random intercepts for study & random slopes for face trustworthiness excluded because singular
m.i.a.1 <- glmer(cuedFaceTrusted_1 ~ 1 +
(1 | participant) +
(1 | face), family = binomial("logit"), data = d2)
summary(m.i.a.1)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ 1 + (1 | participant) + (1 | face)
## Data: d2
##
## AIC BIC logLik deviance df.resid
## 55889.7 55915.6 -27941.9 55883.7 40827
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7453 -0.9796 0.4767 0.9592 1.7594
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.173284 0.41627
## face (Intercept) 0.002452 0.04952
## Number of obs: 40830, groups: participant, 825; face, 141
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.04435 0.01823 2.433 0.015 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(m.i.a.1)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.05 | 1.01 – 1.08 | 0.015 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.17 | ||
| τ00 face | 0.00 | ||
| ICC | 0.05 | ||
| N participant | 825 | ||
| N face | 141 | ||
| Observations | 40830 | ||
| Marginal R2 / Conditional R2 | 0.000 / 0.051 | ||
random intercepts for study excluded because no variance
m.i.a.2 <- glmer(cuedFaceTrusted_1 ~ trust_.5 +
(1 | participant) +
(1 | face), family = binomial("logit"), data = d2)
summary(m.i.a.2)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (1 | participant) + (1 | face)
## Data: d2
##
## AIC BIC logLik deviance df.resid
## 55886.2 55920.7 -27939.1 55878.2 40826
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7532 -0.9768 0.4746 0.9581 1.7459
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.171900 0.41461
## face (Intercept) 0.002442 0.04942
## Number of obs: 40830, groups: participant, 825; face, 141
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.03224 0.01889 1.707 0.0878 .
## trust_.5 0.08592 0.03648 2.355 0.0185 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trust_.5 -0.271tab_model(m.i.a.2)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.03 | 1.00 – 1.07 | 0.088 |
| trust_.5 | 1.09 | 1.01 – 1.17 | 0.019 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.17 | ||
| τ00 face | 0.00 | ||
| ICC | 0.05 | ||
| N participant | 825 | ||
| N face | 141 | ||
| Observations | 40830 | ||
| Marginal R2 / Conditional R2 | 0.000 / 0.051 | ||
m.i.a.2.a <- glmer(cuedFaceTrusted_1 ~ trustYes_0 +
(1 | participant) +
(1 | face), family = binomial("logit"), data = d2)
summary(m.i.a.2.a)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + (1 | participant) + (1 | face)
## Data: d2
##
## AIC BIC logLik deviance df.resid
## 55886.2 55920.7 -27939.1 55878.2 40826
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7532 -0.9768 0.4746 0.9581 1.7459
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.171900 0.41461
## face (Intercept) 0.002442 0.04942
## Number of obs: 40830, groups: participant, 825; face, 141
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.07521 0.02242 3.354 0.000796 ***
## trustYes_0 -0.08593 0.03648 -2.355 0.018506 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trustYes_0 -0.585tab_model(m.i.a.2.a)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.08 | 1.03 – 1.13 | 0.001 |
| trustYes_0 | 0.92 | 0.85 – 0.99 | 0.019 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.17 | ||
| τ00 face | 0.00 | ||
| ICC | 0.05 | ||
| N participant | 825 | ||
| N face | 141 | ||
| Observations | 40830 | ||
| Marginal R2 / Conditional R2 | 0.000 / 0.051 | ||
m.i.a.2.b <- glmer(cuedFaceTrusted_1 ~ trustNo_0 +
(1 | participant) +
(1 | face), family = binomial("logit"), data = d2)
summary(m.i.a.2.b)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + (1 | participant) + (1 | face)
## Data: d2
##
## AIC BIC logLik deviance df.resid
## 55886.2 55920.7 -27939.1 55878.2 40826
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7532 -0.9768 0.4746 0.9581 1.7459
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.171901 0.41461
## face (Intercept) 0.002442 0.04942
## Number of obs: 40830, groups: participant, 825; face, 141
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.01072 0.02961 -0.362 0.7173
## trustNo_0 0.08593 0.03648 2.355 0.0185 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trustNo_0 -0.789tab_model(m.i.a.2.b)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.99 | 0.93 – 1.05 | 0.717 |
| trustNo_0 | 1.09 | 1.01 – 1.17 | 0.019 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.17 | ||
| τ00 face | 0.00 | ||
| ICC | 0.05 | ||
| N participant | 825 | ||
| N face | 141 | ||
| Observations | 40830 | ||
| Marginal R2 / Conditional R2 | 0.000 / 0.051 | ||
random intercepts for study and slopes for face trustworthiness excluded because singular tested all study contrast code variation–no sig effects no significant higher order interactions–so excluded those models
m.i.b.1 <- glmer(cuedFaceTrusted_1 ~ trust_.5 + (S1v23 + S2v3) +
(1 | participant) +
(1 | face), family = binomial("logit"), data = d2)
summary(m.i.b.1)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (S1v23 + S2v3) + (1 | participant) +
## (1 | face)
## Data: d2
##
## AIC BIC logLik deviance df.resid
## 55889.3 55941.0 -27938.7 55877.3 40824
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7520 -0.9758 0.4732 0.9585 1.7492
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.171636 0.41429
## face (Intercept) 0.002421 0.04921
## Number of obs: 40830, groups: participant, 825; face, 141
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.035616 0.020355 1.750 0.0802 .
## trust_.5 0.086772 0.040235 2.157 0.0310 *
## S1v23 0.008924 0.045718 0.195 0.8452
## S2v3 -0.039306 0.042540 -0.924 0.3555
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 S1v23
## trust_.5 -0.363
## S1v23 -0.284 0.418
## S2v3 -0.205 0.012 -0.129tab_model(m.i.b.1)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.04 | 1.00 – 1.08 | 0.080 |
| trust_.5 | 1.09 | 1.01 – 1.18 | 0.031 |
| S1v23 | 1.01 | 0.92 – 1.10 | 0.845 |
| S2v3 | 0.96 | 0.88 – 1.05 | 0.355 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.17 | ||
| τ00 face | 0.00 | ||
| ICC | 0.05 | ||
| N participant | 825 | ||
| N face | 141 | ||
| Observations | 40830 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.051 | ||
m.i.b.1.a <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + S1v23 + S2v3 +
(1 | participant) +
(1 | face), family = binomial("logit"), data = d2)
summary(m.i.b.1.a)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + S1v23 + S2v3 + (1 | participant) +
## (1 | face)
## Data: d2
##
## AIC BIC logLik deviance df.resid
## 55889.3 55941.0 -27938.7 55877.3 40824
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7520 -0.9758 0.4732 0.9585 1.7492
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.171636 0.41429
## face (Intercept) 0.002421 0.04921
## Number of obs: 40830, groups: participant, 825; face, 141
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.079001 0.022850 3.457 0.000545 ***
## trustYes_0 -0.086770 0.040238 -2.156 0.031051 *
## S1v23 0.008919 0.045723 0.195 0.845335
## S2v3 -0.039307 0.042544 -0.924 0.355525
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 S1v23
## trustYes_0 -0.557
## S1v23 0.115 -0.418
## S2v3 -0.173 -0.012 -0.129tab_model(m.i.b.1.a)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.08 | 1.03 – 1.13 | 0.001 |
| trustYes_0 | 0.92 | 0.85 – 0.99 | 0.031 |
| S1v23 | 1.01 | 0.92 – 1.10 | 0.845 |
| S2v3 | 0.96 | 0.88 – 1.05 | 0.356 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.17 | ||
| τ00 face | 0.00 | ||
| ICC | 0.05 | ||
| N participant | 825 | ||
| N face | 141 | ||
| Observations | 40830 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.051 | ||
m.i.b.1.b <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + S1v23 + S2v3 +
(1 | participant) +
(1 | face), family = binomial("logit"), data = d2)
summary(m.i.b.1.b)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + S1v23 + S2v3 + (1 | participant) +
## (1 | face)
## Data: d2
##
## AIC BIC logLik deviance df.resid
## 55889.3 55941.0 -27938.7 55877.3 40824
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7520 -0.9758 0.4732 0.9585 1.7492
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.171636 0.41429
## face (Intercept) 0.002421 0.04921
## Number of obs: 40830, groups: participant, 825; face, 141
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.007768 0.033405 -0.233 0.816
## trustNo_0 0.086771 0.040232 2.157 0.031 *
## S1v23 0.008922 0.045716 0.195 0.845
## S2v3 -0.039308 0.042540 -0.924 0.355
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsN_0 S1v23
## trustNo_0 -0.823
## S1v23 -0.424 0.418
## S2v3 -0.132 0.012 -0.129tab_model(m.i.b.1.b)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.99 | 0.93 – 1.06 | 0.816 |
| trustNo_0 | 1.09 | 1.01 – 1.18 | 0.031 |
| S1v23 | 1.01 | 0.92 – 1.10 | 0.845 |
| S2v3 | 0.96 | 0.88 – 1.05 | 0.355 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.17 | ||
| τ00 face | 0.00 | ||
| ICC | 0.05 | ||
| N participant | 825 | ||
| N face | 141 | ||
| Observations | 40830 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.051 | ||
m3 <- glmer(cuedFaceTrusted_1 ~ trust_.5 + (s2_1 + s3_1) +
(1 | participant), family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (s2_1 + s3_1) + (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 28236.6 28276.2 -14113.3 28226.6 20410
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2832 -1.0042 0.7218 0.9686 1.2443
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07119 0.2668
## Number of obs: 20415, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.02657 0.03942 0.674 0.5004
## trust_.5 0.08031 0.03882 2.069 0.0386 *
## s2_1 0.02876 0.05089 0.565 0.5720
## s3_1 -0.00997 0.04590 -0.217 0.8280
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 s2_1
## trust_.5 -0.492
## s2_1 -0.763 0.358
## s3_1 -0.851 0.406 0.649tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.03 | 0.95 – 1.11 | 0.500 |
| trust_.5 | 1.08 | 1.00 – 1.17 | 0.039 |
| s2_1 | 1.03 | 0.93 – 1.14 | 0.572 |
| s3_1 | 0.99 | 0.90 – 1.08 | 0.828 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.07 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20415 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.022 | ||
m3 <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + (s2_1 + s3_1) +
(1 | participant), family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + (s2_1 + s3_1) + (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 28236.6 28276.2 -14113.3 28226.6 20410
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2832 -1.0042 0.7218 0.9686 1.2443
