INEQ = read.csv("INEQ_22.csv",
                header = TRUE,
                dec = ".")

LOAD PACKAGES

library(ggplot2) 
library(gridExtra)
library(lattice) 
library(lme4) 
## Loading required package: Matrix
library(arm)
## Loading required package: MASS
## 
## arm (Version 1.12-2, built: 2021-10-15)
## Working directory is C:/Users/jc486457/Documents/PhD Thesis/EQUALITY R/R equality cris/Equality paper cris
library(scales) 
## 
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## 
##     rescale
library(DHARMa)
## This is DHARMa 0.4.5. For overview type '?DHARMa'. For recent changes, type news(package = 'DHARMa')
library(insight)
## Warning: package 'insight' was built under R version 4.1.3
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##     display
#library(parameters)
library(performance)
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#library(sjPlot)
library(tidyverse)
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library(graphics)
library(vcd)
## Loading required package: grid
#library(ggstatsplot)
library(dplyr)
library(ggpubr)
library(MuMIn)
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library(car)
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PREPARE DATA FOR ANALYSIS

Meaning of variables:

Eliminate don’t knows and NAs from OrgEffLivelihood and OrgEffComm.

str(INEQ$OrgEffLivelihood)
##  int [1:1191] 3 4 3 4 3 5 2 4 1 4 ...
INEQ$OrgEffLivelihood_cat<-as.factor(INEQ$OrgEffLivelihood)
str(INEQ$OrgEffComm)
##  int [1:1191] 1 5 1 3 5 4 1 4 2 4 ...
INEQ$OrgEffComm_cat<-as.factor(INEQ$OrgEffComm)

ggplot(INEQ,aes(OrgEffLivelihood_cat))+geom_bar()#there are more than 150 don't knows

ggplot(INEQ,aes(OrgEffComm_cat))+geom_bar()#there are less than 200 don't knows

#eliminate don't knows
INEQ <- INEQ[INEQ$OrgEffLivelihood!=0,]
INEQ <- INEQ[INEQ$OrgEffComm!=0,]

#eliminate NAs

which(is.na(INEQ$OrgEffLivelihood))
## [1] 847 858 882 895 904 906 927 960
INEQ<-INEQ[-c(847, 858, 882, 895, 904, 906, 927, 960),]

which(is.na(INEQ$OrgEffComm))
## integer(0)
colSums(is.na(INEQ))
##                 dbid             sampleid             datatype 
##                    0                    0                    0 
##              country                 site     OrgEffLivelihood 
##                    0                    0                    0 
##           OrgEffComm                  sex         yrseducation 
##                    0                    0                    1 
##              migrant           marlivprim               occdiv 
##                    1                    0                    0 
##               mslall         trustleaders            decispart 
##                    0                    1                    2 
##            CommEvent                 popn           distmktreg 
##                   35                    0                    0 
##                 regr                 regg                 rega 
##                    0                    0                    0 
##      clearboundaries        confmechinter        GradSanctions 
##                    0                    0                    0 
## OrgEffLivelihood_cat       OrgEffComm_cat 
##                    0                    0
dim(INEQ)#968
## [1] 968  26

CALCULATE DISPARITY

In this script disparity = inequality

Calculate Disparity 1 (Objective)

Disparity 1 (Objective disparity) = (how the individual think he/she benefits from co-management) - (average of how individuals think they benefit from co-management)

# Inequality1 (or Disparity 1) = OrgEffLivelihood - average at 
#community level of all OrgEffLivelihood
avgeffliv.table <- setNames(aggregate(as.numeric(INEQ$OrgEffLivelihood)~INEQ$site,FUN=mean),
     c("site","avgeffliv"))#table with averages of how individual think they benefit 
#per site ("objective measure" of how the community benefits)
INEQ.outcomes <- merge(INEQ,avgeffliv.table, by = intersect("site", "site"), 
                       all=FALSE) #average has been added to the data frame INEQ
INEQ.outcomes$Inequality1_rec = as.numeric(INEQ.outcomes$OrgEffLivelihood) - 
  as.numeric(INEQ.outcomes$avgeffliv)

Calculate Disparity 2 (Subjective, perceived)

Disparity 2 (Subjective disparity, or perceived disparity) = (how the individual think he/she benefits from co-management) - (how the individual thinks the community benefits)

 # Calculate Inequality2 (or Disparity 2) =(OrgEffLivelihood - OrgEffComm)
INEQ.outcomes$Inequality2_rec = as.numeric(INEQ.outcomes$OrgEffLivelihood) - 
  as.numeric(INEQ.outcomes$OrgEffComm)

# check distribution of new response variables
par(mfrow = c(1,2),mar=c(2,2,2,2))
hist(INEQ.outcomes$Inequality1_rec,main="Inequality1 (recalculated)",
     breaks = c(-5,-4,-3,-2,-1,0,1,2,3,4,5))
hist(INEQ.outcomes$Inequality2_rec,main="Inequality2 (recalculated)",
     breaks = c(-5,-4,-3,-2,-1,0,1,2,3,4,5))

INEQ=INEQ.outcomes

Type of Disparity

### Split Disparity1 data into losses (-5 to 0)  and gains (0 to 5)
# 1. Create binary variable: 0 for equal, 1 for any disparity
INEQ$Ineq1_bin <- as.integer(ifelse(INEQ$Inequality1_rec < 0.5 &  INEQ$Inequality1_rec > -0.5,0,1)
              )#transform to 0 values between 0.5 and -0.5 (equality), transform to 1 the rest 


#2. Create categorical variable with type of disparity: "equal", "loss", "gain"
INEQ$Ineq1_bintype <- as.factor(ifelse(INEQ$Inequality1_rec < -0.5,"loss", 
                                       ifelse(INEQ$Inequality1_rec>0.5,"gain","equal")))

### Split Disparity2 data into losses (-5 to 0)  and gains (0 to 5)
# 1. Create binary variable: 0 for equal, 1 for any disparity
INEQ$Ineq2_bin <- as.integer(ifelse(INEQ$Inequality2_rec == 0,0,1))
# 2. Create categorical variable with type of disparity: "equal", "loss", "gain"
INEQ$Ineq2_bintype <- as.factor(ifelse(INEQ$Inequality2_rec < 0,"loss", 
                                       ifelse(INEQ$Inequality2_rec>0,"gain","equal")))

Eliminate rest of don’t knows and NAs

which(is.na(INEQ$trustleaders))
## [1] 597
INEQ<-INEQ[-c(597),]#eliminate NAs

INEQ <- INEQ[INEQ$trustleaders!=0,] #Eliminate don't knows


which(is.na(INEQ$ yrseducation))
## [1] 906
INEQ<-INEQ[-c(906),] #eliminate NAs


which(is.na(INEQ$migrant))
## [1] 913
INEQ<-INEQ[-c(913),] #eliminate NAs

which(is.na(INEQ$ decispart ))
## [1] 237 485
INEQ<-INEQ[-c(237,485),] #eliminate NAs
colSums(is.na(INEQ))
##                 site                 dbid             sampleid 
##                    0                    0                    0 
##             datatype              country     OrgEffLivelihood 
##                    0                    0                    0 
##           OrgEffComm                  sex         yrseducation 
##                    0                    0                    0 
##              migrant           marlivprim               occdiv 
##                    0                    0                    0 
##               mslall         trustleaders            decispart 
##                    0                    0                    0 
##            CommEvent                 popn           distmktreg 
##                   34                    0                    0 
##                 regr                 regg                 rega 
##                    0                    0                    0 
##      clearboundaries        confmechinter        GradSanctions 
##                    0                    0                    0 
## OrgEffLivelihood_cat       OrgEffComm_cat            avgeffliv 
##                    0                    0                    0 
##      Inequality1_rec      Inequality2_rec            Ineq1_bin 
##                    0                    0                    0 
##        Ineq1_bintype            Ineq2_bin        Ineq2_bintype 
##                    0                    0                    0
colSums(is.na(INEQ))
##                 site                 dbid             sampleid 
##                    0                    0                    0 
##             datatype              country     OrgEffLivelihood 
##                    0                    0                    0 
##           OrgEffComm                  sex         yrseducation 
##                    0                    0                    0 
##              migrant           marlivprim               occdiv 
##                    0                    0                    0 
##               mslall         trustleaders            decispart 
##                    0                    0                    0 
##            CommEvent                 popn           distmktreg 
##                   34                    0                    0 
##                 regr                 regg                 rega 
##                    0                    0                    0 
##      clearboundaries        confmechinter        GradSanctions 
##                    0                    0                    0 
## OrgEffLivelihood_cat       OrgEffComm_cat            avgeffliv 
##                    0                    0                    0 
##      Inequality1_rec      Inequality2_rec            Ineq1_bin 
##                    0                    0                    0 
##        Ineq1_bintype            Ineq2_bin        Ineq2_bintype 
##                    0                    0                    0
dim(INEQ)#955
## [1] 955  33
#oultiers community events

boxplot(INEQ$CommEvent, plot=FALSE)$out  #Identify the outliers
##  [1]  2.648649  2.648649  3.027027  2.648649  3.205212  4.273616  3.154102
##  [8]  5.933763  2.736925  2.736925  4.273616  5.128339  3.205212  2.991531
## [15]  2.564169 10.684039  4.273616 10.684039  3.154102  4.352661  3.091020
## [22]  2.523282  3.154102  2.775610  3.154102  3.154102  3.193079  3.154102
## [29]  2.564169  3.205212  2.564169  2.736925 18.918919  2.648649  3.027027
## [36]  2.736925  2.736925  2.736925  2.554463  2.736925  3.154102  2.523282
## [43]  3.154102  2.964856  2.523282  3.154102  2.736925  2.736925  2.736925
## [50]  2.777850  4.747010  2.564169
hist(INEQ$CommEvent)

which(is.na(INEQ$ CommEvent )) #Identify NAs in community events
##  [1]  99 207 210 213 214 215 217 218 220 231 250 257 379 403 404 406 408 409 410
## [20] 413 454 501 549 607 610 623 669 670 672 677 795 852 949 954
INEQ<-INEQ[-c(99, 207, 210, 213, 214, 215, 217, 218, 220, 231, 250, 257, 379, 403, 404, 
               406, 408, 409, 410, 413, 454, 501, 549, 607, 610, 623, 669, 670, 672, 677, 
              795, 852, 949, 954),]  #eliminate NAs


colSums(is.na(INEQ))
##                 site                 dbid             sampleid 
##                    0                    0                    0 
##             datatype              country     OrgEffLivelihood 
##                    0                    0                    0 
##           OrgEffComm                  sex         yrseducation 
##                    0                    0                    0 
##              migrant           marlivprim               occdiv 
##                    0                    0                    0 
##               mslall         trustleaders            decispart 
##                    0                    0                    0 
##            CommEvent                 popn           distmktreg 
##                    0                    0                    0 
##                 regr                 regg                 rega 
##                    0                    0                    0 
##      clearboundaries        confmechinter        GradSanctions 
##                    0                    0                    0 
## OrgEffLivelihood_cat       OrgEffComm_cat            avgeffliv 
##                    0                    0                    0 
##      Inequality1_rec      Inequality2_rec            Ineq1_bin 
##                    0                    0                    0 
##        Ineq1_bintype            Ineq2_bin        Ineq2_bintype 
##                    0                    0                    0
INEQ<-subset(INEQ,CommEvent<18)#Eliminate this outlier. It is an error

dim(INEQ)#920
## [1] 920  33
which(is.na(INEQ$ CommEvent ))
## integer(0)

Define factors and standardize predictors

Define factors

INEQ$decispart_cat <- as.factor(INEQ$decispart)
INEQ$sex_cat<-as.factor(INEQ$sex)
INEQ$site_cat<-as.factor(INEQ$site)
INEQ$migrant_cat<-as.factor(INEQ$migrant)
INEQ$marlivprim_cat<-as.factor(INEQ$marlivprim)
INEQ$regr_cat<-as.factor(INEQ$regr)
INEQ$regg_cat<-as.factor(INEQ$regg)
INEQ$rega_cat<-as.factor(INEQ$rega)
INEQ$GradSanctions_cat<-as.factor(INEQ$GradSanctions)
INEQ$clearboundaries_cat<-as.factor(INEQ$clearboundaries)

Standardize predictors

#continuous

#standardize (2sd) 
INEQ$distmktreg_sd<-scale(INEQ$distmktreg)*0.5
INEQ$popn_sd<-scale(INEQ$popn)*0.5
INEQ$yrseducation_sd<-scale(INEQ$yrseducation)*0.5
INEQ$mslall_sd<-scale(INEQ$mslall)*0.5
INEQ$occdiv_sd<-scale(INEQ$occdiv)*0.5
INEQ$CommEvent_sd<-scale(INEQ$CommEvent)*0.5
INEQ$trustleaders_sd<-scale(INEQ$trustleaders)*0.5
INEQ$confmechinter_sd<-scale(INEQ$confmechinter)*0.5

Split data in 4

Split initial INEQ data set into 4 data sets that will be used in the models: losses and gains for Inequality 1 (actual) and Inequality 2 (perceived) The 4 data sets are: 1) INEQ_loss1 (for objective losses) 2) INEQ_gain1 (for objective gains) 3) INEQ_loss2 (for subjective losses) 4) INEQ_gain2 (for subjective gains)

# LOSSES 1 (actual/objective): table containing only data for losses 
#and equality for Inequality1 (disparity 1)

INEQ_loss1 <- INEQ[INEQ$Ineq1_bintype %in% c("loss","equal"),]
INEQ_loss1$site <- factor(INEQ_loss1$site)
INEQ_loss1$country <- factor(INEQ_loss1$country)

# GAINS 1 (actual): table containing only data for gains 
#and equality for Inequality1 (disparity 1)
INEQ_gain1 <- INEQ[INEQ$Ineq1_bintype %in% c("gain","equal"),]
INEQ_gain1$site <- factor(INEQ_gain1$site)
INEQ_gain1$country <- factor(INEQ_gain1$country)


