finalAnalyses

Set-Up

library(groundhog)
pkgs <-  c("lmerTest", "ggeffects","r2glmm", "tidyverse","here", "sjPlot", "ggpubr", "wesanderson", "effectsize")
groundhog.day <- '2022-07-25'
groundhog.library(pkgs, groundhog.day)
here::i_am("analysis/finalStableAnalyses.qmd")
plotDir <- "/Volumes/Research Project/Trait_TestRetest/WeekTRT/plots/"

Import Data

fullLong <- as.data.frame( arrow::read_parquet(here("data/longChangeInds.parquet")) ) 
ICCmat <- as.data.frame( arrow::read_parquet(here("data/ICCdf.parquet")) )

Scaling

fullLong$absDS.Z <- scale(fullLong$absDS)
fullLong$selfRespT1.Z <- scale(fullLong$selfRespT1)
fullLong$selfRespT2.Z <- scale(fullLong$selfRespT2)
fullLong$outDegree.Z <- scale(fullLong$outDegree)
fullLong$inDegree.Z <- scale(fullLong$inDegree)
fullLong$SE_C.Z <- scale(fullLong$SE_C)
fullLong$SCC_C.Z <- scale(fullLong$SCC_C)
fullLong$DS_C.Z <- scale(fullLong$DS_C)
fullLong$CESD_C.Z <- scale(fullLong$CESD_C)
fullLong$SWLS_C.Z <- scale(fullLong$SWLS_C)
fullLong$MAIA.All_C.Z <- scale(fullLong$MAIA.All_C)

More Dependent Self-Beliefs Are Less Reliable

m <- lm(scale(ICC) ~ scale(outDegree) + scale(inDegree) + valence, data = ICCmat)
eta_squared(m)

# Effect Size for ANOVA (Type I)

Parameter        | Eta2 (partial) |       95% CI
------------------------------------------------
scale(outDegree) |       2.73e-03 | [0.00, 1.00]
scale(inDegree)  |           0.02 | [0.00, 1.00]
valence          |       9.02e-04 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "Coef.", string.se = "SE", string.stat = "t", digits = 3, emph.p = F, dv.labels = "Reliability", show.df=T)

	Reliability
Fixed Effects	Coef.	SE	t	p	df
(Intercept)	0.030 (-0.131 – 0.190)	0.082	0.363	0.717	292.000
outDegree	-0.027 (-0.142 – 0.089)	0.059	-0.458	0.647	292.000
inDegree	-0.154 (-0.270 – -0.039)	0.059	-2.630	0.009	292.000
valence [positive]	-0.059 (-0.287 – 0.168)	0.115	-0.513	0.608	292.000
Observations	296
R² / R² adjusted	0.026 / 0.016

Self-Beliefs with More Implications Change Less

m<-lmer(absDS.Z ~ outDegree.Z + valence + inDegree.Z +  ( valence + outDegree.Z + inDegree.Z |subID) + (1 | traits), data = fullLong, control=lmerControl(optimizer="bobyqa",
                                 optCtrl=list(maxfun=2e5)))

boundary (singular) fit: see help('isSingular')

r2beta(m)

           Effect   Rsq upper.CL lower.CL
1           Model 0.001    0.002    0.001
2     outDegree.Z 0.001    0.002    0.000
4      inDegree.Z 0.000    0.000    0.000
3 valencepositive 0.000    0.000    0.000

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "Coef.", string.se = "SE", string.stat = "t", digits = 3, emph.p = F, dv.labels = "Confidence", df.method = "satterthwaite",show.df=T)

	Confidence
Fixed Effects	Coef.	SE	t	p	df
(Intercept)	0.006 (-0.045 – 0.058)	0.026	0.247	0.805	141.164
outDegree Z	-0.033 (-0.050 – -0.016)	0.009	-3.787	<0.001	252.429
valence [positive]	-0.013 (-0.059 – 0.033)	0.024	-0.552	0.581	185.666
inDegree Z	-0.010 (-0.027 – 0.007)	0.009	-1.144	0.254	235.067
Random Effects
σ²	0.93
τ₀₀ _traits	0.01
τ₀₀ _subID	0.06
τ₁₁ _{subID.valencepositive}	0.03
τ₁₁ _{subID.outDegree.Z}	0.00
τ₁₁ _{subID.inDegree.Z}	0.00
ρ₀₁ _{subID.valencepositive}	-0.42
ρ₀₁ _{subID.outDegree.Z}	-0.14
ρ₀₁ _{subID.inDegree.Z}	0.23
N _subID	114
N _traits	296
Observations	33731
Marginal R² / Conditional R²	0.001 / NA

