## 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)
library(performance)
library(see)
library(pscl)
Results2 <- read.csv2(here("results","RESULTS.CSV"), header=TRUE, row.names = 1, stringsAsFactors = T,sep = ",", dec = ".")
###### 1 - Crating functions to aid its interpertation
### Overdispersion function
overdisp_fun <- function(model) {
rdf <- df.residual(model)
rp <- residuals(model,type="pearson")
Pearson.chisq <- sum(rp^2)
prat <- Pearson.chisq/rdf
pval <- pchisq(Pearson.chisq, df=rdf, lower.tail=FALSE)
c(chisq=Pearson.chisq,ratio=prat,rdf=rdf,p=pval)
}
## Making a function to extract all the necessary outputs out of each model
model.output <- function(model) {
print(" ")
print("CHECKING COLINEARITY")
print(" ")
#print(check_conversion(model))
print(" ")
print("CHECKING COLINEARITY")
print(" ")
print(check_collinearity(model))
print(" ")
print("TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion")
print(" ")
#print(check_overdispersion(model))
print(" ")
print(overdisp_fun(model))
print(" ")
print("STANDARD R2")
print(" ")
print(performance::r2(model))
print(" ")
print("NAKAGAWA R2")
print(" ")
print(performance::r2_nakagawa(model))
print(" ")
print("MODEL SUMMARY")
print(" ")
print(summary(model))
print(" ")
print("CHECKING MODEL GRAPHICALLY")
print(" ")
print(check_model(model))
print(" ")
}
TAlphaAll.glmm.1 = glmmTMB(TAlphaAll ~ (1 | ForestID), data= Results2,family = "poisson")
model.output(TAlphaAll.glmm.1)
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 10.1261416 0.5329548 19.0000000 0.9497652
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## # R2 for Mixed Models
##
## Conditional R2: 0.450
## Marginal R2: 0.000
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## # R2 for Mixed Models
##
## Conditional R2: 0.450
## Marginal R2: 0.000
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: poisson ( log )
## Formula: TAlphaAll ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 113.8 115.9 -54.9 109.8 19
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.06576 0.2564
## Number of obs: 21, groups: ForestID, 5
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.4814 0.1356 18.29 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 9.0067
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.23
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 2.3083e-16
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 2.9516
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 6.7368420 0.3545706 19.0000000 0.9954900
## [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: 0.000
## [1] " "
## [1] "NAKAGAWA 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: 0.000
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: poisson ( log )
## Formula: TAlphaNat ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 95.9 98.0 -46.0 91.9 19
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 9.861e-11 9.93e-06
## Number of obs: 21, groups: ForestID, 5
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.20250 0.07255 30.36 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning in predict.lm(model, newdata = data.frame(x = xseq), se.fit = se, :
## prediction from a rank-deficient fit may be misleading
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 9.0476
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 9.0266e-09
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 4.6239e-16
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 8.0356e-17
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 18.0549702 0.9502616 19.0000000 0.5187692
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: mu of 2.4 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.644
## Marginal R2: 0.000
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: mu of 2.4 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.644
## Marginal R2: 0.000
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: poisson ( log )
## Formula: TAlphaNInd ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 85.9 88.0 -41.0 81.9 19
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.6236 0.7897
## Number of obs: 21, groups: ForestID, 5
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.8873 0.3857 2.3 0.0214 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: mu of 2.4 is too close to zero, estimate of random effect variances may be unreliable.
