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Last data update: 2014.03.03

R: Tuning and evaluation of ENMs with Maxent

ENMevaluate

R Documentation

Tuning and evaluation of ENMs with Maxent

Description

ENMevaluate automatically executes Maxent (Phillips et al. 2006; Phillips and Dudik 2008) across a range of settings, returning a data.frame of evaluation metrics to aid in identifying settings that balance model fit and predictive ability. The function calls Maxent using the maxent function in the dismo package (Hijmans et al. 2011). Users should consult ENMeval-package and help documentation of the dismo package for guidelines on how to run Maxent in R.

Usage

ENMevaluate(occ, env, bg.coords = NULL, occ.grp = NULL, 
		bg.grp = NULL, RMvalues = seq(0.5, 4, 0.5), 
		fc = c("L", "LQ", "H", "LQH", "LQHP", "LQHPT"),
		categoricals = NULL, n.bg = 10000, method = NULL, 
		overlap = FALSE, aggregation.factor = c(2, 2), 
		kfolds = NA, bin.output = FALSE, clamp = TRUE,
		rasterPreds = TRUE, parallel = FALSE, numCores = NULL)

tuning(occ, env, bg.coords, occ.grp, bg.grp, method, 
	maxent.args, args.lab, categoricals, aggregation.factor, 
	kfolds, bin.output, clamp, rasterPreds, parallel, numCores)

Arguments

`occ`	Two-column matrix or data.frame of longitude and latitude (in that order) of occurrence localities.
`env`	RasterStack of model predictor variables (environmental layers).
`bg.coords`	Two-column matrix or data.frame of longitude and latitude (in that order) of background localities (required for '`user`' method).
`occ.grp`	Vector of bins of occurrence localities (required for '`user`' method).
`bg.grp`	Vector of bins of background localities (required for '`user`' method).
`RMvalues`	Vector of (non-negative) values to use for the regularization multiplier.
`fc`	Character vector of feature class combinations to be included in analysis.
`categoricals`	Vector indicating which (if any) of the input environmental layers are categorical.
`n.bg`	The number of random background localities to draw from the study extent.
`method`	Character string designating the method used for data partitioning. Choices are: `"jackknife", "randomkfold", "user", "block", "checkerboard1", "checkerboard2"`. See details and `get.evaluation.bins` for more information.
`overlap`	logical; If `TRUE`, provides pairwise metric of niche overlap (see details and `calc.niche.overlap`).
`aggregation.factor`	List giving the factor by which the original input grid should be aggregated for checkerboard partitioning methods (see details and `get.evaluation.bins`).
`kfolds`	Number of bins to use in the k-fold random method of data partitioning.
`bin.output`	logical; If `TRUE`, appends evaluations metrics for each evaluation bin to results table (i.e., in addition to the average values across bins).
`maxent.args`	Arguments to pass to Maxent that are generated by the `make.args` function
`args.lab`	Character labels describing feature classes and regularization multiplier values for Maxent runs provided by the `make.args` function.
`clamp`	logical; If `TRUE`, 'clamping' is used (see Maxent documentation and tutorial for more details).
`rasterPreds`	logical; If `TRUE`, the `predict` function from `dismo` is used to predict each full model across the extent of the input environmental variables. Setting to `FALSE` can significantly reduce run time if visualizing predictions in geographic space is not required.
`parallel`	logical; If `TRUE`, parallel processing is used to execute tuning function.
`numCores`	numeric; indicates the number of cores to use if running in parallel. If `parallel=TRUE` and this is not specified, the total number of available cores are used.

Details

ENMevaluate is the primary function for general use in the ENMeval package; the tuning function is used internally.

Maxent settings: In the current default implementation of Maxent, the combination of feature classes (fcs) allowed depends on the number of occurrence localities, and the value for the regularization multiplier (RM) is 1.0. ENMevaluate provides an automated way to execute ecological niche models in Maxent across a user-specified range of (RM) values and (fc) combinations, regardless of sample size. Acceptable values for the fc argument include: L=linear, Q=quadratic, P=product, T=threshold, and H=hinge (see Maxent help documentation, Phillips et al. (2006), Phillips and Dudik (2008), Elith et al. (2011), and Merow et al. (2013) for additional details on RM and fcs). Categorical feature classes (C) are specified by the categoricals argument.

Methods for partitioning data: ENMevaluate includes six methods to partition occurrence and background localities into bins for training and testing ('jackknife', 'randomkfold', 'user', 'block', 'checkerboard1', 'checkerboard2'). The jackknife method is a special case of k-fold cross validation where the number of folds (k) is equal to the number of occurrence localities (n) in the dataset. The randomkfold method partitions occurrence localities randomly into a user-specified number of (k) bins - this is equivalent to the method of k-fold cross validation currently provided by Maxent. The user method enables users to define bins a priori. For this method, the user is required to provide background coordinates (bg.coords) and bin designations for both occurrence localities (occ.grp) and background localities (bg.grp). The block method partitions the data into four bins according to the lines of latitude and longitude that divide the occurrence localities into bins of as equal number as possible. The checkerboard1 (and checkerboard2) methods partition data into two (or four) bins based on one (or two) checkerboard patterns with grain size defined as one (or two) aggregation factor(s) of the original environmental layers. Although the checkerboard1 (and checkerboard2) methods are designed to partition occurrence localities into two (and four) evaluation bins, they may give fewer bins depending on the location of occurrence localities with respect to the checkerboard grid(s) (e.g., all records happen to fall in the "black" squares). A warning is given if the number of bins is < 4 for the checkerboard2 method, and an error is given if all localities fall in a single evaluation bin. Additional details can be found in get.evaluation.bins.

Evaluation metrics: Four evaluation metrics are calculated using the partitioned dataset, and one additional metric is provided based on the full dataset. ENMevaluate uses the same background localities and evaluation bin designations for each of the k iterations (for each unique combination of RM and fc) to facilitate valid comparisons among model settings.

Mean.AUC is the area under the curve of the receiver operating characteristic plot made based on the testing data (i.e., AUCtest), averaged across k bins. In each iteration, as currently implemented, the AUCtest value is calculated with respect to the full set of background localities to enable comparisons across the k iterations (Radosavljevic and Anderson 2014). As a relative measure for a given study species and region, high values of Mean.AUC are associated with the degree to which a model can successfully discriminate occurrence from background localities. This rank-based non-parametric metric, however, does not reveal the model goodness-of-fit (Lobo et al. 2008; Peterson et al. 2011).

