custom 0.7166667 0.2750000 0.90000 0.90000 0.8750000
m2.11 mahal.custom 0.6072222 0.3916667 0.85825 1.00000 1.0000000
m2.21 mahal.custom 0.7058333 0.3333333 0.90000 0.95000 0.9333333
m2.31 mahal.custom 0.6833333 0.2833333 0.90825 0.90000 0.8750000
m3.11 mahal.custom 0.7966667 0.3166667 0.90000 0.85000 0.8750000
m3.21 mahal.custom 0.7713889 0.4333333 0.86675 1.00000 1.0000000
m3.31 mahal.custom 0.7666667 0.3916667 0.86675 1.00000 1.0000000
m4.11 mahal.custom 0.6844444 0.2833333 0.90000 0.95000 0.8750000
m4.21 mahal.custom 0.7100000 0.3333333 0.90000 1.00000 1.0000000
m4.31 mahal.custom 0.7483333 0.3750000 0.90000 0.95000 0.9375000
m5.11 mahal.custom 0.7866667 0.4250000 0.90825 1.00000 1.0000000
m5.21 mahal.custom 0.7836111 0.4333333 0.90000 1.00000 1.0000000
m5.31 mahal.custom 0.7250000 0.2916667 0.75825 0.95000 0.9167500
m6.11 mahal.custom 0.6633333 0.2500000 0.90825 0.90000 0.7917500
m6.21 mahal.custom 0.6158333 0.1916667 0.75825 0.90000 0.8666667
m6.31 mahal.custom 0.7033333 0.4166667 0.80000 0.95000 0.8750000
m7.11 mahal.custom 0.6800000 0.3000000 0.73325 0.95825 0.9167500
m7.21 mahal.custom 0.7150000 0.4416667 0.86675 1.00000 1.0000000
m7.31 mahal.custom 0.6266667 0.3583333 0.90825 1.00000 1.0000000
m8.11 mahal.custom 0.6450000 0.3750000 0.90000 1.00000 1.0000000
m8.21 mahal.custom 0.6650000 0.2333333 0.80825 0.90000 0.9000000
m8.31 mahal.custom 0.5916667 0.4500000 0.81675 1.00000 1.0000000
m9.11 mahal.custom 0.7283333 0.2916667 0.90825 0.95000 0.9167500
m9.21 mahal.custom 0.6800000 0.3833333 0.76675 0.95000 0.9375000
m9.31 mahal.custom 0.6933333 0.4083333 0.81675 0.91675 0.8332500
m10.11 mahal.custom 0.6483333 0.3916667 0.85000 1.00000 1.0000000
m10.21 mahal.custom 0.7533333 0.2750000 0.90000 0.85000 0.7556667
m10.31 mahal.custom 0.6666667 0.3083333 0.85000 0.90000 0.7776667
Neg Pred Value Precision Recall F1 Prevalence Detection Rate
m1.11 0.7082500 1.0000000 0.85000 0.7070000 0.5 0.42950
m1.21 0.7917500 1.0000000 0.85000 0.6690000 0.5 0.42500
m1.31 0.6250000 0.8750000 0.90000 0.7030000 0.5 0.45000
m2.11 0.6480000 1.0000000 0.85825 0.6775000 0.5 0.42925
m2.21 0.8666667 0.9333333 0.90000 0.6827500 0.5 0.45000
m2.31 0.5915000 0.8750000 0.90825 0.6995000 0.5 0.45250
m3.11 0.8667500 0.8750000 0.90000 0.7270000 0.5 0.45225
m3.21 0.8335000 1.0000000 0.86675 0.7180000 0.5 0.43325
m3.31 0.8250000 1.0000000 0.86675 0.7245000 0.5 0.42975
m4.11 0.7000000 0.8750000 0.90000 0.7087500 0.5 0.45000
m4.21 0.6667500 1.0000000 0.90000 0.7082500 0.5 0.45225
m4.31 0.7917500 0.9375000 0.90000 0.7002500 0.5 0.45000
m5.11 0.8667500 1.0000000 0.90825 0.7355000 0.5 0.45250
m5.21 0.8750000 1.0000000 0.90000 0.7322500 0.5 0.45000
m5.31 0.6832500 0.9167500 0.75825 0.6617500 0.5 0.37750
m6.11 0.8335000 0.7917500 0.90825 0.7042500 0.5 0.45250
m6.21 0.7000000 0.8666667 0.75825 0.6480000 0.5 0.37925
m6.31 0.7500000 0.8750000 0.80000 0.6510000 0.5 0.40000
m7.11 0.7750000 0.9167500 0.73325 0.6180000 0.5 0.36375
m7.21 0.7750000 1.0000000 0.86675 0.6912500 0.5 0.42975
m7.31 0.8542500 1.0000000 0.90825 0.8295000 0.5 0.45475
m8.11 0.7917500 1.0000000 0.90000 0.6880000 0.5 0.45225
m8.21 0.7085000 0.9000000 0.80825 0.6745000 0.5 0.40700
m8.31 0.6657500 1.0000000 0.81675 0.7366667 0.5 0.40475
m9.11 0.8250000 0.9167500 0.90825 0.7030000 0.5 0.45425
m9.21 0.7260000 0.9375000 0.76675 0.6107500 0.5 0.38650
m9.31 0.7500000 0.8332500 0.81675 0.6495000 0.5 0.40925
m10.11 0.8125000 1.0000000 0.85000 0.6755000 0.5 0.42500
m10.21 0.8542500 0.7556667 0.90000 0.7120000 0.5 0.45225
m10.31 0.8750000 0.7776667 0.85000 0.6877500 0.5 0.43175
Detection Prevalence Balanced Accuracy Accuracy Kappa AccuracyLower
m1.11 0.76125 0.70825 0.71125 0.41650 0.37575
m1.21 0.75675 0.68750 0.69075 0.37250 0.35100
m1.31 0.78850 0.63750 0.64075 0.27250 0.30825
m2.11 0.78325 0.69575 0.69575 0.39175 0.36575
m2.21 0.80825 0.66675 0.66675 0.33325 0.33200
m2.31 0.80900 0.64175 0.64550 0.28900 0.31575
m3.11 0.74075 0.65825 0.66350 0.32000 0.32600
m3.21 0.70000 0.71675 0.71675 0.43325 0.37850
m3.31 0.69075 0.69575 0.69525 0.39200 0.36125
m4.11 0.78325 0.64175 0.64175 0.28325 0.30800
m4.21 0.78175 0.66675 0.66575 0.33325 0.32700
m4.31 0.78325 0.68750 0.68750 0.37500 0.34650
m5.11 0.73625 0.71250 0.70900 0.42250 0.37525
m5.21 0.72925 0.71675 0.71675 0.43325 0.37225
m5.31 0.64075 0.64575 0.64075 0.29375 0.31000
m6.11 0.78400 0.62500 0.62050 0.24875 0.29175
m6.21 0.66675 0.59575 0.59575 0.19175 0.28050
m6.31 0.70900 0.70825 0.70675 0.41475 0.37950
m7.11 0.62950 0.65000 0.64525 0.29875 0.31200
m7.21 0.74300 0.72075 0.71350 0.44200 0.37925
m7.31 0.76575 0.67925 0.68175 0.36025 0.36075
m8.11 0.80900 0.68750 0.68625 0.37425 0.34900
m8.21 0.69550 0.61675 0.61800 0.23150 0.28775
m8.31 0.73850 0.72500 0.71825 0.45525 0.38800
m9.11 0.79575 0.64575 0.64575 0.29175 0.31125
m9.21 0.72275 0.69175 0.69075 0.38325 0.34850
m9.31 0.74300 0.70400 0.70000 0.40400 0.38425
m10.11 0.73625 0.69575 0.68625 0.38850 0.37100
m10.21 0.76350 0.63750 0.64075 0.27775 0.30550
m10.31 0.72275 0.65425 0.66350 0.30500 0.34675
AccuracyUpper AccuracyNull AccuracyPValue McnemarPValue Positive
m1.11 0.92625 0.5225 0.44900 0.38725 5.25
m1.21 0.92100 0.5225 0.44250 0.47125 5.25
m1.31 0.89425 0.5225 0.42375 0.40750 5.25
m2.11 0.90825 0.5000 0.48650 0.45125 5.25
m2.21 0.90550 0.5000 0.39275 0.36400 5.25
m2.31 0.88975 0.5225 0.47100 0.38625 5.25
m3.11 0.90900 0.5225 0.39200 0.49975 5.25
m3.21 0.92725 0.5000 0.22875 0.52825 5.25
m3.31 0.91350 0.5225 0.30900 0.59550 5.25
m4.11 0.89500 0.5000 0.36350 0.37225 5.25
m4.21 0.91050 0.5225 0.38750 0.36900 5.25
m4.31 0.92225 0.5000 0.34150 0.40750 5.25
m5.11 0.92350 0.5225 0.30050 0.47125 5.25
m5.21 0.93550 0.5000 0.29825 0.58050 5.25
m5.31 0.89150 0.5225 0.41275 0.70425 5.25
m6.11 0.88375 0.5225 0.38875 0.38900 5.25
m6.21 0.86800 0.5000 0.39275 0.68775 5.25
m6.31 0.91650 0.5225 0.45350 0.59550 5.25
m7.11 0.89425 0.5225 0.44775 0.43825 5.25
m7.21 0.92425 0.5225 0.39200 0.43825 5.25
m7.31 0.90025 0.5225 0.33000 0.40700 5.25
m8.11 0.91825 0.5225 0.43875 0.30500 5.25
m8.21 0.88450 0.5225 0.39125 0.55900 5.25
m8.31 0.91825 0.5225 0.49900 0.46525 5.25
m9.11 0.89475 0.5000 0.36250 0.40975 5.25
m9.21 0.92425 0.5225 0.54825 0.48775 5.25
m9.31 0.89350 0.5225 0.49700 0.51625 5.25
m10.11 0.89475 0.5225 0.37075 0.43600 5.25
m10.21 0.89675 0.5225 0.44000 0.45625 5.25
m10.31 0.88600 0.5225 0.39625 0.56700 5.25
Negative True Positive False Positive True Negative False Negative
m1.11 5.25 4.50 3.00 5.25 3.50
m1.21 5.25 4.50 3.25 5.25 3.50
m1.31 5.25 4.75 3.25 4.75 3.50
m2.11 5.25 4.50 3.25 5.25 3.75
m2.21 5.25 4.75 3.25 5.00 3.75
m2.31 5.25 4.75 3.25 4.75 3.75
m3.11 5.25 4.75 3.25 4.50 3.00
m3.21 5.25 4.50 3.00 5.25 2.75
m3.31 5.25 4.50 3.25 5.25 2.75
m4.11 5.25 4.75 3.50 5.00 3.50
m4.21 5.25 4.75 3.50 5.25 3.50
m4.31 5.25 4.75 3.00 5.00 3.50
m5.11 5.25 4.75 3.00 5.25 3.00
m5.21 5.25 4.75 3.00 5.25 3.00
m5.31 5.25 4.00 3.50 5.00 2.75
m6.11 5.25 4.75 3.50 4.75 3.50
m6.21 5.25 4.00 3.75 4.75 3.00
m6.31 5.25 4.25 2.75 5.00 3.25
m7.11 5.25 3.75 3.50 5.00 2.75
m7.21 5.25 4.50 3.00 5.25 3.25
m7.31 5.25 4.75 3.25 5.25 3.25
m8.11 5.25 4.75 3.25 5.25 3.75
m8.21 5.25 4.25 3.50 4.75 3.00
m8.31 5.25 4.25 3.00 5.25 3.50
m9.11 5.25 4.75 3.50 5.00 3.50
m9.21 5.25 4.00 3.00 5.00 3.50
m9.31 5.25 4.25 2.75 4.75 3.50
m10.11 5.25 4.50 3.25 5.25 3.25
m10.21 5.25 4.75 3.50 4.50 3.25
m10.31 5.25 4.50 3.00 4.75 3.00
ROCSD TSSSD SensitivitySD SpecificitySD Pos Pred ValueSD
m1.11 0.19077813 0.3774917 0.3000926 0.20059640 0.15591237
m1.21 0.10754844 0.2061553 0.2061553 0.09428105 0.04201091
m1.31 0.25655842 0.3862210 0.1258306 0.27537853 0.25000000