# LOSSES 2 (perceived): table containing only data for losses 
#and equality for Inequality2 (disparity 2)
INEQ_loss2 <- INEQ[INEQ$Ineq2_bintype %in% c("loss","equal"),]
INEQ_loss2$site <- factor(INEQ_loss2$site)
INEQ_loss2$country <- factor(INEQ_loss2$country)
# GAINS 2 (perceived): table containing only data for gains 
#and equality for Inequality2 (disparity 2)
INEQ_gain2 <- INEQ[INEQ$Ineq2_bintype %in% c("gain","equal"),]
INEQ_gain2$site <- factor(INEQ_gain2$site)
INEQ_gain2$country <- factor(INEQ_gain2$country)

MODELS

MODEL OBJECTIVE LOSSES (Disparity 1)

Define the global model

M.loss1_global.model<-glmer(Ineq1_bin ~ migrant_cat+ sex_cat + yrseducation_sd + 
                              trustleaders_sd +decispart_cat + marlivprim_cat + 
                              mslall_sd + occdiv_sd + CommEvent_sd+ 
              popn_sd + distmktreg_sd  +  regr_cat + regg_cat + rega_cat + 
              clearboundaries_cat  + confmechinter_sd + GradSanctions_cat +(1 | site_cat), 
              data = INEQ_loss1,family=binomial(link = "logit"), na.action='na.fail',
              control = glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=1000000))) 
summary(M.loss1_global.model)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## Ineq1_bin ~ migrant_cat + sex_cat + yrseducation_sd + trustleaders_sd +  
##     decispart_cat + marlivprim_cat + mslall_sd + occdiv_sd +  
##     CommEvent_sd + popn_sd + distmktreg_sd + regr_cat + regg_cat +  
##     rega_cat + clearboundaries_cat + confmechinter_sd + GradSanctions_cat +  
##     (1 | site_cat)
##    Data: INEQ_loss1
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+06))
## 
##      AIC      BIC   logLik deviance df.resid 
##    790.7    880.8   -375.3    750.7      650 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7786 -0.6043 -0.3383  0.7261  3.0061 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  site_cat (Intercept) 1.695    1.302   
## Number of obs: 670, groups:  site_cat, 47
## 
## Fixed effects:
##                       Estimate Std. Error z value Pr(>|z|)    
## (Intercept)          -0.001068   0.840220  -0.001 0.998985    
## migrant_cat1          0.277207   0.412852   0.671 0.501938    
## sex_catM             -0.175309   0.476396  -0.368 0.712880    
## yrseducation_sd      -0.030168   0.217163  -0.139 0.889516    
## trustleaders_sd       0.279245   0.204582   1.365 0.172267    
## decispart_cat1       -0.786738   0.297836  -2.642 0.008254 ** 
## decispart_cat2       -1.016430   0.275585  -3.688 0.000226 ***
## marlivprim_cat1      -0.485853   0.287127  -1.692 0.090624 .  
## mslall_sd            -0.151454   0.347532  -0.436 0.662983    
## occdiv_sd            -0.171685   0.281999  -0.609 0.542647    
## CommEvent_sd          0.484582   0.227075   2.134 0.032841 *  
## popn_sd              -1.178851   0.591258  -1.994 0.046174 *  
## distmktreg_sd         0.320699   0.409207   0.784 0.433211    
## regr_cat1            -2.139454   0.675588  -3.167 0.001541 ** 
## regg_cat1             1.204969   0.709896   1.697 0.089623 .  
## rega_cat1             0.392067   0.569423   0.689 0.491117    
## clearboundaries_cat1 -0.297213   0.514986  -0.577 0.563852    
## confmechinter_sd      0.488268   0.485464   1.006 0.314523    
## GradSanctions_cat1    0.663163   0.536663   1.236 0.216564    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 19 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
vif(M.loss1_global.model)
##                         GVIF Df GVIF^(1/(2*Df))
## migrant_cat         1.058065  1        1.028623
## sex_cat             1.047986  1        1.023712
## yrseducation_sd     1.062701  1        1.030874
## trustleaders_sd     1.041249  1        1.020416
## decispart_cat       1.103221  2        1.024863
## marlivprim_cat      1.107630  1        1.052440
## mslall_sd           1.285081  1        1.133614
## occdiv_sd           1.160308  1        1.077176
## CommEvent_sd        1.045756  1        1.022622
## popn_sd             1.432817  1        1.197003
## distmktreg_sd       1.241261  1        1.114119
## regr_cat            1.355431  1        1.164230
## regg_cat            1.843736  1        1.357843
## rega_cat            1.589977  1        1.260943
## clearboundaries_cat 1.292948  1        1.137079
## confmechinter_sd    1.138007  1        1.066774
## GradSanctions_cat   1.362081  1        1.167082

We can conclude there is not multicollinearity

check_overdispersion(M.loss1_global.model)
## # Overdispersion test
## 
##        dispersion ratio =   0.835
##   Pearson's Chi-Squared = 542.564
##                 p-value =   0.999
## No overdispersion detected.
check_singularity(M.loss1_global.model)#singularity is fine
## [1] FALSE
check_convergence(M.loss1_global.model)#convergence is fine
## [1] TRUE
## attr(,"gradient")
## [1] 3.129966e-06

Dharma residuals

resid.loss1=simulateResiduals(M.loss1_global.model, plot=TRUE)

#loss1.dredge<-dredge(global.model = M.loss1_global.model, trace = TRUE)#This line of code was 
#run on the HPC of James Cook University. It will take a long time to run. 
#Consider running this code in your own computer.

Load dredge output

loss1.2_dredge_sd<-readRDS(file="loss1.2.dredge.sd.rds")

Model average

loss1.average_sd<-summary(model.avg(loss1.2_dredge_sd, subset = delta <= 2))
loss1.average_sd
## 
## Call:
## model.avg(object = loss1.2_dredge_sd, subset = delta <= 2)
## 
## Component model call: 
## glmer(formula = Ineq1_bin ~ <26 unique rhs>, data = INEQ_loss1, family 
##      = binomial(link = "logit"), control = glmerControl(optimizer = 
##      "bobyqa", optCtrl = list(maxfun = 1e+06)), na.action = na.fail)
## 
## Component models: 
##                       df  logLik   AICc delta weight
## 1+5+7+10+12+13         9 -378.93 776.13  0.00   0.07
## 1+5+7+10+12+13+14     10 -377.95 776.23  0.10   0.07
## 1+3+5+7+10+12+13      10 -378.15 776.64  0.51   0.05
## 1+5+10+12+13           8 -380.23 776.68  0.55   0.05
## 1+3+5+7+10+12+13+14   11 -377.19 776.77  0.64   0.05
## 1+2+5+7+10+12+13      10 -378.28 776.90  0.77   0.05
## 1+5+10+12+13+14        9 -379.32 776.91  0.78   0.05
## 1+2+5+7+10+12+13+14   11 -377.31 777.01  0.88   0.04
## 1+3+5+10+12+13         9 -379.48 777.23  1.10   0.04
## 1+2+5+10+12+13         9 -379.58 777.42  1.29   0.04
## 1+4+5+7+10+12+13      10 -378.54 777.42  1.29   0.04
## 1+3+5+10+12+13+14     10 -378.58 777.50  1.37   0.03
## 1+4+5+7+10+12+13+14   11 -377.56 777.52  1.39   0.03
## 1+5+6+7+10+12+13      10 -378.65 777.63  1.50   0.03
## 1+5+7+9+10+12+13      10 -378.67 777.67  1.54   0.03
## 1+2+5+10+12+13+14     10 -378.67 777.67  1.54   0.03
## 1+5+6+7+10+12+13+14   11 -377.66 777.72  1.59   0.03
## 1+4+5+10+12+13         9 -379.74 777.75  1.62   0.03
## 1+2+3+5+7+10+12+13    11 -377.68 777.77  1.64   0.03
## 1+5+7+9+10+12+13+14   11 -377.69 777.79  1.66   0.03
## 1+5+7+8+10+12+13      10 -378.74 777.81  1.68   0.03
## 1+2+3+5+7+10+12+13+14 12 -376.72 777.92  1.79   0.03
## 1+5+7+10+11+12+13     10 -378.81 777.95  1.82   0.03
## 1+4+5+10+12+13+14     10 -378.82 777.98  1.85   0.03
## 1+5+7+8+10+12+13+14   11 -377.80 778.00  1.87   0.03
## 1+5+7+10+11+12+13+14  11 -377.85 778.10  1.97   0.03
## 
## Term codes: 
##        CommEvent_sd   GradSanctions_cat clearboundaries_cat    confmechinter_sd 
##                   1                   2                   3                   4 
##       decispart_cat       distmktreg_sd      marlivprim_cat         migrant_cat 
##                   5                   6                   7                   8 
##           occdiv_sd             popn_sd            rega_cat            regg_cat 
##                   9                  10                  11                  12 
##            regr_cat     trustleaders_sd 
##                  13                  14 
## 
## Model-averaged coefficients:  
## (full average) 
##                      Estimate Std. Error Adjusted SE z value Pr(>|z|)    
## (Intercept)          -0.13451    0.64156     0.64256   0.209 0.834187    
## CommEvent_sd          0.47091    0.22903     0.22945   2.052 0.040132 *  
## decispart_cat1       -0.77252    0.29560     0.29614   2.609 0.009090 ** 
## decispart_cat2       -1.03338    0.27345     0.27394   3.772 0.000162 ***
## marlivprim_cat1      -0.32350    0.31638     0.31670   1.021 0.307023    
## popn_sd              -1.27897    0.59014     0.59120   2.163 0.030516 *  
## regg_cat1             1.63410    0.64311     0.64426   2.536 0.011200 *  
## regr_cat1            -2.18065    0.66817     0.66939   3.258 0.001123 ** 
## trustleaders_sd       0.13872    0.20432     0.20451   0.678 0.497581    
## clearboundaries_cat1 -0.13806    0.33940     0.33969   0.406 0.684431    
## GradSanctions_cat1    0.12434    0.33592     0.33623   0.370 0.711533    
## confmechinter_sd      0.05932    0.23438     0.23462   0.253 0.800380    
## distmktreg_sd         0.01909    0.12325     0.12340   0.155 0.877038    
## occdiv_sd            -0.01315    0.08824     0.08835   0.149 0.881688    
## migrant_cat1          0.01358    0.11171     0.11186   0.121 0.903396    
## rega_cat1             0.01371    0.13835     0.13856   0.099 0.921196    
##  
## (conditional average) 
##                      Estimate Std. Error Adjusted SE z value Pr(>|z|)    
## (Intercept)           -0.1345     0.6416      0.6426   0.209 0.834187    
## CommEvent_sd           0.4709     0.2290      0.2294   2.052 0.040132 *  
## decispart_cat1        -0.7725     0.2956      0.2961   2.609 0.009090 ** 
## decispart_cat2        -1.0334     0.2734      0.2739   3.772 0.000162 ***
## marlivprim_cat1       -0.4631     0.2804      0.2810   1.648 0.099300 .  
## popn_sd               -1.2790     0.5901      0.5912   2.163 0.030516 *  
## regg_cat1              1.6341     0.6431      0.6443   2.536 0.011200 *  
## regr_cat1             -2.1807     0.6682      0.6694   3.258 0.001123 ** 
## trustleaders_sd        0.2888     0.2087      0.2091   1.381 0.167273    
## clearboundaries_cat1  -0.5802     0.4770      0.4779   1.214 0.224757    
## GradSanctions_cat1     0.5670     0.5134      0.5143   1.102 0.270322    
## confmechinter_sd       0.4587     0.4915      0.4924   0.932 0.351549    
## distmktreg_sd          0.2981     0.3924      0.3932   0.758 0.448317    
## occdiv_sd             -0.2106     0.2883      0.2889   0.729 0.465945    
## migrant_cat1           0.2375     0.4064      0.4071   0.584 0.559545    
## rega_cat1              0.2550     0.5427      0.5437   0.469 0.639047    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Relative influence

sw(loss1.average_sd)#Sum of model weights over all models including each explanatory variable.
##                      CommEvent_sd decispart_cat popn_sd regg_cat regr_cat
## Sum of weights:      1.00         1.00          1.00    1.00     1.00    
## N containing models:   26           26            26      26       26    
##                      marlivprim_cat trustleaders_sd clearboundaries_cat
## Sum of weights:      0.70           0.48            0.24               
## N containing models:   18             13               6               
##                      GradSanctions_cat confmechinter_sd distmktreg_sd occdiv_sd
## Sum of weights:      0.22              0.13             0.06          0.06     
## N containing models:    6                 4                2             2     
##                      migrant_cat rega_cat
## Sum of weights:      0.06        0.05    
## N containing models:    2           2

