# Name: Rui Miguel Carvalho
# Date of creation: 7/2/2020
# Libraries -----------
library(BAT)
##
## Attaching package: 'BAT'
## The following object is masked from 'package:base':
##
## beta
library(readr)
library(FD)
## Loading required package: ade4
##
## Attaching package: 'ade4'
## The following object is masked from 'package:BAT':
##
## originality
## Loading required package: ape
## Loading required package: geometry
## Loading required package: vegan
## Loading required package: permute
## Loading required package: lattice
## This is vegan 2.5-5
library(alphahull)
## Registered S3 method overwritten by 'R.oo':
## method from
## throw.default R.methodsS3
##
## Attaching package: 'alphahull'
## The following object is masked from 'package:ape':
##
## complement
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':
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## 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
library(DHARMa)
## Registered S3 method overwritten by 'DHARMa':
## method from
## refit.glmmTMB glmmTMB
library(rlang)
##
## Attaching package: 'rlang'
## The following object is masked from 'package:magrittr':
##
## set_names
## The following object is masked from 'package:data.table':
##
## :=
library(sjPlot)
# Load files -----------
plot.all <- read.csv2(here("data.veg","plots.alpha.csv"), row.names=1, header=TRUE, stringsAsFactors = T, dec = ".", sep = ",")
plot.inv <- read.csv2(here("data.veg","plots.alphainv.csv"), row.names=1, header=TRUE, stringsAsFactors = T, dec = ".", sep = ",")
###########################################################################
# Generating models ###
###########################################################################
## Fake database
dummy1 <- read.csv2(here("data.veg","dummy3.csv"), header=TRUE, row.names = 1, stringsAsFactors = T,sep = ",", dec = ".")
alpha.all <- dredge(glmmTMB(resp ~ dist_begin + dist_trail + (1 | ID), data= dummy1 , family = "poisson"))
## Fixed terms are "cond((Int))" and "disp((Int))"
alpha.all
## Global model call: glmmTMB(formula = resp ~ dist_begin + dist_trail + (1 | ID),
## data = dummy1, family = "poisson", ziformula = ~0, dispformula = ~1)
## ---
## Model selection table
## cnd((Int)) dsp((Int)) cnd(dst_bgn) cnd(dst_trl) df logLik AICc
## 4 2.888 + 0.3883 0.3574 4 -55.641 125.0
## 3 3.563 + 0.5148 3 -285.708 580.4
## 2 2.922 + 0.5288 3 -324.027 657.1
## 1 4.033 + 2 -915.760 1836.9
## delta weight
## 4 0.00 1
## 3 455.42 0
## 2 532.06 0
## 1 1711.86 0
## Models ranked by AICc(x)
## Random terms (all models):
## 'cond(1 | ID)'
alpha.all1 <- glmmTMB(resp ~ dist_begin + dist_trail + (1 | ID), data= dummy1 , family = "poisson")
summary(alpha.all1)
## Family: poisson ( log )
## Formula: resp ~ dist_begin + dist_trail + (1 | ID)
## Data: dummy1
##
## AIC BIC logLik deviance df.resid
## 119.3 121.2 -55.6 111.3 8
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ID (Intercept) 7.051 2.655
## Number of obs: 12, groups: ID, 2
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.88818 1.88083 1.536 0.125
## dist_begin 0.38831 0.01858 20.899 <2e-16 ***
## dist_trail 0.35744 0.01573 22.721 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
performance::r2(alpha.all1)
## # R2 for Mixed Models
##
## Conditional R2: 0.998
## Marginal R2: 0.059
alpha.all2 <- glm(resp ~ dist_begin , data= dummy1 , family = "poisson")
summary(alpha.all2)
##
## Call:
## glm(formula = resp ~ dist_begin, family = "poisson", data = dummy1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -35.277 -26.587 -5.217 10.390 33.398
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.88514 0.04168 117.19 <2e-16 ***
