library(alphahull)
## Registered S3 method overwritten by 'R.oo':
## method from
## throw.default R.methodsS3
library(hypervolume)
## Loading required package: Rcpp
## Loading required package: rgl
library(car)
## Loading required package: carData
library(MASS)
library(lme4)
## Loading required package: Matrix
## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod car
#source(file = "HighstatLibV10.R") #cool tools to support
#library(factoextra) # Useful for PCA analysis
library(here)
## here() starts at /Users/santorui/Documents/GitHub/FinlandAnalysis
library(data.table) # to work with data
library(dplyr) # to manage data
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:data.table':
##
## between, first, last
## The following object is masked from 'package:MASS':
##
## select
## The following object is masked from 'package:car':
##
## recode
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(magrittr) # to use the pipe operator %>%
library(MuMIn)
## Registered S3 method overwritten by 'MuMIn':
## method from
## predict.merMod lme4
library(glmmTMB)
## Warning in checkMatrixPackageVersion(): Package version inconsistency detected.
## TMB was built with Matrix version 1.2.17
## Current Matrix version is 1.2.18
## Please re-install 'TMB' from source using install.packages('TMB', type = 'source') or ask CRAN for a binary version of 'TMB' matching CRAN's 'Matrix' package
library(bbmle)
## Loading required package: stats4
##
## Attaching package: 'bbmle'
## The following object is masked from 'package:MuMIn':
##
## AICc
## The following object is masked from 'package:dplyr':
##
## slice
library(performance)
library(see)
library(pscl)
## Classes and Methods for R developed in the
## Political Science Computational Laboratory
## Department of Political Science
## Stanford University
## Simon Jackman
## hurdle and zeroinfl functions by Achim Zeileis
Results2 <- read.csv2(here("results","RESULTS.CSV"), header=TRUE, row.names = 1, stringsAsFactors = T,sep = ",", dec = ".")
Results2
## ForestID Dist_trail Dist_edge Dist_trail_beginning
## 1 Lagoinha 0 0 250
## 2 Lagoinha 50 293 250
## 3 Lagoinha 235 50 250
## 4 Mist\xe9rios_Negros 0 0 0
## 5 Mist\xe9rios_Negros 0 0 200
## 6 Mist\xe9rios_Negros 0 25 960
## 7 Mist\xe9rios_Negros 50 50 200
## 8 Mist\xe9rios_Negros 600 600 1880
## 9 Santa_Barbara 0 510 0
## 10 Santa_Barbara 0 481 50
## 11 Santa_Barbara 0 436 250
## 12 Santa_Barbara 0 404 401
## 13 Santa_Barbara 50 441 401
## 14 Santa_Barbara 300 600 401
## 15 Pico_Vara 0 0 0
## 16 Pico_Vara 0 0 50
## 17 Pico_Vara 0 0 250
## 18 Pico_Vara 0 0 400
## Dist_trail_std Dist_edge_std Dist_trail_beginning_std TAlphaAll
## 1 0.000000 0.00000000 0.47411353 5
## 2 0.312279 0.96318857 0.47411353 10
## 3 1.467711 0.16436665 0.47411353 8
## 4 0.000000 0.00000000 0.00000000 17
## 5 0.000000 0.00000000 0.37929082 16
## 6 0.000000 0.08218333 1.82059594 11
## 7 0.312279 0.16436665 0.37929082 11
## 8 3.747348 1.97239980 3.56533373 13
## 9 0.000000 1.67653983 0.00000000 12
## 10 0.000000 1.58120718 0.09482271 11
## 11 0.000000 1.43327719 0.47411353 7
## 12 0.000000 1.32808253 0.76047810 14
## 13 0.312279 1.44971386 0.76047810 10
## 14 1.873674 1.97239980 0.76047810 9
## 15 0.000000 0.00000000 0.00000000 10
## 16 0.000000 0.00000000 0.09482271 11
## 17 0.000000 0.00000000 0.47411353 12
## 18 0.000000 0.00000000 0.75858164 11
## TAlphaNat TAlphaNInd TAlphaEnd FAlphaAll FAlphaNat FAlphaNInd FAlphaEnd
## 1 5 0 3 2.507196 2.507196 0.0001000 1.458813
## 2 8 2 5 4.571896 3.649118 1.2911438 2.426661
## 3 8 0 6 3.748115 3.748115 0.0001000 2.736208
## 4 10 7 6 7.055960 4.811024 2.9836466 2.963138
## 5 11 5 6 6.188667 4.841288 1.7638506 2.963138
## 6 10 1 6 4.419375 4.062232 0.6455719 2.653394
## 7 9 2 5 4.455256 3.885097 1.0704329 2.426661
## 8 10 3 7 5.157504 4.091758 1.4415752 3.405778
## 9 11 1 7 