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07119 0.2668
## Number of obs: 20415, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.066721 0.034322 1.944 0.0519 .
## trustYes_0 -0.080311 0.038832 -2.068 0.0386 *
## s2_1 0.028762 0.050895 0.565 0.5720
## s3_1 -0.009966 0.045915 -0.217 0.8282
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 s2_1
## trustYes_0 0.000
## s2_1 -0.674 -0.358
## s3_1 -0.748 -0.406 0.650tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.07 | 1.00 – 1.14 | 0.052 |
| trustYes_0 | 0.92 | 0.86 – 1.00 | 0.039 |
| s2_1 | 1.03 | 0.93 – 1.14 | 0.572 |
| s3_1 | 0.99 | 0.90 – 1.08 | 0.828 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.07 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20415 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.022 | ||
m3 <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + (s2_1 + s3_1) +
(1 | participant), family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + (s2_1 + s3_1) + (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 28236.6 28276.2 -14113.3 28226.6 20410
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2832 -1.0042 0.7218 0.9686 1.2443
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07119 0.2668
## Number of obs: 20415, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.013591 0.051837 -0.262 0.7932
## trustNo_0 0.080312 0.038834 2.068 0.0386 *
## s2_1 0.028765 0.050903 0.565 0.5720
## s3_1 -0.009968 0.045921 -0.217 0.8282
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsN_0 s2_1
## trustNo_0 -0.749
## s2_1 -0.715 0.358
## s3_1 -0.800 0.407 0.650tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.99 | 0.89 – 1.09 | 0.793 |
| trustNo_0 | 1.08 | 1.00 – 1.17 | 0.039 |
| s2_1 | 1.03 | 0.93 – 1.14 | 0.572 |
| s3_1 | 0.99 | 0.90 – 1.08 | 0.828 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.07 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20415 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.022 | ||
m3 <- glmer(cuedFaceTrusted_1 ~ trust_.5 + (s1_1 + s3_1) +
(1 | participant), family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (s1_1 + s3_1) + (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 28236.6 28276.2 -14113.3 28226.6 20410
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2832 -1.0042 0.7218 0.9686 1.2443
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07119 0.2668
## Number of obs: 20415, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.05533 0.03289 1.682 0.0926 .
## trust_.5 0.08031 0.03882 2.069 0.0386 *
## s1_1 -0.02876 0.05089 -0.565 0.5719
## s3_1 -0.03873 0.04077 -0.950 0.3421
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 s1_1
## trust_.5 -0.037
## s1_1 -0.632 -0.358
## s3_1 -0.806 0.011 0.517tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.06 | 0.99 – 1.13 | 0.093 |
| trust_.5 | 1.08 | 1.00 – 1.17 | 0.039 |
| s1_1 | 0.97 | 0.88 – 1.07 | 0.572 |
| s3_1 | 0.96 | 0.89 – 1.04 | 0.342 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.07 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20415 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.022 | ||
m3 <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + (s1_1 + s3_1) +
(1 | participant), family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + (s1_1 + s3_1) + (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 28236.6 28276.2 -14113.3 28226.6 20410
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2832 -1.0042 0.7218 0.9686 1.2443
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07119 0.2668
## Number of obs: 20415, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.09548 0.03759 2.540 0.0111 *
## trustYes_0 -0.08031 0.03883 -2.068 0.0386 *
## s1_1 -0.02877 0.05090 -0.565 0.5720
## s3_1 -0.03873 0.04078 -0.950 0.3423
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 s1_1
## trustYes_0 -0.485
## s1_1 -0.739 0.358
## s3_1 -0.700 -0.011 0.517tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.10 | 1.02 – 1.18 | 0.011 |
| trustYes_0 | 0.92 | 0.86 – 1.00 | 0.039 |
| s1_1 | 0.97 | 0.88 – 1.07 | 0.572 |
| s3_1 | 0.96 | 0.89 – 1.04 | 0.342 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.07 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20415 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.022 | ||
m3 <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + (s1_1 + s3_1) +
(1 | participant), family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + (s1_1 + s3_1) + (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 28236.6 28276.2 -14113.3 28226.6 20410
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2832 -1.0042 0.7218 0.9686 1.2443
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07119 0.2668
## Number of obs: 20415, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.09548 0.03759 2.540 0.0111 *
## trustYes_0 -0.08031 0.03883 -2.068 0.0386 *
## s1_1 -0.02877 0.05090 -0.565 0.5720
## s3_1 -0.03873 0.04078 -0.950 0.3423
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 s1_1
## trustYes_0 -0.485
## s1_1 -0.739 0.358
## s3_1 -0.700 -0.011 0.517tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.10 | 1.02 – 1.18 | 0.011 |
| trustYes_0 | 0.92 | 0.86 – 1.00 | 0.039 |
| s1_1 | 0.97 | 0.88 – 1.07 | 0.572 |
| s3_1 | 0.96 | 0.89 – 1.04 | 0.342 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.07 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20415 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.022 | ||
m3 <- glmer(cuedFaceTrusted_1 ~ trust_.5 + (s1_1 + s2_1) +
(1 | participant), family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (s1_1 + s2_1) + (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 28236.6 28276.2 -14113.3 28226.6 20410
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2832 -1.0042 0.7218 0.9686 1.2443
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07119 0.2668
## Number of obs: 20415, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.016598 0.024133 0.688 0.4916
## trust_.5 0.080314 0.038829 2.068 0.0386 *
## s1_1 0.009964 0.045907 0.217 0.8282
## s2_1 0.038727 0.040776 0.950 0.3422
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 s1_1
## trust_.5 -0.031
## s1_1 -0.512 -0.406
## s2_1 -0.591 -0.011 0.315tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.02 | 0.97 – 1.07 | 0.492 |
| trust_.5 | 1.08 | 1.00 – 1.17 | 0.039 |
| s1_1 | 1.01 | 0.92 – 1.11 | 0.828 |
| s2_1 | 1.04 | 0.96 – 1.13 | 0.342 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.07 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20415 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.022 | ||
m3 <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + (s1_1 + s2_1) +
(1 | participant), family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + (s1_1 + s2_1) + (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 28236.6 28276.2 -14113.3 28226.6 20410
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2832 -1.0042 0.7218 0.9686 1.2443
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07119 0.2668
## Number of obs: 20415, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.056753 0.030488 1.862 0.0627 .
## trustYes_0 -0.080313 0.038820 -2.069 0.0386 *
## s1_1 0.009967 0.045900 0.217 0.8281
## s2_1 0.038732 0.040774 0.950 0.3422
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 s1_1
## trustYes_0 -0.612
## s1_1 -0.664 0.406
## s2_1 -0.475 0.011 0.315tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.06 | 1.00 – 1.12 | 0.063 |
| trustYes_0 | 0.92 | 0.86 – 1.00 | 0.039 |
| s1_1 | 1.01 | 0.92 – 1.11 | 0.828 |
| s2_1 | 1.04 | 0.96 – 1.13 | 0.342 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.07 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20415 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.022 | ||
m3 <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + (s1_1 + s2_1) +
(1 | participant), family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + (s1_1 + s2_1) + (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 28236.6 28276.2 -14113.3 28226.6 20410
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2832 -1.0042 0.7218 0.9686 1.2443
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07119 0.2668
## Number of obs: 20415, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.023559 0.031443 -0.749 0.4537
## trustNo_0 0.080313 0.038825 2.069 0.0386 *
## s1_1 0.009968 0.045904 0.217 0.8281
## s2_1 0.038731 0.040777 0.950 0.3422
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsN_0 s1_1
## trustNo_0 -0.641
## s1_1 -0.142 -0.406
## s2_1 -0.447 -0.011 0.315tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.98 | 0.92 – 1.04 | 0.454 |
| trustNo_0 | 1.08 | 1.00 – 1.17 | 0.039 |
| s1_1 | 1.01 | 0.92 – 1.11 | 0.828 |
| s2_1 | 1.04 | 0.96 – 1.13 | 0.342 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.07 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20415 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.022 | ||
describe(d$trustAvg, na.rm = T)describe(d$trustDiff.CUC, na.rm = T)m3 <- glmer(cuedFaceTrusted_1 ~ trust_.5 + (S1v23 + S2v3) + trustDiff.CUC.c +
(1 | participant),
family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (S1v23 + S2v3) + trustDiff.CUC.c +
## (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27157.7 27205.1 -13572.8 27145.7 20026
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2463 -0.9749 0.5882 0.9557 2.0230
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07614 0.2759
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.031485 0.019710 1.597 0.1102
## trust_.5 0.095698 0.039788 2.405 0.0162 *
## S1v23 0.006949 0.045331 0.153 0.8782
## S2v3 -0.041262 0.041926 -0.984 0.3250
## trustDiff.CUC.c -0.734894 0.032401 -22.681 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 S1v23 S2v3
## trust_.5 -0.370
## S1v23 -0.296 0.417
## S2v3 -0.224 0.011 -0.139
## trstDf.CUC. -0.009 -0.007 0.010 0.006tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.03 | 0.99 – 1.07 | 0.110 |
| trust_.5 | 1.10 | 1.02 – 1.19 | 0.016 |
| S1v23 | 1.01 | 0.92 – 1.10 | 0.878 |
| S2v3 | 0.96 | 0.88 – 1.04 | 0.325 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.08 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.035 / 0.057 | ||
no sig 3-way interaction no sig 2-way interaction for trustManip X trustDiff model with interaction failed to converge–used bobyqa optimizer & increased iterations
m3 <- glmer(cuedFaceTrusted_1 ~ trust_.5 + trustDiff.CUC.c * (S1v23 + S2v3) +
(1 | participant),
family = binomial("logit"),
control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + trustDiff.CUC.c * (S1v23 + S2v3) +