Confident intervals of the full model

confint(loss1.average_sd,full=T,level=0.9) #full model averages
##                              5 %       95 %
## (Intercept)          -1.19104579  0.9220230
## CommEvent_sd          0.09366994  0.8481576
## decispart_cat1       -1.25942311 -0.2856242
## decispart_cat2       -1.48377739 -0.5829792
## marlivprim_cat1      -0.84430315  0.1972955
## popn_sd              -2.25099543 -0.3069369
## regg_cat1             0.57483656  2.6933723
## regr_cat1            -3.28123020 -1.0800713
## trustleaders_sd      -0.19759915  0.4750408
## clearboundaries_cat1 -0.69668887  0.4205711
## GradSanctions_cat1   -0.42859548  0.6772734
## confmechinter_sd     -0.32649574  0.4451454
## distmktreg_sd        -0.18381953  0.2220044
## occdiv_sd            -0.15842986  0.1321318
## migrant_cat1         -0.17035806  0.1975117
## rega_cat1            -0.21411910  0.2415335
ma.loss1<-summary(loss1.average_sd)#pulling out model averages
df.loss1<-as.data.frame(ma.loss1$coefmat.full)#selecting full model coefficient averages
CI<-as.data.frame(confint(loss1.average_sd,level=0.9,full=T))# to get confident intervals 
#for full model
CI
##                              5 %       95 %
## (Intercept)          -1.19104579  0.9220230
## CommEvent_sd          0.09366994  0.8481576
## decispart_cat1       -1.25942311 -0.2856242
## decispart_cat2       -1.48377739 -0.5829792
## marlivprim_cat1      -0.84430315  0.1972955
## popn_sd              -2.25099543 -0.3069369
## regg_cat1             0.57483656  2.6933723
## regr_cat1            -3.28123020 -1.0800713
## trustleaders_sd      -0.19759915  0.4750408
## clearboundaries_cat1 -0.69668887  0.4205711
## GradSanctions_cat1   -0.42859548  0.6772734
## confmechinter_sd     -0.32649574  0.4451454
## distmktreg_sd        -0.18381953  0.2220044
## occdiv_sd            -0.15842986  0.1321318
## migrant_cat1         -0.17035806  0.1975117
## rega_cat1            -0.21411910  0.2415335
df.loss1$CI.min <-CI$`5 %` #pulling out CIs and putting into same df as coefficient estimates
df.loss1$CI.max <-CI$`95 %`
library('data.table')
## 
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
## 
##     between, first, last
## The following object is masked from 'package:purrr':
## 
##     transpose
df.loss1
##                         Estimate Std. Error Adjusted SE    z value  Pr(>|z|)
## (Intercept)          -0.13451139 0.64155579  0.64256424 0.20933532 0.8341865
## CommEvent_sd          0.47091376 0.22902640  0.22944669 2.05238854 0.0401319
## decispart_cat1       -0.77252365 0.29560354  0.29613984 2.60864476 0.0090902
## decispart_cat2       -1.03337830 0.27344687  0.27393876 3.77229674 0.0001618
## marlivprim_cat1      -0.32350384 0.31637982  0.31669836 1.02148885 0.3070229
## popn_sd              -1.27896617 0.59013595  0.59120236 2.16333063 0.0305158
## regg_cat1             1.63410445 0.64310565  0.64426044 2.53640353 0.0111998
## regr_cat1            -2.18065075 0.66817069  0.66939162 3.25766067 0.0011233
## trustleaders_sd       0.13872083 0.20432398  0.20451229 0.67830074 0.4975810
## clearboundaries_cat1 -0.13805888 0.33939933  0.33969161 0.40642417 0.6844310
## GradSanctions_cat1    0.12433897 0.33591963  0.33623420 0.36979869 0.7115325
## confmechinter_sd      0.05932481 0.23437528  0.23461967 0.25285524 0.8003801
## distmktreg_sd         0.01909246 0.12324913  0.12339631 0.15472469 0.8770384
## occdiv_sd            -0.01314904 0.08824171  0.08834988 0.14882914 0.8816885
## migrant_cat1          0.01357684 0.11170575  0.11186094 0.12137247 0.9033960
## rega_cat1             0.01370718 0.13834763  0.13855796 0.09892738 0.9211959
##                           CI.min     CI.max
## (Intercept)          -1.19104579  0.9220230
## CommEvent_sd          0.09366994  0.8481576
## decispart_cat1       -1.25942311 -0.2856242
## decispart_cat2       -1.48377739 -0.5829792
## marlivprim_cat1      -0.84430315  0.1972955
## popn_sd              -2.25099543 -0.3069369
## regg_cat1             0.57483656  2.6933723
## regr_cat1            -3.28123020 -1.0800713
## trustleaders_sd      -0.19759915  0.4750408
## clearboundaries_cat1 -0.69668887  0.4205711
## GradSanctions_cat1   -0.42859548  0.6772734
## confmechinter_sd     -0.32649574  0.4451454
## distmktreg_sd        -0.18381953  0.2220044
## occdiv_sd            -0.15842986  0.1321318
## migrant_cat1         -0.17035806  0.1975117
## rega_cat1            -0.21411910  0.2415335
setDT(df.loss1, keep.rownames='coefficient')#put row names into columns
names(df.loss1) <- gsub(" ",'',names(df.loss1))#remove spaces from column headers

Plot standardized effects

ggplot(data=df.loss1, aes(x=coefficient, y=Estimate))+
  geom_hline(yintercept=0, color='grey', linetype='dashed', lwd=1)+
  geom_errorbar(aes(ymin=Estimate-Std.Error, ymax=Estimate+Std.Error), colour="pink",#SE
         width=.1, lwd=1)+
    coord_flip()+
    geom_point(size=3)+theme_classic(base_size=20)+xlab("")+
  geom_errorbar(aes(ymin=CI.min, ymax=CI.max), colour="blue", # CIs
                width=.2,lwd=1) 

MODEL OBJECTIVE GAINS (Disparity 1)

Define global model

M.gains1_global.model<-glmer(Ineq1_bin ~ migrant_cat+ sex_cat + yrseducation_sd + 
 trustleaders_sd + decispart_cat + marlivprim_cat + mslall_sd + occdiv_sd + CommEvent_sd+ 
   popn_sd + distmktreg_sd  +  regr_cat + regg_cat + rega_cat + 
clearboundaries_cat  + confmechinter_sd + GradSanctions_cat +(1 | site_cat), 
                    data = INEQ_gain1,family=binomial(link = "logit"), na.action='na.fail',
                    control = glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=1000000))) 
summary(M.gains1_global.model)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## Ineq1_bin ~ migrant_cat + sex_cat + yrseducation_sd + trustleaders_sd +  
##     decispart_cat + marlivprim_cat + mslall_sd + occdiv_sd +  
##     CommEvent_sd + popn_sd + distmktreg_sd + regr_cat + regg_cat +  
##     rega_cat + clearboundaries_cat + confmechinter_sd + GradSanctions_cat +  
##     (1 | site_cat)
##    Data: INEQ_gain1
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+06))
## 
##      AIC      BIC   logLik deviance df.resid 
##    724.0    814.1   -342.0    684.0      651 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0320 -0.5639 -0.1804  0.5585  2.9540 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  site_cat (Intercept) 3.713    1.927   
## Number of obs: 671, groups:  site_cat, 47
## 
## Fixed effects:
##                      Estimate Std. Error z value Pr(>|z|)  
## (Intercept)          -0.80617    1.06821  -0.755   0.4504  
## migrant_cat1         -0.11685    0.43052  -0.271   0.7861  
## sex_catM             -0.42489    0.51688  -0.822   0.4111  
## yrseducation_sd       0.10765    0.22925   0.470   0.6387  
## trustleaders_sd       0.36568    0.23111   1.582   0.1136  
## decispart_cat1        0.07891    0.31043   0.254   0.7993  
## decispart_cat2       -0.30516    0.29983  -1.018   0.3088  
## marlivprim_cat1      -0.29669    0.31637  -0.938   0.3483  
## mslall_sd             0.27085    0.34773   0.779   0.4360  
## occdiv_sd             0.20746    0.29765   0.697   0.4858  
## CommEvent_sd         -0.16413    0.25176  -0.652   0.5144  
## popn_sd              -0.82336    0.80211  -1.026   0.3047  
## distmktreg_sd         0.50535    0.56425   0.896   0.3705  
## regr_cat1            -1.69247    0.86598  -1.954   0.0507 .
## regg_cat1             0.78121    0.95756   0.816   0.4146  
## rega_cat1             0.69590    0.76365   0.911   0.3621  
## clearboundaries_cat1  0.03733    0.69756   0.054   0.9573  
## confmechinter_sd      0.96005    0.67999   1.412   0.1580  
## GradSanctions_cat1    0.95870    0.72695   1.319   0.1872  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 19 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
check_overdispersion(M.gains1_global.model)
## # Overdispersion test
## 
##        dispersion ratio =   0.750
##   Pearson's Chi-Squared = 488.179
##                 p-value =       1
## No overdispersion detected.
check_singularity(M.gains1_global.model)
## [1] FALSE
check_convergence(M.gains1_global.model)
## [1] TRUE
## attr(,"gradient")
## [1] 6.824324e-06
resid.gains1=simulateResiduals(M.gains1_global.model, plot=TRUE)

Although diagnostics showed minor problems with the deviation, they are known to be very sensitive with large data sets. Visually, the evaluation does not show any major problem

vif(M.gains1_global.model)
##                         GVIF Df GVIF^(1/(2*Df))
## migrant_cat         1.041859  1        1.020715
## sex_cat             1.026230  1        1.013030
## yrseducation_sd     1.101419  1        1.049485
## trustleaders_sd     1.039803  1        1.019707
## decispart_cat       1.105651  2        1.025426
## marlivprim_cat      1.090304  1        1.044176
## mslall_sd           1.194955  1        1.093140
## occdiv_sd           1.110375  1        1.053743
## CommEvent_sd        1.071672  1        1.035216
## popn_sd             1.273856  1        1.128652
## distmktreg_sd       1.238654  1        1.112948
## regr_cat            1.271643  1        1.127671
## regg_cat            1.733880  1        1.316769
## rega_cat            1.488955  1        1.220227
## clearboundaries_cat 1.230416  1        1.109241
## confmechinter_sd    1.105343  1        1.051353
## GradSanctions_cat   1.300527  1        1.140406
#gains1.dredge<-dredge(global.model = M.gains1_global.model, trace = TRUE)#This line of 
#code was run on the HPC of James Cook University. It will take a long time to run. 
#Consider running this code in your own computer.

Load dredge object

gains1.2_dredge_sd<-readRDS(file="gains1.2.dredge.sd.rds")