m2.11 0.37074666 0.5000000 0.3714910 0.34996035 0.19341320
m2.21 0.12893797 0.2494438 0.3283122 0.25163913 0.11547005
m2.31 0.09714441 0.4255715 0.1667001 0.32015621 0.25000000
m3.11 0.05347204 0.2061553 0.2061553 0.30000000 0.25000000
m3.21 0.11993954 0.2581989 0.2582420 0.13350000 0.03934463
m3.31 0.18102588 0.2910708 0.2909850 0.25000000 0.11199107
m4.11 0.20646346 0.3333333 0.1154701 0.33993468 0.25000000
m4.21 0.12806248 0.3415650 0.1154701 0.25166115 0.13874437
m4.31 0.06540472 0.1763834 0.1707825 0.10000000 0.12500000
m5.11 0.14636332 0.2872281 0.2872281 0.25824197 0.11518789
m5.21 0.15881603 0.3468109 0.2000000 0.26287941 0.14752034
m5.31 0.08766519 0.3457413 0.2005964 0.26880399 0.16870586
m6.11 0.15818414 0.2061553 0.1165000 0.11650000 0.24994449
m6.21 0.24608603 0.4229526 0.2629428 0.28722813 0.23094011
m6.31 0.20551651 0.3785939 0.3127655 0.10000000 0.25000000
m7.11 0.15748016 0.2018434 0.3267347 0.25000000 0.16650000
m7.21 0.17086924 0.2629956 0.2628794 0.20615528 0.07387828
m7.31 0.24962194 0.4245913 0.4244670 0.12583057 0.03603124
m8.11 0.12897028 0.2061553 0.2061553 0.12572026 0.09136192
m8.21 0.17725270 0.1663887 0.1885619 0.20000000 0.20000000
m8.31 0.42768542 0.4776486 0.3415650 0.28493669 0.13947760
m9.11 0.09758187 0.2061553 0.2005964 0.23350000 0.16650000
m9.21 0.09797959 0.1575272 0.2905551 0.23350000 0.12500000
m9.31 0.26975984 0.4961892 0.3918404 0.16650000 0.33350000
m10.11 0.29471267 0.4374802 0.4374230 0.12583057 0.15427573
m10.21 0.09933110 0.1374369 0.2494439 0.19148542 0.21423430
m10.31 0.28394053 0.4879094 0.3773666 0.28722813 0.38509263
Neg Pred ValueSD PrecisionSD RecallSD F1SD PrevalenceSD
m1.11 0.3437134 0.15591237 0.3000926 0.17275513 0.03674235
m1.21 0.2499445 0.04201091 0.2061553 0.21942709 0.03674235
m1.31 0.4787136 0.25000000 0.1258306 0.14594377 0.03674235
m2.11 0.4787136 0.19341320 0.3714910 0.31575993 0.00000000
m2.21 0.2309401 0.11547005 0.3283122 0.15678116 0.00000000
m2.31 0.5773503 0.25000000 0.1667001 0.18681631 0.03674235
m3.11 0.1631633 0.25000000 0.2061553 0.16529872 0.03674235
m3.21 0.1922576 0.03934463 0.2582420 0.23294402 0.00000000
m3.31 0.2362908 0.11199107 0.2909850 0.27597509 0.03674235
m4.11 0.4760952 0.25000000 0.1154701 0.13488514 0.00000000
m4.21 0.4714046 0.13874437 0.1154701 0.13710671 0.03674235
m4.31 0.2499445 0.12500000 0.1707825 0.15763566 0.00000000
m5.11 0.1631633 0.11518789 0.2872281 0.27243776 0.03674235
m5.21 0.2500000 0.14752034 0.2000000 0.15093348 0.00000000
m5.31 0.2849367 0.16870586 0.2005964 0.20967813 0.03674235
m6.11 0.1922576 0.24994449 0.1165000 0.15238766 0.03674235
m6.21 0.3188521 0.23094011 0.2629428 0.19362507 0.00000000
m6.31 0.2886751 0.25000000 0.3127655 0.31160338 0.03674235
m7.11 0.2629956 0.16650000 0.3267347 0.20967813 0.03674235
m7.21 0.2629956 0.07387828 0.2628794 0.24635797 0.03674235
m7.31 0.1716748 0.03603124 0.4244670 0.11242998 0.03674235
m8.11 0.2499445 0.09136192 0.2061553 0.21942709 0.03674235
m8.21 0.2096736 0.20000000 0.1885619 0.13176874 0.03674235
m8.31 0.5000556 0.13947760 0.3415650 0.16686596 0.03674235
m9.11 0.2362908 0.16650000 0.2005964 0.20967813 0.00000000
m9.21 0.3309713 0.12500000 0.2905551 0.13000865 0.03674235
m9.31 0.2886751 0.33350000 0.3918404 0.36530672 0.03674235
m10.11 0.3750000 0.15427573 0.4374230 0.35941202 0.03674235
m10.21 0.1716748 0.21423430 0.2494439 0.06271098 0.03674235
m10.31 0.2500000 0.38509263 0.3773666 0.34389000 0.03674235
Detection RateSD Detection PrevalenceSD Balanced AccuracySD AccuracySD
m1.11 0.15988642 0.15988642 0.18887099 0.20149835
m1.21 0.11088432 0.13505894 0.10307764 0.09033779
m1.31 0.07502444 0.12972535 0.19311050 0.19420500
m2.11 0.18584649 0.18584649 0.25000000 0.25000000
m2.21 0.16413079 0.21665391 0.12472202 0.12472202
m2.31 0.08230836 0.11816796 0.21269442 0.19754578
m3.11 0.11088432 0.23789774 0.10307764 0.09386648
m3.21 0.12905651 0.14142136 0.12905651 0.12905651
m3.31 0.14569489 0.16781215 0.14562137 0.15058857
m4.11 0.05773503 0.19145648 0.16670008 0.16670008
m4.21 0.08446054 0.10144416 0.17078251 0.19127184
m4.31 0.08539126 0.12500000 0.08825484 0.08825484
m5.11 0.13150760 0.17262363 0.14361407 0.14615745
m5.21 0.10000000 0.15670221 0.17346157 0.17346157
m5.31 0.08948929 0.14595290 0.17302601 0.17135830
m6.11 0.05219435 0.09342733 0.10322588 0.12311648
m6.21 0.13155069 0.21879728 0.21151576 0.21151576
m6.31 0.15242047 0.13012302 0.18922363 0.18922892
m7.11 0.16165472 0.28075672 0.10095874 0.10974326
m7.21 0.11257701 0.14984214 0.13161402 0.14941776
m7.31 0.22290357 0.22290357 0.21241998 0.21312966
m8.11 0.09988160 0.09988160 0.10307764 0.11116504
m8.21 0.09824799 0.19224355 0.08317251 0.08425359
m8.31 0.16732877 0.16732877 0.23880885 0.22341218
m9.11 0.10044360 0.14946209 0.10314512 0.10314512
m9.21 0.15065524 0.25268871 0.07892824 0.06843488
m9.31 0.19619803 0.16741764 0.24827001 0.24768932
m10.11 0.19678838 0.19678838 0.21879728 0.22603005
m10.21 0.12495733 0.20979593 0.06883979 0.05995206
m10.31 0.20352621 0.21760113 0.24407427 0.23264924
KappaSD AccuracyLowerSD AccuracyUpperSD AccuracyNullSD
m1.11 0.3772377 0.16006353 0.12904521 0.02598076
m1.21 0.1977768 0.08152709 0.04481815 0.02598076
m1.31 0.3877563 0.16890505 0.11103453 0.02598076
m2.11 0.5000000 0.20487618 0.14554953 0.00000000
m2.21 0.2494439 0.10663645 0.06870953 0.00000000
m2.31 0.4231488 0.15992368 0.12004825 0.02598076
m3.11 0.1952323 0.08499951 0.05232272 0.02598076
m3.21 0.2582420 0.12453246 0.05168091 0.00000000
m3.31 0.2914653 0.13752909 0.06913513 0.02598076
m4.11 0.3333000 0.13678785 0.09905891 0.00000000
m4.21 0.3382021 0.14916965 0.12605125 0.02598076
m4.31 0.1763205 0.07786527 0.05078304 0.00000000
m5.11 0.2916316 0.15352171 0.05762812 0.02598076
m5.21 0.3467548 0.15668945 0.08316800 0.00000000
m5.31 0.3438879 0.14848906 0.09895117 0.02598076
m6.11 0.2106949 0.09876698 0.08042958 0.02598076
m6.21 0.4229132 0.16057086 0.13928987 0.00000000
m6.31 0.3788363 0.18656992 0.08659677 0.02598076
m7.11 0.2039532 0.09313789 0.05929235 0.02598076
m7.21 0.2737736 0.14856059 0.06811449 0.02598076
m7.31 0.4238399 0.20515746 0.10111174 0.02598076
m8.11 0.2097338 0.10725049 0.07248678 0.02598076
m8.21 0.1665873 0.06976329 0.05007994 0.02598076
m8.31 0.4754706 0.18144122 0.14097990 0.02598076
m9.11 0.2061553 0.10183442 0.05533760 0.00000000
m9.21 0.1596277 0.04482094 0.05293628 0.02598076
m9.31 0.4999653 0.23668034 0.11239958 0.02598076
m10.11 0.4396919 0.23908855 0.14287145 0.02598076
m10.21 0.1298820 0.06036279 0.02947739 0.02598076
m10.31 0.4831494 0.20848881 0.12271648 0.02598076
AccuracyPValueSD McnemarPValueSD PositiveSD NegativeSD True PositiveSD
m1.11 0.4051601 0.1973989 0.5 0.5 1.7078251
m1.21 0.1600708 0.4196406 0.5 0.5 1.2909944
m1.31 0.3617857 0.3986389 0.5 0.5 0.9574271
m2.11 0.3934103 0.4392251 0.5 0.5 1.8257419
m2.21 0.2489244 0.4439020 0.5 0.5 1.6329932
m2.31 0.4098203 0.2446717 0.5 0.5 0.8164966
m3.11 0.1801074 0.4174734 0.5 0.5 1.1547005
m3.21 0.1498232 0.4174734 0.5 0.5 1.2583057
m3.31 0.2646638 0.4693893 0.5 0.5 1.4142136
m4.11 0.3365734 0.4205048 0.5 0.5 0.9574271
m4.21 0.3882624 0.2199318 0.5 0.5 0.9574271
m4.31 0.2089569 0.3986389 0.5 0.5 0.9574271
m5.11 0.1753957 0.4196406 0.5 0.5 1.5000000
m5.21 0.2657008 0.4895777 0.5 0.5 1.2583057
m5.31 0.3152823 0.4084820 0.5 0.5 1.1547005
m6.11 0.3020491 0.2726866 0.5 0.5 0.5000000
m6.21 0.3910689 0.4416831 0.5 0.5 1.4142136
m6.31 0.2862278 0.4693893 0.5 0.5 1.7320508
m7.11 0.2324213 0.3868026 0.5 0.5 1.5000000
m7.21 0.2619523 0.4132227 0.5 0.5 1.2583057
m7.31 0.3388943 0.4392251 0.5 0.5 2.4494897