## dist_begin 0.52885 0.01677 31.54 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 8130.7 on 11 degrees of freedom
## Residual deviance: 6947.2 on 10 degrees of freedom
## AIC: 7019.5
##
## Number of Fisher Scoring iterations: 6
performance::r2(alpha.all2)
## $R2_Nagelkerke
## Nagelkerke's R2
## 1
alpha.all3 <- glmmTMB(resp ~ dist_trail + (1 | ID), data= dummy , family = "poisson")
## Error in eval(substitute(offset), data, enclos = environment(formula)): invalid 'envir' argument of type 'closure'
summary(alpha.all3)
## Error in summary(alpha.all3): object 'alpha.all3' not found
performance::r2(alpha.all3)
## Error in performance::r2(alpha.all3): object 'alpha.all3' not found
alpha.all4 <- glmmTMB(resp ~ dist_begin + (1 | ID), data= dummy , family = "poisson")
## Error in eval(substitute(offset), data, enclos = environment(formula)): invalid 'envir' argument of type 'closure'
summary(alpha.all4)
## Error in summary(alpha.all4): object 'alpha.all4' not found
performance::r2(alpha.all4)
## Error in performance::r2(alpha.all4): object 'alpha.all4' not found
## Uploading results for vegetation
test1veg <- read.csv2(here("data.veg","test1veg.CSV"), header=TRUE, row.names = 1, stringsAsFactors = T,sep = ",", dec = ".")
test1veg$percent.alfa[test1veg$percent.alfa == 0] <- 0.001
test1veg$percent.alfa[test1veg$percent.alfa == 1] <- 0.999
test1veg$percent.abund[test1veg$percent.abund == 0] <- 0.001
test1veg$percent.abund[test1veg$percent.abund == 1] <- 0.999
alpha.all <- dredge(glmmTMB(alpha.all ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test1veg , family = "poisson"))
## Fixed terms are "cond((Int))" and "disp((Int))"
alpha.all
## Global model call: glmmTMB(formula = alpha.all ~ Dist_edge_std + Dist_trail_std +
## Dist_trail_beginning_std + (1 | ForestID), data = test1veg,
## family = "poisson", ziformula = ~0, dispformula = ~1)
## ---
## Model selection table
## cnd((Int)) dsp((Int)) cnd(Dst_edg_std) cnd(Dst_trl_bgn_std)
## 1 2.091 +
## 5 2.075 +
## 3 2.101 + -0.02198
## 2 2.086 + 0.013110
## 7 2.084 + -0.02074
## 6 2.076 + -0.005063
## 4 2.096 + 0.020980 -0.03154
## 8 2.084 + 0.001905 -0.02164
## cnd(Dst_trl_std) df logLik AICc delta weight
## 1 2 -162.004 328.2 0.00 0.385
## 5 0.03729 3 -161.715 329.8 1.62 0.171
## 3 3 -161.953 330.3 2.10 0.135
## 2 3 -161.972 330.3 2.13 0.132
## 7 0.03691 4 -161.670 332.0 3.80 0.057
## 6 0.03946 4 -161.711 332.1 3.89 0.055
## 4 4 -161.879 332.4 4.22 0.047
## 8 0.03608 5 -161.670 334.4 6.15 0.018
## Models ranked by AICc(x)
## Random terms (all models):
## 'cond(1 | ForestID)'
alpha.all1 <- glmer(alpha.all ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test1veg , family = "poisson")
summary (alpha.all1)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula:
## alpha.all ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std +
## (1 | ForestID)
## Data: test1veg
##
## AIC BIC logLik deviance df.resid
## 333.3 344.2 -161.7 323.3 60
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4983 -0.5602 -0.1193 0.3897 2.3903
##
## Random effects:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.1469 0.3833
## Number of obs: 65, groups: ForestID, 4
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.083997 0.202375 10.298 <2e-16 ***
## Dist_edge_std 0.001908 0.061934 0.031 0.975
## Dist_trail_std 0.036083 0.055326 0.652 0.514
## Dist_trail_beginning_std -0.021639 0.075024 -0.288 0.773
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Dst_d_ Dst_t_
## Dist_dg_std 0.000
## Dst_trl_std -0.096 -0.489
## Dst_trl_bg_ -0.142 -0.389 0.209
performance::r2(alpha.all1)
