O pacote ecoCopula permite visualizar dados discretos multivariados com modelos gráficos e ordenação. O pacote foi projetado principalmente para dados de abundância multivariada em ecologia, porém pode ser aplicado a quaisquer dados discretos multivariados.
As duas funções principais são:
- Ordenação de cópula ( cord) para visualizar como amostras (sítios) e variáveis estão localizadas ao longo de diversas variáveis latentes (gradiente ambiental não observado).\
- Modelos gráficos de cópula (cgr) para traçar um gráfico que distingue entre associações diretas e indiretas entre variáveis.
crg: Ajustando lasso gráfico de cópula gaussiana a dados de coocorrência
Valor: Três objetos são retornados; best_graph é uma lista com parâmetros para o ‘melhor’ modelo gráfico, escolhido pelo método selecionado; all_graphs é uma lista com verossimilhança, BIC e AIC para todos os modelos ao longo do caminho de lambda; obj é o objeto de entrada.
Detalhes: cgr é usado para ajustar um modelo gráfico de cópula gaussiana a dados discretos multivariados, como dados de coocorrência (multi espécies) em ecologia. O modelo é estimado usando amostragem de importância com conjuntos n.samp de quantis aleatorizados ou resíduos “Dunn-Smyth” (Dunn & Smyth 1996), e o pacote glasso para ajustar modelos gráficos gaussianos.
Spider data
stackedsdm ajusta uma regressão separada para cada resposta de espécie, ou seja, coluna de y. Diferentes famílias podem ser permitidas para cada espécie, o que permite tipos de respostas mistas.
data(spider)
X <- spider$x
abund <- spider$abund
myfamily <- "negative.binomial"
fit0 <- stackedsdm(abund, formula_X = ~. -bare.sand, data = X, family = myfamily, ncores = 2);fit0## $call