To quantify the degree of overfitting, ENMevaluate calculates three metrics. The first is the difference between training and testing AUC, averaged across k bins (Mean.AUC.DIFF) (Warren and Seifert 2011). Mean.AUC.DIFF is expected to be high for models overfit to the training data. ENMevaluate also calculates two threshold-dependent omission rates that quantify overfitting when compared with the omission rate expected by the threshold employed: the proportion of testing localities with Maxent output values lower than the value associated with (1) the training locality with the lowest value (i.e., the minimum training presence, MTP; = 0 percent training omission) (Mean.ORmin) and (2) the value that excludes the 10 percent of training localities with the lowest predicted suitability (Mean.OR10) (Pearson et al. 2007). ENMevaluate uses corrected.var to calculate the variance for each of these metrics across k bins (i.e., variances are corrected for non-independence of cross-validation iterations; see Shcheglovitova and Anderson 2013). The value of these metrics for each of the individual k bins is returned if bin.output = TRUE.

Based on the unpartitioned (full) dataset, ENMevaluate uses calc.aicc to calculate the AICc value for each model run and provides delta.AIC, AICc weights, as well as the number of parameters for each model (Warren and Seifert 2011). The AUCtrain value for the full model is also returned (full.AUC).

To quantify how resulting predictions differ in geographic space depending on the settings used, ENMevaluate includes an option to compute pairwise niche overlap between all pairs of full models (i.e., using the unpartitioned dataset) with Schoener's D statistic (Schoener 1968; Warren et al. 2009).

Value

An object of class ENMevaluation with named slots:

@results data.frame of evaluation metrics. If bin.output=TRUE, evaluation metrics calculated separately for each evaluation bin are included in addition to the averages across k bins.

@predictions RasterStack of full model predictions with each layer named as: fc_RM (e.g., L_1). This will be an empty RasterStack if the rasterPreds argument is set to FALSE.

@models List of objects of class "MaxEnt" from the dismo package. Each of these entries include slots for lambda values and the original Maxent results table. See Maxent documentation for more information.

@partition.method character vector with the method used for data partitioning.

@occ.pts data.frame of the latitude/longitude of input occurrence localities.

@occ.grp vector identifying the bin for each occurrence locality.

@bg.pts data.frame of the latitude/longitude of input background localities.

@bg.grp vector identifying the bin for each background locality.

@overlap matrix of pairwise niche overlap (blank if overlap = FALSE).

Author(s)

Uses the maxent function in the dismo package (Hijmans et al. 2011, Phillips et al. 2006)

Robert Muscarella <bob.muscarella@gmail.com> and Jamie M. Kass <jkass@gc.cuny.edu>

References

Elith, J., Phillips, S. J., Hastie, T., Dudik, M., Chee, Y. E., and Yates, C. J. (2011) A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17: 43-57.

Hijmans, R. J., Phillips, S., Leathwick, J. and Elith, J. (2011) dismo package for R. Available online at: https://cran.r-project.org/package=dismo.

Lobo, J. M., Jimenez-Valverde, A., and Real, R. (2008) AUC: A misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography, 17: 145-151.

Muscarella, R., Galante, P.J., Soley-Guardia, M., Boria, R.A., Kass, J., Uriarte, M. and Anderson, R.P. (2014) ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for ecological niche models. Methods in Ecology and Evolution, 5: 1198-1205.

Pearson, R. G., Raxworthy, C. J., Nakamura, M. and Peterson, A. T. 2007. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. Journal of Biogeography, 34: 102-117.

Peterson, A. T., Soberon, J., Pearson, R. G., Anderson, R. P., Martinez-Meyer, E., Nakamura, M. and Araujo, M. B. (2011) Ecological Niches and Geographic Distributions. Monographs in Population Biology, 49. Princeton University Press, Princeton, NJ.

Phillips, S. J., Anderson, R. P., and Schapire, R. E. (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190: 231-259.

Phillips, S. J. and Dudik, M. (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31: 161-175.

Merow, C., Smith, M., and Silander, J. A. (2013) A practical guide to Maxent: what it does, and why inputs and settings matter. Ecography, 36: 1-12.

Radosavljevic, A. and Anderson, R. P. 2014. Making better Maxent models of species distributions: complexity, overfitting and evaluation. Journal of Biogeography, 41: 629-643.

Schoener, T. W. (1968) The Anolis lizards of Bimini: resource partitioning in a complex fauna. Ecology, 49: 704-726.

Shcheglovitova, M. and Anderson, R. P. (2013) Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes. Ecological Modelling, 269: 9-17.

Warren, D. L., Glor, R. E., Turelli, M. and Funk, D. (2009) Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution, 62: 2868-2883; Erratum: Evolution, 65: 1215.

Warren, D.L. and Seifert, S.N. (2011) Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological Applications, 21: 335-342.

Examples

### Simulated data environmental covariates
set.seed(1)
r1 <- raster(matrix(nrow=50, ncol=50, data=runif(10000, 0, 25)))
r2 <- raster(matrix(nrow=50, ncol=50, data=rep(1:100, each=100), byrow=TRUE))
r3 <- raster(matrix(nrow=50, ncol=50, data=rep(1:100, each=100)))
r4 <- raster(matrix(nrow=50, ncol=50, data=c(rep(1,1000),rep(2,500)),byrow=TRUE))
values(r4) <- as.factor(values(r4))
env <- stack(r1,r2,r3,r4)

### Simulate occurrence localities
nocc <- 50
x <- (rpois(nocc, 2) + abs(rnorm(nocc)))/11
y <- runif(nocc, 0, .99)
occ <- cbind(x,y)

## Not run: 
### This call gives the results loaded below
enmeval_results <- ENMevaluate(occ, env, method="block", n.bg=500, overlap=TRUE, 
bin.output=TRUE, clamp=TRUE)

## End(Not run)

data(enmeval_results)
enmeval_results

### See table of evaluation metrics
enmeval_results@results

### Plot prediction with lowest AICc
plot(enmeval_results@predictions[[which (enmeval_results@results$delta.AICc == 0) ]])
points(enmeval_results@occ.pts, pch=21, bg=enmeval_results@occ.grp)