Model average

gains1.average_sd<-summary(model.avg(gains1.2_dredge_sd, subset = delta <= 2))
gains1.average_sd
## 
## Call:
## model.avg(object = gains1.2_dredge_sd, subset = delta <= 2)
## 
## Component model call: 
## glmer(formula = Ineq1_bin ~ <384 unique rhs>, data = INEQ_gain1, family 
##      = binomial(link = "logit"), control = glmerControl(optimizer = 
##      "bobyqa", optCtrl = list(maxfun = 1e+06)), na.action = na.fail)
## 
## Component models: 
##                    df  logLik   AICc delta weight
## 2+13                4 -349.71 707.48  0.00   0.01
## 2+13+15             5 -348.70 707.50  0.02   0.01
## 2+7+13+15           6 -347.69 707.50  0.03   0.01
## 12+13+15            5 -348.71 707.51  0.03   0.01
## 4+12+13+15          6 -347.71 707.55  0.07   0.01
## 4+10+12+13+15       7 -346.70 707.57  0.09   0.00
## 4+12+13             5 -348.77 707.63  0.15   0.00
## 4+10+12+13          6 -347.77 707.67  0.19   0.00
## 12+13               4 -349.82 707.69  0.21   0.00
## 2+4+13              5 -348.83 707.75  0.27   0.00
## 10+12+13+15         6 -347.84 707.81  0.33   0.00
## 7+12+13+15          6 -347.85 707.82  0.34   0.00
## 2+7+13              5 -348.87 707.83  0.35   0.00
## 13+15               4 -349.89 707.85  0.37   0.00
## 4+13                4 -349.90 707.85  0.37   0.00
## 2+4+13+15           6 -347.87 707.87  0.39   0.00
## 13                  3 -350.92 707.87  0.39   0.00
## 4+7+12+13+15        7 -346.85 707.87  0.39   0.00
## 2+4+7+13+15         7 -346.86 707.88  0.40   0.00
## 2+11+13             5 -348.90 707.90  0.42   0.00
## 7+13+15             5 -348.91 707.92  0.44   0.00
## 4+13+15             5 -348.92 707.93  0.45   0.00
## 2+15                4 -349.96 707.98  0.50   0.00
## 2+11+13+15          6 -347.93 707.98  0.51   0.00
## 4+15                4 -349.97 707.99  0.51   0.00
## 2+4                 4 -349.97 708.00  0.53   0.00
## 4                   3 -350.99 708.01  0.53   0.00
## 4+7+13+15           6 -347.94 708.01  0.53   0.00
## 10+12+13            5 -348.96 708.01  0.53   0.00
## 2+4+15              5 -348.96 708.02  0.54   0.00
## 2+7+11+13+15        7 -346.95 708.06  0.58   0.00
## 2                   3 -351.02 708.08  0.60   0.00
## 2+4+11+13           6 -347.98 708.09  0.61   0.00
## 2+4+7+13            6 -347.99 708.11  0.63   0.00
## 2+7+15              5 -349.04 708.17  0.69   0.00
## 2+4+7+15            6 -348.04 708.21  0.73   0.00
## 15                  3 -351.09 708.22  0.74   0.00
## 2+10+12+13+15       7 -347.03 708.22  0.74   0.00
## 4+7+15              5 -349.07 708.23  0.75   0.00
## 2+4+10+12+13+15     8 -346.01 708.24  0.77   0.00
## 2+4+10+12+13        7 -347.05 708.26  0.78   0.00
## 4+7+10+12+13+15     8 -346.02 708.27  0.79   0.00
## 4+7+13              5 -349.09 708.27  0.79   0.00
## 2+4+11+13+15        7 -347.05 708.27  0.80   0.00
## 4+7+12+13           6 -348.08 708.28  0.80   0.00
## 2+12+13+15          6 -348.08 708.29  0.81   0.00
## 7+13                4 -350.12 708.29  0.81   0.00
## 2+9+13+15           6 -348.09 708.31  0.83   0.00
## 2+7+11+13           6 -348.10 708.32  0.84   0.00
## 7+12+13             5 -349.12 708.33  0.86   0.00
## 2+10+12+13          6 -348.11 708.34  0.86   0.00
## 2+9+13              5 -349.13 708.34  0.87   0.00
## (Null)              2 -352.17 708.35  0.87   0.00
## 5+12+13+15          7 -347.09 708.36  0.88   0.00
## 2+4+7+11+13+15      8 -346.07 708.36  0.88   0.00
## 2+10+13             5 -349.14 708.37  0.89   0.00
## 2+12+13             5 -349.15 708.39  0.92   0.00
## 9+12+13+15          6 -348.14 708.40  0.92   0.00
## 2+4+6               5 -349.16 708.42  0.94   0.00
## 2+10+13+15          6 -348.15 708.43  0.95   0.00
## 2+5+13+15           7 -347.14 708.45  0.97   0.00
## 7+10+12+13+15       7 -347.14 708.46  0.98   0.00
## 7+15                4 -350.20 708.46  0.98   0.00
## 4+6                 4 -350.20 708.46  0.98   0.00
## 4+9+12+13+15        7 -347.15 708.47  0.99   0.00
## 2+4+10+13           6 -348.18 708.48  1.00   0.00
## 2+7+12+13+15        7 -347.16 708.49  1.02   0.00
## 4+5+12+13+15        8 -346.14 708.50  1.02   0.00
## 2+4+7+11+13         7 -347.17 708.51  1.03   0.00
## 2+4+7               5 -349.22 708.52  1.05   0.00
## 2+4+6+15            6 -348.20 708.53  1.05   0.00
## 9+13+15             5 -349.22 708.53  1.05   0.00
## 4+6+15              5 -349.22 708.54  1.06   0.00
## 2+4+6+13            6 -348.21 708.54  1.06   0.00
## 2+4+12+13+15        7 -347.20 708.56  1.08   0.00
## 4+7                 4 -350.26 708.57  1.09   0.00
## 2+4+12+13           6 -348.23 708.58  1.10   0.00
## 4+5+10+12+13+15     9 -345.16 708.59  1.11   0.00
## 4+9+12+13           6 -348.24 708.60  1.12   0.00
## 4+12+13+14+15       7 -347.21 708.60  1.12   0.00
## 2+7                 4 -350.27 708.60  1.12   0.00
## 9+13                4 -350.28 708.61  1.13   0.00
## 2+4+6+7+15          7 -347.22 708.61  1.13   0.00
## 4+9+13              5 -349.27 708.62  1.14   0.00
## 9+12+13             5 -349.27 708.63  1.15   0.00
## 2+4+9+13            6 -348.25 708.63  1.16   0.00
## 4+9+13+15           6 -348.26 708.64  1.16   0.00
## 2+4+10+13+15        7 -347.24 708.65  1.17   0.00
## 2+7+10+13+15        7 -347.24 708.65  1.17   0.00
## 4+7+10+12+13        7 -347.24 708.65  1.17   0.00
## 8+10+12+13+15       7 -347.24 708.66  1.18   0.00
## 2+13+14+15          6 -348.27 708.66  1.18   0.00
## 2+4+6+7+13+15       8 -346.22 708.67  1.19   0.00
## 4+6+13              5 -349.29 708.67  1.19   0.00
## 4+6+7+15            6 -348.27 708.67  1.19   0.00
## 2+4+9+13+15         7 -347.26 708.69  1.21   0.00
## 12+13+14+15         6 -348.28 708.69  1.21   0.00
## 8+12+13+15          6 -348.28 708.69  1.21   0.00
## 5+13+15             6 -348.28 708.70  1.22   0.00
## 4+10+12+13+14+15    8 -346.25 708.71  1.23   0.00
## 5+10+12+13+15       8 -346.25 708.71  1.23   0.00
## 2+5+7+13+15         8 -346.25 708.72  1.24   0.00
## 1+2+7+13+15         7 -347.28 708.72  1.25   0.00
## 2+4+6+13+15         7 -347.28 708.74  1.26   0.00
## 2+13+14             5 -349.33 708.74  1.27   0.00
## 8+10+12+13          6 -348.31 708.75  1.27   0.00
## 8+12+13             5 -349.34 708.78  1.30   0.00
## 4+12+13+14          6 -348.33 708.79  1.31   0.00
## 2+9+11+13           6 -348.33 708.79  1.32   0.00
## 2+4+7+12+13+15      8 -346.29 708.79  1.32   0.00
## 2+7+10+12+13+15     8 -346.29 708.80  1.32   0.00
## 1+2+13              5 -349.36 708.81  1.33   0.00
## 2+4+6+7+13          7 -347.32 708.81  1.33   0.00
## 4+6+7+13+15         7 -347.32 708.82  1.34   0.00
## 2+9+11+13+15        7 -347.33 708.82  1.34   0.00
## 4+8+10+12+13+15     8 -346.30 708.82  1.35   0.00
## 4+6+13+15           6 -348.35 708.83  1.35   0.00
## 2+6+13              5 -349.37 708.83  1.35   0.00
## 2+4+6+7             6 -348.35 708.83  1.35   0.00
## 4+8+10+12+13        7 -347.33 708.84  1.36   0.00
## 2+6+7+13+15         7 -347.34 708.85  1.37   0.00
## 3+12+13+15          6 -348.36 708.85  1.37   0.00
## 2+7+9+13+15         7 -347.35 708.86  1.38   0.00
## 2+7+13+14+15        7 -347.35 708.87  1.39   0.00
## 2+4+7+10+12+13+15   9 -345.30 708.87  1.40   0.00
## 2+4+6+11+13         7 -347.36 708.88  1.40   0.00
## 4+5+13+15           7 -347.36 708.88  1.40   0.00
## 2+5+13              6 -348.38 708.89  1.41   0.00
## 2+10+11+13          6 -348.38 708.89  1.41   0.00
## 2+4+13+14           6 -348.38 708.89  1.42   0.00
## 2+4+7+10+13+15      8 -346.34 708.90  1.42   0.00
## 2+6+13+15           6 -348.39 708.90  1.42   0.00
## 2+4+13+14+15        7 -347.37 708.90  1.43   0.00
## 4+11+13             5 -349.41 708.91  1.43   0.00
## 4+10+12+13+14       7 -347.37 708.91  1.43   0.00
## 2+4+5+13+15         8 -346.35 708.91  1.43   0.00
## 5+7+12+13+15        8 -346.35 708.92  1.44   0.00
## 2+7+10+13           6 -348.40 708.92  1.44   0.00
## 3+12+13             5 -349.42 708.93  1.45   0.00
## 4+9+15              5 -349.42 708.93  1.45   0.00
## 7                   3 -351.45 708.93  1.45   0.00
## 4+6+7               5 -349.42 708.93  1.45   0.00
## 2+4+10+11+13        7 -347.38 708.93  1.45   0.00
## 4+9+10+12+13+15     8 -346.36 708.93  1.45   0.00
## 2+7+12+13           6 -348.40 708.93  1.45   0.00
## 1+2+13+15           6 -348.40 708.93  1.45   0.00
## 2+5+15              6 -348.41 708.94  1.46   0.00
## 4+12+15             5 -349.42 708.94  1.46   0.00
## 1+12+13+15          6 -348.41 708.94  1.47   0.00
## 1+2+7+13            6 -348.41 708.95  1.47   0.00
## 2+5+11+13+15        8 -346.37 708.95  1.47   0.00
## 7+10+12+13          6 -348.42 708.96  1.48   0.00
## 4+13+14+15          6 -348.42 708.96  1.48   0.00
## 2+8+13              5 -349.44 708.96  1.48   0.00
## 4+5+15              6 -348.42 708.96  1.48   0.00
## 4+6+12+13+15        7 -347.40 708.96  1.48   0.00
## 4+6+12+13           6 -348.42 708.97  1.49   0.00
## 1+4+12+13           6 -348.42 708.97  1.49   0.00
## 12+13+14            5 -349.45 708.98  1.50   0.00
## 4+13+14             5 -349.45 708.98  1.51   0.00
## 1+4+12+13+15        7 -347.41 709.00  1.52   0.00
## 2+4+9+11+13         7 -347.42 709.00  1.52   0.00
## 4+9                 4 -350.47 709.00  1.52   0.00
## 2+3+13              5 -349.46 709.01  1.53   0.00
## 13+14+15            5 -349.46 709.01  1.53   0.00
## 4+6+7+13            6 -348.44 709.01  1.53   0.00
## 2+10+11+13+15       7 -347.42 709.01  1.53   0.00
## 5+12+13             6 -348.45 709.02  1.54   0.00
## 1+12+13             5 -349.47 709.02  1.54   0.00
## 5+7+13+15           7 -347.43 709.02  1.54   0.00
## 11+13               4 -350.48 709.03  1.55   0.00
## 2+9+15              5 -349.47 709.03  1.55   0.00
## 4+11+13+15          6 -348.46 709.04  1.56   0.00
## 4+5+12+13           7 -347.44 709.04  1.56   0.00
## 3+4+12+13           6 -348.46 709.04  1.56   0.00
## 11+13+15            5 -349.48 709.05  1.57   0.00
## 2+13+16             5 -349.48 709.05  1.57   0.00
## 3+4+12+13+15        7 -347.44 709.05  1.57   0.00
## 2+8+13+15           6 -348.46 709.06  1.58   0.00
## 2+4+7+10+13         7 -347.44 709.06  1.58   0.00
## 2+4+10              5 -349.49 709.06  1.58   0.00
## 4+5+7+12+13+15      9 -345.40 709.07  1.59   0.00
## 4+9+10+12+13        7 -347.45 709.07  1.59   0.00
## 1+7+12+13+15        7 -347.45 709.07  1.59   0.00
## 4+8+12+13+15        7 -347.45 709.07  1.59   0.00
## 2+4+9+15            6 -348.47 709.07  1.59   0.00
## 4+12                4 -350.50 709.07  1.59   0.00
## 10+12+13+14+15      7 -347.45 709.08  1.60   0.00
## 2+4+5+15            7 -347.45 709.08  1.60   0.00
## 4+8+12+13           6 -348.48 709.08  1.60   0.00
## 5+15                5 -349.50 709.09  1.61   0.00
## 1+4+10+12+13        7 -347.46 709.09  1.61   0.00
## 1+4+10+12+13+15     8 -346.44 709.09  1.61   0.00
## 2+3+7+13+15         7 -347.46 709.09  1.61   0.00
## 1+2+4+13            6 -348.48 709.10  1.62   0.00
## 9+10+12+13+15       7 -347.47 709.10  1.62   0.00
## 2+3+13+15           6 -348.49 709.11  1.63   0.00
## 2+4+10+15           6 -348.49 709.11  1.63   0.00
## 2+4+9               5 -349.51 709.11  1.64   0.00
## 1+2+4+7+13+15       8 -346.45 709.12  1.64   0.00
## 2+4+9+11+13+15      8 -346.45 709.12  1.64   0.00
## 2+8                 4 -350.53 709.12  1.64   0.00
## 2+6+7+13            6 -348.50 709.13  1.65   0.00
## 4+7+12+13+14+15     8 -346.46 709.13  1.65   0.00
## 2+6+15              5 -349.52 709.13  1.65   0.00
## 13+14               4 -350.54 709.13  1.65   0.00
## 2+8+15              5 -349.52 709.13  1.65   0.00
## 1+4+7+12+13+15      8 -346.46 709.13  1.65   0.00
## 2+4+7+12+13         7 -347.48 709.13  1.66   0.00
## 3+7+12+13+15        7 -347.48 709.14  1.66   0.00
## 7+8+12+13+15        7 -347.49 709.14  1.66   0.00
## 2+4+7+13+14+15      8 -346.46 709.14  1.66   0.00
## 9+15                4 -350.54 709.15  1.67   0.00
## 4+5+10+12+13        8 -346.47 709.15  1.67   0.00
## 2+4+6+11+13+15      8 -346.47 709.15  1.67   0.00
## 8+13                4 -350.55 709.15  1.67   0.00
## 2+4+6+7+11+13+15    9 -345.44 709.15  1.67   0.00
## 2+11+13+14+15       7 -347.49 709.15  1.68   0.00
## 7+9+13+15           6 -348.51 709.16  1.68   0.00
## 4+14+15             5 -349.53 709.16  1.68   0.00
## 2+4+10+11+13+15     8 -346.47 709.16  1.68   0.00
## 2+7+8+13+15         7 -347.50 709.16  1.68   0.00
## 1+2+11+13           6 -348.52 709.17  1.69   0.00
## 2+11+13+14          6 -348.52 709.17  1.69   0.00
## 2+6                 4 -350.56 709.17  1.69   0.00
## 12+13+15+16         6 -348.52 709.17  1.69   0.00
## 7+11+13+15          6 -348.52 709.17  1.70   0.00
## 4+7+11+13+15        7 -347.50 709.18  1.70   0.00
## 2+9                 4 -350.56 709.18  1.70   0.00
## 8+15                4 -350.56 709.18  1.70   0.00
## 2+4+7+10+12+13      8 -346.48 709.18  1.70   0.00
## 5+13                5 -349.54 709.18  1.70   0.00
## 8                   3 -351.57 709.18  1.70   0.00
## 2+4+5+7+13+15       9 -345.46 709.18  1.71   0.00
## 1+7+13+15           6 -348.53 709.19  1.71   0.00
## 2+7+9+13            6 -348.53 709.19  1.71   0.00
## 7+9+12+13+15        7 -347.51 709.19  1.71   0.00
## 5+9+12+13+15        8 -346.49 709.19  1.71   0.00
## 3+13                4 -350.57 709.19  1.71   0.00
## 7+12+13+14+15       7 -347.51 709.20  1.72   0.00
## 2+5+9+13+15         8 -346.49 709.20  1.72   0.00
## 2+4+14+15           6 -348.54 709.20  1.72   0.00
## 2+13+15+16          6 -348.54 709.20  1.72   0.00
## 4+11+12+13+15       7 -347.52 709.20  1.72   0.00
## 4+5+7+13+15         8 -346.49 709.21  1.73   0.00
## 4+6+7+12+13+15      8 -346.50 709.21  1.73   0.00
## 12+13+16            5 -349.56 709.21  1.73   0.00
## 4+10+13             5 -349.56 709.21  1.73   0.00
## 1+2+7+11+13+15      8 -346.50 709.21  1.73   0.00
## 2+7+10+12+13        7 -347.52 709.21  1.73   0.00
## 2+4+6+7+11+13       8 -346.50 709.22  1.74   0.00
## 8+13+15             5 -349.56 709.22  1.74   0.00
## 2+5+12+13+15        8 -346.50 709.22  1.74   0.00
## 2+9+12+13+15        7 -347.53 709.22  1.74   0.00
## 11+12+13+15         6 -348.55 709.23  1.75   0.00
## 2+4+11+13+14        7 -347.53 709.23  1.75   0.00
## 4+11+12+13          6 -348.55 709.23  1.75   0.00
## 2+5+10+12+13+15     9 -345.48 709.23  1.75   0.00
## 2+4+5+13            7 -347.53 709.23  1.76   0.00
## 1+4+13              5 -349.57 709.24  1.76   0.00
## 1+2+4+7+13          7 -347.53 709.24  1.76   0.00
## 2+10+15             5 -349.57 709.24  1.76   0.00
## 4+5+13              6 -348.56 709.24  1.76   0.00
## 1+13                4 -350.59 709.24  1.76   0.00
## 2+6+7+15            6 -348.56 709.25  1.77   0.00
## 2+4+7+9+13+15       8 -346.52 709.25  1.78   0.00
## 4+7+13+14+15        7 -347.54 709.25  1.78   0.00
## 2+6+11+13           6 -348.57 709.26  1.78   0.00
## 4+12+13+16          6 -348.57 709.26  1.78   0.00
## 4+6+10+12+13+15     8 -346.52 709.26  1.78   0.00
## 12+15               4 -350.60 709.27  1.79   0.00
## 4+7+9+12+13+15      8 -346.52 709.27  1.79   0.00
## 3+13+15             5 -349.59 709.27  1.79   0.00
## 4+14                4 -350.60 709.27  1.79   0.00
## 4+7+9+13+15         7 -347.55 709.27  1.79   0.00
## 3+4+10+12+13+15     8 -346.53 709.28  1.80   0.00
## 4+8                 4 -350.61 709.28  1.80   0.00
## 6+13                4 -350.61 709.28  1.80   0.00
## 2+4+6+9+13          7 -347.56 709.28  1.80   0.00
## 7+13+14+15          6 -348.58 709.28  1.80   0.00
## 2+8+11+13           6 -348.58 709.28  1.80   0.00
## 2+7+13+14           6 -348.58 709.28  1.81   0.00
## 4+6+9               5 -349.60 709.29  1.81   0.00
## 2+4+14              5 -349.60 709.29  1.81   0.00
## 4+6+9+13            6 -348.58 709.29  1.81   0.00
## 2+7+13+15+16        7 -347.56 709.29  1.81   0.00
## 4+6+10+12+13        7 -347.56 709.29  1.81   0.00
## 2+14+15             5 -349.60 709.29  1.81   0.00
## 3+4+10+12+13        7 -347.56 709.29  1.81   0.00
## 5+8+10+12+13+15     9 -345.51 709.29  1.81   0.00
## 1+4+7+13+15         7 -347.56 709.29  1.81   0.00
## 2+5+7+11+13+15      9 -345.51 709.29  1.82   0.00
## 6+13+15             5 -349.60 709.30  1.82   0.00
## 4+10+12+13+16       7 -347.56 709.30  1.82   0.00
## 2+7+10+11+13+15     8 -346.54 709.30  1.82   0.00
## 4+6+9+15            6 -348.59 709.30  1.82   0.00
## 4+12+13+15+16       7 -347.57 709.30  1.82   0.00
## 5+9+13+15           7 -347.57 709.31  1.83   0.00
## 6+12+13+15          6 -348.59 709.31  1.83   0.00
## 2+10                4 -350.62 709.31  1.83   0.00
## 3+7+13+15           6 -348.59 709.31  1.83   0.00
## 1+10+12+13+15       7 -347.57 709.31  1.83   0.00
## 2+8+10+12+13+15     8 -346.55 709.31  1.83   0.00
## 6+7+13+15           6 -348.59 709.31  1.83   0.00
## 2+4+11+13+14+15     8 -346.55 709.31  1.83   0.00
## 1+2+4+13+15         7 -347.57 709.32  1.84   0.00
## 4+10+13+15          6 -348.60 709.32  1.84   0.00
## 1+13+15             5 -349.61 709.32  1.84   0.00
## 1+2+4               5 -349.61 709.32  1.84   0.00
## 2+5+11+13           7 -347.58 709.32  1.84   0.00
## 5+8+12+13+15        8 -346.55 709.33  1.85   0.00
## 4+10+12+13+15+16    8 -346.56 709.33  1.85   0.00
## 9                   3 -351.65 709.33  1.85   0.00
## 2+8+10+12+13        7 -347.58 709.33  1.85   0.00
## 2+3+7+13            6 -348.60 709.33  1.85   0.00
## 9+10+12+13          6 -348.61 709.34  1.86   0.00
## 2+4+5+11+13+15      9 -345.53 709.34  1.86   0.00
## 3+4+13              5 -349.63 709.35  1.87   0.00
## 4+8+15              5 -349.63 709.35  1.87   0.00
## 4+7+12+15           6 -348.61 709.36  1.88   0.00
## 3+10+12+13+15       7 -347.59 709.36  1.88   0.00
## 1+2+7+11+13         7 -347.59 709.36  1.88   0.00
## 2+4+5+10+12+13+15  10 -344.51 709.36  1.88   0.00
## 1+4                 4 -350.65 709.36  1.88   0.00
## 2+4+6+9             6 -348.62 709.36  1.88   0.00
## 3+4+7+12+13+15      8 -346.57 709.36  1.88   0.00
## 1+2+11+13+15        7 -347.60 709.36  1.88   0.00
## 4+5+9+12+13+15      9 -345.54 709.36  1.88   0.00
## 11+12+13            5 -349.64 709.36  1.88   0.00
## 10+13+15            5 -349.64 709.36  1.88   0.00
## 10+13               4 -350.65 709.37  1.89   0.00
## 1+2+4+11+13         7 -347.60 709.37  1.89   0.00
## 1+2+7+15            6 -348.62 709.37  1.89   0.00
## 1+2                 4 -350.66 709.38  1.90   0.00
## 2+9+12+13           6 -348.62 709.38  1.90   0.00
## 4+6+9+13+15         7 -347.60 709.38  1.90   0.00
## 10+12+13+14         6 -348.63 709.38  1.90   0.00
## 4+7+11+13           6 -348.63 709.38  1.90   0.00
## 1+2+15              5 -349.65 709.38  1.90   0.00
## 2+6+11+13+15        7 -347.61 709.39  1.91   0.00
## 2+5+7+15            7 -347.61 709.39  1.91   0.00
## 2+4+10+12+13+14+15  9 -345.56 709.39  1.91   0.00
## 13+16               4 -350.67 709.39  1.91   0.00
## 5+10+12+13          7 -347.61 709.40  1.92   0.00
## 2+7+8+13            6 -348.64 709.40  1.93   0.00
## 2+11+13+16          6 -348.64 709.41  1.93   0.00
## 7+8+13+15           6 -348.64 709.41  1.93   0.00
## 2+3+4+13            6 -348.64 709.41  1.93   0.00
## 2+4+6+9+15          7 -347.62 709.41  1.93   0.00
## 2+4+6+9+13+15       8 -346.60 709.41  1.93   0.00
## 2+6+7+11+13+15      8 -346.60 709.41  1.93   0.00
## 1+10+12+13          6 -348.64 709.42  1.94   0.00
## 1+4+13+15           6 -348.65 709.42  1.94   0.00
## 2+4+13+16           6 -348.65 709.42  1.94   0.00
## 1+4+7+12+13         7 -347.63 709.43  1.95   0.00
## 2+4+6+10+13         7 -347.63 709.43  1.95   0.00
## 1+2+4+7+15          7 -347.63 709.43  1.95   0.00
## 2+4+8               5 -349.67 709.43  1.95   0.00
## 2+7+9+11+13+15      8 -346.61 709.44  1.96   0.00
## 2+7+11+13+14+15     8 -346.61 709.44  1.96   0.00
## 6+15                4 -350.69 709.44  1.96   0.00
## 4+5+7+15            7 -347.64 709.44  1.96   0.00
## 2+5+10+13+15        8 -346.61 709.44  1.96   0.00
## 1+2+4+15            6 -348.66 709.44  1.96   0.00
## 4+5                 5 -349.68 709.44  1.96   0.00
## 2+7+8+15            6 -348.66 709.45  1.97   0.00
## 2+11+12+13+15       7 -347.64 709.45  1.97   0.00
## 2+8+11+13+15        7 -347.64 709.45  1.97   0.00
## 4+8+13              5 -349.68 709.45  1.97   0.00
## 6+12+13             5 -349.68 709.45  1.97   0.00
## 1+4+15              5 -349.68 709.45  1.97   0.00
## 1+4+7+13            6 -348.66 709.45  1.97   0.00
## 2+4+7+13+14         7 -347.64 709.45  1.97   0.00
## 1+7+13              5 -349.68 709.46  1.98   0.00
## 2+11+12+13          6 -348.67 709.46  1.98   0.00
## 3+10+12+13          6 -348.67 709.46  1.98   0.00
## 4+10+11+12+13+15    8 -346.63 709.47  1.99   0.00
## 10+12+13+15+16      7 -347.65 709.47  1.99   0.00
## 2+12+13+14+15       7 -347.65 709.47  1.99   0.00
## 1+7+12+13           6 -348.67 709.47  1.99   0.00
## 2+5+7+13            7 -347.65 709.47  1.99   0.00
## 2+7+13+16           6 -348.67 709.48  2.00   0.00
## 2+4+7+10+11+13+15   9 -345.60 709.48  2.00   0.00
## 7+8+10+12+13+15     8 -346.63 709.48  2.00   0.00
## 
## Term codes: 
##        CommEvent_sd   GradSanctions_cat clearboundaries_cat    confmechinter_sd 
##                   1                   2                   3                   4 
##       decispart_cat       distmktreg_sd      marlivprim_cat           mslall_sd 
##                   5                   6                   7                   8 
##           occdiv_sd             popn_sd            rega_cat            regg_cat 
##                   9                  10                  11                  12 
##            regr_cat             sex_cat     trustleaders_sd     yrseducation_sd 
##                  13                  14                  15                  16 
## 
## Model-averaged coefficients:  
## (full average) 
##                       Estimate Std. Error Adjusted SE z value Pr(>|z|)
## (Intercept)          -0.609077   0.762034    0.762916   0.798    0.425
## GradSanctions_cat1    0.495862   0.695063    0.695617   0.713    0.476
## regr_cat1            -1.206107   0.966257    0.967287   1.247    0.212
## trustleaders_sd       0.193580   0.241356    0.241576   0.801    0.423
## marlivprim_cat1      -0.124430   0.252611    0.252816   0.492    0.623
## regg_cat1             0.440505   0.783141    0.783676   0.562    0.574
## confmechinter_sd      0.468704   0.667769    0.668341   0.701    0.483
## popn_sd              -0.193279   0.525255    0.525664   0.368    0.713
## rega_cat1             0.110552   0.378076    0.378386   0.292    0.770
## occdiv_sd             0.038123   0.145818    0.145952   0.261    0.794
## decispart_cat1        0.010173   0.102954    0.103126   0.099    0.921
## decispart_cat2       -0.031014   0.130183    0.130304   0.238    0.812
## distmktreg_sd         0.062067   0.242802    0.243023   0.255    0.798
## sex_catM             -0.040904   0.201324    0.201527   0.203    0.839
## mslall_sd             0.021389   0.116869    0.116988   0.183    0.855
## CommEvent_sd         -0.015971   0.080263    0.080351   0.199    0.842
## clearboundaries_cat1 -0.020767   0.166686    0.166882   0.124    0.901
## yrseducation_sd       0.004102   0.044338    0.044396   0.092    0.926
##  
## (conditional average) 
##                      Estimate Std. Error Adjusted SE z value Pr(>|z|)  
## (Intercept)          -0.60908    0.76203     0.76292   0.798   0.4247  
## GradSanctions_cat1    1.03037    0.67315     0.67434   1.528   0.1265  
## regr_cat1            -1.49661    0.85074     0.85219   1.756   0.0791 .
## trustleaders_sd       0.34314    0.22790     0.22831   1.503   0.1329  
## marlivprim_cat1      -0.40692    0.30616     0.30671   1.327   0.1846  
## regg_cat1             1.26750    0.84647     0.84789   1.495   0.1349  
## confmechinter_sd      0.96573    0.66241     0.66360   1.455   0.1456  
## popn_sd              -0.98337    0.79167     0.79305   1.240   0.2150  
## rega_cat1             0.80130    0.69467     0.69589   1.151   0.2495  
## occdiv_sd             0.31173    0.29761     0.29815   1.046   0.2958  
## decispart_cat1        0.09782    0.30553     0.30609   0.320   0.7493  
## decispart_cat2       -0.29823    0.28857     0.28910   1.032   0.3023  
## distmktreg_sd         0.51475    0.50587     0.50675   1.016   0.3097  
## sex_catM             -0.48302    0.51485     0.51578   0.936   0.3490  
## mslall_sd             0.29411    0.32802     0.32860   0.895   0.3708  
## CommEvent_sd         -0.17739    0.20717     0.20755   0.855   0.3927  
## clearboundaries_cat1 -0.49173    0.65291     0.65410   0.752   0.4522  
## yrseducation_sd       0.13914    0.21883     0.21923   0.635   0.5256  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Relative influence