## (1 | participant)
## Data: d
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## AIC BIC logLik deviance df.resid
## 27153.6 27216.8 -13568.8 27137.6 20024
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2861 -0.9724 0.5985 0.9519 2.0493
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07614 0.2759
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.030539 0.019705 1.550 0.12119
## trust_.5 0.097087 0.039833 2.437 0.01480 *
## trustDiff.CUC.c -0.819243 0.046283 -17.701 < 2e-16 ***
## S1v23 0.008393 0.045277 0.185 0.85295
## S2v3 -0.042255 0.041965 -1.007 0.31399
## trustDiff.CUC.c:S1v23 -0.223806 0.080665 -2.775 0.00553 **
## trustDiff.CUC.c:S2v3 -0.283307 0.130258 -2.175 0.02963 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 trD.CUC. S1v23 S2v3 tD.CUC.:S1
## trust_.5 -0.371
## trstDf.CUC. 0.004 -0.014
## S1v23 -0.294 0.418 0.004
## S2v3 -0.224 0.011 0.009 -0.139
## tD.CUC.:S12 0.017 -0.012 0.556 -0.014 0.008
## tD.CUC.:S23 0.011 -0.011 0.694 0.000 0.006 0.597tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.03 | 0.99 – 1.07 | 0.121 |
| trust_.5 | 1.10 | 1.02 – 1.19 | 0.015 |
| trustDiff.CUC.c | 0.44 | 0.40 – 0.48 | <0.001 |
| S1v23 | 1.01 | 0.92 – 1.10 | 0.853 |
| S2v3 | 0.96 | 0.88 – 1.04 | 0.314 |
| trustDiff.CUC.c * S1v23 | 0.80 | 0.68 – 0.94 | 0.006 |
| trustDiff.CUC.c * S2v3 | 0.75 | 0.58 – 0.97 | 0.030 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.08 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.036 / 0.057 | ||
m3 <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + (S1v23 + S2v3) + trustDiff.CUC.c +
(1 | participant), family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + (S1v23 + S2v3) + trustDiff.CUC.c +
## (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27157.7 27205.1 -13572.8 27145.7 20026
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2463 -0.9749 0.5882 0.9557 2.0230
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07614 0.2759
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.079334 0.022223 3.570 0.000357 ***
## trustYes_0 -0.095700 0.039788 -2.405 0.016162 *
## S1v23 0.006954 0.045331 0.153 0.878078
## S2v3 -0.041264 0.041926 -0.984 0.325014
## trustDiff.CUC.c -0.734894 0.032401 -22.681 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 S1v23 S2v3
## trustYes_0 -0.567
## S1v23 0.110 -0.417
## S2v3 -0.189 -0.011 -0.139
## trstDf.CUC. -0.014 0.007 0.010 0.006tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.08 | 1.04 – 1.13 | <0.001 |
| trustYes_0 | 0.91 | 0.84 – 0.98 | 0.016 |
| S1v23 | 1.01 | 0.92 – 1.10 | 0.878 |
| S2v3 | 0.96 | 0.88 – 1.04 | 0.325 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.08 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.035 / 0.057 | ||
m3 <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + (S1v23 + S2v3) + trustDiff.CUC.c +
(1 | participant), family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + (S1v23 + S2v3) + trustDiff.CUC.c +
## (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27157.7 27205.1 -13572.8 27145.7 20026
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2463 -0.9749 0.5882 0.9557 2.0230
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07614 0.2759
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.016369 0.032783 -0.499 0.6176
## trustNo_0 0.095702 0.039789 2.405 0.0162 *
## S1v23 0.006948 0.045332 0.153 0.8782
## S2v3 -0.041264 0.041927 -0.984 0.3250
## trustDiff.CUC.c -0.734893 0.032401 -22.681 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsN_0 S1v23 S2v3
## trustNo_0 -0.830
## S1v23 -0.431 0.417
## S2v3 -0.141 0.011 -0.139
## trstDf.CUC. -0.001 -0.007 0.010 0.006tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.98 | 0.92 – 1.05 | 0.618 |
| trustNo_0 | 1.10 | 1.02 – 1.19 | 0.016 |
| S1v23 | 1.01 | 0.92 – 1.10 | 0.878 |
| S2v3 | 0.96 | 0.88 – 1.04 | 0.325 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.08 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.035 / 0.057 | ||
no 3-way int no higher order int for trustDiff x trustManip
m3 <- glmer(cuedFaceTrusted_1 ~ trust_.5 + trustDiff.CUC.c + (s2_1 + s3_1) +
(1 | participant), family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + trustDiff.CUC.c + (s2_1 + s3_1) +
## (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27157.7 27205.1 -13572.8 27145.7 20026
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2463 -0.9749 0.5882 0.9557 2.0230
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07614 0.2759
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.02685 0.04068 0.660 0.5092
## trust_.5 0.09570 0.03979 2.405 0.0162 *
## trustDiff.CUC.c -0.73490 0.03240 -22.681 <2e-16 ***
## s2_1 0.02758 0.05252 0.525 0.5995
## s3_1 -0.01368 0.04723 -0.290 0.7721
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 tD.CUC s2_1
## trust_.5 -0.489
## trstDf.CUC. -0.012 -0.007
## s2_1 -0.763 0.355 0.007
## s3_1 -0.853 0.405 0.013 0.651tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.03 | 0.95 – 1.11 | 0.509 |
| trust_.5 | 1.10 | 1.02 – 1.19 | 0.016 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| s2_1 | 1.03 | 0.93 – 1.14 | 0.599 |
| s3_1 | 0.99 | 0.90 – 1.08 | 0.772 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.08 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.035 / 0.057 | ||
m3 <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + (s2_1 + s3_1) + trustDiff.CUC.c +
(1 | participant), family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + (s2_1 + s3_1) + trustDiff.CUC.c +
## (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27157.7 27205.1 -13572.8 27145.7 20026
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2463 -0.9749 0.5882 0.9557 2.0230
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07614 0.2759
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.07470 0.03548 2.105 0.0353 *
## trustYes_0 -0.09570 0.03979 -2.405 0.0162 *
## s2_1 0.02758 0.05252 0.525 0.5995
## s3_1 -0.01368 0.04723 -0.290 0.7721
## trustDiff.CUC.c -0.73489 0.03240 -22.681 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 s2_1 s3_1
## trustYes_0 0.000
## s2_1 -0.676 -0.355
## s3_1 -0.751 -0.405 0.651
## trstDf.CUC. -0.018 0.007 0.007 0.013tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.08 | 1.01 – 1.16 | 0.035 |
| trustYes_0 | 0.91 | 0.84 – 0.98 | 0.016 |
| s2_1 | 1.03 | 0.93 – 1.14 | 0.599 |
| s3_1 | 0.99 | 0.90 – 1.08 | 0.772 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.08 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.035 / 0.057 | ||
m3 <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + (s2_1 + s3_1) + trustDiff.CUC.c +
(1 | participant), family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + (s2_1 + s3_1) + trustDiff.CUC.c +
## (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27157.7 27205.1 -13572.8 27145.7 20026
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2463 -0.9749 0.5882 0.9557 2.0230
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07614 0.2759
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.02100 0.05331 -0.394 0.6937
## trustNo_0 0.09570 0.03979 2.405 0.0162 *
## s2_1 0.02758 0.05252 0.525 0.5994
## s3_1 -0.01368 0.04723 -0.290 0.7721
## trustDiff.CUC.c -0.73489 0.03240 -22.681 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsN_0 s2_1 s3_1
## trustNo_0 -0.746
## s2_1 -0.715 0.355
## s3_1 -0.802 0.405 0.651
## trstDf.CUC. -0.007 -0.007 0.007 0.013tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.98 | 0.88 – 1.09 | 0.694 |
| trustNo_0 | 1.10 | 1.02 – 1.19 | 0.016 |
| s2_1 | 1.03 | 0.93 – 1.14 | 0.599 |
| s3_1 | 0.99 | 0.90 – 1.08 | 0.772 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.08 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.035 / 0.057 | ||
no 3-way int no higher order int for trustDiff x trustManip
m3 <- glmer(cuedFaceTrusted_1 ~ trust_.5 * trustDiff.CUC.c + (s1_1 + s3_1) +
(1 | participant), family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 * trustDiff.CUC.c + (s1_1 + s3_1) +
## (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27159.4 27214.7 -13572.7 27145.4 20025
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3192 -0.9753 0.5902 0.9553 2.0063
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07616 0.276
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.05435 0.03397 1.600 0.1096
## trust_.5 0.09581 0.03980 2.407 0.0161 *
## trustDiff.CUC.c -0.74494 0.03693 -20.173 <2e-16 ***
## s1_1 -0.02776 0.05251 -0.529 0.5970
## s3_1 -0.04128 0.04194 -0.984 0.3250
## trust_.5:trustDiff.CUC.c 0.04200 0.07374 0.570 0.5690
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 tD.CUC s1_1 s3_1
## trust_.5 -0.037
## trstDf.CUC. -0.002 -0.009
## s1_1 -0.633 -0.355 -0.003
## s3_1 -0.809 0.012 0.005 0.519
## t_.5:D.CUC. -0.004 0.005 -0.480 -0.006 -0.001tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.06 | 0.99 – 1.13 | 0.110 |
| trust_.5 | 1.10 | 1.02 – 1.19 | 0.016 |
| trustDiff.CUC.c | 0.47 | 0.44 – 0.51 | <0.001 |
| s1_1 | 0.97 | 0.88 – 1.08 | 0.597 |
| s3_1 | 0.96 | 0.88 – 1.04 | 0.325 |
|
trust_.5 * trustDiff.CUC.c |
1.04 | 0.90 – 1.21 | 0.569 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.08 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.035 / 0.057 | ||
m3 <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + (s1_1 + s3_1) + trustDiff.CUC.c +
(1 | participant), family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + (s1_1 + s3_1) + trustDiff.CUC.c +
## (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27157.7 27205.1 -13572.8 27145.7 20026
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2463 -0.9749 0.5882 0.9557 2.0230
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07614 0.2759
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.10228 0.03872 2.641 0.00826 **
## trustYes_0 -0.09570 0.03979 -2.405 0.01617 *
## s1_1 -0.02758 0.05252 -0.525 0.59945
## s3_1 -0.04126 0.04193 -0.984 0.32502
## trustDiff.CUC.c -0.73490 0.03240 -22.681 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 s1_1 s3_1
## trustYes_0 -0.482
## s1_1 -0.737 0.355
## s3_1 -0.704 -0.011 0.519
## trstDf.CUC. -0.007 0.007 -0.007 0.006tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.11 | 1.03 – 1.20 | 0.008 |
| trustYes_0 | 0.91 | 0.84 – 0.98 | 0.016 |
| s1_1 | 0.97 | 0.88 – 1.08 | 0.599 |
| s3_1 | 0.96 | 0.88 – 1.04 | 0.325 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.08 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.035 / 0.057 | ||
m3 <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + (s1_1 + s3_1) + trustDiff.CUC.c +
(1 | participant), family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + (s1_1 + s3_1) + trustDiff.CUC.c +