4.877473 4.591758 0.6455719 3.405778
## 10 9 2 6 4.826500 4.026895 1.2911438 2.804438
## 11 6 1 4 3.438483 3.152768 0.6455719 2.104385
## 12 11 3 8 5.248713 4.678871 1.0704329 3.759558
## 13 9 1 6 3.913632 3.739558 0.6455719 2.484387
## 14 9 0 5 3.839360 3.839360 0.0001000 2.199731
## 15 8 2 5 3.662528 3.186972 1.0704329 2.265014
## 16 9 2 6 4.669753 4.026895 1.1820494 2.804438
## 17 11 1 5 4.320189 4.034474 0.6455719 2.158866
## 18 9 2 5 4.424199 3.658167 1.1820494 2.199731
## abund.all abund.nat abund.nind abund.end prop.Talpha prop.Falpha
## 1 117 117 0 105 0.00010000 0.9999000
## 2 92 90 2 81 0.20000000 0.7981628
## 3 139 139 0 111 0.00010000 0.9999000
## 4 111 99 12 64 0.41176471 0.6818383
## 5 147 115 32 94 0.31250000 0.7822829
## 6 103 102 1 71 0.09090909 0.9191869
## 7 167 148 19 128 0.18181818 0.8720254
## 8 159 114 45 82 0.23076923 0.7933601
## 9 134 130 4 115 0.08333333 0.9414216
## 10 66 63 3 43 0.18181818 0.8343304
## 11 188 176 12 145 0.14285714 0.9169068
## 12 207 184 23 169 0.21428571 0.8914321
## 13 115 113 2 108 0.10000000 0.9555211
## 14 154 154 0 135 0.00010000 0.9999000
## 15 87 77 10 66 0.20000000 0.8701564
## 16 90 82 8 70 0.18181818 0.8623358
## 17 99 96 3 79 0.08333333 0.9338652
## 18 159 157 2 138 0.18181818 0.8268540
## prop.abund prop.end all.tax.btotal all.tax.brich all.tax.brepl
## 1 0.000100000 0.8974359 0.3750000 0.37500000 0.0010000
## 2 0.021739130 0.8804348 0.5000000 0.16666667 0.3333333
## 3 0.000100000 0.7985612 0.0010000 0.00100000 0.0010000
## 4 0.108108108 0.5765766 0.5000000 0.20000000 0.3000000
## 5 0.217687075 0.6394558 0.3888889 0.16666667 0.2222222
## 6 0.009708738 0.6893204 0.5882353 0.11764706 0.4705882
## 7 0.113772455 0.7664671 0.5000000 0.12500000 0.3750000
## 8 0.283018868 0.5157233 0.0010000 0.00100000 0.0010000
## 9 0.029850746 0.8582090 0.5000000 0.21428571 0.2857143
## 10 0.045454545 0.6515152 0.5714286 0.14285714 0.4285714
## 11 0.063829787 0.7712766 0.5454545 0.18181818 0.3636364
## 12 0.111111111 0.8164251 0.5625000 0.31250000 0.2500000
## 13 0.017391304 0.9391304 0.4166667 0.08333333 0.3333333
## 14 0.000100000 0.8766234 0.0010000 0.00100000 0.0010000
## 15 0.114942529 0.7586207 0.5000000 0.07142857 0.4285714
## 16 0.088888889 0.7777778 0.5333333 0.00100000 0.5333333
## 17 0.030303030 0.7979798 0.4666667 0.06666667 0.4000000
## 18 0.012578616 0.8679245 0.0010000 0.00100000 0.0010000
## nat.tax.btotal nat.tax.brich nat.tax.brepl nind.tax.btotal
## 1 0.3750000 0.37500000 0.0010000 0.0010000
## 2 0.4000000 0.00100000 0.4000000 0.9990000
## 3 0.0010000 0.00100000 0.0010000 0.0010000
## 4 0.3333333 0.00100000 0.3333333 0.7500000
## 5 0.3846154 0.07692308 0.3076923 0.4000000
## 6 0.4615385 0.00100000 0.4615385 0.9990000
## 7 0.4166667 0.08333333 0.3333333 0.7500000
## 8 0.0010000 0.00100000 0.0010000 0.0010000
## 9 0.4615385 0.15384615 0.3076923 0.9990000
## 10 0.5000000 0.00100000 0.5000000 0.9990000
## 11 0.5000000 0.30000000 0.2000000 0.9990000
## 12 0.4615385 0.15384615 0.3076923 0.9990000
## 13 0.3636364 0.00100000 0.3636364 0.9990000
## 14 0.0010000 0.00100000 0.0010000 0.0010000
## 15 0.4545455 0.09090909 0.3636364 0.6666667
## 16 0.5000000 0.00100000 0.5000000 0.6666667
## 17 0.3333333 0.16666667 0.1666667 0.9990000
## 18 0.0010000 0.00100000 0.0010000 0.0010000
## nind.tax.brich nind.tax.brepl end.tax.btotal end.tax.brich
## 1 0.0010000 0.0010000 0.5000000 0.5000000
## 2 0.9990000 0.0010000 0.4285714 0.1428571
## 3 0.0010000 0.0010000 0.0010000 0.0010000
## 4 0.5000000 0.2500000 0.1428571 0.1428571
## 5 0.4000000 0.0010000 0.1428571 0.1428571
## 6 0.5000000 0.5000000 0.3750000 0.1250000
## 7 0.2500000 0.5000000 0.2857143 0.2857143
## 8 0.0010000 0.0010000 0.0010000 0.0010000
## 9 0.9990000 0.0010000 0.5000000 0.2500000
## 10 0.9990000 0.0010000 0.4285714 0.1428571
## 11 0.9990000 0.0010000 0.5000000 