sw(gains1.average_sd)
##                      regr_cat trustleaders_sd confmechinter_sd
## Sum of weights:      0.81     0.56            0.49            
## N containing models:  308      216             185            
##                      GradSanctions_cat regg_cat marlivprim_cat popn_sd rega_cat
## Sum of weights:      0.48              0.35     0.31           0.20    0.14    
## N containing models:  184               132      118             77      57    
##                      occdiv_sd distmktreg_sd decispart_cat CommEvent_sd sex_cat
## Sum of weights:      0.12      0.12          0.10          0.09         0.08   
## N containing models:   50        50            44            41           36   
##                      mslall_sd clearboundaries_cat yrseducation_sd
## Sum of weights:      0.07      0.04                0.03           
## N containing models:   32        19                  14
confint(gains1.average_sd,full=T,level=0.9) #full model averages
##                              5 %       95 %
## (Intercept)          -1.86362129 0.64546790
## GradSanctions_cat1   -0.64811257 1.63983613
## regr_cat1            -2.79675534 0.38454066
## trustleaders_sd      -0.20369167 0.59085127
## marlivprim_cat1      -0.54019536 0.29133583
## regg_cat1            -0.84831974 1.72933054
## confmechinter_sd     -0.63039849 1.56780672
## popn_sd              -1.05776141 0.67120336
## rega_cat1            -0.51171701 0.73282183
## occdiv_sd            -0.20189431 0.27814099
## decispart_cat1       -0.15938814 0.17973325
## decispart_cat2       -0.24529849 0.18326956
## distmktreg_sd        -0.33758494 0.46171805
## sex_catM             -0.37230823 0.29049966
## mslall_sd            -0.17099337 0.21377203
## CommEvent_sd         -0.14810187 0.11616065
## clearboundaries_cat1 -0.29518786 0.25365352
## yrseducation_sd      -0.06890069 0.07710551
ma.gains1<-summary(gains1.average_sd)#pulling out model averages
df.gains1<-as.data.frame(ma.gains1$coefmat.full)#selecting full model coefficient averages
CI<-as.data.frame(confint(gains1.average_sd,level=0.9,full=T))# to get confident intervals 
#for full model
CI
##                              5 %       95 %
## (Intercept)          -1.86362129 0.64546790
## GradSanctions_cat1   -0.64811257 1.63983613
## regr_cat1            -2.79675534 0.38454066
## trustleaders_sd      -0.20369167 0.59085127
## marlivprim_cat1      -0.54019536 0.29133583
## regg_cat1            -0.84831974 1.72933054
## confmechinter_sd     -0.63039849 1.56780672
## popn_sd              -1.05776141 0.67120336
## rega_cat1            -0.51171701 0.73282183
## occdiv_sd            -0.20189431 0.27814099
## decispart_cat1       -0.15938814 0.17973325
## decispart_cat2       -0.24529849 0.18326956
## distmktreg_sd        -0.33758494 0.46171805
## sex_catM             -0.37230823 0.29049966
## mslall_sd            -0.17099337 0.21377203
## CommEvent_sd         -0.14810187 0.11616065
## clearboundaries_cat1 -0.29518786 0.25365352
## yrseducation_sd      -0.06890069 0.07710551
df.gains1$CI.min <-CI$`5 %` #pulling out CIs and putting into same df as coefficient estimates
df.gains1$CI.max <-CI$`95 %`
df.gains1
##                          Estimate Std. Error Adjusted SE    z value  Pr(>|z|)
## (Intercept)          -0.609076692 0.76203361  0.76291639 0.79835313 0.4246656
## GradSanctions_cat1    0.495861784 0.69506313  0.69561725 0.71283711 0.4759466
## regr_cat1            -1.206107339 0.96625735  0.96728736 1.24689662 0.2124354
## trustleaders_sd       0.193579798 0.24135567  0.24157558 0.80132187 0.4229453
## marlivprim_cat1      -0.124429767 0.25261062  0.25281574 0.49217572 0.6225951
## regg_cat1             0.440505403 0.78314149  0.78367557 0.56210174 0.5740467
## confmechinter_sd      0.468704113 0.66776920  0.66834141 0.70129444 0.4831193
## popn_sd              -0.193279028 0.52525509  0.52566409 0.36768543 0.7131078
## rega_cat1             0.110552413 0.37807641  0.37838561 0.29216865 0.7701577
## occdiv_sd             0.038123340 0.14581791  0.14595185 0.26120492 0.7939345
## decispart_cat1        0.010172554 0.10295418  0.10312593 0.09864205 0.9214225
## decispart_cat2       -0.031014467 0.13018327  0.13030375 0.23801669 0.8118682
## distmktreg_sd         0.062066556 0.24280202  0.24302272 0.25539405 0.7984188
## sex_catM             -0.040904286 0.20132423  0.20152692 0.20297182 0.8391571
## mslall_sd             0.021389330 0.11686905  0.11698843 0.18283287 0.8549292
## CommEvent_sd         -0.015970609 0.08026328  0.08035063 0.19876147 0.8424493
## clearboundaries_cat1 -0.020767169 0.16668642  0.16688185 0.12444235 0.9009650
## yrseducation_sd       0.004102408 0.04433849  0.04439632 0.09240423 0.9263769
##                           CI.min     CI.max
## (Intercept)          -1.86362129 0.64546790
## GradSanctions_cat1   -0.64811257 1.63983613
## regr_cat1            -2.79675534 0.38454066
## trustleaders_sd      -0.20369167 0.59085127
## marlivprim_cat1      -0.54019536 0.29133583
## regg_cat1            -0.84831974 1.72933054
## confmechinter_sd     -0.63039849 1.56780672
## popn_sd              -1.05776141 0.67120336
## rega_cat1            -0.51171701 0.73282183
## occdiv_sd            -0.20189431 0.27814099
## decispart_cat1       -0.15938814 0.17973325
## decispart_cat2       -0.24529849 0.18326956
## distmktreg_sd        -0.33758494 0.46171805
## sex_catM             -0.37230823 0.29049966
## mslall_sd            -0.17099337 0.21377203
## CommEvent_sd         -0.14810187 0.11616065
## clearboundaries_cat1 -0.29518786 0.25365352
## yrseducation_sd      -0.06890069 0.07710551
setDT(df.gains1, keep.rownames='coefficient')#put row names into columns
names(df.gains1) <- gsub(" ",'',names(df.gains1))#remove spaces from column headers