## # R2 for Mixed Models
##
## Conditional R2: 0.560
## Marginal R2: 0.005
alpha.inv<- (glmmTMB(alpha.inv ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test1veg , family = "poisson"))
alpha.inv
## Formula:
## alpha.inv ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std +
## (1 | ForestID)
## Data: test1veg
## AIC BIC logLik df.resid
## 140.27114 151.14308 -65.13557 60
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.8867
##
## Number of obs: 65 / Conditional model: ForestID, 4
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_edge_std
## -0.30632 -0.01404
## Dist_trail_std Dist_trail_beginning_std
## -0.10677 -0.32615
alpha.inv1<- glmmTMB(alpha.inv ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test1veg , family = "poisson")
summary(alpha.inv1)
## Family: poisson ( log )
## Formula:
## alpha.inv ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std +
## (1 | ForestID)
## Data: test1veg
##
## AIC BIC logLik deviance df.resid
## 140.3 151.1 -65.1 130.3 60
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.7863 0.8867
## Number of obs: 65, groups: ForestID, 4
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.30632 0.50548 -0.606 0.545
## Dist_edge_std -0.01404 0.25538 -0.055 0.956
## Dist_trail_std -0.10677 0.21160 -0.505 0.614
## Dist_trail_beginning_std -0.32615 0.33575 -0.971 0.331
performance::r2(alpha.inv1)
## Warning: mu of 0.6 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.474
## Marginal R2: 0.045
percent.alfa <- dredge(glmmTMB(percent.alfa ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test1veg , family = "beta_family"))
## Fixed terms are "cond((Int))" and "disp((Int))"
percent.alfa1 <- glmmTMB(percent.alfa ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test1veg , family = "beta_family")
summary(percent.alfa1)
## Family: beta ( logit )
## Formula:
## percent.alfa ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std +
## (1 | ForestID)
## Data: test1veg
##
## AIC BIC logLik deviance df.resid
## -270.2 -257.2 141.1 -282.2 59
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.2666 0.5163
## Number of obs: 65, groups: ForestID, 4
##
## Overdispersion parameter for beta family (): 5.16
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.33574 0.33564 -6.959 3.43e-12 ***
## Dist_edge_std -0.05027 0.18130 -0.277 0.782
## Dist_trail_std -0.02667 0.15343 -0.174 0.862
## Dist_trail_beginning_std -0.12707 0.17424 -0.729 0.466
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
performance::r2(percent.alfa1)
## Warning: mu of 0.1 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.223
## Marginal R2: 0.012
percent.alfa2 <- glmmTMB(percent.alfa ~ Dist_trail_beginning_std + (1 | ForestID), data= test1veg , family = "beta_family")
summary(percent.alfa2)
## Family: beta ( logit )
## Formula: percent.alfa ~ Dist_trail_beginning_std + (1 | ForestID)
## Data: test1veg
##
## AIC BIC logLik deviance df.resid
## -274.1 -265.4 141.0 -282.1 61
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.2589 0.5089
## Number of obs: 65, groups: ForestID, 4
##
## Overdispersion parameter for beta family (): 5.12
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.3581 0.3278 -7.194 6.27e-13 ***
## Dist_trail_beginning_std -0.1412 0.1667 -0.847 0.397
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
performance::r2_nakagawa(percent.alfa2)
## Warning: mu of 0.1 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.216
## Marginal R2: 0.011
performance::r2(percent.alfa2)
## Warning: mu of 0.1 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.216
## Marginal R2: 0.011
percent.alfa3 <- glmmTMB(percent.alfa ~ Dist_edge_std + (1 | ForestID), data= test1veg , family = "beta_family")
summary(percent.alfa3)