0.1666667
## 12 0.9990000 0.0010000 0.3750000 0.3750000
## 13 0.9990000 0.0010000 0.1666667 0.1666667
## 14 0.0010000 0.0010000 0.0010000 0.0010000
## 15 0.0010000 0.6666667 0.5714286 0.0010000
## 16 0.0010000 0.6666667 0.4285714 0.1428571
## 17 0.3333333 0.6666667 0.3333333 0.0010000
## 18 0.0010000 0.0010000 0.0010000 0.0010000
## end.tax.brepl all.func.btotal all.func.brich all.func.brepl
## 1 0.0010000 0.4703047 0.13145819 0.33884650
## 2 0.2857143 0.4510427 0.31515581 0.13588692
## 3 0.0010000 0.0010000 0.00100000 0.00100000
## 4 0.0010000 0.5584348 0.25627825 0.30215660
## 5 0.0010000 0.2792181 0.06748164 0.21173649
## 6 0.2500000 0.5247621 0.31531803 0.20944404
## 7 0.0010000 0.3148084 0.04135440 0.27345396
## 8 0.0010000 0.0010000 0.00100000 0.00100000
## 9 0.2500000 0.5260492 0.10235769 0.42369155
## 10 0.2857143 0.6572888 0.53708446 0.12020438
## 11 0.3333333 0.5266739 0.14647102 0.38020285
## 12 0.0010000 0.5870997 0.20743412 0.37966555
## 13 0.0010000 0.4914517 0.21871146 0.27274028
## 14 0.0010000 0.0010000 0.00100000 0.00100000
## 15 0.5714286 0.5250878 0.43168162 0.09340618
## 16 0.2857143 0.6401026 0.37683904 0.26326358
## 17 0.3333333 0.4641417 0.35717635 0.10696535
## 18 0.0010000 0.0010000 0.00100000 0.00100000
## nat.func.btotal nat.func.brich nat.func.brepl nind.func.btotal
## 1 0.4703047 0.13145819 0.33884650 0.0010000
## 2 0.4474493 0.33220517 0.11524413 0.9990000
## 3 0.0010000 0.00100000 0.00100000 0.0010000
## 4 0.5256001 0.09645607 0.42914408 0.8330498
## 5 0.3446691 0.01439418 0.33027495 0.3296413
## 6 0.3675332 0.09069260 0.27684058 0.9963128
## 7 0.3791924 0.21033381 0.16885860 0.5975177
## 8 0.0010000 0.00100000 0.00100000 0.0010000
## 9 0.5332315 0.12395226 0.40927928 0.9990000
## 10 0.6601833 0.56185864 0.09832462 0.9990000
## 11 0.5228679 0.09847547 0.42439247 0.9990000
## 12 0.5762955 0.13399572 0.44229977 0.9990000
## 13 0.4956483 0.23100532 0.26464296 0.9990000
## 14 0.0010000 0.00100000 0.00100000 0.0010000
## 15 0.5567426 0.49342135 0.06332120 0.8920702
## 16 0.6518425 0.42306199 0.22878048 0.8450143
## 17 0.4634681 0.37046817 0.09299989 0.8861977
## 18 0.0010000 0.00100000 0.00100000 0.0010000
## nind.func.brich nind.func.brepl end.func.btotal end.func.brich
## 1 0.0010000 0.00100000 0.4325629 0.04353992
## 2 0.9990000 0.00100000 0.3865546 0.25210084
## 3 0.0010000 0.00100000 0.0010000 0.00100000
## 4 0.7085055 0.12454426 0.6243414 0.16960174
## 5 0.3076977 0.02194364 0.3808905 0.11039383
## 6 0.9600486 0.03626414 0.3013586 0.12212455
## 7 0.5697584 0.02775928 0.4366802 0.34244148
## 8 0.0010000 0.00100000 0.0010000 0.00100000
## 9 0.9990000 0.00100000 0.5605254 0.11515797
## 10 0.9990000 0.00100000 0.7247930 0.65909574
## 11 0.9990000 0.00100000 0.5347506 0.05233033
## 12 0.9990000 0.00100000 0.5908064 0.15760718
## 13 0.9990000 0.00100000 0.4641196 0.17065338
## 14 0.0010000 0.00100000 0.0010000 0.00100000
## 15 0.7386199 0.15345034 0.5533273 0.51059037
## 16 0.6929914 0.15202286 0.6707071 0.43457653
## 17 0.2227605 0.66343721 0.4915034 0.41014424
## 18 0.0010000 0.00100000 0.0010000 0.00100000
## end.func.brepl
## 1 0.38902297
## 2 0.13445378
## 3 0.00100000
## 4 0.45473967
## 5 0.27049662
## 6 0.17923405
## 7 0.09423873
## 8 0.00100000
## 9 0.44536745
## 10 0.06569727
## 11 0.48242031
## 12 0.43319925
## 13 0.29346624
## 14 0.00100000
## 15 0.04273692
## 16 0.23613055
## 17 0.08135913
## 18 0.00100000
withoutcontrols <- Results2[-c(1,6,11,17,21),]
withoutcontrols