## stackedsdm(y = abund, formula_X = ~. - bare.sand, data = X, family = myfamily,
## ncores = 2)
##
## $fits
## $fits[[1]]
## $fits[[1]]$params
## $fits[[1]]$params$coefficients
## resp
## (Intercept) -2.6144490
## soil.dry -0.8139362
## fallen.leaves -0.3623206
## moss 0.1457828
## herb.layer 0.7919684
## reflection 0.8805509
##
## $fits[[1]]$params$dispparam
## resp
## 0.1364675
##
##
## $fits[[1]]$fit
##
## Call: manyglm(formula = formula_X, family = "negative.binomial", data = data.frame(resp = y[, j], data))
## [1] "negative.binomial"
##
## Nuisance Parameter(s) phi estimated by the PHI method.
## resp
## 0.136
##
## Degrees of Freedom: 27 Total (i.e. Null); 22 Residual
##
## resp
## 2*log-likelihood: -99.36
## Residual Deviance: 22.67
## AIC: 113.36
##
##
## $fits[[2]]
## $fits[[2]]$params
## $fits[[2]]$params$coefficients
## resp
## (Intercept) -6.08221350
## soil.dry 1.48307414
## fallen.leaves 0.23767728
## moss -0.08640453
## herb.layer 0.62728217
## reflection 0.46986485
##
## $fits[[2]]$params$dispparam
## resp
## 1.014417
##
##
## $fits[[2]]$fit
##
## Call: manyglm(formula = formula_X, family = "negative.binomial", data = data.frame(resp = y[, j], data))
## [1] "negative.binomial"
##
## Nuisance Parameter(s) phi estimated by the PHI method.
## resp
## 1.014
##
## Degrees of Freedom: 27 Total (i.e. Null); 22 Residual
##
## resp
## 2*log-likelihood: -123.92
## Residual Deviance: 27.16
## AIC: 137.92
##
##
## $fits[[3]]
## $fits[[3]]$params
## $fits[[3]]$params$coefficients
## resp
## (Intercept) 3.833094767
## soil.dry -1.963033078
## fallen.leaves -0.311219557
## moss -0.226492084
## herb.layer -0.004973723
## reflection 0.529356681
##
## $fits[[3]]$params$dispparam
## resp
## 0.7209418
##
##
## $fits[[3]]$fit
##
## Call: manyglm(formula = formula_X, family = "negative.binomial", data = data.frame(resp = y[, j], data))
## [1] "negative.binomial"
##
## Nuisance Parameter(s) phi estimated by the PHI method.
## resp
## 0.721
##
## Degrees of Freedom: 27 Total (i.e. Null); 22 Residual
##
## resp
## 2*log-likelihood: -78.74
## Residual Deviance: 19.18
## AIC: 92.74
##
##
## $fits[[4]]
## $fits[[4]]$params
## $fits[[4]]$params$coefficients
## resp
## (Intercept) -20.4761287
## soil.dry 5.5878544
## fallen.leaves -0.7361578
## moss 0.7390316
## herb.layer 1.0022836
## reflection -0.2612570
##
## $fits[[4]]$params$dispparam
## resp
## 0.8670633
##
##
## $fits[[4]]$fit
##
## Call: manyglm(formula = formula_X, family = "negative.binomial", data = data.frame(resp = y[, j], data))
## [1] "negative.binomial"
##
## Nuisance Parameter(s) phi estimated by the PHI method.
## resp
## 0.867
##
## Degrees of Freedom: 27 Total (i.e. Null); 22 Residual
##
## resp
## 2*log-likelihood: -39.7
## Residual Deviance: 10.7
## AIC: 53.7
##
##
## $fits[[5]]
## $fits[[5]]$params
## $fits[[5]]$params$coefficients
## resp
## (Intercept) -14.64523768
## soil.dry -1.99686089
## fallen.leaves -0.96498875
## moss 0.43852083
## herb.layer -0.03205927
## reflection 4.28634513
##
## $fits[[5]]$params$dispparam
## resp
## 0.005611513
##
##
## $fits[[5]]$fit
##
## Call: manyglm(formula = formula_X, family = "negative.binomial", data = data.frame(resp = y[, j], data))
## [1] "negative.binomial"
##
## Nuisance Parameter(s) phi estimated by the PHI method.