## Family: beta ( logit )
## Formula: percent.alfa ~ Dist_edge_std + (1 | ForestID)
## Data: test1veg
##
## AIC BIC logLik deviance df.resid
## -273.7 -265.0 140.8 -281.7 61
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.2941 0.5423
## Number of obs: 65, groups: ForestID, 4
##
## Overdispersion parameter for beta family (): 5.18
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.3904 0.3367 -7.099 1.26e-12 ***
## Dist_edge_std -0.0974 0.1626 -0.599 0.549
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
performance::r2_nakagawa(percent.alfa3)
## Warning: mu of 0.1 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.235
## Marginal R2: 0.006
performance::r2(percent.alfa3)
## Warning: mu of 0.1 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.235
## Marginal R2: 0.006
abund.all <- dredge(glmmTMB(abund.all ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test1veg , family = "poisson"))
## Fixed terms are "cond((Int))" and "disp((Int))"
abund.all
## Global model call: glmmTMB(formula = abund.all ~ Dist_edge_std + Dist_trail_std +
## Dist_trail_beginning_std + (1 | ForestID), data = test1veg,
## family = "poisson", ziformula = ~0, dispformula = ~1)
## ---
## Model selection table
## cnd((Int)) dsp((Int)) cnd(Dst_edg_std) cnd(Dst_trl_bgn_std)
## 5 3.656 +
## 6 3.662 + -0.03118
## 7 3.653 + 0.0066840
## 8 3.654 + -0.03842 0.0226200
## 1 3.705 +
## 2 3.693 + 0.03032
## 3 3.705 + 0.0007069
## 4 3.698 + 0.03314 -0.0121600
## cnd(Dst_trl_std) df logLik AICc delta weight
## 5 0.1086 3 -526.655 1059.7 0.00 0.431
## 6 0.1221 4 -525.870 1060.4 0.70 0.303
## 7 0.1088 4 -526.627 1061.9 2.22 0.142
## 8 0.1259 5 -525.597 1062.2 2.51 0.123
## 1 2 -541.561 1087.3 27.61 0.000
## 2 3 -540.608 1087.6 27.91 0.000
## 3 3 -541.560 1089.5 29.81 0.000
## 4 4 -540.524 1089.7 30.01 0.000
## Models ranked by AICc(x)
## Random terms (all models):
## 'cond(1 | ForestID)'
abund.all1 <- glmmTMB(abund.all ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test1veg , family = "poisson")
summary(abund.all1 )
## Family: poisson ( log )
## Formula:
## abund.all ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std +
## (1 | ForestID)
## Data: test1veg
##
## AIC BIC logLik deviance df.resid
## 1061.2 1072.1 -525.6 1051.2 60
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.1387 0.3724
## Number of obs: 65, groups: ForestID, 4
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.65379 0.18855 19.379 < 2e-16 ***
## Dist_edge_std -0.03842 0.02696 -1.425 0.154
## Dist_trail_std 0.12590 0.02260 5.571 2.54e-08 ***
## Dist_trail_beginning_std 0.02262 0.03046 0.743 0.458
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
performance::r2(alpha.all1)
## # R2 for Mixed Models
##
## Conditional R2: 0.560
## Marginal R2: 0.005
abund.all3 <- glmmTMB(abund.all ~ Dist_trail_std + (1 | ForestID), data= test1veg , family = "poisson")
summary(abund.all3 )
## Family: poisson ( log )
## Formula: abund.all ~ Dist_trail_std + (1 | ForestID)
## Data: test1veg
##
## AIC BIC logLik deviance df.resid
## 1059.3 1065.8 -526.7 1053.3 62
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.1343 0.3665
## Number of obs: 65, groups: ForestID, 4
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.65623 0.18520 19.743 < 2e-16 ***
## Dist_trail_std 0.10863 0.01912 5.682 1.33e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
performance::r2(alpha.all3)
## Error in performance::r2(alpha.all3): object 'alpha.all3' not found
abund.inv <- dredge(glmmTMB(abund.inv ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test1veg , family = "poisson"))
## Fixed terms are "cond((Int))" and "disp((Int))"
abund.inv