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 1.1822
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 0.67531
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.4876e-16
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 0.15177
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 15.8344488 0.8796916 18.0000000 0.6041000
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: mu of 2.4 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.653
## Marginal R2: 0.060
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: mu of 2.4 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.653
## Marginal R2: 0.060
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: poisson ( log )
## Formula: TAlphaNInd ~ Dist_trail_beginning_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 85.6 88.7 -39.8 79.6 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.5899 0.7681
## Number of obs: 21, groups: ForestID, 5
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.0243 0.3866 2.650 0.00806 **
## Dist_trail_beginning_std -0.3052 0.2162 -1.412 0.15806
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: mu of 2.4 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 16.6030729 0.9223929 18.0000000 0.5505350
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: mu of 2.4 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.649
## Marginal R2: 0.030
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: mu of 2.4 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.649
## Marginal R2: 0.030
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: poisson ( log )
## Formula: TAlphaNInd ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 86.7 89.9 -40.4 80.7 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.6079 0.7797
## Number of obs: 21, groups: ForestID, 5
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.9374 0.3839 2.442 0.0146 *
## Dist_trail_std -0.1872 0.1866 -1.004 0.3156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: mu of 2.4 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 17.2869103 0.9603839 18.0000000 0.5034692
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: mu of 2.4 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.625
## Marginal R2: 0.047
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: mu of 2.4 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.625
## Marginal R2: 0.047
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: poisson ( log )
## Formula: TAlphaNInd ~ Dist_edge_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 87.1 90.2 -40.5 81.1 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.5302 0.7282
## Number of obs: 21, groups: ForestID, 5
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.0070 0.3835 2.626 0.00863 **
## Dist_edge_std -0.2665 0.3060 -0.871 0.38375
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: mu of 2.4 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "
## 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
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 0.60177879 0.03343215 18.00000000 1.00000000
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## # R2 for Mixed Models
##
## Conditional R2: 0.048
## Marginal R2: 0.000
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## # R2 for Mixed Models
##
## Conditional R2: 0.048
## Marginal R2: 0.000
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: Gamma ( inverse )
## Formula: FAlphaAll ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 69.0 72.1 -31.5 63.0 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.00192 0.04381
## Number of obs: 21, groups: ForestID, 5
##
## Dispersion estimate for Gamma family (sigma^2): 0.0377
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.20744 0.02177 9.529 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 3.9678
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 0.45131
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 0.15148
## [1] " "
## 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$optCtrl): NA/NaN function evaluation
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 0.52889840 0.03111167 17.00000000 1.00000000
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## # R2 for Mixed Models
##
## Conditional R2: 0.057
## Marginal R2: 0.004
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## # R2 for Mixed Models
##
## Conditional R2: 0.057
## Marginal R2: 0.004
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: Gamma ( inverse )
## Formula: FAlphaAll ~ Dist_trail_beginning_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 69.4 73.6 -30.7 61.4 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.001946 0.04412
## Number of obs: 21, groups: ForestID, 5
##
## Dispersion estimate for Gamma family (sigma^2): 0.0343
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.20020 0.02233 8.965 <2e-16 ***
## Dist_trail_beginning_std 0.01470 0.01200 1.225 0.221
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## [1] " "
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$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
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 0.18188683 0.01010482 18.00000000 1.00000000
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## # R2 for Mixed Models
##
## Conditional R2: 0.127
## Marginal R2: 0.000
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## # R2 for Mixed Models
##
## Conditional R2: 0.127
## Marginal R2: 0.000
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: Gamma ( inverse )
## Formula: FAlphaNat ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 7.9 11.0 -0.9 1.9 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.00157 0.03963
## Number of obs: 21, groups: ForestID, 5
##
## Dispersion estimate for Gamma family (sigma^2): 0.0108
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.4686 0.0217 21.59 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 1.9333
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 0.18283
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 5.1475e-17
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 0.016626
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 8.0651806 0.4244832 19.0000000 0.9860029
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.8 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: 0.000
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.8 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: 0.000
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: poisson ( log )
## Formula: FAlphaNInd ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 45.9 48.0 -21.0 41.9 19
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 7.396e-10 2.72e-05
## Number of obs: 21, groups: ForestID, 5
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2706 0.2498 -1.083 0.279
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.8 is too close to zero, estimate of random effect variances may be unreliable.