### Niche overlap statistics between model predictions
enmeval_results@overlap

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(ENMeval)
Loading required package: dismo
Loading required package: raster
Loading required package: sp
Loading required package: rJava
Loading required package: parallel
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/ENMeval/ENMevaluate.Rd_%03d_medium.png", width=480, height=480)
> ### Name: ENMevaluate 
> ### Title: Tuning and evaluation of ENMs with Maxent
> ### Aliases: ENMevaluate tuning
> 
> ### ** Examples
> 
> ### Simulated data environmental covariates
> set.seed(1)
> r1 <- raster(matrix(nrow=50, ncol=50, data=runif(10000, 0, 25)))
> r2 <- raster(matrix(nrow=50, ncol=50, data=rep(1:100, each=100), byrow=TRUE))
> r3 <- raster(matrix(nrow=50, ncol=50, data=rep(1:100, each=100)))
> r4 <- raster(matrix(nrow=50, ncol=50, data=c(rep(1,1000),rep(2,500)),byrow=TRUE))
> values(r4) <- as.factor(values(r4))
> env <- stack(r1,r2,r3,r4)
> 
> ### Simulate occurrence localities
> nocc <- 50
> x <- (rpois(nocc, 2) + abs(rnorm(nocc)))/11
> y <- runif(nocc, 0, .99)
> occ <- cbind(x,y)
> 
> ## Not run: 
> ##D ### This call gives the results loaded below
> ##D enmeval_results <- ENMevaluate(occ, env, method="block", n.bg=500, overlap=TRUE, 
> ##D bin.output=TRUE, clamp=TRUE)
> ## End(Not run)
> 
> data(enmeval_results)
> enmeval_results
An object of class: ENMevaluation
 Occurrence/background points: 50/500
 Partition method: block
 Feature classes: L, LQ, H, LQH, LQHP, LQHPT
 Regularization multipliers: 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4
 @results     : data.frame of evaluation results
 @predictions : RasterStack of model predictions
 @models      : list of model objects
 @occ.pts     : data.frame of occurrence coordinates
 @occ.grp     : vector of bins for occurrence points
 @bg.pts      : data.frame of background coordinates
 @bg.grp      : vector of bins for background points
 @overlap     : matrix of pairwise niche overlap metric
> 
> ### See table of evaluation metrics
> enmeval_results@results
    settings features  rm full.AUC  Mean.AUC   Var.AUC Mean.AUC.DIFF
1      L_0.5        L 0.5   0.7512 0.6193366 0.4162066    0.06261513
2     LQ_0.5       LQ 0.5   0.8151 0.6540218 0.5342977    0.02642105
3      H_0.5        H 0.5   0.8473 0.6303295 0.4975472    0.06092553
4    LQH_0.5      LQH 0.5   0.8479 0.6271692 0.4956675    0.06728342
5   LQHP_0.5     LQHP 0.5   0.8642 0.6244603 0.4920282    0.07618212
6  LQHPT_0.5    LQHPT 0.5   0.8872 0.6384680 0.4696086    0.08332131
7        L_1        L 1.0   0.7503 0.6200212 0.4118639    0.06199232
8       LQ_1       LQ 1.0   0.8123 0.6596782 0.5484075    0.02989474
9        H_1        H 1.0   0.8383 0.6419115 0.5154462    0.05477407
10     LQH_1      LQH 1.0   0.8376 0.6385064 0.5153454    0.05908519
11    LQHP_1     LQHP 1.0   0.8444 0.6362192 0.5083415    0.06101274
12   LQHPT_1    LQHPT 1.0   0.8452 0.6301295 0.4956974    0.05601758
13     L_1.5        L 1.5   0.7490 0.6203152 0.4050825    0.06176864
14    LQ_1.5       LQ 1.5   0.8077 0.6581090 0.5540265    0.03563596
15     H_1.5        H 1.5   0.8328 0.6417103 0.5124453    0.04554796
16   LQH_1.5      LQH 1.5   0.8280 0.6391090 0.5148464    0.04854654
17  LQHP_1.5     LQHP 1.5   0.8327 0.6338244 0.5039880    0.05676753
18 LQHPT_1.5    LQHPT 1.5   0.8327 0.6279295 0.4927212    0.06564786
19       L_2        L 2.0   0.7486 0.6194352 0.3972672    0.06209101
20      LQ_2       LQ 2.0   0.8011 0.6441396 0.5358973    0.04371930
21       H_2        H 2.0   0.8278 0.6413410 0.5085090    0.03703365
22     LQH_2      LQH 2.0   0.8201 0.6466718 0.5287108    0.03818311
23    LQHP_2     LQHP 2.0   0.8278 0.6320910 0.4993993    0.04964220
24   LQHPT_2    LQHPT 2.0   0.8292 0.6327833 0.5006643    0.05112384
25     L_2.5        L 2.5   0.7488 0.6203313 0.3934561    0.06133443
26    LQ_2.5       LQ 2.5   0.7924 0.6353536 0.4623535    0.06138816
27     H_2.5        H 2.5   0.8205 0.6360231 0.4994163    0.03271469
28   LQH_2.5      LQH 2.5   0.8135 0.6464451 0.5230157    0.04071711
29  LQHP_2.5     LQHP 2.5   0.8215 0.6315760 0.4584322    0.04374678
30 LQHPT_2.5    LQHPT 2.5   0.8229 0.6314067 0.4640383    0.04216102
31       L_3        L 3.0   0.7476 0.6205008 0.3887178    0.06066338
32      LQ_3       LQ 3.0   0.7798 0.6297099 0.3949870    0.07006140
33       H_3        H 3.0   0.8169 0.6387718 0.4410546    0.03759456
34     LQH_3      LQH 3.0   0.8094 0.6495825 0.4904597    0.04950439
35    LQHP_3     LQHP 3.0   0.8129 0.6363072 0.3750360    0.05501425
36   LQHPT_3    LQHPT 3.0   0.8150 0.6359031 0.3750489    0.05485855
37     L_3.5        L 3.5   0.7465 0.6202061 0.3828951    0.06184649
38    LQ_3.5       LQ 3.5   0.7684 0.6274159 0.3699853    0.07037500
39     H_3.5        H 3.5   0.8125 0.6346140 0.3793913    0.04628191
40   LQH_3.5      LQH 3.5   0.8038 0.6445879 0.4295549    0.06163816