## Global model call: glmmTMB(formula = abund.inv ~ Dist_edge_std + Dist_trail_std +
## Dist_trail_beginning_std + (1 | ForestID), data = test1veg,
## family = "poisson", ziformula = ~0, dispformula = ~1)
## ---
## Model selection table
## cnd((Int)) dsp((Int)) cnd(Dst_edg_std) cnd(Dst_trl_bgn_std)
## 7 1.186 + -0.2456
## 8 1.186 + 0.01946 -0.2575
## 5 1.084 +
## 6 1.103 + -0.08698
## 3 1.109 + -0.2297
## 2 1.072 + -0.15840
## 4 1.123 + -0.10360 -0.1719
## 1 1.016 +
## cnd(Dst_trl_std) df logLik AICc delta weight
## 7 -0.1937 4 -214.596 437.9 0.00 0.475
## 8 -0.2010 5 -214.579 440.2 2.32 0.149
## 5 -0.1836 3 -216.992 440.4 2.52 0.135
## 6 -0.1526 4 -216.566 441.8 3.94 0.066
## 3 3 -217.702 441.8 3.94 0.066
## 2 3 -218.079 442.6 4.69 0.045
## 4 4 -217.099 442.9 5.01 0.039
## 1 2 -219.768 443.7 5.87 0.025
## Models ranked by AICc(x)
## Random terms (all models):
## 'cond(1 | ForestID)'
abund.inv1 <- glmmTMB(abund.inv ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test1veg , family = "poisson")
summary(abund.inv1)
## Family: poisson ( log )
## Formula:
## abund.inv ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std +
## (1 | ForestID)
## Data: test1veg
##
## AIC BIC logLik deviance df.resid
## 439.2 450.0 -214.6 429.2 60
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.34 1.158
## Number of obs: 65, groups: ForestID, 4
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.18640 0.59519 1.993 0.0462 *
## Dist_edge_std 0.01946 0.10759 0.181 0.8565
## Dist_trail_std -0.20099 0.09344 -2.151 0.0315 *
## Dist_trail_beginning_std -0.25749 0.13345 -1.930 0.0537 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
performance::r2(abund.inv1)
## Warning: mu of 2.8 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.821
## Marginal R2: 0.043
abund.inv2 <- glmmTMB(abund.inv ~ Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test1veg , family = "poisson")
summary(abund.inv2)
## Family: poisson ( log )
## Formula:
## abund.inv ~ Dist_trail_std + Dist_trail_beginning_std + (1 |
## ForestID)
## Data: test1veg
##
## AIC BIC logLik deviance df.resid
## 437.2 445.9 -214.6 429.2 61
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.342 1.159
## Number of obs: 65, groups: ForestID, 4
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.18609 0.59567 1.991 0.0465 *
## Dist_trail_std -0.19368 0.08436 -2.296 0.0217 *
## Dist_trail_beginning_std -0.24558 0.11549 -2.126 0.0335 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
performance::r2(abund.inv2)
## Warning: mu of 2.8 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.821
## Marginal R2: 0.041
percent.abund <- dredge(glmmTMB(percent.abund ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test1veg , family = "beta_family"))
## Fixed terms are "cond((Int))" and "disp((Int))"
percent.abund
## Global model call: glmmTMB(formula = percent.abund ~ Dist_edge_std + Dist_trail_std +
## Dist_trail_beginning_std + (1 | ForestID), data = test1veg,
## family = "beta_family", ziformula = ~0, dispformula = ~1)
## ---
## Model selection table
## cnd((Int)) dsp((Int)) cnd(Dst_edg_std) cnd(Dst_trl_bgn_std)
## 1 -2.089 +
## 3 -1.978 + -0.2340
## 2 -2.047 + -0.10920
## 5 -2.072 +
## 7 -1.955 + -0.2362
## 4 -1.963 + -0.05595 -0.2178
## 6 -2.045 + -0.10550
## 8 -1.951 + -0.03747 -0.2249
## cnd(Dst_trl_std) df logLik AICc delta weight
## 1 3 124.424 -242.5 0.00 0.304
## 3 4 125.354 -242.0 0.41 0.247
## 2 4 124.650 -240.6 1.82 0.122
## 5 -0.043160 4 124.470 -240.3 2.18 0.102
## 7 -0.053440 5 125.424 -239.8 2.62 0.082
## 4 5 125.412 -239.8 2.65 0.081
## 6 -0.008489 5 124.652 -238.3 4.17 0.038
## 8 -0.041040 6 125.446 -237.4 5.01 0.025
## Models ranked by AICc(x)
## Random terms (all models):
## 'cond(1 | ForestID)'
percent.abund1 <- glmmTMB(percent.abund ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test1veg , family = "beta_family")