## Warning in predict.lm(model, newdata = data.frame(x = xseq), se.fit = se, :
## prediction from a rank-deficient fit may be misleading
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.76291
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 9.0456e-10
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 7.9718e-17
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 3.377e-19
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## # Check for Multicollinearity
##
## Low Correlation
##
## Parameter VIF Increased SE
## Dist_edge_std 1.42 1.19
## Dist_trail_beginning_std 2.81 1.68
## Dist_trail_std 3.35 1.83
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 22.1904961 1.4793664 15.0000000 0.1029054
## [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: 0.053
## [1] " "
## [1] "NAKAGAWA 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: 0.053
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: nbinom1 ( log )
## Formula:
## abund.all ~ Dist_edge_std + Dist_trail_beginning_std + Dist_trail_std +
## (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 224.0 230.2 -106.0 212.0 15
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 2.467e-10 1.571e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for nbinom1 family (): 10.3
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.84284 0.09199 52.64 <2e-16 ***
## Dist_edge_std 0.01824 0.09566 0.19 0.849
## Dist_trail_beginning_std 0.02599 0.12015 0.22 0.829
## Dist_trail_std 0.04559 0.11573 0.39 0.694
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## # Check for Multicollinearity
##
## Low Correlation
##
## Parameter VIF Increased SE
## Dist_edge_std 1.39 1.18
## Dist_trail_beginning_std 2.91 1.70
## Dist_trail_std 3.40 1.84
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 20.9146751 1.3943117 15.0000000 0.1395777
## [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: 0.019
## [1] " "
## [1] "NAKAGAWA 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: 0.019
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: nbinom1 ( log )
## Formula:
## abund.nat ~ Dist_edge_std + Dist_trail_beginning_std + Dist_trail_std +
## (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 219.5 225.7 -103.7 207.5 15
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.048e-10 1.024e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for nbinom1 family (): 8.83
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.777317 0.089224 53.54 <2e-16 ***
## Dist_edge_std 0.036709 0.092552 0.40 0.692
## Dist_trail_beginning_std 0.002779 0.121576 0.02 0.982
## Dist_trail_std 0.014419 0.116876 0.12 0.902
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 11.0181641 0.6481273 17.0000000 0.8556182
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## # R2 for Mixed Models
##
## Conditional R2: 0.402
## Marginal R2: 0.047
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## # R2 for Mixed Models
##
## Conditional R2: 0.402
## Marginal R2: 0.047
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: nbinom1 ( log )
## Formula: abund.nind ~ Dist_edge_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 145.1 149.3 -68.5 137.1 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.4855 0.6968
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for nbinom1 family (): 11.3
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.0651 0.4908 4.208 2.58e-05 ***
## Dist_edge_std 0.3248 0.3936 0.825 0.409
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 11.1709757 0.6571162 17.0000000 0.8475390
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## # R2 for Mixed Models
##
## Conditional R2: 0.319
## Marginal R2: 0.014
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## # R2 for Mixed Models
##
## Conditional R2: 0.319
## Marginal R2: 0.014
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: nbinom1 ( log )
## Formula: abund.nind ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 145.4 149.6 -68.7 137.4 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.3749 0.6123
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for nbinom1 family (): 11.7
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.1962 0.4102 5.353 8.63e-08 ***
## Dist_trail_std 0.1418 0.2311 0.614 0.539
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 2.2023134 0.1223507 18.0000000 0.9999976
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: mu of 5.5 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 1.367
## Marginal R2: 0.000
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: mu of 5.5 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 1.367
## Marginal R2: 0.000
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: prop.Talpha ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -44.5 -41.3 25.2 -50.5 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.5481 0.7403
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 4.98
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.7080 0.4264 4.006 6.18e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: mu of 5.5 is too close to zero, estimate of random effect variances may be unreliable.
## Warning in sqrt(insight::get_variance_residual(model)): NaNs produced
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 2.3882907 0.1404877 17.0000000 0.9999869
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: mu of 5.5 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 1.219
## Marginal R2: 0.307
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: mu of 5.5 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 1.219
## Marginal R2: 0.307
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: prop.Talpha ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -45.3 -41.1 26.6 -53.3 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.54 0.7349
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 5.74
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.5663 0.4205 3.725 0.000196 ***
## Dist_trail_std 0.4633 0.3251 1.425 0.154114
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: mu of 5.5 is too close to zero, estimate of random effect variances may be unreliable.