## resp
## 0.006
##
## Degrees of Freedom: 27 Total (i.e. Null); 22 Residual
##
## resp
## 2*log-likelihood: -26.602
## Residual Deviance: 6.045
## AIC: 40.602
##
##
## $fits[[6]]
## $fits[[6]]$params
## $fits[[6]]$params$coefficients
## resp
## (Intercept) -16.3511876
## soil.dry -0.1037164
## fallen.leaves 0.8426832
## moss 0.2535792
## herb.layer 4.2081646
## reflection -0.2789977
##
## $fits[[6]]$params$dispparam
## resp
## 0.1917023
##
##
## $fits[[6]]$fit
##
## Call: manyglm(formula = formula_X, family = "negative.binomial", data = data.frame(resp = y[, j], data))
## [1] "negative.binomial"
##
## Nuisance Parameter(s) phi estimated by the PHI method.
## resp
## 0.192
##
## Degrees of Freedom: 27 Total (i.e. Null); 22 Residual
##
## resp
## 2*log-likelihood: -76.69
## Residual Deviance: 18.34
## AIC: 90.69
##
##
## $fits[[7]]
## $fits[[7]]$params
## $fits[[7]]$params$coefficients
## resp
## (Intercept) 5.0939789
## soil.dry -1.6844420
## fallen.leaves 0.4285403
## moss -0.3362819
## herb.layer 0.5681939
## reflection -1.1738041
##
## $fits[[7]]$params$dispparam
## resp
## 0.2499474
##
##
## $fits[[7]]$fit
##
## Call: manyglm(formula = formula_X, family = "negative.binomial", data = data.frame(resp = y[, j], data))
## [1] "negative.binomial"
##
## Nuisance Parameter(s) phi estimated by the PHI method.
## resp
## 0.25
##
## Degrees of Freedom: 27 Total (i.e. Null); 22 Residual
##
## resp
## 2*log-likelihood: -92.34
## Residual Deviance: 27.09
## AIC: 106.34
##
##
## $fits[[8]]
## $fits[[8]]$params
## $fits[[8]]$params$coefficients
## resp
## (Intercept) -5.6455463
## soil.dry 1.2002696
## fallen.leaves -0.3664443
## moss 0.7593639
## herb.layer 0.8329403
## reflection 0.1681142
##
## $fits[[8]]$params$dispparam
## resp
## 0.5784331
##
##
## $fits[[8]]$fit
##
## Call: manyglm(formula = formula_X, family = "negative.binomial", data = data.frame(resp = y[, j], data))
## [1] "negative.binomial"
##
## Nuisance Parameter(s) phi estimated by the PHI method.
## resp
## 0.578
##
## Degrees of Freedom: 27 Total (i.e. Null); 22 Residual
##
## resp
## 2*log-likelihood: -155.71
## Residual Deviance: 27.98
## AIC: 169.71
##
##
## $fits[[9]]
## $fits[[9]]$params
## $fits[[9]]$params$coefficients
## resp
## (Intercept) -5.3342006
## soil.dry 2.2313336
## fallen.leaves -0.6775421
## moss -0.1997778
## herb.layer 0.9371083
## reflection -0.4896571
##
## $fits[[9]]$params$dispparam
## resp
## 0.8870737
##
##
## $fits[[9]]$fit
##
## Call: manyglm(formula = formula_X, family = "negative.binomial", data = data.frame(resp = y[, j], data))
## [1] "negative.binomial"
##
## Nuisance Parameter(s) phi estimated by the PHI method.
## resp
## 0.887
##
## Degrees of Freedom: 27 Total (i.e. Null); 22 Residual
##
## resp
## 2*log-likelihood: -126.5
## Residual Deviance: 26.5
## AIC: 140.5
##
##
## $fits[[10]]
## $fits[[10]]$params
## $fits[[10]]$params$coefficients
## resp
## (Intercept) -17.65397856
## soil.dry 2.51677974
## fallen.leaves -0.07824795
## moss 0.60379080
## herb.layer 3.08705308
## reflection -0.17243471
##
## $fits[[10]]$params$dispparam
## resp
## 0.2605146
##
##
## $fits[[10]]$fit
##
## Call: manyglm(formula = formula_X, family = "negative.binomial", data = data.frame(resp = y[, j], data))
## [1] "negative.binomial"
##
## Nuisance Parameter(s) phi estimated by the PHI method.
## resp
## 0.261
##
## Degrees of Freedom: 27 Total (i.e. Null); 22 Residual
##
## resp
## 2*log-likelihood: -118.03
## Residual Deviance: 25.54
## AIC: 132.03
##
##
## $fits[[11]]
## $fits[[11]]$params
## $fits[[11]]$params$coefficients
## resp
## (Intercept) -0.8033398
## soil.dry 1.3933952
## fallen.leaves -0.1906417
## moss -0.2255547
## herb.layer 0.4301938
## reflection -0.1960337
##
## $fits[[11]]$params$dispparam
## resp
## 0.3413161
##
##
## $fits[[11]]$fit
##
## Call: manyglm(formula = formula_X, family = "negative.binomial", data = data.frame(resp = y[, j], data))
## [1] "negative.binomial"
##
## Nuisance Parameter(s) phi estimated by the PHI method.