m8.11 0.2688647 0.2136118 0.5 0.5 1.1547005
m8.21 0.1842614 0.4330000 0.5 0.5 0.9574271
m8.31 0.4461353 0.4128134 0.5 0.5 1.7078251
m9.11 0.2251599 0.4090529 0.5 0.5 0.9574271
m9.21 0.2134719 0.4284860 0.5 0.5 1.4142136
m9.31 0.4078540 0.3999553 0.5 0.5 1.9148542
m10.11 0.3828684 0.3760000 0.5 0.5 2.1602469
m10.21 0.1207256 0.4364496 0.5 0.5 1.2583057
m10.31 0.4078794 0.4999853 0.5 0.5 2.2173558
False PositiveSD True NegativeSD False NegativeSD
m1.11 1.4142136 0.9574271 1.2909944
m1.21 0.9574271 0.5000000 0.5773503
m1.31 0.5773503 1.5000000 1.2909944
m2.11 2.0615528 1.7320508 1.8929694
m2.21 1.7078251 1.2909944 1.2583057
m2.31 0.9574271 1.7320508 1.5000000
m3.11 0.9574271 1.7320508 1.5000000
m3.21 1.4142136 1.0000000 0.5000000
m3.31 1.7078251 1.2909944 1.2583057
m4.11 0.5773503 1.7078251 1.7320508
m4.21 0.5773503 1.2583057 1.7320508
m4.31 0.8164966 0.8164966 0.5773503
m5.11 1.4142136 1.2583057 1.4142136
m5.21 1.0000000 1.2583057 1.6329932
m5.31 1.2909944 1.2909944 1.5000000
m6.11 1.0000000 0.9574271 1.0000000
m6.21 1.5000000 1.5000000 1.4142136
m6.31 1.5000000 0.8164966 0.5000000
m7.11 1.9148542 1.2909944 1.2583057
m7.21 1.6329932 1.1547005 0.9574271
m7.31 2.0615528 0.8164966 0.5000000
m8.11 0.9574271 0.5773503 0.9574271
m8.21 1.0000000 1.2583057 1.0000000
m8.31 2.1602469 1.7078251 1.2909944
m9.11 1.2909944 1.5000000 1.0000000
m9.21 1.5000000 1.5000000 1.0000000
m9.31 2.2173558 0.5000000 1.0000000
m10.11 2.3629078 0.8164966 0.5000000
m10.21 1.2909944 1.2909944 0.9574271
m10.31 1.8257419 1.5000000 1.4142136
$`Araucaria angustifolia`
algo ROC TSS Sensitivity Specificity Pos Pred Value
m1.1 mahal.custom 0.9633929 0.7916667 0.98800 1 1
m2.1 mahal.custom 0.9555556 0.7732143 0.94000 1 1
m3.1 mahal.custom 0.9734127 0.7613095 0.92800 1 1
m4.1 mahal.custom 0.9615079 0.7428571 0.95100 1 1
m5.1 mahal.custom 0.9556264 0.7898810 0.95050 1 1
m6.1 mahal.custom 0.9700397 0.6815476 0.89000 1 1
m7.1 mahal.custom 0.9660714 0.7678571 0.97625 1 1
m8.1 mahal.custom 0.9461310 0.7851190 0.95175 1 1
m9.1 mahal.custom 0.9570437 0.7666667 0.96300 1 1
m10.1 mahal.custom 0.9636905 0.7964286 0.96300 1 1
Neg Pred Value Precision Recall F1 Prevalence Detection Rate
m1.1 0.96425 1 0.98800 0.96475 0.76650 0.75725
m2.1 0.85325 1 0.94000 0.94375 0.77350 0.72700
m3.1 0.83325 1 0.92800 0.93675 0.77350 0.71775
m4.1 0.82250 1 0.95100 0.94575 0.77350 0.73600
m5.1 0.85175 1 0.95050 0.95075 0.76650 0.72875
m6.1 0.77525 1 0.89000 0.90900 0.77350 0.68850
m7.1 0.92850 1 0.97625 0.95900 0.77350 0.75500
m8.1 0.83675 1 0.95175 0.95175 0.78125 0.74400
m9.1 0.86650 1 0.96300 0.95150 0.76650 0.73850
m10.1 0.86650 1 0.96300 0.95775 0.78125 0.75250
Detection Prevalence Balanced Accuracy Accuracy Kappa AccuracyLower
m1.1 0.80400 0.89575 0.94425 0.83200 0.78200
m2.1 0.76475 0.88650 0.91575 0.77100 0.74550
m3.1 0.75500 0.88075 0.90675 0.75525 0.73775
m4.1 0.78325 0.87150 0.91500 0.74875 0.74125
m5.1 0.76625 0.89500 0.92500 0.78925 0.75550
m6.1 0.73550 0.84050 0.86800 0.65950 0.68275
m7.1 0.80175 0.88400 0.93500 0.79775 0.77200
m8.1 0.78125 0.89275 0.92525 0.78450 0.75700
m9.1 0.78550 0.88325 0.92500 0.78450 0.75875
m10.1 0.78975 0.89850 0.93375 0.80275 0.76575
AccuracyUpper AccuracyNull AccuracyPValue McnemarPValue Positive Negative
m1.1 0.99500 0.76650 0.98050 0.7400000 20.5 6.25
m2.1 0.98150 0.77350 0.96250 0.7127500 20.5 6.00
m3.1 0.97400 0.77350 0.96275 0.5670000 20.5 6.00
m4.1 0.98550 0.77350 0.97400 1.0000000 20.5 6.00
m5.1 0.98925 0.76650 0.93150 0.8700000 20.5 6.25
m6.1 0.96325 0.77350 0.88725 0.3512500 20.5 6.00
m7.1 0.98775 0.77350 0.93800 0.7493333 20.5 6.00
m8.1 0.98575 0.78125 0.98975 1.0000000 20.5 5.75
m9.1 0.98575 0.76650 0.97275 1.0000000 20.5 6.25
m10.1 0.99125 0.78125 0.98700 1.0000000 20.5 5.75
True Positive False Positive True Negative False Negative ROCSD
m1.1 20.25 11.00 6.25 1.25 0.03023239
m2.1 19.25 11.25 6.00 1.00 0.04038661
m3.1 19.00 11.50 6.00 1.00 0.03261702
m4.1 19.50 11.25 6.00 1.25 0.03677011
m5.1 19.50 11.00 6.25 1.00 0.04473929
m6.1 18.25 11.50 6.00 1.25 0.03441417
m7.1 20.00 11.00 6.00 1.25 0.03050053
m8.1 19.50 11.75 5.75 1.00 0.05257337
m9.1 19.75 11.25 6.25 1.25 0.03565306
m10.1 19.75 10.75 5.75 1.00 0.02423236
TSSSD SensitivitySD SpecificitySD Pos Pred ValueSD Neg Pred ValueSD
m1.1 0.1280339 0.09085657 0.1484644 0.04236744 0.07150000
m2.1 0.1451584 0.14195158 0.1359470 0.03753221 0.20931854
m3.1 0.1770077 0.15516738 0.1359470 0.03626752 0.23570232
m4.1 0.1599910 0.14670236 0.1593683 0.04427942 0.05641144
m5.1 0.1704613 0.17058991 0.1364609 0.03880722 0.10806904
m6.1 0.2354255 0.23528334 0.1593683 0.04201984 0.25955780
m7.1 0.2088146 0.15220710 0.2096736 0.05162929 0.14300000
m8.1 0.1591309 0.11893240 0.1359470 0.03724133 0.11983704
m9.1 0.1710644 0.12114007 0.1484644 0.04543494 0.09034932
m10.1 0.1373337 0.04383587 0.1359470 0.03722454 0.09034932
PrecisionSD RecallSD F1SD PrevalenceSD Detection RateSD
m1.1 0.04236744 0.09085657 0.08567380 0.017521415 0.07625123
m2.1 0.03753221 0.14195158 0.13703862 0.005196152 0.11034793
m3.1 0.03626752 0.15516738 0.15548097 0.005196152 0.12001493
m4.1 0.04427942 0.14670236 0.15362617 0.005196152 0.11330012
m5.1 0.03880722 0.17058991 0.16469871 0.011789826 0.13359266
m6.1 0.04201984 0.23528334 0.23139721 0.005196152 0.18123557
m7.1 0.05162929 0.15220710 0.13875968 0.005196152 0.11946652
m8.1 0.03724133 0.11893240 0.11978696 0.013200379 0.09628560
m9.1 0.04543494 0.12114007 0.12182604 0.017521415 0.09428680
m10.1 0.03722454 0.04383587 0.04053805 0.013200379 0.03192047
Detection PrevalenceSD Balanced AccuracySD AccuracySD KappaSD
m1.1 0.07625123 0.06411123 0.06094807 0.07058801
m2.1 0.11034793 0.07283543 0.11071096 0.16399797
m3.1 0.12001493 0.08845856 0.11920570 0.21282916
m4.1 0.11330012 0.07990620 0.11530250 0.11658009
m5.1 0.13359266 0.08509358 0.13052714 0.13895443
m6.1 0.18123557 0.11773523 0.18170925 0.19442115
m7.1 0.11946652 0.10444456 0.11599713 0.17728955
m8.1 0.09628560 0.07940351 0.09026073 0.15286268
m9.1 0.09428680 0.08566359 0.09567088 0.15369776
m10.1 0.03192047 0.06848114 0.03777455 0.10781891
AccuracyLowerSD AccuracyUpperSD AccuracyNullSD AccuracyPValueSD
m1.1 0.05842944 0.05000333 0.017521415 0.02573584
m2.1 0.10353542 0.09182002 0.005196152 0.20286038
m3.1 0.12014262 0.10017110 0.005196152 0.29012914
m4.1 0.10318915 0.10068888 0.005196152 0.05516641
m5.1 0.12462343 0.10599174 0.011789826 0.11299705
m6.1 0.17047458 0.15024535 0.005196152 0.22112591
m7.1 0.11326775 0.09232010 0.005196152 0.11671332
m8.1 0.07903744 0.07970989 0.013200379 0.05772564
m9.1 0.08705314 0.08171087 0.017521415 0.04847250
m10.1 0.04650717 0.03024759 0.013200379 0.04819665
McnemarPValueSD PositiveSD NegativeSD True PositiveSD False PositiveSD
m1.1 0.300222140 0.5773503 0.5 2.0816660 1.6329932
m2.1 0.334659404 0.5773503 0.0 2.9860788 2.9860788
m3.1 0.396720304 0.5773503 0.0 3.1622777 3.1091264
m4.1 0.003265986 0.5773503 0.0 2.9860788 3.2015621
m5.1 0.260000000 0.5773503 0.5 3.6968455 3.2659863
m6.1 0.191842600 0.5773503 0.0 4.7609523 4.7958315
m7.1 0.434167402 0.5773503 0.0 3.3166248 2.9439203
m8.1 0.003593976 0.5773503 0.5 2.2173558 2.7537853
m9.1 0.003304038 0.5773503 0.5 2.5000000 2.6299556
m10.1 0.001414214 0.5773503 0.5 0.9574271 0.9574271
True NegativeSD False NegativeSD
m1.1 0.8164966 0.9574271
m2.1 0.8164966 0.8164966
m3.1 0.8164966 0.8164966
m4.1 0.9574271 0.9574271
m5.1 0.9574271 0.8164966
m6.1 0.9574271 0.9574271
m7.1 1.2583057 1.2583057
m8.1 0.5000000 0.8164966
m9.1 0.8164966 0.9574271
m10.1 0.5000000 0.8164966
Code
i2$models
Output
caretSDM
.........................