## ForestID Dist_trail Dist_edge Dist_trail_beginning
## 2 Lagoinha 50 293 250
## 3 Lagoinha 235 50 250
## 4 Mist\xe9rios_Negros 0 0 0
## 5 Mist\xe9rios_Negros 0 0 200
## 7 Mist\xe9rios_Negros 50 50 200
## 8 Mist\xe9rios_Negros 600 600 1880
## 9 Santa_Barbara 0 510 0
## 10 Santa_Barbara 0 481 50
## 12 Santa_Barbara 0 404 401
## 13 Santa_Barbara 50 441 401
## 14 Santa_Barbara 300 600 401
## 15 Pico_Vara 0 0 0
## 16 Pico_Vara 0 0 50
## 18 Pico_Vara 0 0 400
## Dist_trail_std Dist_edge_std Dist_trail_beginning_std TAlphaAll
## 2 0.312279 0.9631886 0.47411353 10
## 3 1.467711 0.1643667 0.47411353 8
## 4 0.000000 0.0000000 0.00000000 17
## 5 0.000000 0.0000000 0.37929082 16
## 7 0.312279 0.1643667 0.37929082 11
## 8 3.747348 1.9723998 3.56533373 13
## 9 0.000000 1.6765398 0.00000000 12
## 10 0.000000 1.5812072 0.09482271 11
## 12 0.000000 1.3280825 0.76047810 14
## 13 0.312279 1.4497139 0.76047810 10
## 14 1.873674 1.9723998 0.76047810 9
## 15 0.000000 0.0000000 0.00000000 10
## 16 0.000000 0.0000000 0.09482271 11
## 18 0.000000 0.0000000 0.75858164 11
## TAlphaNat TAlphaNInd TAlphaEnd FAlphaAll FAlphaNat FAlphaNInd FAlphaEnd
## 2 8 2 5 4.571896 3.649118 1.2911438 2.426661
## 3 8 0 6 3.748115 3.748115 0.0001000 2.736208
## 4 10 7 6 7.055960 4.811024 2.9836466 2.963138
## 5 11 5 6 6.188667 4.841288 1.7638506 2.963138
## 7 9 2 5 4.455256 3.885097 1.0704329 2.426661
## 8 10 3 7 5.157504 4.091758 1.4415752 3.405778
## 9 11 1 7 4.877473 4.591758 0.6455719 3.405778
## 10 9 2 6 4.826500 4.026895 1.2911438 2.804438
## 12 11 3 8 5.248713 4.678871 1.0704329 3.759558
## 13 9 1 6 3.913632 3.739558 0.6455719 2.484387
## 14 9 0 5 3.839360 3.839360 0.0001000 2.199731
## 15 8 2 5 3.662528 3.186972 1.0704329 2.265014
## 16 9 2 6 4.669753 4.026895 1.1820494 2.804438
## 18 9 2 5 4.424199 3.658167 1.1820494 2.199731
## abund.all abund.nat abund.nind abund.end prop.Talpha prop.Falpha
## 2 92 90 2 81 0.20000000 0.7981628
## 3 139 139 0 111 0.00010000 0.9999000
## 4 111 99 12 64 0.41176471 0.6818383
## 5 147 115 32 94 0.31250000 0.7822829
## 7 167 148 19 128 0.18181818 0.8720254
## 8 159 114 45 82 0.23076923 0.7933601
## 9 134 130 4 115 0.08333333 0.9414216
## 10 66 63 3 43 0.18181818 0.8343304
## 12 207 184 23 169 0.21428571 0.8914321
## 13 115 113 2 108 0.10000000 0.9555211
## 14 154 154 0 135 0.00010000 0.9999000
## 15 87 77 10 66 0.20000000 0.8701564
## 16 90 82 8 70 0.18181818 0.8623358
## 18 159 157 2 138 0.18181818 0.8268540
## prop.abund prop.end all.tax.btotal all.tax.brich all.tax.brepl
## 2 0.02173913 0.8804348 0.5000000 0.16666667 0.3333333
## 3 0.00010000 0.7985612 0.0010000 0.00100000 0.0010000
## 4 0.10810811 0.5765766 0.5000000 0.20000000 0.3000000
## 5 0.21768707 0.6394558 0.3888889 0.16666667 0.2222222
## 7 0.11377246 0.7664671 0.5000000 0.12500000 0.3750000
## 8 0.28301887 0.5157233 0.0010000 0.00100000 0.0010000
## 9 0.02985075 0.8582090 0.5000000 0.21428571 0.2857143
## 10 0.04545455 0.6515152 0.5714286 0.14285714 0.4285714
## 12 0.11111111 0.8164251 0.5625000 0.31250000 0.2500000
## 13 0.01739130 0.9391304 0.4166667 0.08333333 0.3333333
## 14 0.00010000 0.8766234 0.0010000 0.00100000 0.0010000
## 15 0.11494253 0.7586207 0.5000000 0.07142857 0.4285714
## 16 0.08888889 0.7777778 0.5333333 0.00100000 0.5333333
## 18 0.01257862 0.8679245 0.0010000 0.00100000 0.0010000
## nat.tax.btotal nat.tax.brich nat.tax.brepl nind.tax.btotal
## 2 0.4000000 0.00100000 0.4000000 0.9990000
## 3 0.0010000 0.00100000 0.0010000 0.0010000
## 4 0.3333333 0.00100000 0.3333333 0.7500000
## 5 0.3846154 0.07692308 0.3076923 0.4000000
## 7 0.4166667 0.08333333 0.3333333 0.7500000
## 8 0.0010000 0.00100000 0.0010000 0.0010000
## 9 0.4615385 0.15384615 0.3076923 0.9990000
## 10 0.5000000 0.00100000 0.5000000 0.9990000
## 12 0.4615385 0.15384615 0.3076923 0.9990000
## 13 0.3636364 0.00100000 0.3636364 0.9990000
## 14 0.0010000 0.00100000 0.0010000 0.0010000
## 15 0.4545455 0.09090909 0.3636364 0.6666667
## 16 0.5000000 0.00100000 0.5000000 0.6666667
## 18 0.0010000 0.00100000 0.0010000 0.0010000
## nind.tax.brich nind.tax.brepl end.tax.btotal end.tax.brich
## 2 0.999 0.0010000 0.4285714 0.1428571
## 3 0.001 0.0010000 0.0010000 0.0010000
## 4 0.500 0.2500000 0.1428571 