Plot standardized effects

ggplot(data=df.gains1, aes(x=coefficient, y=Estimate))+
  geom_hline(yintercept=0, color='grey', linetype='dashed', lwd=1)+
  geom_errorbar(aes(ymin=Estimate-Std.Error, ymax=Estimate+Std.Error), colour="pink",#SE
         width=.1, lwd=1)+
    coord_flip()+
    geom_point(size=3)+theme_classic(base_size=20)+xlab("")+
  geom_errorbar(aes(ymin=CI.min, ymax=CI.max), colour="blue", # CIs
                width=.2,lwd=1) 

MODEL SUBJECTIVE LOSSES (Disparity 2)

Global model

M.loss2_global.model<-glmer(Ineq2_bin ~ migrant_cat+ sex_cat + yrseducation_sd + 
    trustleaders_sd + decispart_cat + marlivprim_cat + mslall_sd + occdiv_sd + 
     CommEvent_sd+ popn_sd + distmktreg_sd  +  regr_cat + regg_cat + 
      rega_cat + clearboundaries_cat  + confmechinter_sd + GradSanctions_cat +
         (1 | site_cat), data = INEQ_loss2,family=binomial(link = "logit"), 
          na.action='na.fail',control = glmerControl(optimizer="bobyqa",
              optCtrl=list(maxfun=1000000))) 
summary(M.loss2_global.model)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## Ineq2_bin ~ migrant_cat + sex_cat + yrseducation_sd + trustleaders_sd +  
##     decispart_cat + marlivprim_cat + mslall_sd + occdiv_sd +  
##     CommEvent_sd + popn_sd + distmktreg_sd + regr_cat + regg_cat +  
##     rega_cat + clearboundaries_cat + confmechinter_sd + GradSanctions_cat +  
##     (1 | site_cat)
##    Data: INEQ_loss2
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+06))
## 
##      AIC      BIC   logLik deviance df.resid 
##    941.4   1035.8   -450.7    901.4      808 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.9062 -0.5958 -0.4329  0.7153  4.1493 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  site_cat (Intercept) 0.2025   0.45    
## Number of obs: 828, groups:  site_cat, 47
## 
## Fixed effects:
##                      Estimate Std. Error z value Pr(>|z|)    
## (Intercept)          -0.57596    0.57497  -1.002  0.31647    
## migrant_cat1          0.24940    0.33760   0.739  0.46006    
## sex_catM              0.70237    0.43604   1.611  0.10723    
## yrseducation_sd       0.06809    0.18589   0.366  0.71416    
## trustleaders_sd       0.10030    0.17729   0.566  0.57156    
## decispart_cat1       -0.12099    0.26185  -0.462  0.64404    
## decispart_cat2       -0.06065    0.24451  -0.248  0.80410    
## marlivprim_cat1      -0.30567    0.24896  -1.228  0.21952    
## mslall_sd            -0.76851    0.26171  -2.936  0.00332 ** 
## occdiv_sd            -0.12498    0.24213  -0.516  0.60573    
## CommEvent_sd          0.55632    0.19723   2.821  0.00479 ** 
## popn_sd               0.93795    0.30753   3.050  0.00229 ** 
## distmktreg_sd         0.44208    0.21851   2.023  0.04306 *  
## regr_cat1            -0.06598    0.35213  -0.187  0.85137    
## regg_cat1            -1.58473    0.37068  -4.275 1.91e-05 ***
## rega_cat1             0.88934    0.28080   3.167  0.00154 ** 
## clearboundaries_cat1 -0.25887    0.25701  -1.007  0.31382    
## confmechinter_sd     -0.66157    0.24033  -2.753  0.00591 ** 
## GradSanctions_cat1    0.34969    0.28766   1.216  0.22413    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 19 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
check_overdispersion(M.loss2_global.model)
## # Overdispersion test
## 
##        dispersion ratio =   0.979
##   Pearson's Chi-Squared = 790.703
##                 p-value =   0.662
## No overdispersion detected.
check_singularity(M.loss2_global.model)
## [1] FALSE
check_convergence(M.loss2_global.model)
## [1] TRUE
## attr(,"gradient")
## [1] 2.204706e-06
resid.loss1=simulateResiduals(M.loss2_global.model, plot=TRUE)

vif(M.loss2_global.model)
##                         GVIF Df GVIF^(1/(2*Df))
## migrant_cat         1.158187  1        1.076191
## sex_cat             1.045524  1        1.022509
## yrseducation_sd     1.120330  1        1.058457
## trustleaders_sd     1.035175  1        1.017436
## decispart_cat       1.119675  2        1.028663
## marlivprim_cat      1.252481  1        1.119143
## mslall_sd           1.624475  1        1.274549
## occdiv_sd           1.390088  1        1.179020
## CommEvent_sd        1.060731  1        1.029918
## popn_sd             1.717362  1        1.310482
## distmktreg_sd       1.288776  1        1.135243
## regr_cat            1.386187  1        1.177364
## regg_cat            2.247337  1        1.499112
## rega_cat            1.602246  1        1.265799
## clearboundaries_cat 1.339496  1        1.157366
## confmechinter_sd    1.194874  1        1.093103
## GradSanctions_cat   1.609459  1        1.268644
#loss2.dredge<-dredge(global.model = M.loss2_global.model, trace = TRUE)#This line of code was 
#run on the HPC of James Cook University. It will take a long time to run. 
#Consider running this code in your own computer.

Load dredge output

loss2.2_dredge_sd<-readRDS(file="loss2.2.dredge.sd.rds")