41  LQHP_3.5     LQHP 3.5   0.8046 0.6298516 0.3237805    0.07004057
42 LQHPT_3.5    LQHPT 3.5   0.8046 0.6298823 0.3238391    0.07004057
43       L_4        L 4.0   0.7454 0.6214136 0.3773642    0.05920833
44      LQ_4       LQ 4.0   0.7589 0.6263454 0.3661155    0.06866009
45       H_4        H 4.0   0.8090 0.6238694 0.3223219    0.05993092
46     LQH_4      LQH 4.0   0.7955 0.6401712 0.3951043    0.07164035
47    LQHP_4     LQHP 4.0   0.7913 0.6248170 0.3011792    0.08145285
48   LQHPT_4    LQHPT 4.0   0.7909 0.6248170 0.3011792    0.08145285
   Var.AUC.DIFF Mean.OR10   Var.OR10 Mean.ORmin   Var.ORmin nparm     AICc
1   0.021555548 0.2565833 0.12512990 0.08333333 0.034722222     4 754.7624
2   0.008209327 0.2374859 0.13024829 0.03333333 0.005555556     7 731.2494
3   0.014551695 0.3143756 0.12595201 0.03205128 0.001931295    23 795.8741
4   0.017936059 0.3154923 0.12750029 0.03205128 0.001931295    23 796.0107
5   0.023578151 0.3471423 0.10804811 0.06410256 0.004252959    24 798.3934
6   0.020152440 0.3788244 0.09757874 0.14487179 0.026947732    30 847.3932
7   0.020317756 0.2561500 0.12465092 0.03333333 0.005555556     4 754.8550
8   0.010509857 0.2206346 0.13229507 0.03333333 0.005555556     5 726.9757
9   0.007953456 0.3034731 0.13229925 0.01666667 0.001388889    10 731.9548
10  0.013745375 0.3379731 0.13499786 0.01666667 0.001388889    13 743.8811
11  0.011326146 0.3024064 0.13076020 0.01666667 0.001388889    14 745.3163
12  0.012522779 0.3161064 0.11545178 0.03333333 0.002083333    14 743.2104
13  0.019160331 0.2553167 0.12373508 0.03333333 0.005555556     4 754.9833
14  0.014934283 0.2346500 0.13531700 0.03333333 0.005555556     6 731.1533
15  0.005522896 0.3011064 0.12889987 0.01666667 0.001388889     8 728.1952
16  0.015786176 0.3346397 0.13046500 0.05000000 0.012500000    10 736.8504
17  0.011028710 0.3004064 0.12790515 0.05000000 0.012500000     8 728.1997
18  0.009420551 0.3443603 0.10105325 0.05000000 0.012500000     9 729.4640
19  0.018635849 0.2379167 0.11062587 0.03333333 0.005555556     4 755.1469
20  0.021448290 0.2440167 0.11645331 0.05000000 0.005555556     5 730.6153
21  0.004524881 0.2960897 0.12187934 0.01666667 0.001388889     9 733.5521
22  0.017145494 0.2821192 0.12626586 0.05000000 0.012500000     8 734.1916
23  0.010783042 0.2960897 0.12187934 0.05000000 0.012500000     9 733.5521
24  0.010426811 0.2968564 0.12293595 0.05000000 0.012500000     9 732.1412
25  0.017573997 0.1874833 0.08982292 0.03333333 0.005555556     4 755.3458
26  0.025164736 0.2339833 0.10292503 0.05000000 0.005555556     6 735.5974
27  0.005117769 0.2937538 0.09666601 0.05000000 0.005555556     6 728.3221
28  0.017710366 0.2773526 0.11973040 0.03333333 0.005555556     8 736.6908
29  0.008186308 0.2901872 0.09202537 0.06666667 0.008333333     7 731.1407
30  0.008308722 0.2907872 0.09279714 0.06666667 0.008333333     8 733.1847
31  0.016592966 0.1701500 0.08774817 0.03333333 0.005555556     4 755.5800
32  0.023126829 0.2068833 0.08326746 0.06666667 0.008333333     4 733.1730
33  0.004400608 0.2865038 0.08736645 0.05000000 0.005555556     5 728.0378
34  0.017739716 0.2724346 0.09758013 0.06666667 0.008333333     6 732.7286
35  0.012514599 0.2611192 0.07834442 0.05000000 0.005555556     6 731.2429
36  0.012469965 0.2612859 0.07854204 0.05000000 0.005555556     7 733.3601
37  0.016917701 0.1694500 0.08685123 0.03333333 0.005555556     4 755.8493
38  0.020671365 0.2009500 0.07611285 0.06666667 0.008333333     4 735.9920
39  0.006037249 0.2941705 0.07679630 0.08333333 0.020833333     4 728.0842
40  0.019560396 0.2474013 0.08570658 0.06666667 0.008333333     5 731.8876
41  0.019348862 0.2704013 0.07311552 0.08333333 0.020833333     5 731.8226
42  0.019348862 0.2704013 0.07311552 0.08333333 0.020833333     6 734.0140
43  0.014728898 0.1853833 0.08027587 0.03333333 0.002083333     4 756.1539
44  0.019184643 0.2160833 0.07434545 0.08333333 0.013888889     4 738.9550
45  0.014083470 0.2401167 0.08535646 0.08333333 0.020833333     4 730.3244
46  0.023505093 0.2096500 0.08672395 0.06666667 0.008333333     4 731.2224
47  0.026094785 0.2312167 0.07550712 0.08333333 0.020833333     5 735.0602
48  0.026094785 0.2312167 0.07550712 0.08333333 0.020833333     6 737.4876
   delta.AICc        w.AIC nparam   AUC_bin.1 AUC_bin.2 AUC_bin.3 AUC_bin.4
1   27.786727 1.618056e-07      4 0.069048246 0.8279231 0.6126667 0.8874615
2    4.273726 2.064320e-02      7 0.000000000 0.8336154 0.7315000 0.8380769
3   68.898439 1.912929e-16     23 0.000000000 0.8040000 0.7466667 0.7342308
4   69.034986 1.786685e-16     23 0.000000000 0.8040000 0.7266667 0.7328462
5   71.417768 5.427921e-17     24 0.000000000 0.8040000 0.7540000 0.7003846
6  120.417502 1.243015e-27     30 0.027013158 0.8388462 0.7570000 0.7202308
7   27.879347 1.544833e-07      4 0.072214912 0.8279231 0.6161667 0.8863846
8    0.000000 1.749061e-01      5 0.000000000 0.8562308 0.7165000 0.8660769