0.1428571
## 5 0.400 0.0010000 0.1428571 0.1428571
## 7 0.250 0.5000000 0.2857143 0.2857143
## 8 0.001 0.0010000 0.0010000 0.0010000
## 9 0.999 0.0010000 0.5000000 0.2500000
## 10 0.999 0.0010000 0.4285714 0.1428571
## 12 0.999 0.0010000 0.3750000 0.3750000
## 13 0.999 0.0010000 0.1666667 0.1666667
## 14 0.001 0.0010000 0.0010000 0.0010000
## 15 0.001 0.6666667 0.5714286 0.0010000
## 16 0.001 0.6666667 0.4285714 0.1428571
## 18 0.001 0.0010000 0.0010000 0.0010000
## end.tax.brepl all.func.btotal all.func.brich all.func.brepl
## 2 0.2857143 0.4510427 0.31515581 0.13588692
## 3 0.0010000 0.0010000 0.00100000 0.00100000
## 4 0.0010000 0.5584348 0.25627825 0.30215660
## 5 0.0010000 0.2792181 0.06748164 0.21173649
## 7 0.0010000 0.3148084 0.04135440 0.27345396
## 8 0.0010000 0.0010000 0.00100000 0.00100000
## 9 0.2500000 0.5260492 0.10235769 0.42369155
## 10 0.2857143 0.6572888 0.53708446 0.12020438
## 12 0.0010000 0.5870997 0.20743412 0.37966555
## 13 0.0010000 0.4914517 0.21871146 0.27274028
## 14 0.0010000 0.0010000 0.00100000 0.00100000
## 15 0.5714286 0.5250878 0.43168162 0.09340618
## 16 0.2857143 0.6401026 0.37683904 0.26326358
## 18 0.0010000 0.0010000 0.00100000 0.00100000
## nat.func.btotal nat.func.brich nat.func.brepl nind.func.btotal
## 2 0.4474493 0.33220517 0.11524413 0.9990000
## 3 0.0010000 0.00100000 0.00100000 0.0010000
## 4 0.5256001 0.09645607 0.42914408 0.8330498
## 5 0.3446691 0.01439418 0.33027495 0.3296413
## 7 0.3791924 0.21033381 0.16885860 0.5975177
## 8 0.0010000 0.00100000 0.00100000 0.0010000
## 9 0.5332315 0.12395226 0.40927928 0.9990000
## 10 0.6601833 0.56185864 0.09832462 0.9990000
## 12 0.5762955 0.13399572 0.44229977 0.9990000
## 13 0.4956483 0.23100532 0.26464296 0.9990000
## 14 0.0010000 0.00100000 0.00100000 0.0010000
## 15 0.5567426 0.49342135 0.06332120 0.8920702
## 16 0.6518425 0.42306199 0.22878048 0.8450143
## 18 0.0010000 0.00100000 0.00100000 0.0010000
## nind.func.brich nind.func.brepl end.func.btotal end.func.brich
## 2 0.9990000 0.00100000 0.3865546 0.2521008
## 3 0.0010000 0.00100000 0.0010000 0.0010000
## 4 0.7085055 0.12454426 0.6243414 0.1696017
## 5 0.3076977 0.02194364 0.3808905 0.1103938
## 7 0.5697584 0.02775928 0.4366802 0.3424415
## 8 0.0010000 0.00100000 0.0010000 0.0010000
## 9 0.9990000 0.00100000 0.5605254 0.1151580
## 10 0.9990000 0.00100000 0.7247930 0.6590957
## 12 0.9990000 0.00100000 0.5908064 0.1576072
## 13 0.9990000 0.00100000 0.4641196 0.1706534
## 14 0.0010000 0.00100000 0.0010000 0.0010000
## 15 0.7386199 0.15345034 0.5533273 0.5105904
## 16 0.6929914 0.15202286 0.6707071 0.4345765
## 18 0.0010000 0.00100000 0.0010000 0.0010000
## end.func.brepl
## 2 0.13445378
## 3 0.00100000
## 4 0.45473967
## 5 0.27049662
## 7 0.09423873
## 8 0.00100000
## 9 0.44536745
## 10 0.06569727
## 12 0.43319925
## 13 0.29346624
## 14 0.00100000
## 15 0.04273692
## 16 0.23613055
## 18 0.00100000
## Obtaining the several R2 values
###############
###CRUDE SOLUTION FOR CALCULATING R
##################3
version1 <- function(m) {
lmfit <- lm(model.response(model.frame(m)) ~ fitted(m))
summary(lmfit)$r.squared
}
## Ω20 (Xu 2003), which is almost the same, is based on comparing the residual variance of the full model against the
##residual variance of a (fixed) intercept-only null model:
version2 <- function(m){
1-var(residuals(m))/var(model.response(model.frame(m)))
}
## Another possibility is the squared correlation between the response variable and the predicted values:
version3 <- function(m){
cor(model.response(model.frame(m)),predict(m,type="response"))^2
}
r2 <- function(model){
print(" ")
print("STANDARD R2")
print(" ")
print(performance::r2(model))
print(" ")
print(" ")
print("NAKAGAWA")
print(" ")
print(performance::r2_nakagawa(model))
print(" ")
print("VERSION1")
print(" ")
print(version1(model))
print(" ")
print(" ")