## (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27157.7 27205.1 -13572.8 27145.7 20026
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2463 -0.9749 0.5882 0.9557 2.0230
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07614 0.2759
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.10228 0.03872 2.641 0.00826 **
## trustYes_0 -0.09570 0.03979 -2.405 0.01617 *
## s1_1 -0.02758 0.05252 -0.525 0.59945
## s3_1 -0.04126 0.04193 -0.984 0.32502
## trustDiff.CUC.c -0.73490 0.03240 -22.681 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 s1_1 s3_1
## trustYes_0 -0.482
## s1_1 -0.737 0.355
## s3_1 -0.704 -0.011 0.519
## trstDf.CUC. -0.007 0.007 -0.007 0.006tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.11 | 1.03 – 1.20 | 0.008 |
| trustYes_0 | 0.91 | 0.84 – 0.98 | 0.016 |
| s1_1 | 0.97 | 0.88 – 1.08 | 0.599 |
| s3_1 | 0.96 | 0.88 – 1.04 | 0.325 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.08 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.035 / 0.057 | ||
m3 <- glmer(cuedFaceTrusted_1 ~ trust_.5 + (s1_1 + s2_1) + trustDiff.CUC.c +
(1 | participant),
family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (s1_1 + s2_1) + trustDiff.CUC.c +
## (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27157.7 27205.1 -13572.8 27145.7 20026
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2463 -0.9749 0.5882 0.9557 2.0230
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07613 0.2759
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.01317 0.02464 0.535 0.5930
## trust_.5 0.09570 0.03979 2.405 0.0162 *
## s1_1 0.01368 0.04723 0.290 0.7720
## s2_1 0.04126 0.04193 0.984 0.3250
## trustDiff.CUC.c -0.73490 0.03240 -22.681 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 s1_1 s2_1
## trust_.5 -0.032
## s1_1 -0.508 -0.405
## s2_1 -0.587 -0.011 0.311
## trstDf.CUC. 0.004 -0.007 -0.013 -0.006tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.01 | 0.97 – 1.06 | 0.593 |
| trust_.5 | 1.10 | 1.02 – 1.19 | 0.016 |
| s1_1 | 1.01 | 0.92 – 1.11 | 0.772 |
| s2_1 | 1.04 | 0.96 – 1.13 | 0.325 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.08 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.035 / 0.057 | ||
no sig 3-way interaction no sig 2-way interaction for trustManip X trustDiff model with interaction failed to converge–used bobyqa optimizer & increased iterations
m3 <- glmer(cuedFaceTrusted_1 ~ trust_.5 + trustDiff.CUC.c * (s1_1 + s2_1) +
(1 | participant),
family = binomial("logit"),
control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + trustDiff.CUC.c * (s1_1 + s2_1) +
## (1 | participant)
## Data: d
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## AIC BIC logLik deviance df.resid
## 27153.6 27216.8 -13568.8 27137.6 20024
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2861 -0.9724 0.5985 0.9519 2.0493
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07614 0.2759
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.01221 0.02467 0.495 0.6207
## trust_.5 0.09708 0.03983 2.437 0.0148 *
## trustDiff.CUC.c -1.03550 0.12153 -8.520 <2e-16 ***
## s1_1 0.01274 0.04718 0.270 0.7872
## s2_1 0.04225 0.04197 1.007 0.3140
## trustDiff.CUC.c:s1_1 0.36546 0.13049 2.801 0.0051 **
## trustDiff.CUC.c:s2_1 0.28331 0.13026 2.175 0.0296 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 trD.CUC. s1_1 s2_1 tD.CUC.:1
## trust_.5 -0.032
## trstDf.CUC. 0.015 -0.014
## s1_1 -0.509 -0.405 -0.002
## s2_1 -0.587 -0.011 -0.009 0.311
## tD.CUC.:1_1 -0.014 0.013 -0.931 -0.004 0.008
## tD.CUC.:2_1 -0.014 0.011 -0.933 0.003 0.006 0.868tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.01 | 0.96 – 1.06 | 0.621 |
| trust_.5 | 1.10 | 1.02 – 1.19 | 0.015 |
| trustDiff.CUC.c | 0.36 | 0.28 – 0.45 | <0.001 |
| s1_1 | 1.01 | 0.92 – 1.11 | 0.787 |
| s2_1 | 1.04 | 0.96 – 1.13 | 0.314 |
| trustDiff.CUC.c * s1_1 | 1.44 | 1.12 – 1.86 | 0.005 |
| trustDiff.CUC.c * s2_1 | 1.33 | 1.03 – 1.71 | 0.030 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.08 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.036 / 0.057 | ||
m3 <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + trustDiff.CUC.c + (s1_1 + s2_1) +
(1 | participant), family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + trustDiff.CUC.c + (s1_1 + s2_1) +
## (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27157.7 27205.1 -13572.8 27145.7 20026
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2463 -0.9749 0.5882 0.9557 2.0230
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07614 0.2759
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.06102 0.03117 1.957 0.0503 .
## trustYes_0 -0.09570 0.03979 -2.405 0.0162 *
## trustDiff.CUC.c -0.73490 0.03240 -22.681 <2e-16 ***
## s1_1 0.01369 0.04723 0.290 0.7719
## s2_1 0.04126 0.04193 0.984 0.3250
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 tD.CUC s1_1
## trustYes_0 -0.613
## trstDf.CUC. -0.001 0.007
## s1_1 -0.660 0.405 -0.013
## s2_1 -0.471 0.011 -0.006 0.311tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.06 | 1.00 – 1.13 | 0.050 |
| trustYes_0 | 0.91 | 0.84 – 0.98 | 0.016 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| s1_1 | 1.01 | 0.92 – 1.11 | 0.772 |
| s2_1 | 1.04 | 0.96 – 1.13 | 0.325 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.08 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.035 / 0.057 | ||
no sig 2-way interaction for trustManip X trustDiff
m3 <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + trustDiff.CUC.c * (s1_1 + s2_1) +
(1 | participant), family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + trustDiff.CUC.c * (s1_1 + s2_1) +
## (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27153.6 27216.8 -13568.8 27137.6 20024
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2861 -0.9724 0.5985 0.9519 2.0493
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07614 0.2759
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.06075 0.03121 1.946 0.05161 .
## trustYes_0 -0.09708 0.03983 -2.437 0.01480 *
## trustDiff.CUC.c -1.03549 0.12155 -8.519 < 2e-16 ***
## s1_1 0.01273 0.04718 0.270 0.78728
## s2_1 0.04225 0.04197 1.007 0.31402
## trustDiff.CUC.c:s1_1 0.36545 0.13051 2.800 0.00511 **
## trustDiff.CUC.c:s2_1 0.28330 0.13028 2.174 0.02967 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 trD.CUC. s1_1 s2_1 tD.CUC.:1
## trustYes_0 -0.613
## trstDf.CUC. 0.003 0.014
## s1_1 -0.661 0.405 -0.002
## s2_1 -0.471 0.011 -0.009 0.311
## tD.CUC.:1_1 -0.003 -0.013 -0.931 -0.004 0.008
## tD.CUC.:2_1 -0.004 -0.011 -0.933 0.003 0.006 0.869tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.06 | 1.00 – 1.13 | 0.052 |
| trustYes_0 | 0.91 | 0.84 – 0.98 | 0.015 |
| trustDiff.CUC.c | 0.36 | 0.28 – 0.45 | <0.001 |
| s1_1 | 1.01 | 0.92 – 1.11 | 0.787 |
| s2_1 | 1.04 | 0.96 – 1.13 | 0.314 |
| trustDiff.CUC.c * s1_1 | 1.44 | 1.12 – 1.86 | 0.005 |
| trustDiff.CUC.c * s2_1 | 1.33 | 1.03 – 1.71 | 0.030 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.08 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.036 / 0.057 | ||
m3 <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + (s1_1 + s2_1) + trustDiff.CUC.c +
(1 | participant), family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + (s1_1 + s2_1) + trustDiff.CUC.c +
## (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27157.7 27205.1 -13572.8 27145.7 20026
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2463 -0.9749 0.5882 0.9557 2.0230
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07614 0.2759
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.03468 0.03215 -1.079 0.2808
## trustNo_0 0.09570 0.03979 2.405 0.0162 *
## s1_1 0.01368 0.04723 0.290 0.7721
## s2_1 0.04126 0.04193 0.984 0.3251
## trustDiff.CUC.c -0.73489 0.03240 -22.681 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsN_0 s1_1 s2_1
## trustNo_0 -0.643
## s1_1 -0.139 -0.405
## s2_1 -0.443 -0.011 0.311
## trstDf.CUC. 0.007 -0.007 -0.013 -0.006tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.97 | 0.91 – 1.03 | 0.281 |
| trustNo_0 | 1.10 | 1.02 – 1.19 | 0.016 |
| s1_1 | 1.01 | 0.92 – 1.11 | 0.772 |
| s2_1 | 1.04 | 0.96 – 1.13 | 0.325 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.08 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.035 / 0.057 | ||
no sig 2-way interaction for trustManip X trustDiff
m3 <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + (s1_1 + s2_1) * trustDiff.CUC.c +
(1 |participant), family = binomial("logit"), data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + (s1_1 + s2_1) * trustDiff.CUC.c +
## (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27153.6 27216.8 -13568.8 27137.6 20024
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2861 -0.9724 0.5985 0.9519 2.0493
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07613 0.2759
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.03633 0.03220 -1.128 0.25918
## trustNo_0 0.09708 0.03983 2.437 0.01481 *
## s1_1 0.01274 0.04718 0.270 0.78714
## s2_1 0.04225 0.04197 1.007 0.31407
## trustDiff.CUC.c -1.03550 0.12157 -8.518 < 2e-16 ***
## s1_1:trustDiff.CUC.c 0.36547 0.13052 2.800 0.00511 **
## s2_1:trustDiff.CUC.c 0.28331 0.13030 2.174 0.02968 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsN_0 s1_1 s2_1 tD.CUC s1_1:D
## trustNo_0 -0.643
## s1_1 -0.140 -0.405
## s2_1 -0.443 -0.011 0.311
## trstDf.CUC. 0.020 -0.014 -0.002 -0.009
## s1_1:D.CUC. -0.019 0.013 -0.004 0.008 -0.931
## s2_1:D.CUC. -0.018 0.011 0.003 0.006 -0.933 0.869tab_model(m3)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.96 | 0.91 – 1.03 | 0.259 |
| trustNo_0 | 1.10 | 1.02 – 1.19 | 0.015 |
| s1_1 | 1.01 | 0.92 – 1.11 | 0.787 |
| s2_1 | 1.04 | 0.96 – 1.13 | 0.314 |
| trustDiff.CUC.c | 0.36 | 0.28 – 0.45 | <0.001 |
| s1_1 * trustDiff.CUC.c | 1.44 | 1.12 – 1.86 | 0.005 |
| s2_1 * trustDiff.CUC.c | 1.33 | 1.03 – 1.71 | 0.030 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.08 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.036 / 0.057 | ||
singular when including random slopes for face trustworthiness – so incorporated trustworthiness difference as fixed effect no higher order interactions with trustDiff
m7a <- glmer(cuedFaceTrusted_1 ~ trust_.5 * respTime.log.c + trustDiff.CUC.c + (S1v23 + S2v3) +
(1 | participant),
family = binomial("logit"),
data = d)
summary(m7a)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 * respTime.log.c + trustDiff.CUC.c +
## (S1v23 + S2v3) + (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27155.8 27219.0 -13569.9 27139.8 20024
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2448 -0.9736 0.5853 0.9566 2.0630