summary(percent.abund1)
## Family: beta ( logit )
## Formula:
## percent.abund ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std +
## (1 | ForestID)
## Data: test1veg
##
## AIC BIC logLik deviance df.resid
## -238.9 -225.8 125.4 -250.9 59
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.1833 0.4282
## Number of obs: 65, groups: ForestID, 4
##
## Overdispersion parameter for beta family (): 3.31
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.95057 0.30755 -6.342 2.26e-10 ***
## Dist_edge_std -0.03747 0.17903 -0.209 0.834
## Dist_trail_std -0.04104 0.15666 -0.262 0.793
## Dist_trail_beginning_std -0.22488 0.18361 -1.225 0.221
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
performance::r2(percent.abund1)
## Warning: mu of 0.1 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.187
## Marginal R2: 0.033
percent.abund2 <- glmmTMB(percent.abund ~ Dist_trail_std + (1 | ForestID), data= test1veg , family = "beta_family")
summary(percent.abund2)
## Family: beta ( logit )
## Formula: percent.abund ~ Dist_trail_std + (1 | ForestID)
## Data: test1veg
##
## AIC BIC logLik deviance df.resid
## -240.9 -232.2 124.5 -248.9 61
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.2129 0.4614
## Number of obs: 65, groups: ForestID, 4
##
## Overdispersion parameter for beta family (): 3.27
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.07157 0.30890 -6.706 2e-11 ***
## Dist_trail_std -0.04316 0.14445 -0.299 0.765
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
performance::r2(percent.abund2)
## Warning: mu of 0.1 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.180
## Marginal R2: 0.001
percent.abund3 <- glmmTMB(percent.abund ~ Dist_edge_std + (1 | ForestID), data= test1veg , family = "beta_family")
summary(percent.abund3)
## Family: beta ( logit )
## Formula: percent.abund ~ Dist_edge_std + (1 | ForestID)
## Data: test1veg
##
## AIC BIC logLik deviance df.resid
## -241.3 -232.6 124.7 -249.3 61
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.2198 0.4688
## Number of obs: 65, groups: ForestID, 4
##
## Overdispersion parameter for beta family (): 3.3
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.0473 0.3118 -6.566 5.16e-11 ***
## Dist_edge_std -0.1092 0.1653 -0.660 0.509
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
performance::r2(percent.abund3)
## Warning: mu of 0.1 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.190
## Marginal R2: 0.008
#R2
Ralpha.all <- performance::r2(glmmTMB(alpha.all ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test1veg , family = "poisson"))
Rpercent.alfa <- performance::r2(glmmTMB(percent.alfa ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test1veg , family = "beta_family"))
## Warning: mu of 0.1 is too close to zero, estimate of random effect variances may be unreliable.
Rabund.all <- performance::r2(glmmTMB(abund.all ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test1veg , family = "poisson"))
Rabund.inv <- performance::r2(glmmTMB(abund.inv ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test1veg , family = "poisson"))
## Warning: mu of 2.8 is too close to zero, estimate of random effect variances may be unreliable.
#Rpercent.abund <- performance::r2(glmmTMB(percent.abund ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test1veg , family = "beta_family"))
test2veg <- read.csv2(here("data.veg","test2veg.CSV"), header=TRUE, row.names = 1, stringsAsFactors = T,sep = ",", dec = ".")