print("VERSION2")
print(" ")
print(version2(model))
print(" ")
print("VERSION3")
print(" ")
print(version3(model))
}
################
###CHECKING R2
#################
TAlphaNInd.glmm.5 = glmmTMB(TAlphaNInd ~ Dist_trail_beginning_std +(1 | ForestID), data= Results2,family = "poisson")
r2( TAlphaNInd.glmm.5 )
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: mu of 1.7 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.385
## Marginal R2: 0.040
## [1] " "
## [1] " "
## [1] "NAKAGAWA"
## [1] " "
## Warning: mu of 1.7 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.385
## Marginal R2: 0.040
## [1] " "
## [1] "VERSION1"
## [1] " "
## [1] 0.525133
## [1] " "
## [1] " "
## [1] "VERSION2"
## [1] " "
## [1] 0.4673168
## [1] " "
## [1] "VERSION3"
## [1] " "
## [1] 0.525133
TAlphaNInd.glmm.6 = glmmTMB(TAlphaNInd ~ Dist_edge_std + (1 | ForestID), data= Results2,family = "poisson")
r2( TAlphaNInd.glmm.6 )
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: mu of 1.7 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.283
## Marginal R2: 0.030
## [1] " "
## [1] " "
## [1] "NAKAGAWA"
## [1] " "
## Warning: mu of 1.7 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.283
## Marginal R2: 0.030
## [1] " "
## [1] "VERSION1"
## [1] " "
## [1] 0.3985183
## [1] " "
## [1] " "
## [1] "VERSION2"
## [1] " "
## [1] 0.3513795
## [1] " "
## [1] "VERSION3"
## [1] " "
## [1] 0.3985183
FAlphaAll.glmm.9 = glmmTMB(FAlphaAll ~ Dist_trail_beginning_std + (1 | ForestID), data= Results2,family = "Gamma")
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
r2( FAlphaAll.glmm.9 )
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## # R2 for Mixed Models
##
## Conditional R2: 0.023
## Marginal R2: 0.001
## [1] " "
## [1] " "
## [1] "NAKAGAWA"
## [1] " "
## # R2 for Mixed Models
##
## Conditional R2: 0.023
## Marginal R2: 0.001
## [1] " "
## [1] "VERSION1"
## [1] " "
## [1] 0.4749602
## [1] " "
## [1] " "
## [1] "VERSION2"
## [1] " "
## [1] 0.4110315
## [1] " "
## [1] "VERSION3"
## [1] " "
## [1] 0.4749602
abund.all.glmm.14 = glmmTMB(abund.all ~ Dist_trail_std +(1 | ForestID), data= Results2,family = "nbinom1")
r2( abund.all.glmm.14 )
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.083
## [1] " "
## [1] " "
## [1] "NAKAGAWA"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.083
## [1] " "
## [1] "VERSION1"
## [1] " "
## [1] 0.06579715
## [1] " "
## [1] " "
## [1] "VERSION2"
## [1] " "
## [1] 0.06326355
## [1] " "
## [1] "VERSION3"
## [1] " "
## [1] 0.06579715
prop.Talpha.glmm.19 = glmmTMB(prop.Talpha ~ Dist_trail_std +(1 | ForestID), data= Results2,family = "beta_family")
r2( prop.Talpha.glmm.19 )
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: mu of 0.1 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.560
## Marginal R2: 0.198
## [1] " "
## [1] " "
## [1] "NAKAGAWA"
## [1] " "
## Warning: mu of 0.1 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.560
## Marginal R2: 0.198
## [1] " "
## [1] "VERSION1"
## [1] " "
## [1] 0.3085268
## [1] " "
## [1] " "
## [1] "VERSION2"
## [1] " "
## [1] 0.2902301
## [1] " "
## [1] "VERSION3"
## [1] " "
## [1] 0.3085268
prop.Falpha.glmm.21 = glmmTMB(prop.Falpha ~ Dist_trail_std + (1 | ForestID), data= Results2,family = "beta_family")
r2( prop.Falpha.glmm.21 )
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## # R2 for Mixed Models
##
## Conditional R2: 1.190
## Marginal R2: 0.336
## [1] " "
## [1] " "
## [1] "NAKAGAWA"
## [1] " "
## # R2 for Mixed Models
##
## Conditional R2: 1.190
## Marginal R2: 0.336
## [1] " "
## [1] "VERSION1"
## [1] " "
## [1] 0.2343031
## [1] " "
## [1] " "
## [1] "VERSION2"
## [1] " "
## [1] 0.2058367
## [1] " "
## [1] "VERSION3"
## [1] " "
## [1] 0.2343031
dredge.24 = dredge(glmmTMB(all.tax.btotal ~ Dist_trail_beginning_std + Dist_trail_std + Dist_edge_std + (1 | ForestID), data= withoutcontrols,family = "beta_family"))