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07359 0.2713
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.02585 0.02017 1.282 0.1999
## trust_.5 0.09367 0.04029 2.325 0.0201 *
## respTime.log.c -0.03863 0.02211 -1.747 0.0806 .
## trustDiff.CUC.c -0.73603 0.03240 -22.719 <2e-16 ***
## S1v23 0.03021 0.04613 0.655 0.5125
## S2v3 -0.04451 0.04173 -1.067 0.2861
## trust_.5:respTime.log.c -0.05352 0.04428 -1.209 0.2268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 rspT.. tD.CUC S1v23 S2v3
## trust_.5 -0.389
## respTm.lg.c -0.059 0.143
## trstDf.CUC. -0.007 -0.006 0.015
## S1v23 -0.312 0.401 -0.129 0.006
## S2v3 -0.211 0.009 0.015 0.006 -0.143
## trst_.5:T.. 0.234 -0.151 -0.243 0.007 -0.130 0.026tab_model(m7a)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.03 | 0.99 – 1.07 | 0.200 |
| trust_.5 | 1.10 | 1.01 – 1.19 | 0.020 |
| respTime.log.c | 0.96 | 0.92 – 1.00 | 0.081 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| S1v23 | 1.03 | 0.94 – 1.13 | 0.513 |
| S2v3 | 0.96 | 0.88 – 1.04 | 0.286 |
| trust_.5 * respTime.log.c | 0.95 | 0.87 – 1.03 | 0.227 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.07 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.036 / 0.057 | ||
m7 <- glmer(cuedFaceTrusted_1 ~ trustYes_0 * respTime.log.c + trustDiff.CUC.c + (S1v23 + S2v3) +
(1 | participant),
family = binomial("logit"),
data = d)
summary(m7)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 * respTime.log.c + trustDiff.CUC.c +
## (S1v23 + S2v3) + (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27155.8 27219.0 -13569.9 27139.8 20024
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2448 -0.9736 0.5853 0.9566 2.0630
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07359 0.2713
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.07269 0.02228 3.262 0.0011 **
## trustYes_0 -0.09367 0.04029 -2.325 0.0201 *
## respTime.log.c -0.06539 0.02722 -2.402 0.0163 *
## trustDiff.CUC.c -0.73602 0.03240 -22.719 <2e-16 ***
## S1v23 0.03021 0.04613 0.655 0.5125
## S2v3 -0.04452 0.04173 -1.067 0.2861
## trustYes_0:respTime.log.c 0.05352 0.04428 1.209 0.2268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 rspT.. tD.CUC S1v23 S2v3
## trustYes_0 -0.552
## respTm.lg.c 0.123 0.006
## trstDf.CUC. -0.012 0.006 0.018
## S1v23 0.081 -0.401 -0.210 0.007
## S2v3 -0.183 -0.009 0.033 0.006 -0.143
## trstY_0:T.. -0.076 -0.151 -0.616 -0.007 0.130 -0.026tab_model(m7)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.08 | 1.03 – 1.12 | 0.001 |
| trustYes_0 | 0.91 | 0.84 – 0.99 | 0.020 |
| respTime.log.c | 0.94 | 0.89 – 0.99 | 0.016 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| S1v23 | 1.03 | 0.94 – 1.13 | 0.513 |
| S2v3 | 0.96 | 0.88 – 1.04 | 0.286 |
|
trustYes_0 * respTime.log.c |
1.05 | 0.97 – 1.15 | 0.227 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.07 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.036 / 0.057 | ||
m7 <- glmer(cuedFaceTrusted_1 ~ trustNo_0 * respTime.log.c + trustDiff.CUC.c + (S1v23 + S2v3) +
(1 | participant),
family = binomial("logit"),
data = d)
summary(m7)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 * respTime.log.c + trustDiff.CUC.c +
## (S1v23 + S2v3) + (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27155.8 27219.0 -13569.9 27139.8 20024
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2447 -0.9736 0.5853 0.9566 2.0630
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07359 0.2713
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.02099 0.03360 -0.625 0.5321
## trustNo_0 0.09368 0.04029 2.325 0.0201 *
## respTime.log.c -0.01185 0.03489 -0.340 0.7342
## trustDiff.CUC.c -0.73601 0.03240 -22.719 <2e-16 ***
## S1v23 0.03021 0.04613 0.655 0.5125
## S2v3 -0.04452 0.04173 -1.067 0.2861
## trustNo_0:respTime.log.c -0.05355 0.04428 -1.209 0.2265
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsN_0 rspT.. tD.CUC S1v23 S2v3
## trustNo_0 -0.833
## respTm.lg.c -0.223 0.186
## trstDf.CUC. -0.001 -0.006 0.005
## S1v23 -0.428 0.401 0.001 0.006
## S2v3 -0.132 0.009 -0.007 0.006 -0.143
## trstN_0:T.. 0.231 -0.151 -0.789 0.007 -0.130 0.026tab_model(m7)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.98 | 0.92 – 1.05 | 0.532 |
| trustNo_0 | 1.10 | 1.01 – 1.19 | 0.020 |
| respTime.log.c | 0.99 | 0.92 – 1.06 | 0.734 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| S1v23 | 1.03 | 0.94 – 1.13 | 0.513 |
| S2v3 | 0.96 | 0.88 – 1.04 | 0.286 |
|
trustNo_0 * respTime.log.c |
0.95 | 0.87 – 1.03 | 0.227 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.07 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.036 / 0.057 | ||
m7a <- glmer(cuedFaceTrusted_1 ~ trust_.5 * respTime.log.c + trustDiff.CUC.c + (s2_1 + s3_1) +
(1 | participant),
family = binomial("logit"),
data = d)
summary(m7a)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 * respTime.log.c + trustDiff.CUC.c +
## (s2_1 + s3_1) + (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27155.8 27219.0 -13569.9 27139.8 20024
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2448 -0.9736 0.5853 0.9566 2.0630
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07359 0.2713
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.005711 0.041704 0.137 0.8911
## trust_.5 0.093674 0.040289 2.325 0.0201 *
## respTime.log.c -0.038626 0.022114 -1.747 0.0807 .
## trustDiff.CUC.c -0.736022 0.032397 -22.719 <2e-16 ***
## s2_1 0.052470 0.053274 0.985 0.3247
## s3_1 0.007953 0.047843 0.166 0.8680
## trust_.5:respTime.log.c -0.053523 0.044279 -1.209 0.2267
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 rspT.. tD.CUC s2_1 s3_1
## trust_.5 -0.484
## respTm.lg.c 0.066 0.143
## trstDf.CUC. -0.008 -0.006 0.015
## s2_1 -0.770 0.344 -0.117 0.003
## s3_1 -0.855 0.391 -0.117 0.009 0.664
## trst_.5:T.. 0.209 -0.151 -0.243 0.007 -0.123 -0.114tab_model(m7a)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.01 | 0.93 – 1.09 | 0.891 |
| trust_.5 | 1.10 | 1.01 – 1.19 | 0.020 |
| respTime.log.c | 0.96 | 0.92 – 1.00 | 0.081 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| s2_1 | 1.05 | 0.95 – 1.17 | 0.325 |
| s3_1 | 1.01 | 0.92 – 1.11 | 0.868 |
| trust_.5 * respTime.log.c | 0.95 | 0.87 – 1.03 | 0.227 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.07 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.036 / 0.057 | ||
m7 <- glmer(cuedFaceTrusted_1 ~ trustYes_0 * respTime.log.c + trustDiff.CUC.c + (S1v23 + S2v3) +
(1 | participant),
family = binomial("logit"),
data = d)
summary(m7)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 * respTime.log.c + trustDiff.CUC.c +
## (S1v23 + S2v3) + (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27155.8 27219.0 -13569.9 27139.8 20024
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2448 -0.9736 0.5853 0.9566 2.0630
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07359 0.2713
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.07269 0.02228 3.262 0.0011 **
## trustYes_0 -0.09367 0.04029 -2.325 0.0201 *
## respTime.log.c -0.06539 0.02722 -2.402 0.0163 *
## trustDiff.CUC.c -0.73602 0.03240 -22.719 <2e-16 ***
## S1v23 0.03021 0.04613 0.655 0.5125
## S2v3 -0.04452 0.04173 -1.067 0.2861
## trustYes_0:respTime.log.c 0.05352 0.04428 1.209 0.2268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 rspT.. tD.CUC S1v23 S2v3
## trustYes_0 -0.552
## respTm.lg.c 0.123 0.006
## trstDf.CUC. -0.012 0.006 0.018
## S1v23 0.081 -0.401 -0.210 0.007
## S2v3 -0.183 -0.009 0.033 0.006 -0.143
## trstY_0:T.. -0.076 -0.151 -0.616 -0.007 0.130 -0.026tab_model(m7)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.08 | 1.03 – 1.12 | 0.001 |
| trustYes_0 | 0.91 | 0.84 – 0.99 | 0.020 |
| respTime.log.c | 0.94 | 0.89 – 0.99 | 0.016 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| S1v23 | 1.03 | 0.94 – 1.13 | 0.513 |
| S2v3 | 0.96 | 0.88 – 1.04 | 0.286 |
|
trustYes_0 * respTime.log.c |
1.05 | 0.97 – 1.15 | 0.227 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.07 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.036 / 0.057 | ||
m7 <- glmer(cuedFaceTrusted_1 ~ trustNo_0 * respTime.log.c + trustDiff.CUC.c + (S1v23 + S2v3) +
(1 | participant),
family = binomial("logit"),
data = d)
summary(m7)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 * respTime.log.c + trustDiff.CUC.c +
## (S1v23 + S2v3) + (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27155.8 27219.0 -13569.9 27139.8 20024
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2447 -0.9736 0.5853 0.9566 2.0630
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07359 0.2713
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.02099 0.03360 -0.625 0.5321
## trustNo_0 0.09368 0.04029 2.325 0.0201 *
## respTime.log.c -0.01185 0.03489 -0.340 0.7342
## trustDiff.CUC.c -0.73601 0.03240 -22.719 <2e-16 ***
## S1v23 0.03021 0.04613 0.655 0.5125
## S2v3 -0.04452 0.04173 -1.067 0.2861
## trustNo_0:respTime.log.c -0.05355 0.04428 -1.209 0.2265
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsN_0 rspT.. tD.CUC S1v23 S2v3
## trustNo_0 -0.833
## respTm.lg.c -0.223 0.186
## trstDf.CUC. -0.001 -0.006 0.005
## S1v23 -0.428 0.401 0.001 0.006
## S2v3 -0.132 0.009 -0.007 0.006 -0.143
## trstN_0:T.. 0.231 -0.151 -0.789 0.007 -0.130 0.026tab_model(m7)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.98 | 0.92 – 1.05 | 0.532 |
| trustNo_0 | 1.10 | 1.01 – 1.19 | 0.020 |
| respTime.log.c | 0.99 | 0.92 – 1.06 | 0.734 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| S1v23 | 1.03 | 0.94 – 1.13 | 0.513 |
| S2v3 | 0.96 | 0.88 – 1.04 | 0.286 |
|
trustNo_0 * respTime.log.c |
0.95 | 0.87 – 1.03 | 0.227 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.07 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.036 / 0.057 | ||
singular when including random slopes for face trustworthiness – so incorporated trustworthiness difference as fixed effect no higher order interactions with trustDiff
m7a <- glmer(cuedFaceTrusted_1 ~ trust_.5 * respTime.log.c + trustDiff.CUC.c + (S1v23 + S2v3) +
(1 | participant),
family = binomial("logit"),
data = d)
summary(m7a)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 * respTime.log.c + trustDiff.CUC.c +
## (S1v23 + S2v3) + (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27155.8 27219.0 -13569.9 27139.8 20024
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2448 -0.9736 0.5853 0.9566 2.0630