Class : Models
Algorithms Names : kknn
Variables Names : bio1 bio4 bio12
Model Validation :
Method : repeatedcv
Number : 4
Metrics :
$`Salminus brasiliensis`
algo ROC TSS Sensitivity Specificity Pos Pred Value
m1.11 kknn 0.9500000 0.9000000 0.95000 0.95000 0.95825
m1.21 kknn 0.8000000 0.5250000 0.71675 0.85825 0.81675
m1.31 kknn 0.9750000 0.8500000 0.90000 0.95000 0.95825
m2.11 kknn 0.8891667 0.6583333 0.80000 0.85825 0.86425
m2.21 kknn 0.8259722 0.4416667 0.71675 0.77500 0.79275
m2.31 kknn 0.9583333 0.7416667 0.86675 0.87500 0.89275
m3.11 kknn 0.9791667 0.9583333 0.95825 1.00000 1.00000
m3.21 kknn 0.8433333 0.6333333 0.67500 0.95825 0.95825
m3.31 kknn 0.9291667 0.8583333 0.90825 0.95000 0.95825
m4.11 kknn 0.9291667 0.8583333 0.90000 0.95825 0.95000
m4.21 kknn 0.7958333 0.5916667 0.68325 0.90825 0.88750
m4.31 kknn 0.9650000 0.8583333 0.90825 0.95000 0.95825
m5.11 kknn 0.9541667 0.9083333 0.95825 0.95000 0.95825
m5.21 kknn 0.8391667 0.5166667 0.66675 0.90000 0.88750
m5.31 kknn 0.9375000 0.8750000 0.91675 0.95825 0.95825
m6.11 kknn 0.9041667 0.8083333 0.85825 0.95000 0.93750
m6.21 kknn 0.8941667 0.6666667 0.80000 0.86675 0.88100
m6.31 kknn 0.9750000 0.9500000 0.95000 1.00000 1.00000
m7.11 kknn 0.9000000 0.8000000 0.85000 0.95000 0.96425
m7.21 kknn 0.7466667 0.4083333 0.55825 0.85000 0.81250
m7.31 kknn 0.9900000 0.9000000 0.95000 0.95000 0.95825
m8.11 kknn 0.9291667 0.8583333 0.90825 0.95000 0.95000
m8.21 kknn 0.8958333 0.5083333 0.76675 0.74175 0.82500
m8.31 kknn 0.9250000 0.8500000 0.90000 0.95000 0.95000
m9.11 kknn 0.8833333 0.7666667 0.81675 0.95000 0.95825
m9.21 kknn 0.9030556 0.7083333 0.80000 0.90825 0.91425
m9.31 kknn 0.9250000 0.8500000 0.95000 0.90000 0.92250
m10.11 kknn 0.9750000 0.9500000 0.95000 1.00000 1.00000
m10.21 kknn 0.7875000 0.4750000 0.62500 0.85000 0.82500
m10.31 kknn 0.9750000 0.9500000 0.95000 1.00000 1.00000
Neg Pred Value Precision Recall F1 Prevalence Detection Rate
m1.11 0.95825 0.95825 0.95000 0.94950 0.5 0.47500
m1.21 0.73675 0.81675 0.71675 0.75425 0.5 0.35700
m1.31 0.92850 0.95825 0.90000 0.91475 0.5 0.45000
m2.11 0.81650 0.86425 0.80000 0.82525 0.5 0.40000
m2.21 0.71675 0.79275 0.71675 0.72050 0.5 0.35825
m2.31 0.86600 0.89275 0.86675 0.86675 0.5 0.43200
m3.11 0.95825 1.00000 0.95825 0.97725 0.5 0.47750
m3.21 0.76325 0.95825 0.67500 0.76900 0.5 0.33200
m3.31 0.90825 0.95825 0.90825 0.93050 0.5 0.45250
m4.11 0.91650 0.95000 0.90000 0.92225 0.5 0.45225
m4.21 0.76400 0.88750 0.68325 0.74175 0.5 0.33650
m4.31 0.92250 0.95825 0.90825 0.92675 0.5 0.45425
m5.11 0.95825 0.95825 0.95825 0.95450 0.5 0.47750
m5.21 0.71200 0.88750 0.66675 0.71675 0.5 0.33425
m5.31 0.92850 0.95825 0.91675 0.92725 0.5 0.45475
m6.11 0.87500 0.93750 0.85825 0.89400 0.5 0.42750
m6.21 0.83675 0.88100 0.80000 0.82225 0.5 0.40225
m6.31 0.95825 1.00000 0.95000 0.97225 0.5 0.47500
m7.11 0.89575 0.96425 0.85000 0.89050 0.5 0.42950
m7.21 0.67625 0.81250 0.55825 0.64275 0.5 0.28425
m7.31 0.95825 0.95825 0.95000 0.94950 0.5 0.47500
m8.11 0.90825 0.95000 0.90825 0.92725 0.5 0.45250
m8.21 0.80350 0.82500 0.76675 0.75825 0.5 0.37975
m8.31 0.91425 0.95000 0.90000 0.92225 0.5 0.45225
m9.11 0.85700 0.95825 0.81675 0.86475 0.5 0.40475
m9.21 0.83675 0.91425 0.80000 0.84050 0.5 0.40000
m9.31 0.95825 0.92250 0.95000 0.93025 0.5 0.47500
m10.11 0.95825 1.00000 0.95000 0.97225 0.5 0.47500
m10.21 0.69700 0.82500 0.62500 0.70425 0.5 0.31150
m10.31 0.95825 1.00000 0.95000 0.97225 0.5 0.47500
Detection Prevalence Balanced Accuracy Accuracy Kappa AccuracyLower
m1.11 0.50000 0.95000 0.95000 0.90000 0.63425
m1.21 0.45000 0.76250 0.76350 0.52500 0.41950
m1.31 0.47500 0.92500 0.92500 0.85000 0.60650
m2.11 0.47075 0.82925 0.82925 0.65825 0.49675
m2.21 0.49575 0.72075 0.72075 0.44175 0.37825
m2.31 0.50000 0.87075 0.86350 0.73375 0.54350
m3.11 0.47750 0.97925 0.97725 0.95500 0.67150
m3.21 0.35450 0.81675 0.80900 0.62400 0.46625
m3.31 0.47500 0.92925 0.92950 0.85825 0.61100
m4.11 0.47500 0.92925 0.92950 0.85825 0.61100
m4.21 0.38425 0.79600 0.78850 0.58625 0.45750
m4.31 0.47925 0.92925 0.92925 0.85825 0.60425
m5.11 0.50250 0.95425 0.95225 0.90500 0.63725
m5.21 0.42975 0.75825 0.76125 0.51650 0.41600
m5.31 0.47725 0.93750 0.93175 0.86625 0.61325
m6.11 0.45250 0.90425 0.90225 0.80500 0.58550
m6.21 0.47025 0.83325 0.83400 0.66825 0.49400
m6.31 0.47500 0.97500 0.97500 0.95000 0.66925
m7.11 0.45225 0.90000 0.90675 0.80875 0.57900
m7.21 0.35675 0.70425 0.71125 0.41350 0.37050
m7.31 0.50000 0.95000 0.95000 0.90000 0.63425
m8.11 0.47750 0.92925 0.92725 0.85500 0.60950
m8.21 0.51600 0.75400 0.74300 0.50325 0.40575
m8.31 0.47725 0.92500 0.92725 0.85350 0.60950
m9.11 0.42975 0.88325 0.87950 0.76125 0.54900
m9.21 0.44575 0.85425 0.85425 0.70825 0.51450
m9.31 0.52275 0.92500 0.92725 0.85350 0.60300
m10.11 0.47500 0.97500 0.97500 0.95000 0.66925
m10.21 0.40925 0.73750 0.73850 0.47775 0.39550
m10.31 0.47500 0.97500 0.97500 0.95000 0.66850
AccuracyUpper AccuracyNull AccuracyPValue McnemarPValue Positive
m1.11 0.99850 0.5000 0.00575 1.0000000 5.25
m1.21 0.95450 0.5225 0.19800 0.8700000 5.25
m1.31 0.99300 0.5000 0.01675 0.7400000 5.25
m2.11 0.96750 0.5000 0.10050 1.0000000 5.25
m2.21 0.93275 0.5000 0.16725 1.0000000 5.25
m2.31 0.96700 0.5225 0.11200 0.5485000 5.25
m3.11 0.99950 0.5225 0.00400 1.0000000 5.25
m3.21 0.97200 0.5225 0.07650 0.5520000 5.25
m3.31 0.99350 0.5225 0.01850 1.0000000 5.25
m4.11 0.99350 0.5225 0.01850 1.0000000 5.25
m4.21 0.95025 0.5225 0.15475 0.7113333 5.25
m4.31 0.99800 0.5000 0.00650 1.0000000 5.25
m5.11 0.99875 0.5225 0.00650 1.0000000 5.25
m5.21 0.95625 0.5225 0.16825 0.8700000 5.25
m5.31 0.99375 0.5225 0.01900 0.7400000 5.25
m6.11 0.98275 0.5225 0.04675 1.0000000 5.25
m6.21 0.97750 0.5225 0.06550 0.8700000 5.25
m6.31 0.99925 0.5225 0.00350 1.0000000 5.25
m7.11 0.99300 0.5225 0.02150 0.8266667 5.25
m7.21 0.93200 0.5225 0.19825 0.7162500 5.25
m7.31 0.99850 0.5000 0.00575 1.0000000 5.25
m8.11 0.99325 0.5225 0.01750 1.0000000 5.25
m8.21 0.93625 0.5225 0.24675 0.6385000 5.25
m8.31 0.99325 0.5225 0.01750 1.0000000 5.25
m9.11 0.98725 0.5225 0.03200 0.6533333 5.25
m9.21 0.98625 0.5000 0.07125 0.8700000 5.25
m9.31 0.99800 0.5225 0.00900 1.0000000 5.25
m10.11 0.99925 0.5225 0.00350 1.0000000 5.25
m10.21 0.94400 0.5225 0.21950 0.7742500 5.25
m10.31 0.99925 0.5000 0.00325 1.0000000 5.25
Negative True Positive False Positive True Negative False Negative
m1.11 5.25 5.00 0.25 5.00 0.25
m1.21 5.25 3.75 2.00 4.50 1.00
m1.31 5.25 4.75 0.50 5.00 0.25
m2.11 5.25 4.25 1.00 4.50 0.75
m2.21 5.25 3.75 1.75 4.00 1.50
m2.31 5.25 4.50 0.75 4.50 0.75
m3.11 5.25 5.00 0.25 5.25 0.00
m3.21 5.25 3.50 1.75 5.00 0.25
m3.31 5.25 4.75 0.50 5.00 0.25
m4.11 5.25 4.75 0.50 5.00 0.25
m4.21 5.25 3.50 1.75 4.75 0.50
m4.31 5.25 4.75 0.50 5.00 0.25
m5.11 5.25 5.00 0.25 5.00 0.25
m5.21 5.25 3.50 2.00 4.75 1.00
m5.31 5.25 4.75 0.50 5.00 0.25
m6.11 5.25 4.50 0.75 5.00 0.25
m6.21 5.25 4.25 1.00 4.50 0.75
m6.31 5.25 5.00 0.25 5.25 0.00
m7.11 5.25 4.50 0.75 5.00 0.25
m7.21 5.25 3.00 2.25 4.50 0.75
m7.31 5.25 5.00 0.25 5.00 0.25
m8.11 5.25 4.75 0.50 5.00 0.25
m8.21 5.25 4.00 1.50 3.75 1.50
m8.31 5.25 4.75 0.50 5.00 0.25
m9.11 5.25 4.25 1.00 5.00 0.25
m9.21 5.25 4.25 1.25 4.75 0.75
m9.31 5.25 5.00 0.25 4.75 0.50
m10.11 5.25 5.00 0.25 5.25 0.00
m10.21 5.25 3.25 2.00 4.50 1.00
m10.31 5.25 5.00 0.25 5.25 0.00
ROCSD TSSSD SensitivitySD SpecificitySD Pos Pred ValueSD
m1.11 0.05773503 0.11547005 0.1000000 0.1000000 0.08350000
m1.21 0.11970256 0.23940513 0.2212591 0.1641308 0.13731563
m1.31 0.05000000 0.19148542 0.2000000 0.1000000 0.08350000
m2.11 0.03370625 0.30595933 0.1632993 0.1893117 0.18862374
m2.21 0.11069680 0.20615528 0.1885619 0.2061553 0.17533848