Individual Differences in Self-Concept Stability as a Function of Outdegree

Self-Concept Clarity

m<-lmer( absDS.Z ~ SCC_C.Z*outDegree.Z + ( outDegree.Z | subID ) + ( SCC_C.Z | traits), data=fullLong, control=lmerControl(optimizer="bobyqa",
                                 optCtrl=list(maxfun=2e5)))
rb <- r2beta(m)
data.frame(rb$Effect,rb$Rsq)

            rb.Effect       rb.Rsq
1               Model 1.445609e-03
2         outDegree.Z 1.205068e-03
3 SCC_C.Z:outDegree.Z 2.106537e-04
4             SCC_C.Z 3.027464e-05

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "Coef.", string.se = "SE", string.stat = "t", digits = 3, emph.p = F, dv.labels = "Evaluation", df.method = "satterthwaite",show.df=T)

	Evaluation
Fixed Effects	Coef.	SE	t	p	df
(Intercept)	-0.000 (-0.045 – 0.045)	0.023	-0.003	0.998	129.487
SCC C Z	0.005 (-0.039 – 0.049)	0.022	0.242	0.809	116.567
outDegree Z	-0.034 (-0.051 – -0.017)	0.008	-4.032	<0.001	206.837
SCC C Z * outDegree Z	-0.014 (-0.027 – -0.001)	0.007	-2.162	0.032	128.053
Random Effects
σ²	0.93
τ₀₀ _traits	0.01
τ₀₀ _subID	0.05
τ₁₁ _{traits.SCC_C.Z}	0.00
τ₁₁ _{subID.outDegree.Z}	0.00
ρ₀₁ _traits	0.46
ρ₀₁ _subID	-0.24
ICC	0.07
N _subID	114
N _traits	296
Observations	33731
Marginal R² / Conditional R²	0.001 / 0.068

Plot

m<-lmer( absDS ~ SCC_C*outDegree + ( outDegree | subID ) + ( SCC_C | traits), data= fullLong, control=lmerControl(optimizer="bobyqa",
                                 optCtrl=list(maxfun=2e5)))

Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.0109902 (tol = 0.002, component 1)

Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
 - Rescale variables?

p <- ggpredict(m, c("SCC_C","outDegree"))
SCC.outdeg.Stable.Plot <-ggplot(p, aes(x, predicted)) +  geom_line(aes(linetype=group, color=group)) + geom_ribbon(aes(ymin=conf.low, ymax=conf.high, fill=group), alpha=0.15) + scale_linetype_discrete(labels = c("Low Outdegree","Medium Outdegree","High Outdegree")) + scale_color_manual(labels = c("Low Outdegree","Medium Outdegree","High Outdegree"), values = wes_palette("Darjeeling1")) + scale_fill_manual( 
                      labels=c("Low Outdegree","Medium Outdegree","High Outdegree"), values = wes_palette("Darjeeling1")) + theme(
    legend.position = c(.2, .7),
    legend.justification = c("left", "bottom"),
    legend.box.just = "left",
    legend.margin = margin(6, 6, 6, 6)
    ) + theme(axis.text=element_text(size=12),
        axis.title=element_text(size=12,face="bold")) + theme(legend.text = element_text(size=12)) + theme(panel.border = element_rect(colour = "black", fill = NA, size =1)) + theme(legend.title = element_blank()) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black")) +
  xlab("Self-Concept Clarity") + ylab("Absolute Change")
SCC.outdeg.Stable.Plot

Self-Esteem

m<-lmer( absDS.Z ~ SE_C.Z*outDegree.Z + ( outDegree.Z | subID ) + ( SE_C.Z | traits), data=fullLong, control=lmerControl(optimizer="bobyqa",
                                 optCtrl=list(maxfun=2e5)))
rb <- r2beta(m)
data.frame(rb$Effect,rb$Rsq)

           rb.Effect       rb.Rsq
1              Model 0.0020813527
2        outDegree.Z 0.0012049747
3 SE_C.Z:outDegree.Z 0.0004555649
4             SE_C.Z 0.0004226945