## resp
## 0.341
##
## Degrees of Freedom: 27 Total (i.e. Null); 22 Residual
##
## resp
## 2*log-likelihood: -208.06
## Residual Deviance: 30.89
## AIC: 222.06
##
##
## $fits[[12]]
## $fits[[12]]$params
## $fits[[12]]$params$coefficients
## resp
## (Intercept) -3.12403433
## soil.dry 1.75106244
## fallen.leaves -0.46304204
## moss -0.06552062
## herb.layer 0.65999721
## reflection -0.80362555
##
## $fits[[12]]$params$dispparam
## resp
## 0.6262394
##
##
## $fits[[12]]$fit
##
## Call: manyglm(formula = formula_X, family = "negative.binomial", data = data.frame(resp = y[, j], data))
## [1] "negative.binomial"
##
## Nuisance Parameter(s) phi estimated by the PHI method.
## resp
## 0.626
##
## Degrees of Freedom: 27 Total (i.e. Null); 22 Residual
##
## resp
## 2*log-likelihood: -118.39
## Residual Deviance: 25.48
## AIC: 132.39
##
##
##
## $y
## Alopacce Alopcune Alopfabr Arctlute Arctperi Auloalbi Pardlugu Pardmont
## units1 25 10 0 0 0 4 0 60
## units2 0 2 0 0 0 30 1 1
## units3 15 20 2 2 0 9 1 29
## units4 2 6 0 1 0 24 1 7
## units5 1 20 0 2 0 9 1 2
## units6 0 6 0 6 0 6 0 11
## units7 2 7 0 12 0 16 1 30
## units8 0 11 0 0 0 7 55 2
## units9 1 1 0 0 0 0 0 26
## units10 3 0 1 0 0 0 0 22
## units11 15 1 2 0 0 1 0 95
## units12 16 13 0 0 0 0 0 96
## units13 3 43 1 2 0 18 1 24
## units14 0 2 0 1 0 4 3 14
## units15 0 0 0 0 0 0 6 0
## units16 0 3 0 0 0 0 6 0
## units17 0 0 0 0 0 0 2 0
## units18 0 1 0 0 0 0 5 0
## units19 0 1 0 0 0 0 12 0
## units20 0 2 0 0 0 0 13 0
## units21 0 1 0 0 0 0 16 1
## units22 7 0 16 0 4 0 0 2
## units23 17 0 15 0 7 0 2 6
## units24 11 0 20 0 5 0 0 3
## units25 9 1 9 0 0 2 1 11
## units26 3 0 6 0 18 0 0 0
## units27 29 0 11 0 4 0 0 1
## units28 15 0 14 0 1 0 0 6
## Pardnigr Pardpull Trocterr Zoraspin
## units1 12 45 57 4
## units2 15 37 65 9
## units3 18 45 66 1
## units4 29 94 86 25
## units5 135 76 91 17
## units6 27 24 63 34
## units7 89 105 118 16
## units8 2 1 30 3
## units9 1 1 2 0
## units10 0 0 1 0
## units11 0 1 4 0
## units12 1 8 13 0
## units13 53 72 97 22
## units14 15 72 94 32
## units15 0 0 25 3
## units16 2 0 28 4
## units17 0 0 23 2
## units18 0 0 25 0
## units19 1 0 22 3
## units20 0 0 22 2
## units21 0 1 18 2
## units22 0 0 1 0
## units23 0 0 1 0
## units24 0 0 0 0
## units25 6 0 16 6
## units26 0 0 1 0
## units27 0 0 0 0
## units28 0 0 2 0
##
## $formula_X
## resp ~ soil.dry + fallen.leaves + moss + herb.layer + reflection
##
## $data
## soil.dry bare.sand fallen.leaves moss herb.layer reflection
## 1 2.3321 0.0000 0.0000 3.0445 4.4543 3.9120
## 2 3.0493 0.0000 1.7918 1.0986 4.5643 1.6094
## 3 2.5572 0.0000 0.0000 2.3979 4.6052 3.6889
## 4 2.6741 0.0000 0.0000 2.3979 4.6151 2.9957
## 5 3.0155 0.0000 0.0000 0.0000 4.6151 2.3026
## 6 3.3810 2.3979 3.4340 2.3979 3.4340 0.6931
## 7 3.1781 0.0000 0.0000 0.6931 4.6151 2.3026
## 8 2.6247 0.0000 4.2627 1.0986 3.4340 0.6931
## 9 2.4849 0.0000 0.0000 4.3307 3.2581 3.4012
## 10 2.1972 3.9318 0.0000 3.4340 3.0445 3.6889
## 11 2.2192 0.0000 0.0000 4.1109 3.7136 3.6889
## 12 2.2925 0.0000 0.0000 3.8286 4.0254 3.6889
## 13 3.5175 1.7918 1.7918 0.6931 4.5109 3.4012
## 14 3.0865 0.0000 0.0000 1.7918 4.5643 1.0986
## 15 3.2696 0.0000 4.3944 0.6931 3.0445 0.6931
## 16 3.0301 0.0000 4.6052 0.6931 0.6931 0.0000
## 