m2.31 0.08333333 0.33374974 0.1631633 0.2500000 0.21450000
m3.11 0.04166667 0.08333333 0.0835000 0.0000000 0.00000000
m3.21 0.08814970 0.14142136 0.2217356 0.0835000 0.08350000
m3.31 0.08858455 0.17716910 0.1067969 0.1000000 0.08350000
m4.11 0.08858455 0.17716910 0.1154701 0.0835000 0.10000000
m4.21 0.15053915 0.30107831 0.2849367 0.1067969 0.13149778
m4.31 0.04725816 0.09574271 0.1067969 0.1000000 0.08350000
m5.11 0.05335937 0.10671874 0.0835000 0.1000000 0.08350000
m5.21 0.12583057 0.18358568 0.1667001 0.1373156 0.16520190
m5.31 0.07978559 0.15957118 0.1665000 0.0835000 0.08350000
m6.11 0.14166667 0.28333333 0.1893117 0.1000000 0.12500000
m6.21 0.08247895 0.16329932 0.1632993 0.1631633 0.15779100
m6.31 0.05000000 0.10000000 0.1000000 0.0000000 0.00000000
m7.11 0.08164966 0.16329932 0.1914854 0.1000000 0.07150000
m7.21 0.15930404 0.17716910 0.2060071 0.1000000 0.14218884
m7.31 0.02000000 0.11547005 0.1000000 0.1000000 0.08350000
m8.11 0.09464847 0.18929694 0.1067969 0.1000000 0.10000000
m8.21 0.14101156 0.26579719 0.2516391 0.3945955 0.23629078
m8.31 0.09574271 0.19148542 0.1154701 0.1000000 0.10000000
m9.11 0.08819171 0.17638342 0.2133594 0.1000000 0.08350000
m9.21 0.11665212 0.17716910 0.1914854 0.1067969 0.10806904
m9.31 0.05000000 0.10000000 0.1000000 0.1154701 0.09002407
m10.11 0.05000000 0.10000000 0.1000000 0.0000000 0.00000000
m10.21 0.15478480 0.20615528 0.1258306 0.1632993 0.16520190
m10.31 0.05000000 0.10000000 0.1000000 0.0000000 0.00000000
Neg Pred ValueSD PrecisionSD RecallSD F1SD PrevalenceSD
m1.11 0.08350000 0.08350000 0.1000000 0.05888124 0.00000000
m1.21 0.14981405 0.13731563 0.2212591 0.17129604 0.03674235
m1.31 0.14300000 0.08350000 0.2000000 0.11791346 0.00000000
m2.11 0.16440702 0.18862374 0.1632993 0.15101959 0.00000000
m2.21 0.10873324 0.17533848 0.1885619 0.12085391 0.00000000
m2.31 0.15542630 0.21450000 0.1631633 0.16316326 0.03674235
m3.11 0.08350000 0.00000000 0.0835000 0.04550000 0.03674235
m3.21 0.16331437 0.08350000 0.2217356 0.10120277 0.03674235
m3.31 0.10679693 0.08350000 0.1067969 0.08344459 0.03674235
m4.11 0.09641749 0.10000000 0.1154701 0.09685169 0.03674235
m4.21 0.19419063 0.13149778 0.2849367 0.21143695 0.03674235
m4.31 0.09002407 0.08350000 0.1067969 0.04973513 0.00000000
m5.11 0.08350000 0.08350000 0.0835000 0.05253887 0.03674235
m5.21 0.06271098 0.16520190 0.1667001 0.11160794 0.03674235
m5.31 0.14300000 0.08350000 0.1665000 0.09506270 0.03674235
m6.11 0.15945532 0.12500000 0.1893117 0.15729590 0.03674235
m6.21 0.11983704 0.15779100 0.1632993 0.09814742 0.03674235
m6.31 0.08350000 0.00000000 0.1000000 0.05550000 0.03674235
m7.11 0.12505565 0.07150000 0.1914854 0.10454505 0.03674235
m7.21 0.09409702 0.14218884 0.2060071 0.14411656 0.03674235
m7.31 0.08350000 0.08350000 0.1000000 0.05888124 0.00000000
m8.11 0.10679693 0.10000000 0.1067969 0.09506270 0.03674235
m8.21 0.17126661 0.23629078 0.2516391 0.13348034 0.03674235
m8.31 0.10171324 0.10000000 0.1154701 0.09685169 0.03674235
m9.11 0.16512218 0.08350000 0.2133594 0.11196837 0.03674235
m9.21 0.14719007 0.10806904 0.1914854 0.10711053 0.00000000
m9.31 0.08350000 0.09002407 0.1000000 0.04854809 0.03674235
m10.11 0.08350000 0.00000000 0.1000000 0.05550000 0.03674235
m10.21 0.11192371 0.16520190 0.1258306 0.10307764 0.03674235
m10.31 0.08350000 0.00000000 0.1000000 0.05550000 0.00000000
Detection RateSD Detection PrevalenceSD Balanced AccuracySD AccuracySD
m1.11 0.05000000 0.08164966 0.05773503 0.05773503
m1.21 0.09572704 0.11233581 0.11960212 0.13077557
m1.31 0.10000000 0.12583057 0.09574271 0.09574271
m2.11 0.08164966 0.08844348 0.15304329 0.15304329
m2.21 0.09428105 0.16844064 0.10322588 0.10322588
m2.31 0.07851964 0.11104354 0.16689992 0.17425173
m3.11 0.02598076 0.02598076 0.04150000 0.04550000
m3.21 0.08298193 0.12763620 0.07086783 0.07502444
m3.31 0.04092676 0.06204837 0.08844348 0.08802083
m4.11 0.08446054 0.06204837 0.08844348 0.08802083
m4.21 0.13252798 0.14089801 0.15045930 0.15988642
m4.31 0.05328149 0.09159103 0.04784262 0.04784262
m5.11 0.02598076 0.06837397 0.05328149 0.05525924
m5.21 0.09572704 0.13277927 0.09181322 0.08581181
m5.31 0.06411123 0.07842353 0.07990202 0.08712587
m6.11 0.08760708 0.04092676 0.14167657 0.14149293
m6.21 0.10376375 0.14942194 0.08178580 0.08676789
m6.31 0.06204837 0.06204837 0.05000000 0.05000000
m7.11 0.12065516 0.15373652 0.08164966 0.07443733
m7.21 0.12510895 0.15391204 0.08872570 0.08971575
m7.31 0.05000000 0.08164966 0.05773503 0.05773503
m8.11 0.04092676 0.02598076 0.09463394 0.09506270
m8.21 0.10974326 0.29943391 0.13305888 0.14984214
m8.31 0.08446054 0.07842353 0.09574271 0.09506270
m9.11 0.08987167 0.13010348 0.08825484 0.09136557
m9.21 0.09574271 0.12263326 0.08872570 0.08872570
m9.31 0.06204837 0.11321182 0.05000000 0.04868521
m10.11 0.06204837 0.06204837 0.05000000 0.05000000
m10.21 0.06035727 0.11117966 0.10307764 0.11149140
m10.31 0.05000000 0.05000000 0.05000000 0.05000000
KappaSD AccuracyLowerSD AccuracyUpperSD AccuracyNullSD
m1.11 0.11547005 0.09317859 0.001732051 0.00000000
m1.21 0.24249948 0.11276524 0.071339564 0.02598076
m1.31 0.19148542 0.13276671 0.012083046 0.00000000
m2.11 0.30589582 0.15903537 0.059668529 0.00000000
m2.21 0.20600708 0.08126654 0.059868606 0.00000000
m2.31 0.33988662 0.18560442 0.051813769 0.02598076
m3.11 0.09000000 0.05736724 0.001000000 0.02598076
m3.21 0.14314561 0.08442896 0.023930455 0.02598076
m3.31 0.17731023 0.11128642 0.011090537 0.02598076
m4.11 0.17731023 0.11128642 0.011090537 0.02598076
m4.21 0.30728746 0.17313867 0.048300276 0.02598076
m4.31 0.09577186 0.06497884 0.001414214 0.00000000
m5.11 0.11000000 0.07817235 0.001500000 0.02598076
m5.21 0.17642562 0.08113158 0.040743098 0.02598076
m5.31 0.17016536 0.10052985 0.011206397 0.02598076
m6.11 0.28301943 0.16785013 0.033180064 0.02598076
m6.21 0.17033765 0.09253108 0.027160020 0.02598076
m6.31 0.10000000 0.07693450 0.001500000 0.02598076
m7.11 0.15484481 0.08721238 0.010739336 0.02598076
m7.21 0.17701318 0.09145308 0.040849317 0.02598076
m7.31 0.11547005 0.09317859 0.001732051 0.00000000
m8.11 0.19000000 0.12360286 0.012203142 0.02598076
m8.21 0.27163379 0.13352247 0.072724938 0.02598076
m8.31 0.19039170 0.12360286 0.012203142 0.02598076
m9.11 0.18075651 0.11986937 0.013073510 0.02598076
m9.21 0.17702801 0.11113168 0.027097048 0.00000000
m9.31 0.09788939 0.07617524 0.001414214 0.02598076
m10.11 0.10000000 0.07693450 0.001500000 0.02598076
m10.21 0.21436787 0.10561566 0.053099749 0.02598076
m10.31 0.10000000 0.07833475 0.001500000 0.00000000
AccuracyPValueSD McnemarPValueSD PositiveSD NegativeSD True PositiveSD
m1.11 0.006075909 0.00000000 0.5 0.5 0.8164966
m1.21 0.282014184 0.33465940 0.5 0.5 0.9574271
m1.31 0.025979158 0.36769553 0.5 0.5 1.2583057
m2.11 0.184371907 0.00000000 0.5 0.5 1.2583057
m2.21 0.156538334 0.37600000 0.5 0.5 1.0000000
m2.31 0.184184690 0.09687363 0.5 0.5 0.5773503
m3.11 0.006000000 NA 0.5 0.5 0.0000000
m3.21 0.073708887 0.31806079 0.5 0.5 1.0000000
m3.31 0.028722813 0.00000000 0.5 0.5 0.5000000
m4.11 0.028722813 0.00000000 0.5 0.5 0.9574271
m4.21 0.169056943 0.49998533 0.5 0.5 1.2909944
m4.31 0.005259911 0.00000000 0.5 0.5 0.5000000
m5.11 0.006403124 0.00000000 0.5 0.5 0.0000000
m5.21 0.154189007 0.37921322 0.5 0.5 0.9574271
m5.31 0.028565714 0.36769553 0.5 0.5 0.5000000
m6.11 0.083691397 0.00000000 0.5 0.5 1.0000000
m6.21 0.080917654 0.26000000 0.5 0.5 1.2583057
m6.31 0.005000000 NA 0.5 0.5 0.8164966
m7.11 0.026851443 0.30022214 0.5 0.5 1.2909944
m7.21 0.131289439 0.36061833 0.5 0.5 1.4142136
m7.31 0.006075909 0.00000000 0.5 0.5 0.8164966
m8.11 0.025632011 0.00000000 0.5 0.5 0.5000000