9    4.979182 1.450739e-02     10 0.000000000 0.8144615 0.7525000 0.7598462
10  16.905406 3.731149e-05     13 0.000000000 0.8144615 0.7123333 0.7771538
11  18.340626 1.820491e-05     14 0.000000000 0.8144615 0.7288333 0.7603846
12  16.234731 5.217684e-05     14 0.000000000 0.7929231 0.7298333 0.7653077
13  28.007589 1.448886e-07      4 0.077223684 0.8279231 0.6183333 0.8848462
14   4.177670 2.165884e-02      6 0.000000000 0.8686923 0.6921667 0.8897692
15   1.219567 9.505595e-02      8 0.000000000 0.8137692 0.7562500 0.7676154
16   9.874768 1.254661e-03     10 0.000000000 0.8137692 0.7031667 0.8066923
17   1.224067 9.484231e-02      8 0.000000000 0.8137692 0.7181667 0.7697692
18   2.488378 5.040348e-02      9 0.000000000 0.7950000 0.7180000 0.7702308
19  28.171242 1.335050e-07      4 0.081785088 0.8247692 0.6185000 0.8825385
20   3.639616 2.834472e-02      5 0.003057018 0.8485385 0.6599167 0.9057692
21   6.576426 6.527578e-03      9 0.000000000 0.8234615 0.7603333 0.7770769
22   7.215925 4.741181e-03      8 0.000000000 0.8656923 0.6985833 0.8290000
23   6.576426 6.527578e-03      9 0.000000000 0.8234615 0.7140833 0.7770769
24   5.165568 1.321649e-02      9 0.000000000 0.8208462 0.7142500 0.7791538
25  28.370163 1.208655e-07      4 0.084688596 0.8247692 0.6231667 0.8816154
26   8.621731 2.347577e-03      6 0.049960526 0.8370769 0.6282500 0.9082308
27   1.346421 8.921405e-02      6 0.000000000 0.8250000 0.7471667 0.7866154
28   9.715153 1.358896e-03      8 0.005802632 0.8790000 0.6882500 0.8429231
29   4.164985 2.179665e-02      7 0.024815789 0.8250000 0.7103333 0.7942308
30   6.209019 7.843926e-03      8 0.020815789 0.8203846 0.7103333 0.7990000
31  28.604285 1.075136e-07      4 0.088074561 0.8247692 0.6255000 0.8800769
32   6.197319 7.889947e-03      4 0.095600877 0.8247692 0.6242500 0.9028462
33   1.062124 1.028413e-01      5 0.041416667 0.8288462 0.7406667 0.7978462
34   5.752912 9.853165e-03      6 0.034592105 0.8927692 0.6759167 0.8593846
35   4.267260 2.071005e-02      6 0.091811404 0.8288462 0.7023333 0.8234615
36   6.384448 7.185211e-03      7 0.091188596 0.8288462 0.7023333 0.8212308
37  28.873613 9.396789e-08      4 0.092364035 0.8247692 0.6250833 0.8782308
38   9.016338 1.927223e-03      4 0.115425439 0.8247692 0.6250833 0.9003846
39   1.108542 1.004819e-01      4 0.081153509 0.8131538 0.7418333 0.7968462
40   4.911899 1.500374e-02      5 0.074478070 0.8768462 0.6615833 0.8666154
41   4.846935 1.549909e-02      5 0.132302632 0.8283846 0.6726667 0.8241538
42   7.038289 5.181551e-03      6 0.132302632 0.8283846 0.6726667 0.8243077
43  29.178230 8.069239e-08      4 0.095978070 0.8247692 0.6342500 0.8751538
44  11.979288 4.380619e-04      4 0.118355263 0.8247692 0.6250833 0.8997692
45   3.348728 3.278217e-02      4 0.118442982 0.7950000 0.7021667 0.8031538
46   4.246702 2.092402e-02      4 0.101714912 0.8695385 0.6399167 0.8747692
47   8.084505 3.070979e-03      5 0.156232456 0.8247692 0.6470000 0.8400000
48  10.511918 9.123695e-04      6 0.156232456 0.8247692 0.6470000 0.8400000
          NA AUC.DIFF_bin.1 AUC.DIFF_bin.2 AUC.DIFF_bin.3 AUC.DIFF_bin.4
1  0.6995833    0.000000000     0.18141228     0.00000000    0.069048246
2  0.8669167    0.000000000     0.10568421     0.00000000    0.000000000
3  0.8667500    0.000000000     0.12114912     0.12255301    0.000000000
4  0.8723333    0.000000000     0.14441228     0.12472141    0.000000000
5  0.8639167    0.000000000     0.13181579     0.17291268    0.000000000
6  0.8492500    0.000000000     0.13439474     0.17187734    0.027013158
7  0.6974167    0.000000000     0.17575439     0.00000000    0.072214912
8  0.8595833    0.000000000     0.11957895     0.00000000    0.000000000
9  0.8827500    0.022430353     0.11005263     0.08661331    0.000000000
10 0.8885833    0.022430353     0.15495614     0.05895426    0.000000000
11 0.8774167    0.022430353     0.13714035     0.08448025    0.000000000
12 0.8625833    0.005725572     0.13619298     0.08215177    0.000000000
13 0.6932500    0.000000000     0.16985088     0.00000000    0.077223684
14 0.8399167    0.000000000     0.14254386     0.00000000    0.000000000
15 0.8709167    0.018311850     0.09117105     0.07270894    0.000000000
16 0.8719167    0.018311850     0.15770175     0.01817256    0.000000000
17 0.8674167    0.018311850     0.13614912     0.07260915    0.000000000
18 0.8564167    0.051648649     0.13657895     0.07436383    0.000000000
19 0.6895833    0.000000000     0.16657895     0.00000000    0.081785088
20 0.8034167    0.000000000     0.17182018     0.00000000    0.003057018
21 0.8458333    0.008376299     0.08142982     0.05832848    0.000000000
22 0.8400833    0.000000000     0.15273246     0.00000000    0.000000000
23 0.8458333    0.008376299     0.13186404     0.05832848    0.000000000
24 0.8496667    0.013694387     0.13217105     0.05862994    0.000000000
25 0.6874167    0.000000000     0.16064912     0.00000000    0.084688596