## Warning in sqrt(insight::get_variance_residual(model)): NaNs produced
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 0.22976531 0.01276474 18.00000000 1.00000000
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: mu of 0.8 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.897
## Marginal R2: 0.000
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: mu of 0.8 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.897
## Marginal R2: 0.000
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: prop.Falpha ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -50.5 -47.4 28.3 -56.5 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.02016 0.142
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 77.1
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.16590 0.09412 -1.762 0.078 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: mu of 0.8 is too close to zero, estimate of random effect variances may be unreliable.
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.48909
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 0.018228
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.5375e-16
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 0.00031945
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 0.19503099 0.01147241 17.00000000 1.00000000
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: mu of 0.8 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.945
## Marginal R2: 0.092
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: mu of 0.8 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.945
## Marginal R2: 0.092
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: prop.Falpha ~ Dist_trail_beginning_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -49.7 -45.5 28.8 -57.7 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.03122 0.1767
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 88.7
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.21851 0.10782 -2.027 0.0427 *
## Dist_trail_beginning_std 0.07238 0.06581 1.100 0.2715
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: mu of 0.8 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 1.01738616 0.05652145 18.00000000 1.00000000
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## # R2 for Mixed Models
##
## Conditional R2: 1.405
## Marginal R2: 0.000
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## # R2 for Mixed Models
##
## Conditional R2: 1.405
## Marginal R2: 0.000
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: prop.abund ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -78.4 -75.3 42.2 -84.4 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.323 0.5683
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 9.46
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.6587 0.3817 6.966 3.26e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning in sqrt(insight::get_variance_residual(model)): NaNs produced
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 2.7779510 0.1634089 17.0000000 0.9999601
## [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: 0.870
## Marginal R2: 0.528
## [1] " "
## [1] "NAKAGAWA 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: 0.870
## Marginal R2: 0.528
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: all.tax.btotal ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -33.9 -29.7 20.9 -41.9 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.8608 0.9278
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 3
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.8334 0.4983 -1.673 0.09443 .
## Dist_trail_std -1.2546 0.4024 -3.118 0.00182 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## # Check for Multicollinearity
##
## Low Correlation
##
## Parameter VIF Increased SE
## Dist_edge_std 1.77 1.33
## Dist_trail_std 1.77 1.33
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 2.8891280 0.1805705 16.0000000 0.9998683
## [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: 0.899
## Marginal R2: 0.619
## [1] " "
## [1] "NAKAGAWA 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: 0.899
## Marginal R2: 0.619
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula:
## all.tax.btotal ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -35.2 -29.9 22.6 -45.2 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.8354 0.914
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 3.46
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.2502 0.5557 -2.250 0.024453 *
## Dist_edge_std 1.1322 0.6595 1.717 0.086022 .
## Dist_trail_std -1.7044 0.4517 -3.773 0.000161 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 0.69342191 0.04078952 17.00000000 1.00000000
## [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: 0.901
## Marginal R2: 0.477
## [1] " "
## [1] "NAKAGAWA 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: 0.901
## Marginal R2: 0.477
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: all.tax.brich ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -79.2 -75.0 43.6 -87.2 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.581 1.258
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 15
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.4243 0.6253 -3.877 0.000106 ***
## Dist_trail_std -1.4497 0.3959 -3.662 0.000250 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: mu of 0.1 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## # Check for Multicollinearity
##
## Low Correlation
##
## Parameter VIF Increased SE
## Dist_edge_std 1.12 1.06
## Dist_trail_std 1.12 1.06
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 0.81349415 0.05084338 16.00000000 0.99999999
## [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: 0.884
## Marginal R2: 0.527
## [1] " "
## [1] "NAKAGAWA 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: 0.884
## Marginal R2: 0.527
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula:
## all.tax.brich ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -79.8 -74.6 44.9 -89.8 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.16 1.077
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 14.7
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.7132 0.5796 -4.681 2.86e-06 ***