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07359 0.2713
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.02585 0.02017 1.282 0.1999
## trust_.5 0.09367 0.04029 2.325 0.0201 *
## respTime.log.c -0.03863 0.02211 -1.747 0.0806 .
## trustDiff.CUC.c -0.73603 0.03240 -22.719 <2e-16 ***
## S1v23 0.03021 0.04613 0.655 0.5125
## S2v3 -0.04451 0.04173 -1.067 0.2861
## trust_.5:respTime.log.c -0.05352 0.04428 -1.209 0.2268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 rspT.. tD.CUC S1v23 S2v3
## trust_.5 -0.389
## respTm.lg.c -0.059 0.143
## trstDf.CUC. -0.007 -0.006 0.015
## S1v23 -0.312 0.401 -0.129 0.006
## S2v3 -0.211 0.009 0.015 0.006 -0.143
## trst_.5:T.. 0.234 -0.151 -0.243 0.007 -0.130 0.026tab_model(m7a)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.03 | 0.99 – 1.07 | 0.200 |
| trust_.5 | 1.10 | 1.01 – 1.19 | 0.020 |
| respTime.log.c | 0.96 | 0.92 – 1.00 | 0.081 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| S1v23 | 1.03 | 0.94 – 1.13 | 0.513 |
| S2v3 | 0.96 | 0.88 – 1.04 | 0.286 |
| trust_.5 * respTime.log.c | 0.95 | 0.87 – 1.03 | 0.227 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.07 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.036 / 0.057 | ||
m7 <- glmer(cuedFaceTrusted_1 ~ trustYes_0 * respTime.log.c + trustDiff.CUC.c + (S1v23 + S2v3) +
(1 | participant),
family = binomial("logit"),
data = d)
summary(m7)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 * respTime.log.c + trustDiff.CUC.c +
## (S1v23 + S2v3) + (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27155.8 27219.0 -13569.9 27139.8 20024
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2448 -0.9736 0.5853 0.9566 2.0630
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07359 0.2713
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.07269 0.02228 3.262 0.0011 **
## trustYes_0 -0.09367 0.04029 -2.325 0.0201 *
## respTime.log.c -0.06539 0.02722 -2.402 0.0163 *
## trustDiff.CUC.c -0.73602 0.03240 -22.719 <2e-16 ***
## S1v23 0.03021 0.04613 0.655 0.5125
## S2v3 -0.04452 0.04173 -1.067 0.2861
## trustYes_0:respTime.log.c 0.05352 0.04428 1.209 0.2268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 rspT.. tD.CUC S1v23 S2v3
## trustYes_0 -0.552
## respTm.lg.c 0.123 0.006
## trstDf.CUC. -0.012 0.006 0.018
## S1v23 0.081 -0.401 -0.210 0.007
## S2v3 -0.183 -0.009 0.033 0.006 -0.143
## trstY_0:T.. -0.076 -0.151 -0.616 -0.007 0.130 -0.026tab_model(m7)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.08 | 1.03 – 1.12 | 0.001 |
| trustYes_0 | 0.91 | 0.84 – 0.99 | 0.020 |
| respTime.log.c | 0.94 | 0.89 – 0.99 | 0.016 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| S1v23 | 1.03 | 0.94 – 1.13 | 0.513 |
| S2v3 | 0.96 | 0.88 – 1.04 | 0.286 |
|
trustYes_0 * respTime.log.c |
1.05 | 0.97 – 1.15 | 0.227 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.07 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.036 / 0.057 | ||
m7 <- glmer(cuedFaceTrusted_1 ~ trustNo_0 * respTime.log.c + trustDiff.CUC.c + (S1v23 + S2v3) +
(1 | participant),
family = binomial("logit"),
data = d)
summary(m7)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 * respTime.log.c + trustDiff.CUC.c +
## (S1v23 + S2v3) + (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27155.8 27219.0 -13569.9 27139.8 20024
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2447 -0.9736 0.5853 0.9566 2.0630
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07359 0.2713
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.02099 0.03360 -0.625 0.5321
## trustNo_0 0.09368 0.04029 2.325 0.0201 *
## respTime.log.c -0.01185 0.03489 -0.340 0.7342
## trustDiff.CUC.c -0.73601 0.03240 -22.719 <2e-16 ***
## S1v23 0.03021 0.04613 0.655 0.5125
## S2v3 -0.04452 0.04173 -1.067 0.2861
## trustNo_0:respTime.log.c -0.05355 0.04428 -1.209 0.2265
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsN_0 rspT.. tD.CUC S1v23 S2v3
## trustNo_0 -0.833
## respTm.lg.c -0.223 0.186
## trstDf.CUC. -0.001 -0.006 0.005
## S1v23 -0.428 0.401 0.001 0.006
## S2v3 -0.132 0.009 -0.007 0.006 -0.143
## trstN_0:T.. 0.231 -0.151 -0.789 0.007 -0.130 0.026tab_model(m7)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.98 | 0.92 – 1.05 | 0.532 |
| trustNo_0 | 1.10 | 1.01 – 1.19 | 0.020 |
| respTime.log.c | 0.99 | 0.92 – 1.06 | 0.734 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| S1v23 | 1.03 | 0.94 – 1.13 | 0.513 |
| S2v3 | 0.96 | 0.88 – 1.04 | 0.286 |
|
trustNo_0 * respTime.log.c |
0.95 | 0.87 – 1.03 | 0.227 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.07 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.036 / 0.057 | ||
singular when including random slopes for face trustworthiness – so incorporated trustworthiness difference as fixed effect no higher order interactions with trustDiff
m7a <- glmer(cuedFaceTrusted_1 ~ trust_.5 * respTime.log.c + trustDiff.CUC.c + (S1v23 + S2v3) +
(1 | participant),
family = binomial("logit"),
data = d)
summary(m7a)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 * respTime.log.c + trustDiff.CUC.c +
## (S1v23 + S2v3) + (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27155.8 27219.0 -13569.9 27139.8 20024
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2448 -0.9736 0.5853 0.9566 2.0630
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07359 0.2713
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.02585 0.02017 1.282 0.1999
## trust_.5 0.09367 0.04029 2.325 0.0201 *
## respTime.log.c -0.03863 0.02211 -1.747 0.0806 .
## trustDiff.CUC.c -0.73603 0.03240 -22.719 <2e-16 ***
## S1v23 0.03021 0.04613 0.655 0.5125
## S2v3 -0.04451 0.04173 -1.067 0.2861
## trust_.5:respTime.log.c -0.05352 0.04428 -1.209 0.2268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 rspT.. tD.CUC S1v23 S2v3
## trust_.5 -0.389
## respTm.lg.c -0.059 0.143
## trstDf.CUC. -0.007 -0.006 0.015
## S1v23 -0.312 0.401 -0.129 0.006
## S2v3 -0.211 0.009 0.015 0.006 -0.143
## trst_.5:T.. 0.234 -0.151 -0.243 0.007 -0.130 0.026tab_model(m7a)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.03 | 0.99 – 1.07 | 0.200 |
| trust_.5 | 1.10 | 1.01 – 1.19 | 0.020 |
| respTime.log.c | 0.96 | 0.92 – 1.00 | 0.081 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| S1v23 | 1.03 | 0.94 – 1.13 | 0.513 |
| S2v3 | 0.96 | 0.88 – 1.04 | 0.286 |
| trust_.5 * respTime.log.c | 0.95 | 0.87 – 1.03 | 0.227 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.07 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.036 / 0.057 | ||
m7 <- glmer(cuedFaceTrusted_1 ~ trustYes_0 * respTime.log.c + trustDiff.CUC.c + (S1v23 + S2v3) +
(1 | participant),
family = binomial("logit"),
data = d)
summary(m7)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 * respTime.log.c + trustDiff.CUC.c +
## (S1v23 + S2v3) + (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27155.8 27219.0 -13569.9 27139.8 20024
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2448 -0.9736 0.5853 0.9566 2.0630
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07359 0.2713
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.07269 0.02228 3.262 0.0011 **
## trustYes_0 -0.09367 0.04029 -2.325 0.0201 *
## respTime.log.c -0.06539 0.02722 -2.402 0.0163 *
## trustDiff.CUC.c -0.73602 0.03240 -22.719 <2e-16 ***
## S1v23 0.03021 0.04613 0.655 0.5125
## S2v3 -0.04452 0.04173 -1.067 0.2861
## trustYes_0:respTime.log.c 0.05352 0.04428 1.209 0.2268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 rspT.. tD.CUC S1v23 S2v3
## trustYes_0 -0.552
## respTm.lg.c 0.123 0.006
## trstDf.CUC. -0.012 0.006 0.018
## S1v23 0.081 -0.401 -0.210 0.007
## S2v3 -0.183 -0.009 0.033 0.006 -0.143
## trstY_0:T.. -0.076 -0.151 -0.616 -0.007 0.130 -0.026tab_model(m7)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.08 | 1.03 – 1.12 | 0.001 |
| trustYes_0 | 0.91 | 0.84 – 0.99 | 0.020 |
| respTime.log.c | 0.94 | 0.89 – 0.99 | 0.016 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| S1v23 | 1.03 | 0.94 – 1.13 | 0.513 |
| S2v3 | 0.96 | 0.88 – 1.04 | 0.286 |
|
trustYes_0 * respTime.log.c |
1.05 | 0.97 – 1.15 | 0.227 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.07 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.036 / 0.057 | ||
m7 <- glmer(cuedFaceTrusted_1 ~ trustNo_0 * respTime.log.c + trustDiff.CUC.c + (S1v23 + S2v3) +
(1 | participant),
family = binomial("logit"),
data = d)
summary(m7)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 * respTime.log.c + trustDiff.CUC.c +
## (S1v23 + S2v3) + (1 | participant)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 27155.8 27219.0 -13569.9 27139.8 20024
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2447 -0.9736 0.5853 0.9566 2.0630
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.07359 0.2713
## Number of obs: 20032, groups: participant, 825
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.02099 0.03360 -0.625 0.5321
## trustNo_0 0.09368 0.04029 2.325 0.0201 *
## respTime.log.c -0.01185 0.03489 -0.340 0.7342
## trustDiff.CUC.c -0.73601 0.03240 -22.719 <2e-16 ***
## S1v23 0.03021 0.04613 0.655 0.5125
## S2v3 -0.04452 0.04173 -1.067 0.2861
## trustNo_0:respTime.log.c -0.05355 0.04428 -1.209 0.2265
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsN_0 rspT.. tD.CUC S1v23 S2v3
## trustNo_0 -0.833
## respTm.lg.c -0.223 0.186
## trstDf.CUC. -0.001 -0.006 0.005
## S1v23 -0.428 0.401 0.001 0.006
## S2v3 -0.132 0.009 -0.007 0.006 -0.143
## trstN_0:T.. 0.231 -0.151 -0.789 0.007 -0.130 0.026tab_model(m7)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.98 | 0.92 – 1.05 | 0.532 |
| trustNo_0 | 1.10 | 1.01 – 1.19 | 0.020 |
| respTime.log.c | 0.99 | 0.92 – 1.06 | 0.734 |
| trustDiff.CUC.c | 0.48 | 0.45 – 0.51 | <0.001 |
| S1v23 | 1.03 | 0.94 – 1.13 | 0.513 |
| S2v3 | 0.96 | 0.88 – 1.04 | 0.286 |
|
trustNo_0 * respTime.log.c |
0.95 | 0.87 – 1.03 | 0.227 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.07 | ||
| ICC | 0.02 | ||
| N participant | 825 | ||
| Observations | 20032 | ||
| Marginal R2 / Conditional R2 | 0.036 / 0.057 | ||
no 2-way trust manipulation by response time interaction no 2-way study by response time interaction no 2-way study by trust manipulation interaction singular when including random slopes for face trustworthiness
m7 <- glmer(cuedFaceTrusted_1 ~ trust_.5 + respTime.log.c + (S1v23 + S2v3) +
(1|face) +
(1|participant),
family = binomial("logit"),
data = d2)
summary(m7)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + respTime.log.c + (S1v23 + S2v3) +
## (1 | face) + (1 | participant)
## Data: d2
##
## AIC BIC logLik deviance df.resid
## 55889.4 55949.7 -27937.7 55875.4 40823
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7559 -0.9767 0.4740 0.9578 1.7665
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.170704 0.4132
## face (Intercept) 0.002431 0.0493
## Number of obs: 40830, groups: participant, 825; face, 141
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.03567 0.02032 1.755 0.0792 .