17 3.3322 0.0000 4.4543 0.6931 3.0445 1.0986
## 18 3.1224 0.0000 4.3944 0.0000 3.0445 1.0986
## 19 2.9232 0.0000 4.5109 1.6094 1.6094 0.0000
## 20 3.1091 0.0000 4.5951 0.6931 0.6931 0.0000
## 21 2.9755 0.0000 4.5643 0.6931 1.7918 0.0000
## 22 1.2528 3.2581 0.0000 4.3307 0.6931 3.9120
## 23 1.1939 3.0445 0.0000 4.0254 3.2581 4.0943
## 24 1.6487 3.2581 0.0000 4.0254 3.0445 4.0073
## 25 1.8245 3.5835 0.0000 1.0986 4.1109 2.3026
## 26 0.9933 4.5109 0.0000 1.7918 1.7918 4.3820
## 27 0.9555 2.3979 0.0000 3.8286 3.4340 3.6889
## 28 0.9555 3.4340 0.0000 3.7136 3.4340 3.6889
##
## $family
## [1] "negative.binomial" "negative.binomial" "negative.binomial"
## [4] "negative.binomial" "negative.binomial" "negative.binomial"
## [7] "negative.binomial" "negative.binomial" "negative.binomial"
## [10] "negative.binomial" "negative.binomial" "negative.binomial"
##
## $trial_size
## [1] 1
##
## $linear_predictor
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 2.9035862 1.745619389 0.6142391 -1.7522773 -1.3416598 1.8319458
## [2,] -0.5534935 2.390375108 -2.1300043 0.2100244 -13.8155106 3.8793586
## [3,] 2.5491632 2.125158575 0.1999601 -0.7627781 -3.0358221 2.3418914
## [4,] 1.8514566 1.979029718 -0.3965178 0.0814681 -6.2408669 2.5648290
## [5,] 0.6136963 2.366877314 -0.8904890 0.3981151 -10.9449901 2.1147359
## [6,] -2.9310740 2.020804829 -4.0659360 0.9213298 -13.8155106 1.0574429
## [7,] 0.5823924 2.548138192 -1.3666599 1.8189230 -10.9657409 2.2736274
## [8,] -2.8051648 1.208384422 -2.5449206 -4.8750422 -13.8155106 1.5047396
## [9,] 1.5695847 0.870737719 -0.2414723 -1.0133923 -3.2338704 -2.7490428
## [10,] 1.7572006 0.522728876 0.6797460 -3.5729591 -1.8125658 -3.9257197
## [11,] 2.3678805 0.916583781 0.4799189 -2.2791478 -1.5811128 -0.9406708
## [12,] 2.5140003 1.245271693 0.3984165 -1.7656747 -1.8612733 0.2922471
## [13,] 0.5417865 3.928194434 -2.0084870 2.0049383 -8.6602230 3.0033481
## [14,] -0.2832947 1.719772728 -2.0727857 2.3826869 -13.8155106 2.6838738
## [15,] -3.7453765 1.986831664 -3.7580885 -2.0584536 -13.8155106 -0.1930722
## [16,] -6.0993604 -0.118916840 -3.7087490 -5.7276192 -13.8155106 -9.6922993
## [17,] -3.4609686 2.284439173 -3.6849622 -1.8586895 -13.8155106 -0.2622217
## [18,] -3.3695438 2.018940327 -3.0974942 -3.4991483 -13.8155106 -0.4666945
## [19,] -5.1189223 0.195735749 -3.6816449 -4.6599741 -13.8155106 -5.6723812
## [20,] -6.1600019 -0.004154524 -3.8606853 -5.2787435 -13.8155106 -9.7090040
## [21,] -5.1699648 0.479581228 -3.5943032 -4.9013983 -13.8155106 -5.0975917
## [22,] 0.9908218 -2.325529721 2.4603337 -10.6024953 1.4981590 -13.5577080
## [23,] 3.1861786 -0.691868363 2.7288485 -8.6340160 2.1810624 -2.8859361
## [24,] 2.5702280 -0.192231958 1.7910694 -6.2840182 0.9068259 -3.8076974
## [25,] 1.3439409 0.189336332 1.2011669 -5.9504709 -8.0688056 0.3950877
## [26,] 2.1159048 -1.580983608 3.7881146 -12.9504526 2.8823426 -9.6822245
## [27,] 3.1338628 -1.108573106 3.0259332 -9.8293866 0.8274896 -2.0577926
## [28,] 3.1170978 -1.098636585 3.0519798 -9.9143753 0.7770597 -2.0869543
## [,7] [,8] [,9] [,10] [,11] [,12]
## [1,] -1.91913419 3.83331448 1.51989181 3.12974052 2.90882446 0.55618328
## [2,] 1.06031614 2.26443044 3.52549851 4.35627776 4.50419098 3.03289100
## [3,] -1.73324604 3.70067489 2.40199351 3.81016300 3.47697275 1.27159551
## [4,] -1.11085120 3.73269572 3.01154410 4.25446812 3.78001010 2.03990191
## [5,] -0.06598566 2.20506916 4.59175002 3.78538126 4.93244379 3.35181939
## [6,] 1.20173168 1.95191105 2.28286026 2.51580603 