m8.21 0.278717898 0.48957771 0.5 0.5 1.2583057
m8.31 0.025632011 0.00000000 0.5 0.5 0.9574271
m9.11 0.030397368 0.30022214 0.5 0.5 0.9574271
m9.21 0.071500000 0.26000000 0.5 0.5 1.4142136
m9.31 0.005416026 0.00000000 0.5 0.5 0.8164966
m10.11 0.005000000 NA 0.5 0.5 0.8164966
m10.21 0.186528818 0.26660630 0.5 0.5 0.5000000
m10.31 0.005188127 NA 0.5 0.5 0.8164966
False PositiveSD True NegativeSD False NegativeSD
m1.11 0.5000000 0.8164966 0.5000000
m1.21 1.4142136 0.9574271 0.8164966
m1.31 1.0000000 0.8164966 0.5000000
m2.11 0.8164966 1.0000000 0.9574271
m2.21 0.9574271 0.8164966 1.2583057
m2.31 0.9574271 1.0000000 1.5000000
m3.11 0.5000000 0.5000000 0.0000000
m3.21 1.2583057 0.0000000 0.5000000
m3.31 0.5773503 0.8164966 0.5000000
m4.11 0.5773503 0.0000000 0.5000000
m4.21 1.7078251 0.5000000 0.5773503
m4.31 0.5773503 0.8164966 0.5000000
m5.11 0.5000000 0.8164966 0.5000000
m5.21 0.8164966 0.9574271 0.8164966
m5.31 1.0000000 0.0000000 0.5000000
m6.11 0.9574271 0.8164966 0.5000000
m6.21 0.8164966 0.5773503 0.9574271
m6.31 0.5000000 0.5000000 0.0000000
m7.11 0.9574271 0.8164966 0.5000000
m7.21 0.9574271 1.0000000 0.5000000
m7.31 0.5000000 0.8164966 0.5000000
m8.11 0.5773503 0.8164966 0.5000000
m8.21 1.2909944 1.8929694 2.3804761
m8.31 0.5773503 0.8164966 0.5000000
m9.11 1.1547005 0.8164966 0.5000000
m9.21 0.9574271 0.5773503 0.5773503
m9.31 0.5000000 0.9574271 0.5773503
m10.11 0.5000000 0.5000000 0.0000000
m10.21 0.8164966 1.2583057 0.8164966
m10.31 0.5000000 0.5000000 0.0000000
$`Araucaria angustifolia`
algo ROC TSS Sensitivity Specificity Pos Pred Value
m1.1 kknn 0.9729167 0.9458333 0.98750 0.95825 0.98750
m2.1 kknn 0.9520833 0.8922619 0.97550 0.91650 0.97550
m3.1 kknn 0.9464286 0.8928571 0.97625 0.91650 0.97675
m4.1 kknn 0.9464286 0.8678571 0.95125 0.91650 0.97625
m5.1 kknn 0.9464286 0.7797619 0.98800 0.79150 0.94800
m6.1 kknn 0.9732143 0.9464286 0.98800 0.95825 0.98800
m7.1 kknn 0.9464286 0.8928571 0.97625 0.91650 0.97675
m8.1 kknn 0.9692460 0.8922619 0.97550 0.91650 0.97675
m9.1 kknn 0.9464286 0.8511905 0.97625 0.87500 0.96625
m10.1 kknn 0.9629960 0.8089286 0.97550 0.83325 0.95625
Neg Pred Value Precision Recall F1 Prevalence Detection Rate
m1.1 0.95825 0.98750 0.98750 0.98750 0.76650 0.75700
m2.1 0.92250 0.97550 0.97550 0.97550 0.77350 0.75475
m3.1 0.93750 0.97675 0.97625 0.97575 0.77350 0.75500
m4.1 0.86600 0.97625 0.95125 0.96275 0.77350 0.73575
m5.1 0.95825 0.94800 0.98800 0.96625 0.76650 0.75725
m6.1 0.96425 0.98800 0.98800 0.98800 0.77350 0.76425
m7.1 0.93750 0.97675 0.97625 0.97575 0.77350 0.75500
m8.1 0.91650 0.97675 0.97550 0.97575 0.78125 0.76200
m9.1 0.94450 0.96625 0.97625 0.97025 0.76650 0.74875
m10.1 0.91650 0.95625 0.97550 0.96475 0.78100 0.76175
Detection Prevalence Balanced Accuracy Accuracy Kappa AccuracyLower
m1.1 0.76650 0.97300 0.98075 0.94575 0.84150
m2.1 0.77400 0.94625 0.96200 0.89175 0.80875
m3.1 0.77400 0.94650 0.96275 0.89500 0.80975
m4.1 0.75425 0.93400 0.94375 0.84725 0.78375
m5.1 0.80350 0.89000 0.94450 0.80575 0.79000
m6.1 0.77400 0.97325 0.98125 0.94600 0.83850
m7.1 0.77400 0.94650 0.96275 0.89500 0.80975
m8.1 0.78050 0.94625 0.96225 0.88900 0.80775
m9.1 0.78650 0.92550 0.95425 0.86700 0.79850
m10.1 0.79900 0.90450 0.94350 0.81925 0.78225
AccuracyUpper AccuracyNull AccuracyPValue McnemarPValue Positive Negative
m1.1 0.99775 0.76650 0.01075 1.0000000 20.5 6.25
m2.1 0.99725 0.77350 0.01550 1.0000000 20.5 6.00
m3.1 0.99725 0.77350 0.01575 0.8266667 20.5 6.00
m4.1 0.99150 0.77350 0.04275 0.8266667 20.5 6.00
m5.1 0.98725 0.76650 0.07350 0.5670000 20.5 6.25
m6.1 0.99950 0.77350 0.00550 1.0000000 20.5 6.00
m7.1 0.99725 0.77350 0.01575 0.8266667 20.5 6.00
m8.1 0.99725 0.78125 0.02000 1.0000000 20.5 5.75
m9.1 0.99525 0.76650 0.02525 0.6533333 20.5 6.25
m10.1 0.99150 0.78100 0.04725 0.7493333 20.5 5.75
True Positive False Positive True Negative False Negative ROCSD
m1.1 20.25 0.25 6.00 0.25 0.05416667
m2.1 20.00 0.50 5.50 0.50 0.05626286
m3.1 20.00 0.50 5.50 0.50 0.03948363
m4.1 19.50 1.00 5.50 0.50 0.06484180
m5.1 20.25 0.25 5.00 1.25 0.06219745
m6.1 20.25 0.25 5.75 0.25 0.03933378
m7.1 20.00 0.50 5.50 0.50 0.03948363
m8.1 20.00 0.50 5.25 0.50 0.05147271
m9.1 20.00 0.50 5.50 1.00 0.07866757
m10.1 20.00 0.50 4.75 1.00 0.04857222
TSSSD SensitivitySD SpecificitySD Pos Pred ValueSD Neg Pred ValueSD
m1.1 0.10833333 0.02500000 0.08350000 0.02500000 0.08350000
m2.1 0.10092870 0.02830194 0.09641749 0.02830194 0.09002407
m3.1 0.07896726 0.04750000 0.09641749 0.02687471 0.12500000
m4.1 0.11034030 0.05632865 0.09641749 0.02750000 0.15542630
m5.1 0.31429473 0.02400000 0.31564167 0.07547185 0.08350000
m6.1 0.07866757 0.02400000 0.08350000 0.02400000 0.07150000
m7.1 0.07896726 0.04750000 0.09641749 0.02687471 0.12500000
m8.1 0.09960950 0.02830194 0.09641749 0.02687471 0.09641749
m9.1 0.16947743 0.04750000 0.19225764 0.05141012 0.11100000
m10.1 0.22596375 0.02830194 0.23570232 0.05907270 0.09641749
PrecisionSD RecallSD F1SD PrevalenceSD Detection RateSD
m1.1 0.02500000 0.02500000 0.02500000 0.011789826 0.02089657
m2.1 0.02830194 0.02830194 0.02042058 0.005196152 0.02233644
m3.1 0.02687471 0.04750000 0.02043486 0.005196152 0.03426368
m4.1 0.02750000 0.05632865 0.03223223 0.005196152 0.04401799
m5.1 0.07547185 0.02400000 0.04209810 0.017521415 0.01912023
m6.1 0.02400000 0.02400000 0.01385641 0.005196152 0.01607016
m7.1 0.02687471 0.04750000 0.02043486 0.005196152 0.03426368
m8.1 0.02687471 0.02830194 0.01963628 0.013200379 0.01581139
m9.1 0.05141012 0.04750000 0.02392175 0.011789826 0.04669315
m10.1 0.05907270 0.02830194 0.02940947 0.018493242 0.02093442
Detection PrevalenceSD Balanced AccuracySD AccuracySD KappaSD
m1.1 0.01178983 0.05400000 0.03850000 0.10850000
m2.1 0.02760435 0.05024191 0.03144307 0.08877453
m3.1 0.05086584 0.03929801 0.03021451 0.07874431
m4.1 0.05150647 0.05515433 0.04817589 0.12794628
m5.1 0.08317251 0.15699045 0.07084961 0.26572715
m6.1 0.02760435 0.03916950 0.02165448 0.06261523
m7.1 0.05086584 0.03929801 0.03021451 0.07874431
m8.1 0.02414539 0.04968819 0.03024759 0.08749095
m9.1 0.08102057 0.08485478 0.03708099 0.11516510
m10.1 0.06000000 0.11292918 0.04804512 0.16584405
AccuracyLowerSD AccuracyUpperSD AccuracyNullSD AccuracyPValueSD
m1.1 0.06177648 0.0045000000 0.011789826 0.020172176
m2.1 0.05031484 0.0041932485 0.005196152 0.017521415
m3.1 0.04549267 0.0041932485 0.005196152 0.018006943
m4.1 0.07007793 0.0110905365 0.005196152 0.053643111
m5.1 0.10004332 0.0199562020 0.017521415 0.119940263
m6.1 0.03649201 0.0005773503 0.005196152 0.005196152
m7.1 0.04549267 0.0041932485 0.005196152 0.018006943
m8.1 0.04600272 0.0041932485 0.013200379 0.018202564
m9.1 0.05588306 0.0049244289 0.011789826 0.019872510
m10.1 0.06938960 0.0110905365 0.018493242 0.050651588
McnemarPValueSD PositiveSD NegativeSD True PositiveSD False PositiveSD
m1.1 NA 0.5773503 0.5 0.9574271 0.5000000
m2.1 0.0000000 0.5773503 0.0 0.8164966 0.5773503
m3.1 0.3002221 0.5773503 0.0 0.8164966 1.0000000
m4.1 0.3002221 0.5773503 0.0 1.2909944 1.1547005
m5.1 0.6123545 0.5773503 0.5 0.5000000 0.5000000
m6.1 0.0000000 0.5773503 0.0 0.5000000 0.5000000
m7.1 0.3002221 0.5773503 0.0 0.8164966 1.0000000
m8.1 0.0000000 0.5773503 0.5 0.8164966 0.5773503
m9.1 0.3002221 0.5773503 0.5 0.8164966 1.0000000
m10.1 0.4341674 0.5773503 0.5 0.8164966 0.5773503
True NegativeSD False NegativeSD
m1.1 0.8164966 0.5000000
m2.1 0.5773503 0.5773503
m3.1 0.5773503 0.5773503
m4.1 0.5773503 0.5773503
m5.1 2.1602469 1.8929694
m6.1 0.5000000 0.5000000
m7.1 0.5773503 0.5773503
m8.1 0.5000000 0.5773503
m9.1 1.5000000 1.1547005
m10.1 1.2583057 1.4142136
train_sdm - independent data
Code
i1
Output
caretSDM
...............................