average model

loss2.average_sd<-summary(model.avg(loss2.2_dredge_sd, subset = delta <= 2))
loss2.average_sd
## 
## Call:
## model.avg(object = loss2.2_dredge_sd, subset = delta <= 2)
## 
## Component model call: 
## glmer(formula = Ineq2_bin ~ <14 unique rhs>, data = INEQ_loss2, family 
##      = binomial(link = "logit"), control = glmerControl(optimizer = 
##      "bobyqa", optCtrl = list(maxfun = 1e+06)), na.action = na.fail)
## 
## Component models: 
##                        df  logLik   AICc delta weight
## 1+4+5+8+9+10+11+12     10 -453.34 926.95  0.00   0.12
## 1+2+4+5+8+9+10+11+12   11 -452.53 927.38  0.43   0.10
## 1+4+5+8+9+10+11         9 -454.61 927.44  0.49   0.10
## 1+3+4+5+8+9+10+11+12   11 -452.69 927.70  0.75   0.09
## 1+4+5+6+8+9+10+11+12   11 -452.81 927.94  1.00   0.08
## 1+2+4+5+8+9+10+11      10 -453.87 928.01  1.06   0.07
## 1+3+4+5+8+9+10+11      10 -453.93 928.13  1.18   0.07
## 1+2+4+5+6+8+9+10+11+12 12 -451.90 928.17  1.23   0.07
## 1+2+3+4+5+8+9+10+11+12 12 -452.13 928.64  1.69   0.05
## 1+3+4+5+6+8+9+10+11+12 12 -452.13 928.65  1.70   0.05
## 1+4+5+7+8+9+10+11+12   11 -453.21 928.75  1.80   0.05
## 1+4+5+8+9+10+11+12+13  11 -453.21 928.75  1.80   0.05
## 1+4+5+6+8+9+10+11      10 -454.28 928.84  1.89   0.05
## 1+4+5+8+9+10+11+12+14  11 -453.28 928.87  1.93   0.05
## 
## Term codes: 
##        CommEvent_sd   GradSanctions_cat clearboundaries_cat    confmechinter_sd 
##                   1                   2                   3                   4 
##       distmktreg_sd      marlivprim_cat         migrant_cat           mslall_sd 
##                   5                   6                   7                   8 
##             popn_sd            rega_cat            regg_cat             sex_cat 
##                   9                  10                  11                  12 
##     trustleaders_sd     yrseducation_sd 
##                  13                  14 
## 
## Model-averaged coefficients:  
## (full average) 
##                       Estimate Std. Error Adjusted SE z value Pr(>|z|)    
## (Intercept)          -0.661523   0.554615    0.555169   1.192  0.23343    
## CommEvent_sd          0.498788   0.180450    0.180717   2.760  0.00578 ** 
## confmechinter_sd     -0.624992   0.245707    0.246067   2.540  0.01109 *  
## distmktreg_sd         0.509849   0.217568    0.217884   2.340  0.01928 *  
## mslall_sd            -0.619684   0.243684    0.244040   2.539  0.01111 *  
## popn_sd               0.944203   0.293486    0.293914   3.213  0.00132 ** 
## rega_cat1             0.884043   0.270567    0.270943   3.263  0.00110 ** 
## regg_cat1            -1.490620   0.341848    0.342305   4.355 1.33e-05 ***
## sex_catM              0.482962   0.477930    0.478343   1.010  0.31266    
## GradSanctions_cat1    0.104807   0.224971    0.225131   0.466  0.64154    
## clearboundaries_cat1 -0.074946   0.182129    0.182269   0.411  0.68094    
## marlivprim_cat1      -0.059454   0.157540    0.157670   0.377  0.70612    
## migrant_cat1          0.008605   0.083823    0.083923   0.103  0.91833    
## trustleaders_sd       0.004456   0.043940    0.043993   0.101  0.91932    
## yrseducation_sd       0.003100   0.042097    0.042152   0.074  0.94137    
##  
## (conditional average) 
##                      Estimate Std. Error Adjusted SE z value Pr(>|z|)    
## (Intercept)          -0.66152    0.55461     0.55517   1.192  0.23343    
## CommEvent_sd          0.49879    0.18045     0.18072   2.760  0.00578 ** 
## confmechinter_sd     -0.62499    0.24571     0.24607   2.540  0.01109 *  
## distmktreg_sd         0.50985    0.21757     0.21788   2.340  0.01928 *  
## mslall_sd            -0.61968    0.24368     0.24404   2.539  0.01111 *  
## popn_sd               0.94420    0.29349     0.29391   3.213  0.00132 ** 
## rega_cat1             0.88404    0.27057     0.27094   3.263  0.00110 ** 
## regg_cat1            -1.49062    0.34185     0.34231   4.355 1.33e-05 ***
## sex_catM              0.67928    0.43349     0.43413   1.565  0.11765    
## GradSanctions_cat1    0.35470    0.28749     0.28791   1.232  0.21795    
## clearboundaries_cat1 -0.28604    0.25733     0.25771   1.110  0.26703    
## marlivprim_cat1      -0.24230    0.23842     0.23877   1.015  0.31020    
## migrant_cat1          0.16940    0.33329     0.33378   0.508  0.61180    
## trustleaders_sd       0.08793    0.17537     0.17563   0.501  0.61663    
## yrseducation_sd       0.06508    0.18211     0.18238   0.357  0.72122    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Relative influence

sw(loss2.average_sd)
##                      CommEvent_sd confmechinter_sd distmktreg_sd mslall_sd
## Sum of weights:      1.00         1.00             1.00          1.00     
## N containing models:   14           14               14            14     
##                      popn_sd rega_cat regg_cat sex_cat GradSanctions_cat
## Sum of weights:      1.00    1.00     1.00     0.71    0.30             
## N containing models:   14      14       14       10       4             
##                      clearboundaries_cat marlivprim_cat migrant_cat
## Sum of weights:      0.26                0.25           0.05       
## N containing models:    4                   4              1       
##                      trustleaders_sd yrseducation_sd
## Sum of weights:      0.05            0.05           
## N containing models:    1               1
confint(loss2.average_sd,full=T,level=0.9) #full model averages
##                              5 %        95 %
## (Intercept)          -1.57448009  0.25143386
## CommEvent_sd          0.20163765  0.79593818
## confmechinter_sd     -1.02959655 -0.22038706
## distmktreg_sd         0.15158348  0.86811416
## mslall_sd            -1.02095624 -0.21841159
## popn_sd               0.46092312  1.42748302
## rega_cat1             0.43852704  1.32955931
## regg_cat1            -2.05348559 -0.92775496
## sex_catM             -0.30368241  1.26960691
## GradSanctions_cat1   -0.26543758  0.47505257
## clearboundaries_cat1 -0.37469787  0.22480671
## marlivprim_cat1      -0.31874708  0.19983982
## migrant_cat1         -0.12939782  0.14660806
## trustleaders_sd      -0.06788479  0.07679734
## yrseducation_sd      -0.06621240  0.07241312
ma.loss2<-summary(loss2.average_sd)#pulling out model averages
df.loss2<-as.data.frame(ma.loss2$coefmat.full)#selecting full model coefficient averages
CI<-as.data.frame(confint(loss2.average_sd,level=0.9,full=T))# to get confident intervals 
#for full model
CI
##                              5 %        95 %
## (Intercept)          -1.57448009  0.25143386
## CommEvent_sd          0.20163765  0.79593818
## confmechinter_sd     -1.02959655 -0.22038706
## distmktreg_sd         0.15158348  0.86811416
## mslall_sd            -1.02095624 -0.21841159
## popn_sd               0.46092312  1.42748302
## rega_cat1             0.43852704  1.32955931
## regg_cat1            -2.05348559 -0.92775496
## sex_catM             -0.30368241  1.26960691
## GradSanctions_cat1   -0.26543758  0.47505257
## clearboundaries_cat1 -0.37469787  0.22480671
## marlivprim_cat1      -0.31874708  0.19983982
## migrant_cat1         -0.12939782  0.14660806
## trustleaders_sd      -0.06788479  0.07679734
## yrseducation_sd      -0.06621240  0.07241312
df.loss2$CI.min <-CI$`5 %` #pulling out CIs and putting into same df as coefficient estimates
df.loss2$CI.max <-CI$`95 %`
df.loss2
##                          Estimate Std. Error Adjusted SE    z value  Pr(>|z|)
## (Intercept)          -0.661523117 0.55461452  0.55516858 1.19157160 0.2334293
## CommEvent_sd          0.498787918 0.18045003  0.18071731 2.76004507 0.0057793
## confmechinter_sd     -0.624991806 0.24570706  0.24606668 2.53992863 0.0110875
## distmktreg_sd         0.509848822 0.21756796  0.21788412 2.33999997 0.0192837
## mslall_sd            -0.619683913 0.24368421  0.24403976 2.53927441 0.0111083
## popn_sd               0.944203070 0.29348551  0.29391398 3.21251498 0.0013158
## rega_cat1             0.884043177 0.27056707  0.27094283 3.26284024 0.0011030
## regg_cat1            -1.490620274 0.34184831  0.34230511 4.35465392 0.0000133
## sex_catM              0.482962249 0.47792993  0.47834304 1.00965669 0.3126598
## GradSanctions_cat1    0.104807498 0.22497089  0.22513053 0.46554103 0.6415441
## clearboundaries_cat1 -0.074945578 0.18212939  0.18226933 0.41118041 0.6809402
## marlivprim_cat1      -0.059453631 0.15753996  0.15766971 0.37707707 0.7061163
## migrant_cat1          0.008605120 0.08382340  0.08392329 0.10253554 0.9183316
## trustleaders_sd       0.004456277 0.04393997  0.04399261 0.10129603 0.9193155
## yrseducation_sd       0.003100364 0.04209656  0.04215225 0.07355158 0.9413672
##                           CI.min      CI.max
## (Intercept)          -1.57448009  0.25143386
## CommEvent_sd          0.20163765  0.79593818
## confmechinter_sd     -1.02959655 -0.22038706
## distmktreg_sd         0.15158348  0.86811416
## mslall_sd            -1.02095624 -0.21841159
## popn_sd               0.46092312  1.42748302
## rega_cat1             0.43852704  1.32955931
## regg_cat1            -2.05348559 -0.92775496
## sex_catM             -0.30368241  1.26960691
## GradSanctions_cat1   -0.26543758  0.47505257
## clearboundaries_cat1 -0.37469787  0.22480671
## marlivprim_cat1      -0.31874708  0.19983982
## migrant_cat1         -0.12939782  0.14660806
## trustleaders_sd      -0.06788479  0.07679734
## yrseducation_sd      -0.06621240  0.07241312
setDT(df.loss2, keep.rownames='coefficient')#put row names into columns
names(df.loss2) <- gsub(" ",'',names(df.loss2))#remove spaces from column headers

Plot standardized effects

ggplot(data=df.loss2, aes(x=coefficient, y=Estimate))+
  geom_hline(yintercept=0, color='grey', linetype='dashed', lwd=1)+
  geom_errorbar(aes(ymin=Estimate-Std.Error, ymax=Estimate+Std.Error), colour="pink",#SE
         width=.1, lwd=1)+
    coord_flip()+
    geom_point(size=3)+theme_classic(base_size=20)+xlab("")+
  geom_errorbar(aes(ymin=CI.min, ymax=CI.max), colour="blue", # CIs
                width=.2,lwd=1) 

MODEL SUBJECTIVE GAINS (Disparity 2)

Define global model

M.gains2_global.model<-glmer(Ineq2_bin ~ migrant_cat+ sex_cat + yrseducation_sd + 
trustleaders_sd +decispart_cat + marlivprim_cat + mslall_sd + occdiv_sd + CommEvent_sd+
       popn_sd + distmktreg_sd  +  regr_cat + regg_cat + rega_cat + clearboundaries_cat  + 
        confmechinter_sd + GradSanctions_cat +(1 | site_cat), 
          data = INEQ_gain2,family=binomial(link = "logit"), na.action='na.fail',
            control = glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=1000000))) 
summary(M.gains2_global.model)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## Ineq2_bin ~ migrant_cat + sex_cat + yrseducation_sd + trustleaders_sd +  
##     decispart_cat + marlivprim_cat + mslall_sd + occdiv_sd +  
##     CommEvent_sd + popn_sd + distmktreg_sd + regr_cat + regg_cat +  
##     rega_cat + clearboundaries_cat + confmechinter_sd + GradSanctions_cat +  
##     (1 | site_cat)
##    Data: INEQ_gain2
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+06))
## 
##      AIC      BIC   logLik deviance df.resid 
##    532.6    622.5   -246.3    492.6      643 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.1603 -0.4236 -0.3269 -0.2363  5.8552 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  site_cat (Intercept) 0.06087  0.2467  
## Number of obs: 663, groups:  site_cat, 47
## 
## Fixed effects:
##                      Estimate Std. Error z value Pr(>|z|)   
## (Intercept)          -2.18671    0.72052  -3.035  0.00241 **
## migrant_cat1          0.66240    0.47048   1.408  0.15916   
## sex_catM             -0.05559    0.51946  -0.107  0.91477   
## yrseducation_sd      -0.13734    0.25759  -0.533  0.59391   
## trustleaders_sd       0.21903    0.24057   0.910  0.36257   
## decispart_cat1        0.23820    0.34786   0.685  0.49349   
## decispart_cat2       -0.02732    0.32885  -0.083  0.93379   
## marlivprim_cat1      -0.07022    0.33548  -0.209  0.83420   
## mslall_sd            -0.72498    0.34490  -2.102  0.03555 * 
## occdiv_sd            -0.21754    0.29548  -0.736  0.46158   
## CommEvent_sd          0.46586    0.22740   2.049  0.04049 * 
## popn_sd               1.01549    0.36282   2.799  0.00513 **
## distmktreg_sd         0.31157    0.33472   0.931  0.35194   
## regr_cat1             0.60831    0.39372   1.545  0.12234   
## regg_cat1            -0.61179    0.47388  -1.291  0.19670   
## rega_cat1            -0.15926    0.35153  -0.453  0.65051   
## clearboundaries_cat1  0.65958    0.32879   2.006  0.04485 * 
## confmechinter_sd     -0.35536    0.30427  -1.168  0.24284   
## GradSanctions_cat1    0.44555    0.36317   1.227  0.21988   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 19 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
check_overdispersion(M.gains2_global.model)
## # Overdispersion test
## 
##        dispersion ratio =   1.044
##   Pearson's Chi-Squared = 671.279
##                 p-value =   0.213
## No overdispersion detected.
check_singularity(M.gains2_global.model)
## [1] FALSE
check_convergence(M.gains2_global.model)
## [1] TRUE
## attr(,"gradient")
## [1] 1.142812e-06
resid.loss1=simulateResiduals(M.gains2_global.model, plot=TRUE)

vif(M.gains2_global.model)
##                         GVIF Df GVIF^(1/(2*Df))
## migrant_cat         1.267181  1        1.125691
## sex_cat             1.081513  1        1.039958
## yrseducation_sd     1.172083  1        1.082628
## trustleaders_sd     1.083550  1        1.040937
## decispart_cat       1.124764  2        1.029830
## marlivprim_cat      1.223472  1        1.106107
## mslall_sd           1.851116  1        1.360557
## occdiv_sd           1.419366  1        1.191371
## CommEvent_sd        1.065046  1        1.032011
## popn_sd             2.132162  1        1.460192
## distmktreg_sd       1.374562  1        1.172417
## regr_cat            1.402307  1        1.184190
## regg_cat            2.334233  1        1.527820
## rega_cat            1.769661  1        1.330286
## clearboundaries_cat 1.517737  1        1.231965
## confmechinter_sd    1.441461  1        1.200608
## GradSanctions_cat   1.772031  1        1.331177
#gains2.dredge<-dredge(global.model = M.gains2_global.model, trace = TRUE) #This line of 
#code was run on the HPC of James Cook University. It will take a long time to run. 
#Consider running this code in your own computer.