4.05362261 2.75854708
## [7,] -0.57295292 2.92654808 4.81609886 4.61309705 5.00267787 3.59112980
## [8,] 3.26773759 -0.24616663 0.29339484 -0.23698396 3.13487629 1.13562656
## [9,] -2.68913710 3.91117352 0.73303325 0.68624695 2.41716289 0.36047627
## [10,] -2.36204880 2.75538481 -0.07082135 -1.28825380 2.07024972 -0.45673053
## [11,] -2.24655724 3.85312450 0.47005755 1.24136856 2.23606913 -0.02095393
## [12,] -2.09793160 3.98644663 0.98220195 2.21794153 2.53601353 0.33168254
## [13,] -1.72554097 2.77522268 3.72380961 4.81597971 4.87381555 2.40412694
## [14,] 0.59626488 3.40619356 4.93415487 5.09673408 4.84139661 4.29278228
## [15,] 2.15293053 -0.15272580 1.35915585 -0.07146230 3.93227125 1.97340379
## [16,] 2.12420322 -2.59253263 -1.18220955 -7.83010783 2.68267896 0.46149051
## [17,] 1.59717647 -0.03136861 1.25969660 0.01147878 3.92858669 1.72941393
## [18,] 2.15797983 -0.78755025 0.97061358 -0.93034196 3.80400380 1.43518959
## [19,] 2.47635964 -1.22725744 -0.68123098 -4.70985256 2.73921337 0.86268569
## [20,] 1.98680405 -2.49401025 -0.99909101 -7.63049193 2.79468266 0.60450117
## [21,] 2.82292106 -1.72792826 -0.24672800 -4.57257845 3.08705081 1.10995985
## [22,] -2.67073255 0.38170227 -4.66999259 -10.42104817 -0.50322049 -3.90039254
## [23,] -1.22541926 2.24631172 -2.42600766 -2.86676753 0.55128066 -2.43713477
## [24,] -2.01074873 2.59965232 -1.56876329 -2.36652882 1.11016230 -1.71181155
## [25,] 1.28426206 1.18981676 1.24226572 -0.10527100 2.80821202 0.86155248
## [26,] -1.30724701 -0.86355138 -3.94234569 -9.29641607 0.08837248 -3.84100801
## [27,] -0.18186254 1.88908534 -2.55529698 -2.97267618 0.41862757 -2.39981029
## [28,] -0.14319012 1.80175850 -2.53232253 -3.04211212 0.44456637 -2.39227542
##
## $fitted
## Alopacce Alopcune Alopfabr Arctlute Arctperi
## units1 18.239439075 5.72944913 1.84824965 1.733787e-01 2.614114e-01
## units2 0.574937738 10.91758845 0.11883678 1.233708e+00 1.000000e-06
## units3 12.796390651 8.37422532 1.22135400 4.663690e-01 4.803516e-02
## units4 6.369090246 7.23571889 0.67265832 1.084879e+00 1.948166e-03
## units5 1.847246864 10.66403979 0.41045498 1.489015e+00 1.764620e-05
## units6 0.053339718 7.54439444 0.01714693 2.512629e+00 1.000000e-06
## units7 1.790316488 12.78328159 0.25495713 6.165215e+00 1.728380e-05
## units8 0.060496800 3.34807121 0.07847928 7.634772e-03 1.000000e-06
## units9 4.804652178 2.38867237 0.78547053 3.629855e-01 3.940469e-02
## units10 5.796189060 1.68662396 1.97337649 2.807266e-02 1.632348e-01
## units11 10.674743518 2.50073273 1.61594333 1.023714e-01 2.057460e-01
## units12 12.354251711 3.47387850 1.48946423 1.710713e-01 1.554745e-01
## units13 1.719075301 50.81514469 0.13419156 7.425636e+00 1.733456e-04
## units14 0.753297790 5.58325940 0.12583476 1.083397e+01 1.000000e-06
## units15 0.023626731 7.29239238 0.02332829 1.276512e-01 1.000000e-06
## units16 0.002244303 0.88788163 0.02450816 3.254817e-03 1.000000e-06
## units17 0.031399335 9.82017726 0.02509812 1.558768e-01 1.000000e-06
## units18 0.034405328 7.53034101 0.04516223 3.022311e-02 1.000000e-06
## units19 0.005982467 1.21620548 0.02518152 9.466707e-03 1.000000e-06
## units20 0.002112249 0.99585409 0.02105357 5.098833e-03 1.000000e-06
## units21 0.005684769 1.61539778 0.02747983 7.436178e-03 1.000000e-06
## units22 2.693446952 0.09773166 11.70871816 2.485391e-05 4.473446e+00
## units23 24.195787923 0.50063982 15.31524154 1.779486e-04 8.855710e+00
## units24 13.068803874 0.82511545 5.99586115 1.865888e-03 2.476450e+00
## units25 