## Dist_edge_std 0.8819 0.5185 1.701 0.089 .
## Dist_trail_std -1.6443 0.3823 -4.301 1.70e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Warning: mu of 0.1 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 3.0185237 0.1775602 17.0000000 0.9999274
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.470
## Marginal R2: 0.337
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.470
## Marginal R2: 0.337
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: all.tax.brepl ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -47.4 -43.2 27.7 -55.4 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.1613 0.4016
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.26
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.2313 0.3787 -3.251 0.00115 **
## Dist_trail_std -0.6954 0.3399 -2.046 0.04074 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## # Check for Multicollinearity
##
## Low Correlation
##
## Parameter VIF Increased SE
## Dist_edge_std 1.45 1.21
## Dist_trail_std 1.45 1.21
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 3.8469197 0.2404325 16.0000000 0.9991423
## [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: 0.475
## [1] " "
## [1] "NAKAGAWA 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: 0.475
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula:
## all.tax.brepl ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -48.2 -42.9 29.1 -58.2 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.327e-08 0.0001152
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.35
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.4674 0.3437 -4.269 1.96e-05 ***
## Dist_edge_std 0.6402 0.3526 1.816 0.06945 .
## Dist_trail_std -0.9475 0.3522 -2.690 0.00714 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [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.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 2.2357030 0.1315119 17.0000000 0.9999920
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.860
## Marginal R2: 0.530
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.860
## Marginal R2: 0.530
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: nat.tax.btotal ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -37.4 -33.3 22.7 -45.4 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.8423 0.9177
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 3.69
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0376 0.4926 -2.106 0.03518 *
## Dist_trail_std -1.2646 0.3991 -3.168 0.00153 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## # Check for Multicollinearity
##
## Low Correlation
##
## Parameter VIF Increased SE
## Dist_edge_std 1.61 1.27
## Dist_trail_std 1.61 1.27
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 2.4201718 0.1512607 16.0000000 0.9999608
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.883
## Marginal R2: 0.621
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.883
## Marginal R2: 0.621
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula:
## nat.tax.btotal ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -38.7 -33.5 24.4 -48.7 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.7393 0.8598
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 4.14
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.4310 0.5293 -2.704 0.006859 **
## Dist_edge_std 1.0727 0.6115 1.754 0.079376 .
## Dist_trail_std -1.6624 0.4307 -3.859 0.000114 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 3.698676 0.205482 18.000000 0.999866
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.1 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: 0.000
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.1 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: 0.000
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: nat.tax.brich ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -135.8 -132.7 70.9 -141.8 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 9.572e-10 3.094e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 3.08
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.6358 0.4236 -6.223 4.88e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.1 is too close to zero, estimate of random effect variances may be unreliable.
## Warning in predict.lm(model, newdata = data.frame(x = xseq), se.fit = se, :
## prediction from a rank-deficient fit may be misleading
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.066869
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 5.0264e-11
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 2.0135e-16
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 7.981e-22
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 3.3206201 0.1953306 17.0000000 0.9998569
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.506
## Marginal R2: 0.340
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.506
## Marginal R2: 0.340
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: nat.tax.brepl ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -48.0 -43.8 28.0 -56.0 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.2137 0.4623
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.32
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.2587 0.3933 -3.200 0.00137 **
## Dist_trail_std -0.7198 0.3466 -2.077 0.03781 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## # Check for Multicollinearity
##
## Low Correlation
##
## Parameter VIF Increased SE
## Dist_edge_std 2.78 1.67
## Dist_trail_std 2.78 1.67
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 3.6036548 0.2252284 16.0000000 0.9994348
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.581
## Marginal R2: 0.497
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.581
## Marginal R2: 0.497
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula:
## nat.tax.brepl ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -48.8 -43.6 29.4 -58.8 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.1223 0.3497
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.56
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.5571 0.4806 -3.240 0.0012 **
## Dist_edge_std 0.7591 0.5949 1.276 0.2020
## Dist_trail_std -1.0586 0.5402 -1.960 0.0500 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## # Check for Multicollinearity
##
## Low Correlation
##
## Parameter VIF Increased SE
## Dist_edge_std 1.36 1.16
## Dist_trail_std 1.36 1.16
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 11.164000 0.697750 16.000000 0.799251
## [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: 1.176
## [1] " "
## [1] "NAKAGAWA 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: 1.176
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula:
## nind.tax.btotal ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -153.4 -148.2 81.7 -163.4 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.876e-09 4.331e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 0.396
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.03587 0.38041 -0.094 0.9249
## Dist_edge_std 0.90713 0.44137 2.055 0.0399 *
## Dist_trail_std -0.97860 0.40175 -2.436 0.0149 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [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.