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "Coef.", string.se = "SE", string.stat = "t", digits = 3, emph.p = F, dv.labels = "Evaluation", df.method = "satterthwaite",show.df=T)

	Evaluation
Fixed Effects	Coef.	SE	t	p	df
(Intercept)	-0.000 (-0.045 – 0.045)	0.023	-0.003	0.998	129.596
SE C Z	-0.020 (-0.064 – 0.024)	0.022	-0.910	0.365	115.708
outDegree Z	-0.034 (-0.050 – -0.018)	0.008	-4.093	<0.001	204.678
SE C Z * outDegree Z	-0.021 (-0.033 – -0.009)	0.006	-3.340	0.001	118.926
Random Effects
σ²	0.93
τ₀₀ _traits	0.01
τ₀₀ _subID	0.05
τ₁₁ _{traits.SE_C.Z}	0.00
τ₁₁ _{subID.outDegree.Z}	0.00
ρ₀₁ _traits	0.61
ρ₀₁ _subID	-0.44
ICC	0.07
N _subID	114
N _traits	296
Observations	33731
Marginal R² / Conditional R²	0.002 / 0.068

Plot

m<-lmer( absDS ~ SE_C*outDegree + ( outDegree | subID ) + ( SE_C | traits), data= fullLong, control=lmerControl(optimizer="bobyqa",
                                 optCtrl=list(maxfun=2e5)))

Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.0748283 (tol = 0.002, component 1)

Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
 - Rescale variables?

p <- ggpredict(m, c("SE_C","outDegree"))
SE.outdeg.Stable.Plot <-ggplot(p, aes(x, predicted)) +  geom_line(aes(linetype=group, color=group)) + geom_ribbon(aes(ymin=conf.low, ymax=conf.high, fill=group), alpha=0.15) + scale_linetype_discrete(labels = c("Low Outdegree","Medium Outdegree","High Outdegree")) + scale_color_manual(labels = c("Low Outdegree","Medium Outdegree","High Outdegree"), values = wes_palette("Darjeeling1")) + scale_fill_manual( 
                      labels=c("Low Outdegree","Medium Outdegree","High Outdegree"), values = wes_palette("Darjeeling1")) + theme(
    legend.position = c(.2, .10),
    legend.justification = c("left", "bottom"),
    legend.box.just = "left",
    legend.margin = margin(6, 6, 6, 6)
    ) + theme(axis.text=element_text(size=12),
        axis.title=element_text(size=12,face="bold")) + theme(legend.text = element_text(size=12)) + theme(panel.border = element_rect(colour = "black", fill = NA, size =1)) + theme(legend.title = element_blank()) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black")) +
  xlab("Self-Esteem") + ylab("Absolute Change")
SE.outdeg.Stable.Plot

Dialectical Self-Views

m<-lmer( absDS.Z ~ DS_C.Z*outDegree.Z + ( outDegree.Z | subID ) + ( DS_C.Z | traits), data=fullLong, control=lmerControl(optimizer="bobyqa",
                                 optCtrl=list(maxfun=2e5)))
rb <- r2beta(m)
data.frame(rb$Effect,rb$Rsq)

           rb.Effect       rb.Rsq
1              Model 0.0018703310
2        outDegree.Z 0.0012053611
3             DS_C.Z 0.0004275102
4 DS_C.Z:outDegree.Z 0.0002391522

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "Coef.", string.se = "SE", string.stat = "t", digits = 3, emph.p = F, dv.labels = "Evaluation", df.method = "satterthwaite",show.df=T)

	Evaluation
Fixed Effects	Coef.	SE	t	p	df
(Intercept)	-0.000 (-0.045 – 0.045)	0.023	-0.003	0.998	129.586
DS C Z	-0.020 (-0.064 – 0.023)	0.022	-0.918	0.361	114.256
outDegree Z	-0.034 (-0.051 – -0.017)	0.008	-4.041	<0.001	206.354
DS C Z * outDegree Z	0.015 (0.003 – 0.027)	0.006	2.462	0.015	108.447
Random Effects
σ²	0.93
τ₀₀ _traits	0.01
τ₀₀ _subID	0.05
τ₁₁ _{traits.DS_C.Z}	0.00
τ₁₁ _{subID.outDegree.Z}	0.00
ρ₀₁ _traits	-0.95
ρ₀₁ _subID	-0.21
ICC	0.07
N _subID	114
N _traits	296
Observations	33731
Marginal R² / Conditional R²	0.002 / 0.067