26 0.7532500    0.000000000     0.19559211     0.00000000    0.049960526
27 0.8213333    0.000000000     0.08712281     0.04373597    0.000000000
28 0.8162500    0.000000000     0.15706579     0.00000000    0.005802632
29 0.8035000    0.000000000     0.12045614     0.02971518    0.024815789
30 0.8065000    0.002047817     0.12045614     0.02532432    0.020815789
31 0.6840833    0.000000000     0.15457895     0.00000000    0.088074561
32 0.7010833    0.000000000     0.18464474     0.00000000    0.095600877
33 0.7850833    0.000000000     0.08978070     0.01918087    0.041416667
34 0.7852500    0.000000000     0.16342544     0.00000000    0.034592105
35 0.7350833    0.000000000     0.12824561     0.00000000    0.091811404
36 0.7359167    0.000000000     0.12824561     0.00000000    0.091188596
37 0.6805833    0.000000000     0.15502193     0.00000000    0.092364035
38 0.6714167    0.000000000     0.16607456     0.00000000    0.115425439
39 0.7400833    0.000000000     0.08898246     0.01499168    0.081153509
40 0.7434167    0.000000000     0.17207456     0.00000000    0.074478070
41 0.6917500    0.000000000     0.14785965     0.00000000    0.132302632
42 0.6917500    0.000000000     0.14785965     0.00000000    0.132302632
43 0.6769167    0.000000000     0.14085526     0.00000000    0.095978070
44 0.6637500    0.000000000     0.15628509     0.00000000    0.118355263
45 0.7005833    0.000000000     0.12128070     0.00000000    0.118442982
46 0.7149167    0.000000000     0.18484649     0.00000000    0.101714912
47 0.6560833    0.000000000     0.16957895     0.00000000    0.156232456
48 0.6560833    0.000000000     0.16957895     0.00000000    0.156232456
   OR10_bin.1 OR10_bin.2 OR10_bin.3 OR10_bin.4         NA ORmin_bin.1
1   0.6995833 0.00000000  0.5833333 0.00000000 0.00000000  0.00000000
2   0.8669167 0.00000000  0.1666667 0.15384615 0.00000000  0.00000000
3   0.8667500 0.07692308  0.1666667 0.46153846 0.00000000  0.00000000
4   0.8723333 0.07692308  0.1666667 0.46153846 0.00000000  0.00000000
5   0.8639167 0.07692308  0.2500000 0.46153846 0.08333333  0.08333333
6   0.8492500 0.00000000  0.2500000 0.46153846 0.33333333  0.33333333
7   0.6974167 0.00000000  0.5833333 0.00000000 0.00000000  0.00000000
8   0.8595833 0.00000000  0.1666667 0.07692308 0.00000000  0.00000000
9   0.8827500 0.00000000  0.2500000 0.38461538 0.00000000  0.00000000
10  0.8885833 0.00000000  0.4166667 0.38461538 0.00000000  0.00000000
11  0.8774167 0.00000000  0.2500000 0.38461538 0.00000000  0.00000000
12  0.8625833 0.00000000  0.2500000 0.38461538 0.08333333  0.08333333
13  0.6932500 0.00000000  0.5833333 0.00000000 0.00000000  0.00000000
14  0.8399167 0.00000000  0.3333333 0.00000000 0.00000000  0.00000000
15  0.8709167 0.00000000  0.2500000 0.38461538 0.00000000  0.00000000
16  0.8719167 0.00000000  0.4166667 0.38461538 0.00000000  0.00000000
17  0.8674167 0.00000000  0.2500000 0.38461538 0.00000000  0.00000000
18  0.8564167 0.23076923  0.2500000 0.38461538 0.00000000  0.00000000
19  0.6895833 0.00000000  0.5000000 0.00000000 0.00000000  0.00000000
20  0.8034167 0.00000000  0.3333333 0.00000000 0.08333333  0.08333333
21  0.8458333 0.00000000  0.2500000 0.38461538 0.00000000  0.00000000
22  0.8400833 0.00000000  0.4166667 0.15384615 0.00000000  0.00000000
23  0.8458333 0.00000000  0.2500000 0.38461538 0.00000000  0.00000000
24  0.8496667 0.00000000  0.2500000 0.38461538 0.00000000  0.00000000
25  0.6874167 0.00000000  0.2500000 0.00000000 0.00000000  0.00000000
26  0.7532500 0.00000000  0.3333333 0.00000000 0.08333333  0.08333333
27  0.8213333 0.00000000  0.2500000 0.23076923 0.16666667  0.16666667
28  0.8162500 0.00000000  0.4166667 0.15384615 0.00000000  0.00000000
29  0.8035000 0.00000000  0.2500000 0.23076923 0.16666667  0.16666667
30  0.8065000 0.00000000  0.2500000 0.23076923 0.16666667  0.16666667
31  0.6840833 0.00000000  0.1666667 0.00000000 0.00000000  0.00000000
32  0.7010833 0.00000000  0.1666667 0.00000000 0.16666667  0.16666667
33  0.7850833 0.00000000  0.2500000 0.23076923 0.16666667  0.16666667
34  0.7852500 0.00000000  0.3333333 0.07692308 0.16666667  0.16666667
35  0.7350833 0.00000000  0.2500000 0.15384615 0.16666667  0.16666667
36  0.7359167 0.00000000  0.2500000 0.15384615 0.16666667  0.16666667
37  0.6805833 0.00000000  0.1666667 0.00000000 0.00000000  0.00000000
38  0.6714167 0.00000000  0.1666667 0.00000000 0.16666667  0.16666667
39  0.7400833 0.00000000  0.1666667 0.23076923 0.33333333  0.33333333
40  0.7434167 0.00000000  0.2500000 0.07692308 0.16666667  0.16666667
41  0.6917500 0.00000000  0.2500000 0.07692308 0.33333333  0.33333333
42  0.6917500 0.00000000  0.2500000 0.07692308 0.33333333  0.33333333
43  0.6769167 0.00000000  0.1666667 0.00000000 0.08333333  0.08333333
44  0.6637500 0.00000000  0.1666667 0.00000000 0.25000000  0.25000000
45  0.7005833 0.00000000  0.1666667 0.00000000 0.33333333  0.33333333