3.834123624 1.20844732 3.32399352 2.604614e-03 3.131571e-04
## units26 8.297089369 0.20577260 44.17303581 2.375144e-06 1.785605e+01
## units27 22.962508395 0.33002954 20.61323125 5.384577e-05 2.287569e+00
## units28 22.580750360 0.33332523 21.15718899 4.945857e-05 2.175068e+00
## Auloalbi Pardlugu Pardmont Pardnigr Pardpull
## units1 6.246029e+00 0.14673395 46.21546509 4.571731e+00 2.286804e+01
## units2 4.839317e+01 2.88728364 9.62564065 3.397070e+01 7.796638e+01
## units3 1.040089e+01 0.17670987 40.47461115 1.104517e+01 4.515780e+01
## units4 1.299844e+01 0.32927856 41.79161499 2.031875e+01 7.041935e+01
## units5 8.287397e+00 0.93614429 9.07087888 9.866695e+01 4.405246e+01
## units6 2.879000e+00 3.32587128 7.04213262 9.804684e+00 1.237658e+01
## units7 9.714575e+00 0.56385795 18.66309570 1.234824e+02 1.007958e+02
## units8 4.502981e+00 26.25187948 0.78179195 1.340972e+00 7.890039e-01
## units9 6.398908e-02 0.06793954 49.95754379 2.081384e+00 1.986247e+00
## units10 1.972793e-02 0.09422697 15.72709170 9.316283e-01 2.757519e-01
## units11 3.903659e-01 0.10576271 47.14012274 1.600086e+00 3.460346e+00
## units12 1.339434e+00 0.12270998 53.86315317 2.670330e+00 9.188397e+00
## units13 2.015290e+01 0.17807669 16.04219884 4.142190e+01 1.234677e+02
## units14 1.464170e+01 1.81532567 30.15026027 1.389557e+02 1.634871e+02
## units15 8.244225e-01 8.61005347 0.85836505 3.892906e+00 9.310314e-01
## units16 6.175724e-05 8.36622883 0.07483028 3.066005e-01 3.975826e-04
## units17 7.693405e-01 4.93906712 0.96911828 3.524352e+00 1.011545e+00
## units18 6.270717e-01 8.65363817 0.45495797 2.639564e+00 3.944188e-01
## units19 3.439665e-03 11.89787290 0.29309531 5.059937e-01 9.006105e-03
## units20 6.073417e-05 7.29219102 0.08257814 3.682140e-01 4.854220e-04
## units21 6.111447e-03 16.82592849 0.17765208 7.813532e-01 1.033129e-02
## units22 1.294083e-06 0.06920151 1.46477591 9.372339e-03 2.979863e-05
## units23 5.580253e-02 0.29363456 9.45280688 8.838901e-02 5.688250e-02
## units24 2.219924e-02 0.13388839 13.45905784 2.083026e-01 9.380578e-02
## units25 1.484514e+00 3.61200153 3.28647894 3.463452e+00 9.000806e-01
## units26 6.238258e-05 0.27056389 0.42166194 1.940265e-02 9.175248e-05
## units27 1.277356e-01 0.83371593 6.61331698 7.766916e-02 5.116620e-02
## units28 1.240644e-01 0.86658930 6.06029510 7.947422e-02 4.773396e-02
## Trocterr Zoraspin
## units1 18.3352321 1.74400341
## units2 90.3951831 20.75715483
## units3 32.3616070 3.56653846
## units4 43.8164845 7.68985486
## units5 138.7180969 28.55463853
## units6 57.6057630 15.77690371
## units7 148.8111236 36.27503647
## units8 22.9857922 3.11312349
## units9 11.2139988 1.43401223
## units10 7.9268024 0.63335099
## units11 9.3564798 0.97926407
## units12 12.6292245 1.39331046
## units13 130.8191121 11.06876236
## units14 126.6461033 73.16976425
## units15 51.0227314 7.19512558
## units16 14.6242186 1.58643682
## units17 50.8350811 5.63734903
## units18 44.8805177 4.20044127
## units19 15.4748074 2.36951595
## units20 16.3574371 1.83033895
## units21 21.9123589 3.03423657
## units22 0.6045805 0.02023397
## units23 1.7354742 0.08741095
## units24 3.0348509 0.18053844
## units25 16.5802466 2.36683229
## units26 1.0923949 0.02147195
## units27 1.5198742 0.09073516
## units28 1.5598137 0.09142142
##
## attr(,"class")
## [1] "stackedsdm"summary.cgr Função de resumo para objeto cgr
abund <- spider$abund[,1:12]
spider_mod <- stackedsdm(abund,~1, data = spider$x, ncores=2)
spid_graph=cgr(spider_mod)
summary(spid_graph)##