Plot

m<-lmer( absDS ~ DS_C*outDegree + ( outDegree | subID ) + ( DS_C | traits), data= fullLong, control=lmerControl(optimizer="bobyqa",
                                 optCtrl=list(maxfun=2e5)))

Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.00340668 (tol = 0.002, component 1)

Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
 - Rescale variables?

p <- ggpredict(m, c("DS_C","outDegree"))
DS.outdeg.Stable.Plot <-ggplot(p, aes(x, predicted)) +  geom_line(aes(linetype=group, color=group)) + geom_ribbon(aes(ymin=conf.low, ymax=conf.high, fill=group), alpha=0.15) + scale_linetype_discrete(labels = c("Low Outdegree","Medium Outdegree","High Outdegree")) + scale_color_manual(labels = c("Low Outdegree","Medium Outdegree","High Outdegree"), values = wes_palette("Darjeeling1")) + scale_fill_manual( 
                      labels=c("Low Outdegree","Medium Outdegree","High Outdegree"), values = wes_palette("Darjeeling1")) + theme(
    legend.position = c(.5, .70),
    legend.justification = c("left", "bottom"),
    legend.box.just = "left",
    legend.margin = margin(6, 6, 6, 6)
    ) + theme(axis.text=element_text(size=12),
        axis.title=element_text(size=12,face="bold")) + theme(legend.text = element_text(size=12)) + theme(panel.border = element_rect(colour = "black", fill = NA, size =1)) + theme(legend.title = element_blank()) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black")) +
  xlab("Dialectical Self-Views") + ylab("Absolute Change")
DS.outdeg.Stable.Plot

Depressive Symptoms

m<-lmer( absDS.Z ~ CESD_C.Z*outDegree.Z + ( outDegree.Z | subID ) + ( SWLS_C.Z | traits), data=fullLong, control=lmerControl(optimizer="bobyqa",
                                 optCtrl=list(maxfun=2e5)))
rb <- r2beta(m)
data.frame(rb$Effect,rb$Rsq)

             rb.Effect       rb.Rsq
1                Model 0.0027066943
2          outDegree.Z 0.0012364280
3             CESD_C.Z 0.0011764386
4 CESD_C.Z:outDegree.Z 0.0002980858

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "Coef.", string.se = "SE", string.stat = "t", digits = 3, emph.p = F, dv.labels = "Evaluation", df.method = "satterthwaite",show.df=T)

	Evaluation
Fixed Effects	Coef.	SE	t	p	df
(Intercept)	-0.000 (-0.045 – 0.045)	0.023	-0.004	0.996	129.743
CESD C Z	0.034 (-0.010 – 0.077)	0.022	1.539	0.127	112.733
outDegree Z	-0.034 (-0.051 – -0.018)	0.008	-4.134	<0.001	201.120
CESD C Z * outDegree Z	0.017 (0.005 – 0.028)	0.006	2.915	0.004	117.082
Random Effects
σ²	0.93
τ₀₀ _traits	0.01
τ₀₀ _subID	0.05
τ₁₁ _{traits.SWLS_C.Z}	0.00
τ₁₁ _{subID.outDegree.Z}	0.00
ρ₀₁ _traits	0.58
ρ₀₁ _subID	-0.40
ICC	0.06
N _subID	114
N _traits	296
Observations	33731
Marginal R² / Conditional R²	0.003 / 0.065

Plot

m<-lmer( absDS ~ CESD_C*outDegree + ( outDegree | subID ) + ( CESD_C | traits), data= fullLong, control=lmerControl(optimizer="bobyqa",
                                 optCtrl=list(maxfun=2e5)))

Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.0367839 (tol = 0.002, component 1)

Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
 - Rescale variables?

p <- ggpredict(m, c("CESD_C","outDegree"))
CESD.outdeg.Stable.Plot <-ggplot(p, aes(x, predicted)) +  geom_line(aes(linetype=group, color=group)) + geom_ribbon(aes(ymin=conf.low, ymax=conf.high, fill=group), alpha=0.15) + scale_linetype_discrete(labels = c("Low Outdegree","Medium Outdegree","High Outdegree")) + scale_color_manual(labels = c("Low Outdegree","Medium Outdegree","High Outdegree"), values = wes_palette("Darjeeling1")) + scale_fill_manual( 
                      labels=c("Low Outdegree","Medium Outdegree","High Outdegree"), values = wes_palette("Darjeeling1")) + theme(
    legend.position = c(.55, .10),
    legend.justification = c("left", "bottom"),
    legend.box.just = "left",
    legend.margin = margin(6, 6, 6, 6)
    ) + theme(axis.text=element_text(size=12),
        axis.title=element_text(size=12,face="bold")) + theme(legend.text = element_text(size=12)) + theme(panel.border = element_rect(colour = "black", fill = NA, size =1)) + theme(legend.title = element_blank()) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black")) +
  xlab("Depressive Symptoms") + ylab("Absolute Change")
CESD.outdeg.Stable.Plot