## Warning in sqrt(insight::get_variance_residual(model)): NaNs produced
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 16.4515482 0.9139749 18.0000000 0.5610653
## [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: -0.000
## [1] " "
## [1] "NAKAGAWA 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: -0.000
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: nind.tax.btotal ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -150.5 -147.4 78.3 -156.5 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 4.211e-09 6.489e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 0.287
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1433 0.3052 0.47 0.639
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## 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.
## Warning in sqrt(insight::get_variance_residual(model)): NaNs produced
## Warning in predict.lm(model, newdata = data.frame(x = xseq), se.fit = se, :
## prediction from a rank-deficient fit may be misleading
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 14.0916376 0.8289199 17.0000000 0.6606050
## [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: 1.740
## [1] " "
## [1] "NAKAGAWA 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: 1.740
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: nind.tax.btotal ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -151.2 -147.1 79.6 -159.2 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 7.475e-09 8.646e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 0.327
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.3882 0.3348 1.160 0.246
## Dist_trail_std -0.5428 0.3386 -1.603 0.109
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## 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.
## Warning in sqrt(insight::get_variance_residual(model)): NaNs produced
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## # Check for Multicollinearity
##
## Low Correlation
##
## Parameter VIF Increased SE
## Dist_edge_std 1.53 1.24
## Dist_trail_std 1.53 1.24
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 8.6087198 0.5380450 16.0000000 0.9286488
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.8 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: 0.908
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.8 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: 0.908
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula:
## nind.tax.brich ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -155.8 -150.5 82.9 -165.8 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 2.665e-09 5.162e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 0.479
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.6909 0.3859 -1.790 0.07342 .
## Dist_edge_std 1.4233 0.4788 2.973 0.00295 **
## Dist_trail_std -1.0907 0.4112 -2.652 0.00799 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.8 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 12.0360372 0.6686687 18.0000000 0.8453714
## [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: 0.000
## [1] " "
## [1] "NAKAGAWA 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: 0.000
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: nind.tax.brepl ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -156.0 -152.9 81.0 -162.0 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.685e-08 0.0001298
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 1.08
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.8618 0.4048 -4.6 4.23e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## 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.
## Warning in predict.lm(model, newdata = data.frame(x = xseq), se.fit = se, :
## prediction from a rank-deficient fit may be misleading
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 9.5742292 0.5631900 17.0000000 0.9205096
## [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: 0.072
## [1] " "
## [1] "NAKAGAWA 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: 0.072
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: nind.tax.brepl ~ Dist_edge_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -155.6 -151.4 81.8 -163.6 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.105e-08 0.0001051
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 1.16
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.6300 0.4351 -3.746 0.00018 ***
## Dist_edge_std -0.3985 0.3195 -1.247 0.21226
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## 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.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 3.0771443 0.1810085 17.0000000 0.9999166
## [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: 0.832
## Marginal R2: 0.542
## [1] " "
## [1] "NAKAGAWA 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: 0.832
## Marginal R2: 0.542
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: all.func.btotal ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -33.4 -29.3 20.7 -41.4 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.6208 0.7879
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.66
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.8169 0.4538 -1.800 0.07186 .
## Dist_trail_std -1.1691 0.4214 -2.775 0.00553 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## # Check for Multicollinearity
##
## Low Correlation
##
## Parameter VIF Increased SE
## Dist_edge_std 1.79 1.34
## Dist_trail_std 1.79 1.34
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 3.1770939 0.1985684 16.0000000 0.9997516
## [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: 0.865
## Marginal R2: 0.660
## [1] " "
## [1] "NAKAGAWA 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: 0.865
## Marginal R2: 0.660
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula:
## all.func.btotal ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -34.9 -29.7 22.5 -44.9 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.5002 0.7072
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 3.03
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.2038 0.4923 -2.445 0.01448 *