test2veg$delta.percent.alpha[test2veg$delta.percent.alpha == 0] <- 0.001
test2veg$delta.percent.alpha[test2veg$delta.percent.alpha == 1] <- 0.999
test2veg$delta.percent.abund[test2veg$delta.percent.abund == 0] <- 0.001
test2veg$delta.percent.abund[test2veg$delta.percent.abund == 1] <- 0.999
delta.alpha <- dredge(glmmTMB(delta.alpha ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test2veg , family = "gaussian"))
## Fixed terms are "cond((Int))" and "disp((Int))"
delta.abund <- dredge(glmmTMB(delta.abund ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test2veg , family = "gaussian"))
## Fixed terms are "cond((Int))" and "disp((Int))"
delta.percent.alpha <- dredge(glmmTMB(delta.percent.alpha ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test2veg , family = "gaussian"))
## Fixed terms are "cond((Int))" and "disp((Int))"
delta.percent.abund <- dredge(glmmTMB(delta.percent.abund ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test2veg , family = "gaussian"))
## Fixed terms are "cond((Int))" and "disp((Int))"
#Rdelta.alpha <- performance::r2(glmmTMB(delta.alpha ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test2veg , family = "gaussian"))
#Rdelta.abund <- performance::r2(glmmTMB(delta.abund ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test2veg , family = "gaussian"))
Rdelta.percent.alpha <- performance::r2(glmmTMB(delta.percent.alpha ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test2veg , family = "gaussian"))
#Rdelta.percent.abund <- performance::r2(glmmTMB(delta.percent.abund ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= test2veg , family = "gaussian"))
fam.veg <- c(NA, NA, NA, NA,NA, NA,NA, "poisson", "poisson","beta_family","beta_family", "beta_family", "beta_family","beta_family", "beta_family")
## GENERATING MODELS FOR PLOT 1
# Alpha
dredge.alpha.plot1 <- dredge(glmmTMB(alpha.plot1.all ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= Results.plot1.import , family = "poisson"))
## Error in eval(substitute(offset), data, enclos = environment(formula)): object 'Results.plot1.import' not found
dredge.alpha.plot2 <- dredge(glmmTMB(alpha.plot2.all ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= Results.plot2.import , family = "poisson"))
## Error in eval(substitute(offset), data, enclos = environment(formula)): object 'Results.plot2.import' not found
dredge.alpha.plot3 <- dredge(glmmTMB(alpha.plot3.all ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= Results.plot3.import , family = "poisson"))
## Error in eval(substitute(offset), data, enclos = environment(formula)): object 'Results.plot3.import' not found
dredge.alpha.plot1.inv <- dredge(glmmTMB(alpha.plot1.inv ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= Results.plot1.import , family = "poisson"))
## Error in eval(substitute(offset), data, enclos = environment(formula)): object 'Results.plot1.import' not found
dredge.alpha.plot2.inv <- dredge(glmmTMB(alpha.plot2.inv ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= Results.plot2.import , family = "poisson"))
## Error in eval(substitute(offset), data, enclos = environment(formula)): object 'Results.plot2.import' not found
dredge.alpha.plot3.inv <- dredge(glmmTMB(alpha.plot3.inv ~ Dist_edge_std + Dist_trail_std + Dist_trail_beginning_std + (1 | ForestID), data= Results.plot3.import , family = "poisson"))
## Error in eval(substitute(offset), data, enclos = environment(formula)): object 'Results.plot3.import' not found
#dredge.alpha.inv <- data.frame(dredge.alpha.inv)
#write.csv(dredge.alpha.inv, file = here("results","test.alpha.inv.csv"), row.names = TRUE)