46  0.7149167 0.00000000  0.1666667 0.00000000 0.16666667  0.16666667
47  0.6560833 0.00000000  0.1666667 0.00000000 0.33333333  0.33333333
48  0.6560833 0.00000000  0.1666667 0.00000000 0.33333333  0.33333333
   ORmin_bin.2 ORmin_bin.3 ORmin_bin.4 NA
1            0  0.41666667  0.00000000  0
2            0  0.16666667  0.00000000  0
3            0  0.08333333  0.07692308  0
4            0  0.08333333  0.07692308  0
5            0  0.08333333  0.15384615  0
6            0  0.08333333  0.30769231  0
7            0  0.16666667  0.00000000  0
8            0  0.16666667  0.00000000  0
9            0  0.08333333  0.00000000  0
10           0  0.08333333  0.00000000  0
11           0  0.08333333  0.00000000  0
12           0  0.08333333  0.00000000  0
13           0  0.16666667  0.00000000  0
14           0  0.16666667  0.00000000  0
15           0  0.08333333  0.00000000  0
16           0  0.25000000  0.00000000  0
17           0  0.25000000  0.00000000  0
18           0  0.25000000  0.00000000  0
19           0  0.16666667  0.00000000  0
20           0  0.16666667  0.00000000  0
21           0  0.08333333  0.00000000  0
22           0  0.25000000  0.00000000  0
23           0  0.25000000  0.00000000  0
24           0  0.25000000  0.00000000  0
25           0  0.16666667  0.00000000  0
26           0  0.16666667  0.00000000  0
27           0  0.08333333  0.00000000  0
28           0  0.16666667  0.00000000  0
29           0  0.16666667  0.00000000  0
30           0  0.16666667  0.00000000  0
31           0  0.16666667  0.00000000  0
32           0  0.16666667  0.00000000  0
33           0  0.08333333  0.00000000  0
34           0  0.16666667  0.00000000  0
35           0  0.08333333  0.00000000  0
36           0  0.08333333  0.00000000  0
37           0  0.16666667  0.00000000  0
38           0  0.16666667  0.00000000  0
39           0  0.08333333  0.00000000  0
40           0  0.16666667  0.00000000  0
41           0  0.08333333  0.00000000  0
42           0  0.08333333  0.00000000  0
43           0  0.08333333  0.00000000  0
44           0  0.16666667  0.00000000  0
45           0  0.08333333  0.00000000  0
46           0  0.16666667  0.00000000  0
47           0  0.08333333  0.00000000  0
48           0  0.08333333  0.00000000  0
> 
> ### Plot prediction with lowest AICc
> plot(enmeval_results@predictions[[which (enmeval_results@results$delta.AICc == 0) ]])
> points(enmeval_results@occ.pts, pch=21, bg=enmeval_results@occ.grp)
> 
> ### Niche overlap statistics between model predictions
> enmeval_results@overlap
              L_0.5    LQ_0.5     H_0.5   LQH_0.5  LQHP_0.5 LQHPT_0.5       L_1
L_0.5            NA        NA        NA        NA        NA        NA        NA
LQ_0.5    0.6904021        NA        NA        NA        NA        NA        NA
H_0.5     0.5977922 0.8082292        NA        NA        NA        NA        NA
LQH_0.5   0.5963977 0.8065003 0.9898059        NA        NA        NA        NA
LQHP_0.5  0.5757667 0.7615979 0.9008728 0.9017413        NA        NA        NA
LQHPT_0.5 0.5557143 0.7217880 0.8570047 0.8574290 0.9077956        NA        NA
L_1       0.9920197 0.6902412 0.5972368 0.5958997 0.5757552 0.5557171        NA
LQ_1      0.7226490 0.9640651 0.7957497 0.7940248 0.7495492 0.7128567 0.7226373
H_1       0.6578847 0.8890675 0.8963782 0.8949056 0.8364911 0.7931691 0.6573157
LQH_1     0.6579679 0.8907528 0.8956683 0.8940527 0.8350299 0.7885177 0.6574491
LQHP_1    0.6558528 0.8829295 0.8978330 0.8964352 0.8459250 0.8011957 0.6553292
LQHPT_1   0.6430928 0.8739146 0.8836195 0.8819847 0.8373947 0.8057621 0.6425464
L_1.5     0.9840556 0.6897632 0.5965479 0.5952768 0.5755935 0.5555836 0.9920355
LQ_1.5    0.7519237 0.9314559 0.7810782 0.7794025 0.7369025 0.7029047 0.7522683
H_1.5     0.6995738 0.9107678 0.8517596 0.8501900 0.7957679 0.7581020 0.6992266
LQH_1.5   0.6994971 0.9220468 0.8383374 0.8369132 0.7860334 0.7452208 0.6991028
LQHP_1.5  0.6997017 0.9108646 0.8517101 0.8501575 0.7957502 0.7580686 0.6993618
LQHPT_1.5 0.6887136 0.9010004 0.8472161 0.8447175 0.7924484 0.7621426 0.6883187
L_2       0.9761065 0.6891042 0.5958049 0.5945457 0.5752273 0.5553255 0.9840862
LQ_2      0.7797875 0.9008764 0.7631425 0.7617440 0.7221269 0.6911319 0.7802670
H_2       0.7290268 0.9062870 0.8242142 0.8226588 0.7738186 0.7408595 0.7290458
LQH_2     0.7263734 0.9151072 0.8095558 0.8081073 0.7609257 0.7239874 0.7261978
LQHP_2    0.7290268 0.9062870 0.8242142 0.8226588 0.7738186 0.7408595 0.7290458
LQHPT_2   0.7199722 0.9014223 0.8223329 0.8206855 0.7732304 0.7440985 0.7198369
L_2.5     0.9681727 0.6883045 0.5949855 0.5937394 0.5747090 0.5549463 0.9761517
LQ_2.5    0.8060263 0.8722648 0.7435987 0.7424685 0.7064120 0.6778335 0.8067289
H_2.5     0.7534223 0.8921222 0.7962112 0.7948937 0.7536563 0.7238594 0.7540062
LQH_2.5   0.7483320 0.9049135 0.7929457 0.7917222 0.7475851 0.7127049 0.7484479
LQHP_2.5  0.7534329 0.8927613 0.7965358 0.7951456 0.7537306 0.7236474 0.7539672