## Dist_edge_std 1.0580 0.6150 1.720 0.08536 .
## Dist_trail_std -1.5955 0.4718 -3.382 0.00072 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 3.0800536 0.1811796 17.0000000 0.9999161
## [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: 0.326
## [1] " "
## [1] "NAKAGAWA 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: 0.326
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: all.func.brich ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -53.8 -49.6 30.9 -61.8 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 2.731e-09 5.226e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 3
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.4636 0.2954 -4.955 7.22e-07 ***
## Dist_trail_std -0.6470 0.2886 -2.242 0.025 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## 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.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## # Check for Multicollinearity
##
## Low Correlation
##
## Parameter VIF Increased SE
## Dist_edge_std 1.36 1.17
## Dist_trail_std 1.36 1.17
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 3.1695803 0.1980988 16.0000000 0.9997555
## [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: 0.453
## [1] " "
## [1] "NAKAGAWA 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: 0.453
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula:
## all.func.brich ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -54.4 -49.2 32.2 -64.4 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.254e-09 3.542e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 3.48
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.7317 0.3294 -5.257 1.46e-07 ***
## Dist_edge_std 0.5463 0.3226 1.694 0.09035 .
## Dist_trail_std -0.9422 0.3389 -2.780 0.00543 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [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.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 1.15079724 0.06769396 17.00000000 0.99999995
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.872
## Marginal R2: 0.510
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.872
## Marginal R2: 0.510
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: all.func.brepl ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -55.3 -51.1 31.6 -63.3 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.069 1.034
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 7.85
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.7452 0.5325 -3.277 0.001048 **
## Dist_trail_std -1.3352 0.3762 -3.549 0.000387 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## # Check for Multicollinearity
##
## Low Correlation
##
## Parameter VIF Increased SE
## Dist_edge_std 1.20 1.09
## Dist_trail_std 1.20 1.09
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 1.25871265 0.07866954 16.00000000 0.99999965
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.842
## Marginal R2: 0.573
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.842
## Marginal R2: 0.573
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula:
## all.func.brepl ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -55.1 -49.9 32.5 -65.1 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.6732 0.8205
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 7.36
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.9694 0.4866 -4.047 5.18e-05 ***
## Dist_edge_std 0.7494 0.5172 1.449 0.147
## Dist_trail_std -1.5033 0.3766 -3.992 6.55e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 3.2690116 0.1922948 17.0000000 0.9998719
## [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: 0.826
## Marginal R2: 0.546
## [1] " "
## [1] "NAKAGAWA 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: 0.826
## Marginal R2: 0.546
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: nat.func.btotal ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -32.4 -28.3 20.2 -40.4 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.5708 0.7555
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.48
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.7555 0.4443 -1.701 0.08903 .
## Dist_trail_std -1.1455 0.4259 -2.690 0.00715 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## # Check for Multicollinearity
##
## Low Correlation
##
## Parameter VIF Increased SE
## Dist_edge_std 1.75 1.32
## Dist_trail_std 1.75 1.32
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 3.4625313 0.2164082 16.0000000 0.9995634
## [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: 0.853
## Marginal R2: 0.645
## [1] " "
## [1] "NAKAGAWA 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: 0.853
## Marginal R2: 0.645
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula:
## nat.func.btotal ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -33.3 -28.1 21.6 -43.3 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.4731 0.6878
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.74
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0996 0.4853 -2.266 0.02345 *
## Dist_edge_std 0.9476 0.6052 1.566 0.11739
## Dist_trail_std -1.5235 0.4747 -3.209 0.00133 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 3.9103731 0.2300219 17.0000000 0.9995565
## [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: 0.260
## [1] " "
## [1] "NAKAGAWA 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: 0.260
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: nat.func.brich ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -56.6 -52.4 32.3 -64.6 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 7.296e-10 2.701e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.75
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.5156 0.3081 -4.919 8.72e-07 ***
## Dist_trail_std -0.5725 0.2789 -2.053 0.0401 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## 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.