## trust_.5 0.08193 0.04031 2.032 0.0421 *
## respTime.log.c -0.02359 0.01696 -1.391 0.1642
## S1v23 0.01735 0.04603 0.377 0.7063
## S2v3 -0.04027 0.04247 -0.948 0.3430
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 rspT.. S1v23
## trust_.5 -0.361
## respTm.lg.c -0.002 0.086
## S1v23 -0.281 0.401 -0.132
## S2v3 -0.205 0.013 0.016 -0.130tab_model(m7)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.04 | 1.00 – 1.08 | 0.079 |
| trust_.5 | 1.09 | 1.00 – 1.17 | 0.042 |
| respTime.log.c | 0.98 | 0.94 – 1.01 | 0.164 |
| S1v23 | 1.02 | 0.93 – 1.11 | 0.706 |
| S2v3 | 0.96 | 0.88 – 1.04 | 0.343 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.17 | ||
| τ00 face | 0.00 | ||
| ICC | 0.05 | ||
| N face | 141 | ||
| N participant | 825 | ||
| Observations | 40830 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.051 | ||
m7 <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + respTime.log.c + (S1v23 + S2v3) +
(1|face) +
(1 | participant),
family = binomial("logit"),
data = d2)
summary(m7)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + respTime.log.c + (S1v23 + S2v3) +
## (1 | face) + (1 | participant)
## Data: d2
##
## AIC BIC logLik deviance df.resid
## 55889.4 55949.7 -27937.7 55875.4 40823
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7559 -0.9767 0.4740 0.9578 1.7665
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.170705 0.4132
## face (Intercept) 0.002431 0.0493
## Number of obs: 40830, groups: participant, 825; face, 141
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.07664 0.02287 3.350 0.000807 ***
## trustYes_0 -0.08193 0.04032 -2.032 0.042134 *
## respTime.log.c -0.02359 0.01696 -1.391 0.164152
## S1v23 0.01735 0.04604 0.377 0.706337
## S2v3 -0.04027 0.04247 -0.948 0.343016
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 rspT.. S1v23
## trustYes_0 -0.560
## respTm.lg.c 0.074 -0.086
## S1v23 0.104 -0.401 -0.132
## S2v3 -0.171 -0.013 0.016 -0.130tab_model(m7)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.08 | 1.03 – 1.13 | 0.001 |
| trustYes_0 | 0.92 | 0.85 – 1.00 | 0.042 |
| respTime.log.c | 0.98 | 0.94 – 1.01 | 0.164 |
| S1v23 | 1.02 | 0.93 – 1.11 | 0.706 |
| S2v3 | 0.96 | 0.88 – 1.04 | 0.343 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.17 | ||
| τ00 face | 0.00 | ||
| ICC | 0.05 | ||
| N face | 141 | ||
| N participant | 825 | ||
| Observations | 40830 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.051 | ||
m7b <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + respTime.log.c + (S1v23 + S2v3) +
(1|face) +
(1 | participant),
family = binomial("logit"),
data = d2)
summary(m7b)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + respTime.log.c + (S1v23 + S2v3) +
## (1 | face) + (1 | participant)
## Data: d2
##
## AIC BIC logLik deviance df.resid
## 55889.4 55949.7 -27937.7 55875.4 40823
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7559 -0.9767 0.4740 0.9578 1.7665
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.170704 0.4132
## face (Intercept) 0.002431 0.0493
## Number of obs: 40830, groups: participant, 825; face, 141
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.00529 0.03340 -0.158 0.8742
## trustNo_0 0.08193 0.04031 2.032 0.0421 *
## respTime.log.c -0.02359 0.01696 -1.391 0.1641
## S1v23 0.01735 0.04604 0.377 0.7063
## S2v3 -0.04027 0.04247 -0.948 0.3430
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsN_0 rspT.. S1v23
## trustNo_0 -0.823
## respTm.lg.c -0.053 0.086
## S1v23 -0.413 0.401 -0.132
## S2v3 -0.133 0.013 0.016 -0.130tab_model(m7b)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.99 | 0.93 – 1.06 | 0.874 |
| trustNo_0 | 1.09 | 1.00 – 1.17 | 0.042 |
| respTime.log.c | 0.98 | 0.94 – 1.01 | 0.164 |
| S1v23 | 1.02 | 0.93 – 1.11 | 0.706 |
| S2v3 | 0.96 | 0.88 – 1.04 | 0.343 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.17 | ||
| τ00 face | 0.00 | ||
| ICC | 0.05 | ||
| N face | 141 | ||
| N participant | 825 | ||
| Observations | 40830 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.051 | ||
m7 <- glmer(cuedFaceTrusted_1 ~ trust_.5 + respTime.log.c + (s2_1 + s3_1) +
(1|face) +
(1|participant),
family = binomial("logit"),
data = d2)
summary(m7)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + respTime.log.c + (s2_1 + s3_1) +
## (1 | face) + (1 | participant)
## Data: d2
##
## AIC BIC logLik deviance df.resid
## 55889.4 55949.7 -27937.7 55875.4 40823
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7559 -0.9767 0.4740 0.9578 1.7665
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.170706 0.4132
## face (Intercept) 0.002431 0.0493
## Number of obs: 40830, groups: participant, 825; face, 141
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.024120 0.041299 0.584 0.5592
## trust_.5 0.081921 0.040315 2.032 0.0422 *
## respTime.log.c -0.023591 0.016957 -1.391 0.1641
## s2_1 0.037473 0.053157 0.705 0.4808
## s3_1 -0.002799 0.048120 -0.058 0.9536
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 rspT.. s2_1
## trust_.5 -0.476
## respTm.lg.c 0.097 0.086
## s2_1 -0.762 0.342 -0.120
## s3_1 -0.845 0.390 -0.119 0.652tab_model(m7)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.02 | 0.94 – 1.11 | 0.559 |
| trust_.5 | 1.09 | 1.00 – 1.17 | 0.042 |
| respTime.log.c | 0.98 | 0.94 – 1.01 | 0.164 |
| s2_1 | 1.04 | 0.94 – 1.15 | 0.481 |
| s3_1 | 1.00 | 0.91 – 1.10 | 0.954 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.17 | ||
| τ00 face | 0.00 | ||
| ICC | 0.05 | ||
| N face | 141 | ||
| N participant | 825 | ||
| Observations | 40830 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.051 | ||
m7 <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + respTime.log.c + (s2_1 + s3_1) +
(1|face) +
(1 | participant),
family = binomial("logit"),
data = d2)
summary(m7)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + respTime.log.c + (s2_1 + s3_1) +
## (1 | face) + (1 | participant)
## Data: d2
##
## AIC BIC logLik deviance df.resid
## 55889.4 55949.7 -27937.7 55875.4 40823
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7559 -0.9767 0.4740 0.9578 1.7665
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.170705 0.4132
## face (Intercept) 0.002431 0.0493
## Number of obs: 40830, groups: participant, 825; face, 141
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.065073 0.036322 1.792 0.0732 .