LQHPT_2.5 0.7475144 0.8927843 0.7963248 0.7948503 0.7537591 0.7252157 0.7477980
L_3       0.9602533 0.6874169 0.5940677 0.5928387 0.5740629 0.5544326 0.9682306
LQ_3      0.8304541 0.8455300 0.7239770 0.7228686 0.6902675 0.6637676 0.8316246
H_3       0.7744981 0.8738420 0.7720510 0.7708504 0.7356892 0.7089552 0.7756144
LQH_3     0.7678591 0.8925710 0.7799681 0.7789387 0.7363775 0.7035991 0.7681866
LQHP_3    0.7756688 0.8751149 0.7729137 0.7716318 0.7354281 0.7076602 0.7765411
LQHPT_3   0.7719063 0.8761386 0.7735229 0.7722257 0.7361360 0.7090602 0.7726110
L_3.5     0.9523467 0.6864758 0.5930124 0.5918606 0.5732709 0.5537596 0.9603219
LQ_3.5    0.8527629 0.8204642 0.7045634 0.7035044 0.6742222 0.6492993 0.8544200
H_3.5     0.7914201 0.8540270 0.7489978 0.7478034 0.7173954 0.6928059 0.7931503
LQH_3.5   0.7854052 0.8778203 0.7656713 0.7645927 0.7250556 0.6939648 0.7859438
LQHP_3.5  0.7958958 0.8539773 0.7491413 0.7479612 0.7159088 0.6895168 0.7971979
LQHPT_3.5 0.7946513 0.8548859 0.7494606 0.7483205 0.7164555 0.6904349 0.7958814
L_4       0.9444517 0.6854788 0.5917005 0.5907615 0.5723776 0.5528559 0.9524247
LQ_4      0.8727434 0.7966719 0.6856163 0.6845037 0.6582986 0.6346740 0.8749665
H_4       0.8060665 0.8394636 0.7314312 0.7304064 0.7029527 0.6792065 0.8082351
LQH_4     0.8024999 0.8627016 0.7512642 0.7503191 0.7132691 0.6838646 0.8033073
LQHP_4    0.8164987 0.8346627 0.7275139 0.7263412 0.6975838 0.6723852 0.8181843
LQHPT_4   0.8159203 0.8356394 0.7280076 0.7268213 0.6979861 0.6728866 0.8175865
               LQ_1       H_1     LQH_1    LQHP_1   LQHPT_1     L_1.5    LQ_1.5
L_0.5            NA        NA        NA        NA        NA        NA        NA
LQ_0.5           NA        NA        NA        NA        NA        NA        NA
H_0.5            NA        NA        NA        NA        NA        NA        NA
LQH_0.5          NA        NA        NA        NA        NA        NA        NA
LQHP_0.5         NA        NA        NA        NA        NA        NA        NA
LQHPT_0.5        NA        NA        NA        NA        NA        NA        NA
L_1              NA        NA        NA        NA        NA        NA        NA
LQ_1             NA        NA        NA        NA        NA        NA        NA
H_1       0.8772520        NA        NA        NA        NA        NA        NA
LQH_1     0.8798412 0.9707122        NA        NA        NA        NA        NA
LQHP_1    0.8718071 0.9847818 0.9648563        NA        NA        NA        NA
LQHPT_1   0.8633145 0.9583148 0.9386373 0.9657626        NA        NA        NA
L_1.5     0.7222522 0.6566232 0.6567614 0.6546838 0.6418808        NA        NA
LQ_1.5    0.9672343 0.8597140 0.8622545 0.8553063 0.8471595 0.7521461        NA
H_1.5     0.9126374 0.9449363 0.9351346 0.9400883 0.9265894 0.6987504 0.9017937
LQH_1.5   0.9234884 0.9222292 0.9369842 0.9162920 0.8951117 0.6985465 0.9091836
LQHP_1.5  0.9127335 0.9449019 0.9350463 0.9400683 0.9266679 0.6988949 0.9018974
LQHPT_1.5 0.9018184 0.9312956 0.9181524 0.9272799 0.9400739 0.6878034 0.8908339
L_2       0.7216737 0.6557387 0.6559536 0.6539482 0.6411270 0.9920505 0.7517528
LQ_2      0.9365239 0.8381948 0.8399773 0.8346777 0.8265872 0.7805890 0.9692291
H_2       0.9208345 0.9061705 0.9026297 0.9034353 0.8944915 0.7289288 0.9215390
LQH_2     0.9266818 0.8853728 0.8997842 0.8801157 0.8607085 0.7258436 0.9235751
LQHP_2    0.9208345 0.9061705 0.9026297 0.9034353 0.8944915 0.7289288 0.9215390
LQHPT_2   0.9136313 0.9015828 0.8952375 0.8991852 0.9039114 0.7196100 0.9123472
L_2.5     0.7210230 0.6546838 0.6550276 0.6530752 0.6402231 0.9841152 0.7512714
LQ_2.5    0.9077143 0.8153322 0.8168928 0.8124158 0.8041058 0.8072749 0.9402637
H_2.5     0.9139502 0.8715063 0.8704785 0.8692241 0.8618081 0.7543217 0.9262453
LQH_2.5   0.9253146 0.8686703 0.8798133 0.8636775 0.8462747 0.7484183 0.9336706
LQHP_2.5  0.9147450 0.8718502 0.8711709 0.8695111 0.8617954 0.7542621 0.9270766
LQHPT_2.5 0.9132506 0.8715866 0.8700805 0.8691930 0.8680978 0.7479184 0.9237226
L_3       0.7202854 0.6534062 0.6539602 0.6519734 0.6391560 0.9761929 0.7507067
LQ_3      0.8806717 0.7927023 0.7943392 0.7900698 0.7811117 0.8324441 0.9130203
H_3       0.8997067 0.8439385 0.8443853 0.8423762 0.8349361 0.7764682 0.9179944
LQH_3     0.9180344 0.8547483 0.8627030 0.8502802 0.8346201 0.7683645 0.9352591
LQHP_3    0.9021159 0.8448436 0.8460219 0.8431170 0.8348454 0.7771901 0.9214538
LQHPT_3   0.9022915 0.8464486 0.8473291 0.8445274 0.8396473 0.7731615 0.9206830
L_3.5     0.7194361 0.6520059 0.6527093 0.6506621 0.6379554 0.9682827 0.7500273
LQ_3.5    0.8552281 0.7711395 0.7727578 0.7686903 0.7587934 0.8557511 0.8873923
H_3.5     0.8826698 0.8181177 0.8189075 0.8167330 0.8093373 0.7946260 0.9048468
LQH_3.5   0.9070246 0.8387517 0.8445358 0.8347677 0.8204047 0.7863063 0.9297411
LQHP_3.5  0.8835106 0.8185843 0.8201500 0.8171305 0.8084017 0.7982365 0.9078138
LQHPT_3.5 0.8841230 0.81