Class : input_sdm
-------- Occurrences --------
Species Names : Salminus brasiliensis Araucaria angustifolia
Number of presences : 76 21
Pseudoabsence methods :
Method to obtain PAs : bioclim
Number of PA sets : 10
Number of PAs in each set : 76 21
Independent Test : TRUE (number of records = 26 )
Data Cleaning : NAs, Capitals, Centroids, Geographically Duplicated, Identical Lat/Long, Institutions, Invalid, Non-terrestrial, Duplicated Cell (grid), Methods also applied in independent_test
-------- Predictors ---------
Number of Predictors : 3
Predictors Names : bio1, bio4, bio12
--------- Scenarios ---------
Number of Scenarios : 1
Scenarios Names : current
----------- Models ----------
Algorithms Names : kknn
Variables Names : bio1 bio4 bio12
Model Validation :
Method : repeatedcv
Number : 4
Metrics :
$`Salminus brasiliensis`
algo ROC TSS Sensitivity Specificity
1 kknn 0.90775 0.7841667 0.848325 0.940825
$`Araucaria angustifolia`
algo ROC TSS Sensitivity Specificity
1 kknn 0.9652412 0.8711842 0.97355 0.897375
Independent Validation :
ROC (mean +- sd) : NA +- NA
Code
i1$models
Output
caretSDM
.........................
Class : Models
Algorithms Names : kknn
Variables Names : bio1 bio4 bio12
Model Validation :
Method : repeatedcv
Number : 4
Metrics :
$`Salminus brasiliensis`
algo ROC TSS Sensitivity Specificity Pos Pred Value
m1.1 kknn 0.9000000 0.8000000 0.90000 0.90000 0.92250
m2.1 kknn 0.9250000 0.8500000 0.85000 1.00000 1.00000
m3.1 kknn 0.8791667 0.7583333 0.80825 0.95000 0.95825
m4.1 kknn 0.9458333 0.8166667 0.86675 0.95000 0.95825
m5.1 kknn 0.9250000 0.8500000 0.85000 1.00000 1.00000
m6.1 kknn 0.9583333 0.8583333 0.90825 0.95000 0.95825
m7.1 kknn 0.9233333 0.7666667 0.81675 0.95000 0.95000
m8.1 kknn 0.8791667 0.7583333 0.80825 0.95000 0.95825
m9.1 kknn 0.8125000 0.5250000 0.81675 0.75825 0.82500
m10.1 kknn 0.9291667 0.8583333 0.85825 1.00000 1.00000
Neg Pred Value Precision Recall F1 Prevalence Detection Rate
m1.1 0.92850 0.92250 0.90000 0.89550 0.5 0.45000
m2.1 0.89275 1.00000 0.85000 0.90975 0.5 0.42725
m3.1 0.87050 0.95825 0.80825 0.84725 0.5 0.40425
m4.1 0.88675 0.95825 0.86675 0.89950 0.5 0.42975
m5.1 0.89575 1.00000 0.85000 0.90975 0.5 0.42950
m6.1 0.90825 0.95825 0.90825 0.93050 0.5 0.45250
m7.1 0.83675 0.95000 0.81675 0.87225 0.5 0.40475
m8.1 0.86450 0.95825 0.80825 0.84725 0.5 0.40250
m9.1 0.74975 0.82500 0.81675 0.79225 0.5 0.40700
m10.1 0.87475 1.00000 0.85825 0.92175 0.5 0.42750
Detection Prevalence Balanced Accuracy Accuracy Kappa AccuracyLower
m1.1 0.52025 0.90000 0.90225 0.80350 0.57525
m2.1 0.42725 0.92500 0.92725 0.85350 0.60950
m3.1 0.42925 0.87925 0.87925 0.75825 0.55250
m4.1 0.45475 0.90825 0.90450 0.81125 0.57675
m5.1 0.42950 0.92500 0.92950 0.85525 0.61100
m6.1 0.47500 0.92925 0.92950 0.85825 0.61100
m7.1 0.42975 0.88325 0.87950 0.76125 0.54900
m8.1 0.42750 0.87925 0.87725 0.75500 0.55125
m9.1 0.54550 0.76250 0.76825 0.52375 0.43975
m10.1 0.42750 0.92925 0.92725 0.85500 0.60300
AccuracyUpper AccuracyNull AccuracyPValue McnemarPValue Positive Negative
m1.1 0.99250 0.5225 0.02300 0.8700000 5.25 5.25
m2.1 0.99325 0.5225 0.01750 0.7400000 5.25 5.25
m3.1 0.98200 0.5000 0.04675 0.7493333 5.25 5.25
m4.1 0.99275 0.5225 0.02400 0.8700000 5.25 5.25
m5.1 0.99350 0.5225 0.01850 0.7400000 5.25 5.25
m6.1 0.99350 0.5225 0.01850 1.0000000 5.25 5.25
m7.1 0.98725 0.5225 0.03200 0.8266667 5.25 5.25
m8.1 0.98200 0.5225 0.04925 0.7493333 5.25 5.25
m9.1 0.93425 0.5225 0.22525 0.9207500 5.25 5.25
m10.1 0.99800 0.5225 0.00900 1.0000000 5.25 5.25
True Positive False Positive True Negative False Negative ROCSD
m1.1 4.75 0.50 4.75 0.75 0.08164966
m2.1 4.50 0.75 5.25 0.00 0.09574271
m3.1 4.25 1.00 5.00 0.25 0.12720281
m4.1 4.50 1.00 5.00 0.25 0.05335937
m5.1 4.50 0.75 5.25 0.00 0.09574271
m6.1 4.75 0.50 5.00 0.25 0.08858455
m7.1 4.25 1.00 5.00 0.25 0.07272475
m8.1 4.25 1.00 5.00 0.25 0.12720281
m9.1 4.25 1.25 4.00 1.50 0.26714367
m10.1 4.50 0.75 5.25 0.00 0.04787136
TSSSD SensitivitySD SpecificitySD Pos Pred ValueSD Neg Pred ValueSD
m1.1 0.16329932 0.20000000 0.1154701 0.09002407 0.1430000
m2.1 0.19148542 0.19148542 0.0000000 0.00000000 0.1369121
m3.1 0.25440563 0.28332358 0.1000000 0.08350000 0.1770056
m4.1 0.13743685 0.16316326 0.1000000 0.08350000 0.1395024
m5.1 0.19148542 0.19148542 0.0000000 0.00000000 0.1250557
m6.1 0.17716910 0.10679693 0.1000000 0.08350000 0.1067969
m7.1 0.17638342 0.13731563 0.1000000 0.10000000 0.1198370
m8.1 0.25440563 0.28332358 0.1000000 0.08350000 0.1780197
m9.1 0.44586578 0.13731563 0.3804019 0.23629078 0.2887714
m10.1 0.09574271 0.09577186 0.0000000 0.00000000 0.0835000
PrecisionSD RecallSD F1SD PrevalenceSD Detection RateSD
m1.1 0.09002407 0.20000000 0.10492696 0.03674235 0.10653638
m2.1 0.00000000 0.19148542 0.11866023 0.03674235 0.11452911
m3.1 0.08350000 0.28332358 0.18909676 0.00000000 0.14167657
m4.1 0.08350000 0.16316326 0.08205892 0.03674235 0.06428063
m5.1 0.00000000 0.19148542 0.11866023 0.03674235 0.12065516
m6.1 0.08350000 0.10679693 0.08344459 0.03674235 0.04092676
m7.1 0.10000000 0.13731563 0.09493989 0.03674235 0.03755330
m8.1 0.08350000 0.28332358 0.18909676 0.03674235 0.13665650
m9.1 0.23629078 0.13731563 0.15492229 0.03674235 0.06428063
m10.1 0.00000000 0.09577186 0.05301179 0.03674235 0.03175426
Detection PrevalenceSD Balanced AccuracySD AccuracySD KappaSD
m1.1 0.15329791 0.08164966 0.08177357 0.16344928
m2.1 0.11452911 0.09574271 0.09506270 0.19039170
m3.1 0.17016340 0.12723567 0.12723567 0.25437292
m4.1 0.11327842 0.06883979 0.07448266 0.14550916
m5.1 0.12065516 0.09574271 0.08802083 0.18241962
m6.1 0.06204837 0.08844348 0.08802083 0.17731023
m7.1 0.05994650 0.08825484 0.09136557 0.18075651
m8.1 0.16635805 0.12723567 0.12650527 0.25317978
m9.1 0.15649601 0.22312403 0.21238075 0.44813568
m10.1 0.03175426 0.04784262 0.04868521 0.09712535
AccuracyLowerSD AccuracyUpperSD AccuracyNullSD AccuracyPValueSD
m1.1 0.11151196 0.011733144 0.02598076 0.023916521
m2.1 0.12360286 0.012203142 0.02598076 0.025632011
m3.1 0.14741438 0.032690468 0.00000000 0.083611702
m4.1 0.09838149 0.010594810 0.02598076 0.027080128
m5.1 0.11128642 0.011090537 0.02598076 0.028722813
m6.1 0.11128642 0.011090537 0.02598076 0.028722813
m7.1 0.11986937 0.013073510 0.02598076 0.030397368
m8.1 0.15211481 0.032690468 0.02598076 0.082001524
m9.1 0.18506103 0.112562205 0.02598076 0.395868981
m10.1 0.07617524 0.001414214 0.02598076 0.005416026
McnemarPValueSD PositiveSD NegativeSD True PositiveSD False PositiveSD
m1.1 0.3002221 0.5 0.5 1.2583057 1.0000000
m2.1 0.3676955 0.5 0.5 1.2909944 0.9574271
m3.1 0.4341674 0.5 0.5 1.5000000 1.4142136
m4.1 0.3002221 0.5 0.5 0.5773503 0.9574271