Satisfaction with Life

m<-lmer( absDS.Z ~ SWLS_C.Z*outDegree.Z + ( outDegree.Z | subID ) + ( SWLS_C.Z | traits), data=fullLong, control=lmerControl(optimizer="bobyqa",
                                 optCtrl=list(maxfun=2e5)))
rb <- r2beta(m)
data.frame(rb$Effect,rb$Rsq)

             rb.Effect       rb.Rsq
1                Model 1.695171e-03
2          outDegree.Z 1.205137e-03
3             SWLS_C.Z 4.180024e-04
4 SWLS_C.Z:outDegree.Z 7.335063e-05

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "Coef.", string.se = "SE", string.stat = "t", digits = 3, emph.p = F, dv.labels = "Evaluation", df.method = "satterthwaite",show.df=T)

	Evaluation
Fixed Effects	Coef.	SE	t	p	df
(Intercept)	-0.000 (-0.045 – 0.045)	0.023	-0.003	0.998	129.578
SWLS C Z	-0.020 (-0.064 – 0.024)	0.022	-0.909	0.365	113.812
outDegree Z	-0.034 (-0.051 – -0.017)	0.008	-4.001	<0.001	207.473
SWLS C Z * outDegree Z	-0.008 (-0.021 – 0.004)	0.006	-1.356	0.178	105.970
Random Effects
σ²	0.93
τ₀₀ _traits	0.01
τ₀₀ _subID	0.05
τ₁₁ _{traits.SWLS_C.Z}	0.00
τ₁₁ _{subID.outDegree.Z}	0.00
ρ₀₁ _traits	0.57
ρ₀₁ _subID	-0.26
ICC	0.06
N _subID	114
N _traits	296
Observations	33731
Marginal R² / Conditional R²	0.002 / 0.066

Plot

m<-lmer( absDS ~ SWLS_C*outDegree + ( outDegree | subID ) + ( SWLS_C | traits), data= fullLong, control=lmerControl(optimizer="bobyqa",
                                 optCtrl=list(maxfun=2e5)))

Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.0293289 (tol = 0.002, component 1)

Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
 - Rescale variables?

p <- ggpredict(m, c("SWLS_C","outDegree"))
SWLS.outdeg.Stable.Plot <-ggplot(p, aes(x, predicted)) +  geom_line(aes(linetype=group, color=group)) + geom_ribbon(aes(ymin=conf.low, ymax=conf.high, fill=group), alpha=0.15) + scale_linetype_discrete(labels = c("Low Outdegree","Medium Outdegree","High Outdegree")) + scale_color_manual(labels = c("Low Outdegree","Medium Outdegree","High Outdegree"), values = wes_palette("Darjeeling1")) + scale_fill_manual( 
                      labels=c("Low Outdegree","Medium Outdegree","High Outdegree"), values = wes_palette("Darjeeling1")) + theme(
    legend.position = c(.2, .10),
    legend.justification = c("left", "bottom"),
    legend.box.just = "left",
    legend.margin = margin(6, 6, 6, 6)
    ) + theme(axis.text=element_text(size=12),
        axis.title=element_text(size=12,face="bold")) + theme(legend.text = element_text(size=12)) + theme(panel.border = element_rect(colour = "black", fill = NA, size =1)) + theme(legend.title = element_blank()) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black")) +
  xlab("Satisfaction with Life") + ylab("Absolute Change")
SWLS.outdeg.Stable.Plot

Interoceptive Awareness

m<-lmer( absDS.Z ~ MAIA.All_C.Z*outDegree.Z + ( outDegree.Z | subID ) + ( SWLS_C.Z | traits), data=fullLong, control=lmerControl(optimizer="bobyqa",
                                 optCtrl=list(maxfun=2e5)))
rb <- r2beta(m)
data.frame(rb$Effect,rb$Rsq)

                 rb.Effect       rb.Rsq
1                    Model 1.968630e-03
2              outDegree.Z 1.099474e-03
3             MAIA.All_C.Z 8.677533e-04
4 MAIA.All_C.Z:outDegree.Z 3.356956e-06