## Call:
## stackedsdm(y = abund, formula_X = ~1, data = spider$x, ncores = 2)
##
## Pairwise associations:
## Alo Alo Alo Arc Arc Aul Par Par Par Par Tro
## Alo 0.000 0.000 0.313 0.000 0.040 0.000 -0.279 0.384 0.000 0.000 -0.028
## Alo 0.000 0.000 0.000 0.000 -0.057 0.042 0.000 0.135 0.230 0.151 0.229
## Alo 0.313 0.000 0.000 0.000 0.246 0.000 0.000 0.000 0.000 0.000 -0.218
## Arc 0.000 0.000 0.000 0.000 0.000 0.037 -0.025 0.000 0.169 0.127 0.040
## Arc 0.040 -0.057 0.246 0.000 0.000 0.000 -0.041 -0.056 0.000 0.000 -0.141
## Aul 0.000 0.042 0.000 0.037 0.000 0.000 0.000 0.020 0.191 0.249 0.100
## Par -0.279 0.000 0.000 -0.025 -0.041 0.000 0.000 -0.202 0.000 0.000 0.106
## Par 0.384 0.135 0.000 0.000 -0.056 0.020 -0.202 0.000 0.000 0.249 0.000
## Par 0.000 0.230 0.000 0.169 0.000 0.191 0.000 0.000 0.000 0.190 0.122
## Par 0.000 0.151 0.000 0.127 0.000 0.249 0.000 0.249 0.190 0.000 0.167
## Tro -0.028 0.229 -0.218 0.040 -0.141 0.100 0.106 0.000 0.122 0.167 0.000
## Zor -0.126 0.000 0.000 0.103 0.000 0.124 0.000 0.000 0.157 0.000 0.407
## Zor
## Alo -0.126
## Alo 0.000
## Alo 0.000
## Arc 0.103
## Arc 0.000
## Aul 0.124
## Par 0.000
## Par 0.000
## Par 0.157
## Par 0.000
## Tro 0.407
## Zor 0.000spid_graph$best_graph$cov## Alopacce Alopcune Alopfabr Arctlute Arctperi
## Alopacce 1.00000000 -0.10326141 0.48581270 -0.10034559 0.25141117
## Alopcune -0.10326141 1.00000000 -0.29700544 0.36603353 -0.31891565
## Alopfabr 0.48581270 -0.29700544 1.00000000 -0.20959147 0.44037904
## Arctlute -0.10034559 0.36603353 -0.20959147 1.00000000 -0.19692001
## Arctperi 0.25141117 -0.31891565 0.44037904 -0.19692001 1.00000000
## Auloalbi -0.13222399 0.50834835 -0.28386170 0.39051744 -0.26950889
## Pardlugu -0.49007120 0.07597942 -0.27944131 0.05361891 -0.18073743
## Pardmont 0.45309879 0.29399867 0.09850137 0.15333125 -0.05056864
## Pardnigr -0.15970796 0.61841611 -0.32215882 0.47898002 -0.30550588
## Pardpull -0.05217294 0.59263774 -0.26822258 0.44482744 -0.28130559
## Trocterr -0.31337335 0.62375327 -0.51051349 0.43047624 -0.44302289
## Zoraspin -0.31458251 0.50107008 -0.40414078 0.42052242 -0.33936880
## Auloalbi Pardlugu Pardmont Pardnigr Pardpull Trocterr
## Alopacce -0.13222399 -0.49007120 0.45309879 -0.1597080 -0.05217294 -0.3133733
## Alopcune 0.50834835 0.07597942 0.29399867 0.6184161 0.59263774 0.6237533
## Alopfabr -0.28386170 -0.27944131 0.09850137 -0.3221588 -0.26822258 -0.5105135
## Arctlute 0.39051744 0.05361891 0.15333125 0.4789800 0.44482744 0.4304762
## Arctperi -0.26950889 -0.18073743 -0.05056864 -0.3055059 -0.28130559 -0.4430229
## Auloalbi 1.00000000 0.09285768 0.22053624 0.6067252 0.61198597 0.5850001
## Pardlugu 0.09285768 1.00000000 -0.35164878 0.1120955 0.03378495 0.2466510
## Pardmont 0.22053624 -0.35164878 1.00000000 0.2228291 0.38376822 0.1350489
## Pardnigr 0.60672516 0.11209551 0.22282908 1.0000000 0.63760327 0.6537467
## Pardpull 0.61198597 0.03378495 0.38376822 0.6376033 1.00000000 0.6125370
## Trocterr 0.58500006 0.24665096 0.13504888 0.6537467 0.61253696 1.0000000
## Zoraspin 0.54064437 0.20850179 0.08708312 0.6033118 0.51069323 0.7277463
## Zoraspin
## Alopacce -0.31458251
## Alopcune 0.50107008
## Alopfabr -0.40414078
## Arctlute 0.42052242
## Arctperi -0.33936880
## Auloalbi 0.54064437
## Pardlugu 0.20850179
## Pardmont 0.08708312
## Pardnigr 0.60331179
## Pardpull 0.51069323
## Trocterr 0.72774632
## Zoraspin 1.00000000spid_graph$best_graph$logL## [1] 94.25275spid_graph$all_graphs$lambda.opt## [1] 0.09787635plot(spid_graph,pad=1)