Load dredge output

gains2.2_dredge_sd<-readRDS(file="gains2.2.dredge.sd.rds")

Model average

gains2.average_sd<-summary(model.avg(gains2.2_dredge_sd, subset = delta <= 2))
gains2.average_sd
## 
## Call:
## model.avg(object = gains2.2_dredge_sd, subset = delta <= 2)
## 
## Component model call: 
## glmer(formula = Ineq2_bin ~ <46 unique rhs>, data = INEQ_gain2, family 
##      = binomial(link = "logit"), control = glmerControl(optimizer = 
##      "bobyqa", optCtrl = list(maxfun = 1e+06)), na.action = na.fail)
## 
## Component models: 
##                  df  logLik   AICc delta weight
## 1+3+7+8+10        7 -250.82 515.81  0.00   0.04
## 1+3+8+10          6 -252.00 516.12  0.31   0.04
## 1+7+8+10          6 -252.05 516.23  0.41   0.03
## 1+3+4+8+10        7 -251.04 516.25  0.44   0.03
## 1+4+8+10          6 -252.19 516.51  0.70   0.03
## 1+3+7+8+9+10      8 -250.16 516.54  0.73   0.03
## 1+3+8+9+10        7 -251.19 516.54  0.73   0.03
## 1+3+4+8+9+10      8 -250.20 516.63  0.81   0.03
## 1+8+10            5 -253.34 516.77  0.95   0.03
## 1+3+4+7+8+10      8 -250.29 516.80  0.99   0.03
## 1+4+7+8+10        7 -251.34 516.86  1.04   0.02
## 1+3+8+10+12       7 -251.36 516.90  1.08   0.02
## 1+3+7+8+10+12     8 -250.34 516.90  1.09   0.02
## 1+7+8+10+11       7 -251.44 517.05  1.24   0.02
## 1+3+7+8+10+13     8 -250.43 517.09  1.28   0.02
## 1+7+8+10+12       7 -251.46 517.10  1.29   0.02
## 1+3+7+8+10+14     8 -250.45 517.12  1.30   0.02
## 1+4+8+10+12       7 -251.51 517.18  1.37   0.02
## 1+4+7+8+10+11     8 -250.48 517.19  1.37   0.02
## 1+3+4+8+10+12     8 -250.48 517.19  1.38   0.02
## 1+8+10+12         6 -252.56 517.24  1.43   0.02
## 1+4+8+10+11       7 -251.54 517.25  1.43   0.02
## 1+3+8+10+14       7 -251.56 517.30  1.49   0.02
## 1+4+8+9+10        7 -251.58 517.32  1.51   0.02
## 1+7+8+9+10        7 -251.58 517.34  1.53   0.02
## 1+3+4+8+10+14     8 -250.56 517.34  1.53   0.02
## 1+3+7+8+9+10+13   9 -249.55 517.38  1.57   0.02
## 1+3+5+7+8+10      8 -250.58 517.38  1.57   0.02
## 1+3+8+9+10+13     8 -250.59 517.40  1.58   0.02
## 1+3+8+10+13       7 -251.64 517.46  1.65   0.02
## 1+3+4+7+8+9+10    9 -249.60 517.47  1.66   0.02
## 1+3+7+8+10+11     8 -250.63 517.49  1.68   0.02
## 1+2+3+7+8+10      8 -250.64 517.49  1.68   0.02
## 1+3+8+10+12+13    8 -250.66 517.55  1.73   0.02
## 1+3+7+8+10+12+13  9 -249.64 517.56  1.74   0.02
## 1+7+8+10+14       7 -251.71 517.60  1.79   0.02
## 1+3+6+7+8+10      8 -250.70 517.63  1.82   0.02
## 1+8+9+10          6 -252.75 517.63  1.82   0.02
## 1+4+8+10+14       7 -251.73 517.64  1.83   0.02
## 1+3+4+8+9+10+13   9 -249.71 517.70  1.88   0.02
## 1+3+5+8+10        7 -251.77 517.72  1.90   0.02
## 1+2+3+7+8+10+12   9 -249.73 517.73  1.91   0.02
## 3+4+8+10          6 -252.80 517.73  1.92   0.02
## 1+3+7+8+10+15     8 -250.76 517.73  1.92   0.02
## 1+3+4+8+10+13     8 -250.77 517.76  1.95   0.02
## 1+4+7+8+10+12     8 -250.79 517.80  1.99   0.02
## 
## Term codes: 
##        CommEvent_sd   GradSanctions_cat clearboundaries_cat    confmechinter_sd 
##                   1                   2                   3                   4 
##       distmktreg_sd      marlivprim_cat         migrant_cat           mslall_sd 
##                   5                   6                   7                   8 
##           occdiv_sd             popn_sd            rega_cat            regg_cat 
##                   9                  10                  11                  12 
##            regr_cat     trustleaders_sd     yrseducation_sd 
##                  13                  14                  15 
## 
## Model-averaged coefficients:  
## (full average) 
##                       Estimate Std. Error Adjusted SE z value Pr(>|z|)    
## (Intercept)          -2.158001   0.347845    0.348224   6.197  < 2e-16 ***
## CommEvent_sd          0.424903   0.220172    0.220551   1.927  0.05404 .  
## clearboundaries_cat1  0.338711   0.353359    0.353687   0.958  0.33824    
## migrant_cat1          0.343213   0.468418    0.468810   0.732  0.46411    
## mslall_sd            -0.835173   0.335166    0.335736   2.488  0.01286 *  
## popn_sd               0.957182   0.346518    0.347112   2.758  0.00582 ** 
## confmechinter_sd     -0.147018   0.262647    0.262857   0.559  0.57595    
## occdiv_sd            -0.073726   0.193247    0.193411   0.381  0.70306    
## regg_cat1            -0.090893   0.251860    0.252078   0.361  0.71842    
## rega_cat1            -0.027721   0.133074    0.133194   0.208  0.83513    
## regr_cat1             0.058685   0.206486    0.206682   0.284  0.77646    
## trustleaders_sd       0.020997   0.099801    0.099908   0.210  0.83354    
## distmktreg_sd         0.006730   0.062097    0.062175   0.108  0.91380    
## GradSanctions_cat1    0.010165   0.085995    0.086084   0.118  0.90601    
## marlivprim_cat1       0.002548   0.045138    0.045206   0.056  0.95505    
## yrseducation_sd      -0.001475   0.033889    0.033945   0.043  0.96534    
##  
## (conditional average) 
##                      Estimate Std. Error Adjusted SE z value Pr(>|z|)    
## (Intercept)          -2.15800    0.34785     0.34822   6.197  < 2e-16 ***
## CommEvent_sd          0.43182    0.21512     0.21552   2.004  0.04511 *  
## clearboundaries_cat1  0.51855    0.31290     0.31346   1.654  0.09808 .  
## migrant_cat1          0.68703    0.45056     0.45137   1.522  0.12799    
## mslall_sd            -0.83517    0.33517     0.33574   2.488  0.01286 *  
## popn_sd               0.95718    0.34652     0.34711   2.758  0.00582 ** 
## confmechinter_sd     -0.40586    0.29220     0.29272   1.387  0.16559    
## occdiv_sd            -0.34396    0.28509     0.28561   1.204  0.22847    
## regg_cat1            -0.45547    0.38963     0.39034   1.167  0.24327    
## rega_cat1            -0.33812    0.33323     0.33381   1.013  0.31111    
## regr_cat1             0.40321    0.39247     0.39317   1.026  0.30512    
## trustleaders_sd       0.22105    0.24624     0.24670   0.896  0.37023    
## distmktreg_sd         0.19123    0.27255     0.27305   0.700  0.48372    
## GradSanctions_cat1    0.29819    0.36202     0.36264   0.822  0.41092    
## marlivprim_cat1       0.15118    0.31371     0.31429   0.481  0.63050    
## yrseducation_sd      -0.09208    0.25171     0.25217   0.365  0.71499    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Relative influence

sw(gains2.average_sd)
##                      mslall_sd popn_sd CommEvent_sd clearboundaries_cat
## Sum of weights:      1.00      1.00    0.98         0.65               
## N containing models:   46        46      45           30               
##                      migrant_cat confmechinter_sd occdiv_sd regg_cat regr_cat
## Sum of weights:      0.50        0.36             0.21      0.20     0.15    
## N containing models:   23          17               10        10        8    
##                      trustleaders_sd rega_cat distmktreg_sd GradSanctions_cat
## Sum of weights:      0.09            0.08     0.04          0.03             
## N containing models:    5               4        2             2             
##                      marlivprim_cat yrseducation_sd
## Sum of weights:      0.02           0.02           
## N containing models:    1              1
confint(gains2.average_sd,full=T,level=0.9) #full model averages
##                              5 %        95 %
## (Intercept)          -2.73063233 -1.58536934
## CommEvent_sd          0.06227531  0.78752979
## clearboundaries_cat1 -0.24292573  0.92034783
## migrant_cat1         -0.42775981  1.11418513
## mslall_sd            -1.38718952 -0.28315668
## popn_sd               0.38646257  1.52790150
## confmechinter_sd     -0.57929826  0.28526260
## occdiv_sd            -0.39179523  0.24434365
## regg_cat1            -0.50544067  0.32365418
## rega_cat1            -0.24675896  0.19131638
## regr_cat1            -0.28120104  0.39857035
## trustleaders_sd      -0.14329495  0.18528942
## distmktreg_sd        -0.09550829  0.10896879
## GradSanctions_cat1   -0.13139609  0.15172540
## marlivprim_cat1      -0.07178261  0.07687870
## yrseducation_sd      -0.05728759  0.05433759
ma.gains2<-summary(gains2.average_sd)#pulling out model averages
df.gains2<-as.data.frame(ma.gains2$coefmat.full)#selecting full model coefficient averages
CI<-as.data.frame(confint(gains2.average_sd,level=0.9,full=T))# to get confident intervals 
#for full model
CI
##                              5 %        95 %
## (Intercept)          -2.73063233 -1.58536934
## CommEvent_sd          0.06227531  0.78752979
## clearboundaries_cat1 -0.24292573  0.92034783
## migrant_cat1         -0.42775981  1.11418513
## mslall_sd            -1.38718952 -0.28315668
## popn_sd               0.38646257  1.52790150
## confmechinter_sd     -0.57929826  0.28526260
## occdiv_sd            -0.39179523  0.24434365
## regg_cat1            -0.50544067  0.32365418
## rega_cat1            -0.24675896  0.19131638
## regr_cat1            -0.28120104  0.39857035
## trustleaders_sd      -0.14329495  0.18528942
## distmktreg_sd        -0.09550829  0.10896879
## GradSanctions_cat1   -0.13139609  0.15172540
## marlivprim_cat1      -0.07178261  0.07687870
## yrseducation_sd      -0.05728759  0.05433759
df.gains2$CI.min <-CI$`5 %` #pulling out CIs and putting into same df as coefficient estimates
df.gains2$CI.max <-CI$`95 %`
df.gains2
##                          Estimate Std. Error Adjusted SE    z value  Pr(>|z|)
## (Intercept)          -2.158000837 0.34784541  0.34822425 6.19715834 0.0000000
## CommEvent_sd          0.424902553 0.22017197  0.22055068 1.92655291 0.0540354
## clearboundaries_cat1  0.338711049 0.35335927  0.35368709 0.95765737 0.3382356
## migrant_cat1          0.343212662 0.46841819  0.46881008 0.73209318 0.4641117
## mslall_sd            -0.835173101 0.33516626  0.33573601 2.48758868 0.0128612
## popn_sd               0.957182036 0.34651799  0.34711246 2.75755598 0.0058235
## confmechinter_sd     -0.147017830 0.26264675  0.26285733 0.55930657 0.5759525
## occdiv_sd            -0.073725790 0.19324724  0.19341097 0.38118722 0.7030643
## regg_cat1            -0.090893245 0.25186036  0.25207811 0.36057572 0.7184166
## rega_cat1            -0.027721290 0.13307363  0.13319365 0.20812771 0.8351292
## regr_cat1             0.058684654 0.20648642  0.20668173 0.28393731 0.7764584
## trustleaders_sd       0.020997239 0.09980107  0.09990759 0.21016661 0.8335376
## distmktreg_sd         0.006730251 0.06209703  0.06217492 0.10824704 0.9137997
## GradSanctions_cat1    0.010164659 0.08599478  0.08608372 0.11807875 0.9060053
## marlivprim_cat1       0.002548045 0.04513777  0.04520581 0.05636543 0.9550507
## yrseducation_sd      -0.001474999 0.03388923  0.03394467 0.04345303 0.9653404
##                           CI.min      CI.max
## (Intercept)          -2.73063233 -1.58536934
## CommEvent_sd          0.06227531  0.78752979
## clearboundaries_cat1 -0.24292573  0.92034783
## migrant_cat1         -0.42775981  1.11418513
## mslall_sd            -1.38718952 -0.28315668
## popn_sd               0.38646257  1.52790150
## confmechinter_sd     -0.57929826  0.28526260
## occdiv_sd            -0.39179523  0.24434365
## regg_cat1            -0.50544067  0.32365418
## rega_cat1            -0.24675896  0.19131638
## regr_cat1            -0.28120104  0.39857035
## trustleaders_sd      -0.14329495  0.18528942
## distmktreg_sd        -0.09550829  0.10896879
## GradSanctions_cat1   -0.13139609  0.15172540
## marlivprim_cat1      -0.07178261  0.07687870
## yrseducation_sd      -0.05728759  0.05433759
setDT(df.gains2, keep.rownames='coefficient')#put row names into columns
names(df.gains2) <- gsub(" ",'',names(df.gains2))#remove spaces from column headers

Plot standardized effects

ggplot(data=df.gains2, aes(x=coefficient, y=Estimate))+
  geom_hline(yintercept=0, color='grey', linetype='dashed', lwd=1)+
  geom_errorbar(aes(ymin=Estimate-Std.Error, ymax=Estimate+Std.Error), colour="pink",#SE
         width=.1, lwd=1)+
    coord_flip()+
    geom_point(size=3)+theme_classic(base_size=20)+xlab("")+
  geom_errorbar(aes(ymin=CI.min, ymax=CI.max), colour="blue", # CIs
                width=.2,lwd=1)