m5.1 0.3676955 0.5 0.5 1.2909944 0.9574271
m6.1 0.0000000 0.5 0.5 0.5000000 0.5773503
m7.1 0.3002221 0.5 0.5 0.5000000 0.8164966
m8.1 0.4341674 0.5 0.5 1.5000000 1.4142136
m9.1 0.1585000 0.5 0.5 0.5000000 0.8164966
m10.1 0.0000000 0.5 0.5 0.5773503 0.5000000
True NegativeSD False NegativeSD
m1.1 0.9574271 0.5773503
m2.1 0.5000000 0.0000000
m3.1 0.8164966 0.5000000
m4.1 0.8164966 0.5000000
m5.1 0.5000000 0.0000000
m6.1 0.8164966 0.5000000
m7.1 0.8164966 0.5000000
m8.1 0.8164966 0.5000000
m9.1 2.0000000 1.8929694
m10.1 0.5000000 0.0000000
$`Araucaria angustifolia`
algo ROC TSS Sensitivity Specificity Pos Pred Value
m1.1 kknn 0.9682018 0.8486842 0.97375 0.87500 0.96375
m2.1 kknn 0.9747807 0.8903509 0.97350 0.91650 0.97425
m3.1 kknn 0.9714912 0.7653509 0.97350 0.79150 0.94325
m4.1 kknn 0.9791667 0.9451754 0.98675 0.95825 0.98750
m5.1 kknn 0.9802632 0.8903509 0.97350 0.91650 0.97500
m6.1 kknn 0.9451754 0.8903509 0.97350 0.91650 0.97425
m7.1 kknn 0.9353070 0.8640351 0.94725 0.91650 0.97275
m8.1 kknn 0.9475877 0.8951754 0.98675 0.90825 0.97425
m9.1 kknn 0.9660088 0.8903509 0.97350 0.91650 0.97425
m10.1 kknn 0.9844298 0.8320175 0.97350 0.85825 0.96175
Neg Pred Value Precision Recall F1 Prevalence Detection Rate
m1.1 0.93750 0.96375 0.97375 0.96700 0.76000 0.74000
m2.1 0.92250 0.97425 0.97350 0.97350 0.76000 0.74000
m3.1 0.92250 0.94325 0.97350 0.95625 0.75275 0.73275
m4.1 0.95825 0.98750 0.98675 0.98675 0.76800 0.75750
m5.1 0.92850 0.97500 0.97350 0.97350 0.76000 0.74000
m6.1 0.92700 0.97425 0.97350 0.97350 0.75275 0.73300
m7.1 0.85100 0.97275 0.94725 0.95975 0.76000 0.72000
m8.1 0.95000 0.97425 0.98675 0.98025 0.76800 0.75750
m9.1 0.91650 0.97425 0.97350 0.97350 0.76800 0.74750
m10.1 0.91425 0.96175 0.97350 0.96700 0.77600 0.75550
Detection Prevalence Balanced Accuracy Accuracy Kappa AccuracyLower
m1.1 0.77000 0.92425 0.95000 0.85975 0.78475
m2.1 0.76000 0.94525 0.96000 0.89025 0.79875
m3.1 0.78275 0.88275 0.93000 0.77725 0.76075
m4.1 0.76750 0.97275 0.97950 0.94150 0.82775
m5.1 0.76000 0.94550 0.96000 0.89000 0.79600
m6.1 0.75300 0.94525 0.96050 0.89275 0.80075
m7.1 0.74000 0.93200 0.94000 0.84150 0.77175
m8.1 0.77800 0.94775 0.96925 0.90775 0.81300
m9.1 0.76750 0.94525 0.95950 0.88675 0.79700
m10.1 0.78625 0.91625 0.94875 0.84775 0.77775
AccuracyUpper AccuracyNull AccuracyPValue McnemarPValue Positive Negative
m1.1 0.99475 0.76000 0.0230 0.6533333 19 6.00
m2.1 0.99700 0.76000 0.0150 1.0000000 19 6.00
m3.1 0.98600 0.75275 0.0745 0.7113333 19 6.25
m4.1 0.99950 0.76800 0.0270 1.0000000 19 5.75
m5.1 0.99900 0.76000 0.0090 1.0000000 19 6.00
m6.1 0.99700 0.75275 0.0135 1.0000000 19 6.25
m7.1 0.99100 0.76000 0.0420 1.0000000 19 6.00
m8.1 0.99725 0.76800 0.0270 1.0000000 19 5.75
m9.1 0.99700 0.76800 0.0195 1.0000000 19 5.75
m10.1 0.99675 0.77600 0.0355 1.0000000 19 5.50
True Positive False Positive True Negative False Negative ROCSD
m1.1 18.50 0.50 5.25 0.75 0.03623402
m2.1 18.50 0.50 5.50 0.50 0.05517432
m3.1 18.50 0.50 5.00 1.25 0.05413307
m4.1 18.75 0.50 5.50 0.25 0.04166667
m5.1 18.50 0.50 5.50 0.50 0.03250254
m6.1 18.50 0.50 5.75 0.50 0.05045449
m7.1 18.00 1.00 5.50 0.50 0.06258010
m8.1 18.75 0.25 5.25 0.50 0.06301305
m9.1 18.50 0.50 5.25 0.50 0.05194175
m10.1 18.50 0.50 4.75 0.75 0.01569783
TSSSD SensitivitySD SpecificitySD Pos Pred ValueSD Neg Pred ValueSD
m1.1 0.13950310 0.05250000 0.15945532 0.04571196 0.12500000
m2.1 0.10090897 0.03059956 0.09641749 0.02975875 0.09002407
m3.1 0.30280104 0.03059956 0.31564167 0.08206248 0.09002407
m4.1 0.08240480 0.05250000 0.08350000 0.02500000 0.14300000
m5.1 0.06583819 0.03059956 0.09641749 0.02886751 0.08256109
m6.1 0.10090897 0.03059956 0.09641749 0.02975875 0.08601938
m7.1 0.13020728 0.04286704 0.09641749 0.03148942 0.11737405
m8.1 0.12602611 0.02650000 0.10679693 0.02975875 0.10000000
m9.1 0.10090897 0.03059956 0.09641749 0.02975875 0.09641749
m10.1 0.08465253 0.03059956 0.09577186 0.02553919 0.10171324
PrecisionSD RecallSD F1SD PrevalenceSD Detection RateSD
m1.1 0.04571196 0.05250000 0.0255342907 0.00000000 0.04000000
m2.1 0.02975875 0.03059956 0.0216410105 0.00000000 0.02309401
m3.1 0.08206248 0.03059956 0.0404423458 0.01450000 0.01889224
m4.1 0.02500000 0.05250000 0.0266520793 0.01600000 0.02600000
m5.1 0.02886751 0.03059956 0.0005773503 0.00000000 0.02309401
m6.1 0.02975875 0.03059956 0.0216410105 0.01450000 0.03320643
m7.1 0.03148942 0.04286704 0.0347311100 0.00000000 0.03265986
m8.1 0.02975875 0.02650000 0.0253294953 0.01600000 0.00500000
m9.1 0.02975875 0.03059956 0.0216410105 0.01600000 0.01892969
m10.1 0.02553919 0.03059956 0.0133416641 0.01847521 0.02968164
Detection PrevalenceSD Balanced AccuracySD AccuracySD KappaSD
m1.1 0.06831301 0.06984447 0.03829708 0.108149202
m2.1 0.03265986 0.05057915 0.03265986 0.089540959
m3.1 0.09304255 0.15129965 0.06831301 0.246935045
m4.1 0.03771825 0.04112582 0.03960955 0.105880436
m5.1 0.04618802 0.03290897 0.00000000 0.006928203
m6.1 0.04482559 0.05057915 0.03267517 0.089893919
m7.1 0.02309401 0.06525846 0.05163978 0.135411718
m8.1 0.02103965 0.06283510 0.03960955 0.120311748
m9.1 0.02217356 0.05057915 0.03267517 0.089514896
m10.1 0.04759814 0.04236646 0.02118765 0.068451321
AccuracyLowerSD AccuracyUpperSD AccuracyNullSD AccuracyPValueSD
m1.1 0.05846580 0.005500000 0.00000000 0.02103965
m2.1 0.05031484 0.004690416 0.00000000 0.01773885
m3.1 0.09660702 0.021150256 0.01450000 0.11699715
m4.1 0.06371290 0.004856267 0.01600000 0.04681880
m5.1 0.00000000 0.000000000 0.00000000 0.00000000
m6.1 0.05032809 0.004690416 0.01450000 0.01864582
m7.1 0.07513710 0.011575837 0.00000000 0.05290243
m8.1 0.06371290 0.004856267 0.01600000 0.04681880
m9.1 0.05056349 0.004690416 0.01600000 0.01799074
m10.1 0.03200391 0.004500000 0.01847521 0.04186884
McnemarPValueSD PositiveSD NegativeSD True PositiveSD False PositiveSD
m1.1 0.3002221 0 0.0000000 1.0000000 1.0000000
m2.1 0.0000000 0 0.0000000 0.5773503 0.5773503
m3.1 0.4999853 0 0.5000000 0.5773503 0.5773503
m4.1 0.3676955 0 0.5000000 1.0000000 1.0000000
m5.1 0.0000000 0 0.0000000 0.5773503 0.5773503
m6.1 0.0000000 0 0.5000000 0.5773503 0.5773503
m7.1 0.0000000 0 0.0000000 0.8164966 0.8164966
m8.1 0.0000000 0 0.5000000 0.5000000 0.5000000
m9.1 0.0000000 0 0.5000000 0.5773503 0.5773503
m10.1 0.0000000 0 0.5773503 0.5773503 0.5773503
True NegativeSD False NegativeSD
m1.1 0.9574271 0.9574271
m2.1 0.5773503 0.5773503
m3.1 2.1602469 1.8929694
m4.1 0.5773503 0.5000000
m5.1 0.5773503 0.5773503
m6.1 0.9574271 0.5773503
m7.1 0.5773503 0.5773503
m8.1 0.9574271 0.5773503
m9.1 0.5000000 0.5773503
m10.1 0.9574271 0.5000000
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