## Fixed terms are "cond((Int))" and "disp((Int))"
dredge.24
## Global model call: glmmTMB(formula = all.tax.btotal ~ Dist_trail_beginning_std +
## Dist_trail_std + Dist_edge_std + (1 | ForestID), data = withoutcontrols,
## family = "beta_family", ziformula = ~0, dispformula = ~1)
## ---
## Model selection table
## cnd((Int)) dsp((Int)) cnd(Dst_edg_std) cnd(Dst_trl_bgn_std)
## 5 -0.4790 +
## 6 -0.8101 + 0.5579
## 3 -0.5005 + -0.78720
## 1 -0.9393 +
## 7 -0.4681 + -0.04445
## 2 -0.7595 + -0.2241
## 4 -0.6036 + 0.1980 -0.88510
## 8 -0.7570 + 0.5615 -0.17960
## cnd(Dst_trl_std) df logLik AICc delta weight
## 5 -0.8739 4 12.035 -11.6 0.00 0.606
## 6 -1.1490 5 13.103 -8.7 2.92 0.141
## 3 4 10.211 -8.0 3.65 0.098
## 1 3 7.990 -7.6 4.05 0.080
## 7 -0.8566 5 12.037 -6.6 5.05 0.048
## 2 4 8.182 -3.9 7.71 0.013
## 4 5 10.352 -3.2 8.42 0.009
## 8 -1.0950 6 13.136 -2.3 9.35 0.006
## Models ranked by AICc(x)
## Random terms (all models):
## 'cond(1 | ForestID)'
all.tax.btotal.glmm.24 = glmmTMB(all.tax.btotal ~ (1 | ForestID), data= withoutcontrols,family = "beta_family")
all.tax.brich.glmm.25 = glmmTMB(all.tax.btotal ~ Dist_trail_std +(1 | ForestID), data= withoutcontrols,family = "beta_family")
r2( all.tax.brich.glmm.25 )
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.717
## Marginal R2: 0.708
## [1] " "
## [1] " "
## [1] "NAKAGAWA"
## [1] " "
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.717
## Marginal R2: 0.708
## [1] " "
## [1] "VERSION1"
## [1] " "
## [1] 0.5989002
## [1] " "
## [1] " "
## [1] "VERSION2"
## [1] " "
## [1] 0.5302905
## [1] " "
## [1] "VERSION3"
## [1] " "
## [1] 0.5989002
all.tax.brich.glmm.25a = glmmTMB(all.tax.btotal ~ Dist_edge_std + Dist_trail_std +(1 | ForestID), data= withoutcontrols,family = "beta_family")
r2( all.tax.brich.glmm.25a )
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.790
## [1] " "
## [1] " "
## [1] "NAKAGAWA"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.790
## [1] " "
## [1] "VERSION1"
## [1] " "
## [1] 0.5874673
## [1] " "
## [1] " "
## [1] "VERSION2"
## [1] " "
## [1] 0.572622
## [1] " "
## [1] "VERSION3"
## [1] " "
## [1] 0.5874673
nind.tax.btotal.glmm.31 = glmmTMB(nind.tax.btotal ~ Dist_edge_std + (1 | ForestID), data= withoutcontrols,family = "beta_family")
r2( nind.tax.btotal.glmm.31 )
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 1.2 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: -1.768
## [1] " "
## [1] " "
## [1] "NAKAGAWA"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 1.2 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: -1.768
## [1] " "
## [1] "VERSION1"
## [1] " "
## [1] 0.01294264
## [1] " "
## [1] " "
## [1] "VERSION2"
## [1] " "
## [1] 0.01176139
## [1] " "
## [1] "VERSION3"
## [1] " "
## [1] 0.01294264
nind.tax.brich.glmm.32 = glmmTMB(nind.tax.brich ~ Dist_edge_std + (1 | ForestID), data= withoutcontrols,family = "beta_family")
r2( nind.tax.brich.glmm.32 )
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.9 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.730
## [1] " "
## [1] " "
## [1] "NAKAGAWA"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.9 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.730
## [1] " "
## [1] "VERSION1"
## [1] " "
## [1] 0.1813694
## [1] " "
## [1] " "
## [1] "VERSION2"
## [1] " "
## [1] 0.154714
## [1] " "
## [1] "VERSION3"
## [1] " "
## [1] 0.1813694
end.tax.btotal.glmm.35 = glmmTMB(end.tax.btotal ~ Dist_trail_std +(1 | ForestID), data= withoutcontrols,family = "beta_family")
r2( end.tax.btotal.glmm.35 )
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.575
## [1] " "
## [1] " "
## [1] "NAKAGAWA"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.575
## [1] " "
## [1] "VERSION1"
## [1] " "
## [1] 0.3969704
## [1] " "
## [1] " "
## [1] "VERSION2"
## [1] " "
## [1] 0.3532717
## [1] " "
## [1] "VERSION3"
## [1] " "
## [1] 0.3969704
all.func.btotal.glmm.39 = glmmTMB(all.func.btotal ~ Dist_edge_std + (1 | ForestID), data= withoutcontrols,family = "beta_family")