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "Coef.", string.se = "SE", string.stat = "t", digits = 3, emph.p = F, dv.labels = "Evaluation", df.method = "satterthwaite",show.df=T)

	Evaluation
Fixed Effects	Coef.	SE	t	p	df
(Intercept)	0.000 (-0.045 – 0.045)	0.023	0.005	0.996	129.619
MAIA All C Z	-0.029 (-0.072 – 0.014)	0.022	-1.319	0.190	112.254
outDegree Z	-0.032 (-0.049 – -0.016)	0.008	-3.841	<0.001	200.613
MAIA All C Z * outDegree Z	-0.002 (-0.013 – 0.010)	0.006	-0.303	0.762	114.406
Random Effects
σ²	0.93
τ₀₀ _traits	0.01
τ₀₀ _subID	0.05
τ₁₁ _{traits.SWLS_C.Z}	0.00
τ₁₁ _{subID.outDegree.Z}	0.00
ρ₀₁ _traits	0.57
ρ₀₁ _subID	-0.24
ICC	0.06
N _subID	114
N _traits	296
Observations	33731
Marginal R² / Conditional R²	0.002 / 0.065

Plot

m<-lmer( absDS ~ MAIA.All_C*outDegree + ( outDegree | subID ) + ( MAIA.All_C | traits), data= fullLong, control=lmerControl(optimizer="bobyqa",
                                 optCtrl=list(maxfun=2e5)))

boundary (singular) fit: see help('isSingular')

Warning: Model failed to converge with 1 negative eigenvalue: -1.6e+03

p <- ggpredict(m, c("MAIA.All_C","outDegree"))
MAIA.All.outdeg.Stable.Plot <-ggplot(p, aes(x, predicted)) +  geom_line(aes(linetype=group, color=group)) + geom_ribbon(aes(ymin=conf.low, ymax=conf.high, fill=group), alpha=0.15) + scale_linetype_discrete(labels = c("Low Outdegree","Medium Outdegree","High Outdegree")) + scale_color_manual(labels = c("Low Outdegree","Medium Outdegree","High Outdegree"), values = wes_palette("Darjeeling1")) + scale_fill_manual( 
                      labels=c("Low Outdegree","Medium Outdegree","High Outdegree"), values = wes_palette("Darjeeling1")) + theme(
    legend.position = c(.2, .07),
    legend.justification = c("left", "bottom"),
    legend.box.just = "left",
    legend.margin = margin(6, 6, 6, 6)
    ) + theme(axis.text=element_text(size=12),
        axis.title=element_text(size=12,face="bold")) + theme(legend.text = element_text(size=12)) + theme(panel.border = element_rect(colour = "black", fill = NA, size =1)) + theme(legend.title = element_blank()) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black")) +
  xlab("Interoceptive Awareness") + ylab("Absolute Change")
MAIA.All.outdeg.Stable.Plot

ggarrange(SE.outdeg.Stable.Plot, SCC.outdeg.Stable.Plot, DS.outdeg.Stable.Plot, CESD.outdeg.Stable.Plot, SWLS.outdeg.Stable.Plot, nrow=2, ncol=3, common.legend = T)

ggsave(paste0(plotDir,"IndDiff.outdegStable",".png"), width = 13, height = 10.5, units = "in")

Warning in dir.create(dir, recursive = TRUE): cannot create dir '/Volumes/
Research Project', reason 'Permission denied'

Warning in normalizePath(dir): path[1]="/Volumes/Research Project/
Trait_TestRetest/WeekTRT/plots": No such file or directory

Warning in grDevices::dev.off(): agg could not write to the given file

ggsave(paste0(plotDir,"IndDiff.outdegStable",".tiff"), width = 13, height = 10.5, units = "in")

Warning in dir.create(dir, recursive = TRUE): cannot create dir '/Volumes/
Research Project', reason 'Permission denied'

Warning in normalizePath(dir): path[1]="/Volumes/Research Project/
Trait_TestRetest/WeekTRT/plots": No such file or directory

Warning in grDevices::dev.off(): agg could not write to the given file