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.18088
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 0.00077524
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 7.6857e-16
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## # Check for Multicollinearity
##
## Low Correlation
##
## Parameter VIF Increased SE
## Dist_edge_std 1.40 1.18
## Dist_trail_std 1.40 1.18
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 4.0314491 0.2519656 16.0000000 0.9988482
## [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: 0.393
## [1] " "
## [1] "NAKAGAWA 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: 0.393
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula:
## nat.func.brich ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -57.1 -51.9 33.6 -67.1 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 8.997e-10 2.999e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 3.18
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.7868 0.3435 -5.202 1.98e-07 ***
## Dist_edge_std 0.5497 0.3319 1.656 0.09767 .
## Dist_trail_std -0.8650 0.3327 -2.600 0.00933 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [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.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 1.64497455 0.09676321 17.00000000 0.99999923
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.866
## Marginal R2: 0.490
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.866
## Marginal R2: 0.490
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: nat.func.brepl ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -52.9 -48.7 30.4 -60.9 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.112 1.054
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 6.22
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.5986 0.5436 -2.941 0.003276 **
## Dist_trail_std -1.3075 0.3824 -3.419 0.000628 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## # Check for Multicollinearity
##
## Low Correlation
##
## Parameter VIF Increased SE
## Dist_edge_std 1.40 1.18
## Dist_trail_std 1.40 1.18
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 11.2042651 0.7002666 16.0000000 0.7967050
## [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: 1.058
## [1] " "
## [1] "NAKAGAWA 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: 1.058
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula:
## nind.func.btotal ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -136.4 -131.2 73.2 -146.4 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.254e-09 3.541e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 0.468
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2244 0.3775 -0.594 0.55231
## Dist_edge_std 1.1157 0.4471 2.496 0.01258 *
## Dist_trail_std -1.0829 0.4067 -2.663 0.00775 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [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.
## Warning in sqrt(insight::get_variance_residual(model)): NaNs produced
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## # Check for Multicollinearity
##
## Low Correlation
##
## Parameter VIF Increased SE
## Dist_edge_std 1.42 1.19
## Dist_trail_std 1.42 1.19
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 9.9632102 0.6227006 16.0000000 0.8685425
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 1.0 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: 1.022
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 1.0 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: 1.022
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula:
## nind.func.brich ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -133.3 -128.1 71.6 -143.3 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.364e-09 3.693e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 0.496
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.3288 0.3773 -0.872 0.38343
## Dist_edge_std 1.2141 0.4522 2.685 0.00726 **
## Dist_trail_std -1.1193 0.4092 -2.735 0.00624 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 1.0 is too close to zero, estimate of random effect variances may be unreliable.
## Warning in sqrt(insight::get_variance_residual(model)): NaNs produced
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 6.3657915 0.3536551 18.0000000 0.9944736
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.0 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: 0.000
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.0 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: 0.000
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: nind.func.brepl ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -172.7 -169.5 89.3 -178.7 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.093e-08 0.0001045
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 3.76
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.9995 0.4635 -6.471 9.71e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.0 is too close to zero, estimate of random effect variances may be unreliable.
## Warning in predict.lm(model, newdata = data.frame(x = xseq), se.fit = se, :
## prediction from a rank-deficient fit may be misleading
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## [1] " "
## [1] "CHECKING COLINEARITY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## NULL
## [1] " "
## [1] "TESTING FOR OVERDISPERSION - P-VALUES < 0.05 meand overdispersion"
## [1] " "
## [1] " "
## chisq ratio rdf p
## 5.1487467 0.3028675 17.0000000 0.9973187
## [1] " "
## [1] "STANDARD R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.0 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: 0.053
## [1] " "
## [1] "NAKAGAWA R2"
## [1] " "
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.0 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: 0.053
## [1] " "
## [1] "MODEL SUMMARY"
## [1] " "
## Family: beta ( logit )
## Formula: nind.func.brepl ~ Dist_edge_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -172.4 -168.2 90.2 -180.4 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.965e-08 0.0001402
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 4.07
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.7956 0.4708 -5.939 2.87e-09 ***
## Dist_edge_std -0.3791 0.2983 -1.271 0.204
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] " "
## [1] "CHECKING MODEL GRAPHICALLY"
## [1] " "
## Not enough model terms in the conditional part of the model to check for multicollinearity.
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.0 is too close to zero, estimate of random effect variances may be unreliable.
## [1] " "