r2( all.func.btotal.glmm.39 )
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.048
## [1] " "
## [1] " "
## [1] "NAKAGAWA"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.048
## [1] " "
## [1] "VERSION1"
## [1] " "
## [1] 0.002098823
## [1] " "
## [1] " "
## [1] "VERSION2"
## [1] " "
## [1] -0.003740786
## [1] " "
## [1] "VERSION3"
## [1] " "
## [1] 0.002098823
nat.func.btotal.glmm.43 = glmmTMB(nat.func.btotal ~ Dist_trail_beginning_std + (1 | ForestID), data= withoutcontrols,family = "beta_family")
r2( nat.func.btotal.glmm.43 )
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.564
## [1] " "
## [1] " "
## [1] "NAKAGAWA"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.564
## [1] " "
## [1] "VERSION1"
## [1] " "
## [1] 0.4119036
## [1] " "
## [1] " "
## [1] "VERSION2"
## [1] " "
## [1] 0.3343611
## [1] " "
## [1] "VERSION3"
## [1] " "
## [1] 0.4119036
nind.func.btotal.glmm.46 = glmmTMB(nind.func.btotal ~ Dist_edge_std + (1 | ForestID), data= withoutcontrols,family = "beta_family")
r2( nind.func.btotal.glmm.46 )
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 1.2 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: -0.613
## [1] " "
## [1] " "
## [1] "NAKAGAWA"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 1.2 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: -0.613
## [1] " "
## [1] "VERSION1"
## [1] " "
## [1] 0.003388029
## [1] " "
## [1] " "
## [1] "VERSION2"
## [1] " "
## [1] -0.0003418351
## [1] " "
## [1] "VERSION3"
## [1] " "
## [1] 0.003388029
nind.func.brich.glmm.48 = glmmTMB(nind.func.brich ~ Dist_edge_std + (1 | ForestID), data= withoutcontrols,family = "beta_family")
r2( nind.func.brich.glmm.48 )
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 1.1 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: -3.676
## [1] " "
## [1] " "
## [1] "NAKAGAWA"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 1.1 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: -3.676
## [1] " "
## [1] "VERSION1"
## [1] " "
## [1] 0.02100292
## [1] " "
## [1] " "
## [1] "VERSION2"
## [1] " "
## [1] 0.02088823
## [1] " "
## [1] "VERSION3"
## [1] " "
## [1] 0.02100292
end.func.btotal.glmm.50 = glmmTMB(end.func.btotal ~ Dist_trail_beginning_std + (1 | ForestID), data= withoutcontrols,family = "beta_family")
r2( end.func.btotal.glmm.50 )
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.588
## [1] " "
## [1] " "
## [1] "NAKAGAWA"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.588
## [1] " "
## [1] "VERSION1"
## [1] " "
## [1] 0.4439095
## [1] " "
## [1] " "
## [1] "VERSION2"
## [1] " "
## [1] 0.3503584
## [1] " "
## [1] "VERSION3"
## [1] " "
## [1] 0.4439095
end.func.brich.glmm.52 = glmmTMB(end.func.brich ~ Dist_trail_beginning_std + (1 | ForestID), data= withoutcontrols,family = "beta_family")
r2( end.func.brich.glmm.52 )
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.438
## [1] " "
## [1] " "
## [1] "NAKAGAWA"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.438
## [1] " "
## [1] "VERSION1"
## [1] " "
## [1] 0.3243946
## [1] " "
## [1] " "
## [1] "VERSION2"
## [1] " "
## [1] 0.2523689
## [1] " "
## [1] "VERSION3"
## [1] " "
## [1] 0.3243946
end.func.brepl.glmm.54 = glmmTMB(end.func.brepl ~ Dist_edge_std + (1 | ForestID), data= withoutcontrols,family = "beta_family")
r2( end.func.brepl.glmm.54 )
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.015
## [1] " "
## [1] " "
## [1] "NAKAGAWA"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.015
## [1] " "
## [1] "VERSION1"
## [1] " "
## [1] 0.0001037741
## [1] " "
## [1] " "
## [1] "VERSION2"
## [1] " "
## [1] -0.008681977
## [1] " "
## [1] "VERSION3"
## [1] " "
## [1] 0.0001037741