## trustYes_0 -0.081928 0.040313 -2.032 0.0421 *
## respTime.log.c -0.023590 0.016957 -1.391 0.1642
## s2_1 0.037482 0.053151 0.705 0.4807
## s3_1 -0.002788 0.048117 -0.058 0.9538
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 rspT.. s2_1
## trustYes_0 -0.014
## respTm.lg.c 0.158 -0.086
## s2_1 -0.676 -0.342 -0.120
## s3_1 -0.745 -0.389 -0.119 0.652tab_model(m7)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.07 | 0.99 – 1.15 | 0.073 |
| trustYes_0 | 0.92 | 0.85 – 1.00 | 0.042 |
| respTime.log.c | 0.98 | 0.94 – 1.01 | 0.164 |
| s2_1 | 1.04 | 0.94 – 1.15 | 0.481 |
| s3_1 | 1.00 | 0.91 – 1.10 | 0.954 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.17 | ||
| τ00 face | 0.00 | ||
| ICC | 0.05 | ||
| N face | 141 | ||
| N participant | 825 | ||
| Observations | 40830 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.051 | ||
m7b <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + respTime.log.c + (s2_1 + s3_1) +
(1|face) +
(1 | participant),
family = binomial("logit"),
data = d2)
summary(m7b)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + respTime.log.c + (s2_1 + s3_1) +
## (1 | face) + (1 | participant)
## Data: d2
##
## AIC BIC logLik deviance df.resid
## 55889.4 55949.7 -27937.7 55875.4 40823
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7559 -0.9767 0.4740 0.9578 1.7665
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.170704 0.4132
## face (Intercept) 0.002431 0.0493
## Number of obs: 40830, groups: participant, 825; face, 141
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.016854 0.053885 -0.313 0.7545
## trustNo_0 0.081927 0.040313 2.032 0.0421 *
## respTime.log.c -0.023590 0.016956 -1.391 0.1642
## s2_1 0.037482 0.053149 0.705 0.4807
## s3_1 -0.002788 0.048115 -0.058 0.9538
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsN_0 rspT.. s2_1
## trustNo_0 -0.739
## respTm.lg.c 0.042 0.086
## s2_1 -0.712 0.342 -0.120
## s3_1 -0.793 0.389 -0.119 0.652tab_model(m7b)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.98 | 0.88 – 1.09 | 0.754 |
| trustNo_0 | 1.09 | 1.00 – 1.17 | 0.042 |
| respTime.log.c | 0.98 | 0.94 – 1.01 | 0.164 |
| s2_1 | 1.04 | 0.94 – 1.15 | 0.481 |
| s3_1 | 1.00 | 0.91 – 1.10 | 0.954 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.17 | ||
| τ00 face | 0.00 | ||
| ICC | 0.05 | ||
| N face | 141 | ||
| N participant | 825 | ||
| Observations | 40830 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.051 | ||
m7 <- glmer(cuedFaceTrusted_1 ~ trust_.5 + respTime.log.c + (s1_1 + s3_1) +
(1|face) +
(1|participant),
family = binomial("logit"),
data = d2)
summary(m7)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + respTime.log.c + (s1_1 + s3_1) +
## (1 | face) + (1 | participant)
## Data: d2
##
## AIC BIC logLik deviance df.resid
## 55889.4 55949.7 -27937.7 55875.4 40823
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7559 -0.9767 0.4740 0.9578 1.7665
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.170704 0.4132
## face (Intercept) 0.002431 0.0493
## Number of obs: 40830, groups: participant, 825; face, 141
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.06159 0.03444 1.788 0.0737 .
## trust_.5 0.08193 0.04031 2.032 0.0421 *
## respTime.log.c -0.02359 0.01696 -1.391 0.1642
## s1_1 -0.03748 0.05315 -0.705 0.4806
## s3_1 -0.04027 0.04247 -0.948 0.3430
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 rspT.. s1_1
## trust_.5 -0.043
## respTm.lg.c -0.070 0.086
## s1_1 -0.630 -0.342 0.120
## s3_1 -0.796 0.013 0.016 0.512tab_model(m7)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.06 | 0.99 – 1.14 | 0.074 |
| trust_.5 | 1.09 | 1.00 – 1.17 | 0.042 |
| respTime.log.c | 0.98 | 0.94 – 1.01 | 0.164 |
| s1_1 | 0.96 | 0.87 – 1.07 | 0.481 |
| s3_1 | 0.96 | 0.88 – 1.04 | 0.343 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.17 | ||
| τ00 face | 0.00 | ||
| ICC | 0.05 | ||
| N face | 141 | ||
| N participant | 825 | ||
| Observations | 40830 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.051 | ||
m7 <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + respTime.log.c + (s1_1 + s3_1) +
(1|face) +
(1 | participant),
family = binomial("logit"),
data = d2)
summary(m7)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + respTime.log.c + (s1_1 + s3_1) +
## (1 | face) + (1 | participant)
## Data: d2
##
## AIC BIC logLik deviance df.resid
## 55889.4 55949.7 -27937.7 55875.4 40823
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7559 -0.9767 0.4740 0.9578 1.7665
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.170704 0.41316
## face (Intercept) 0.002431 0.04931
## Number of obs: 40830, groups: participant, 825; face, 141
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.10256 0.03916 2.619 0.00882 **
## trustYes_0 -0.08193 0.04031 -2.032 0.04213 *
## respTime.log.c -0.02359 0.01696 -1.391 0.16411
## s1_1 -0.03749 0.05315 -0.705 0.48065
## s3_1 -0.04028 0.04247 -0.948 0.34291
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 rspT.. s1_1
## trustYes_0 -0.477
## respTm.lg.c -0.017 -0.086
## s1_1 -0.730 0.342 0.120
## s3_1 -0.693 -0.013 0.016 0.512tab_model(m7)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.11 | 1.03 – 1.20 | 0.009 |
| trustYes_0 | 0.92 | 0.85 – 1.00 | 0.042 |
| respTime.log.c | 0.98 | 0.94 – 1.01 | 0.164 |
| s1_1 | 0.96 | 0.87 – 1.07 | 0.481 |
| s3_1 | 0.96 | 0.88 – 1.04 | 0.343 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.17 | ||
| τ00 face | 0.00 | ||
| ICC | 0.05 | ||
| N face | 141 | ||
| N participant | 825 | ||
| Observations | 40830 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.051 | ||
m7b <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + respTime.log.c + (s1_1 + s3_1) +
(1|face) +
(1 | participant),
family = binomial("logit"),
data = d2)
summary(m7b)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + respTime.log.c + (s1_1 + s3_1) +
## (1 | face) + (1 | participant)
## Data: d2
##
## AIC BIC logLik deviance df.resid
## 55889.4 55949.7 -27937.7 55875.4 40823
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7559 -0.9767 0.4740 0.9578 1.7665
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.170705 0.4132
## face (Intercept) 0.002431 0.0493
## Number of obs: 40830, groups: participant, 825; face, 141
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.02063 0.04064 0.508 0.6118
## trustNo_0 0.08193 0.04032 2.032 0.0421 *
## respTime.log.c -0.02359 0.01696 -1.391 0.1641
## s1_1 -0.03749 0.05316 -0.705 0.4807
## s3_1 -0.04027 0.04247 -0.948 0.3430
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsN_0 rspT.. s1_1
## trustNo_0 -0.532
## respTm.lg.c -0.102 0.086
## s1_1 -0.364 -0.342 0.120
## s3_1 -0.681 0.013 0.016 0.512tab_model(m7b)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.02 | 0.94 – 1.11 | 0.612 |
| trustNo_0 | 1.09 | 1.00 – 1.17 | 0.042 |
| respTime.log.c | 0.98 | 0.94 – 1.01 | 0.164 |
| s1_1 | 0.96 | 0.87 – 1.07 | 0.481 |
| s3_1 | 0.96 | 0.88 – 1.04 | 0.343 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.17 | ||
| τ00 face | 0.00 | ||
| ICC | 0.05 | ||
| N face | 141 | ||
| N participant | 825 | ||
| Observations | 40830 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.051 | ||
m7 <- glmer(cuedFaceTrusted_1 ~ trust_.5 + respTime.log.c + (s1_1 + s2_1) +
(1|face) +
(1|participant),
family = binomial("logit"),
data = d2)
summary(m7)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + respTime.log.c + (s1_1 + s2_1) +
## (1 | face) + (1 | participant)
## Data: d2
##
## AIC BIC logLik deviance df.resid
## 55889.4 55949.7 -27937.7 55875.4 40823
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7559 -0.9767 0.4740 0.9578 1.7665
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.170706 0.4132
## face (Intercept) 0.002431 0.0493
## Number of obs: 40830, groups: participant, 825; face, 141
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.021321 0.025730 0.829 0.4073
## trust_.5 0.081925 0.040314 2.032 0.0421 *
## respTime.log.c -0.023591 0.016957 -1.391 0.1641
## s1_1 0.002789 0.048117 0.058 0.9538
## s2_1 0.040269 0.042472 0.948 0.3431
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 rspT.. s1_1
## trust_.5 -0.035
## respTm.lg.c -0.067 0.086
## s1_1 -0.514 -0.389 0.119
## s2_1 -0.585 -0.013 -0.016 0.317tab_model(m7)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.02 | 0.97 – 1.07 | 0.407 |
| trust_.5 | 1.09 | 1.00 – 1.17 | 0.042 |
| respTime.log.c | 0.98 | 0.94 – 1.01 | 0.164 |
| s1_1 | 1.00 | 0.91 – 1.10 | 0.954 |
| s2_1 | 1.04 | 0.96 – 1.13 | 0.343 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.17 | ||
| τ00 face | 0.00 | ||
| ICC | 0.05 | ||
| N face | 141 | ||
| N participant | 825 | ||
| Observations | 40830 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.051 | ||
m7 <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + respTime.log.c + (s1_1 + s2_1) +
(1|face) +
(1 | participant),
family = binomial("logit"),
data = d2)
summary(m7)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + respTime.log.c + (s1_1 + s2_1) +
## (1 | face) + (1 | participant)
## Data: d2
##
## AIC BIC logLik deviance df.resid
## 55889.4 55949.7 -27937.7 55875.4 40823
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7559 -0.9767 0.4740 0.9578 1.7665
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.170704 0.4132
## face (Intercept) 0.002431 0.0493
## Number of obs: 40830, groups: participant, 825; face, 141
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.062281 0.032118 1.939 0.0525 .
## trustYes_0 -0.081925 0.040316 -2.032 0.0421 *
## respTime.log.c -0.023591 0.016957 -1.391 0.1642
## s1_1 0.002791 0.048120 0.058 0.9537
## s2_1 0.040273 0.042473 0.948 0.3430
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 rspT.. s1_1
## trustYes_0 -0.599
## respTm.lg.c 0.001 -0.086
## s1_1 -0.656 0.390 0.119
## s2_1 -0.477 0.013 -0.016 0.317tab_model(m7)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.06 | 1.00 – 1.13 | 0.052 |
| trustYes_0 | 0.92 | 0.85 – 1.00 | 0.042 |
| respTime.log.c | 0.98 | 0.94 – 1.01 | 0.164 |
| s1_1 | 1.00 | 0.91 – 1.10 | 0.954 |
| s2_1 | 1.04 | 0.96 – 1.13 | 0.343 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.17 | ||
| τ00 face | 0.00 | ||
| ICC | 0.05 | ||
| N face | 141 | ||
| N participant | 825 | ||
| Observations | 40830 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.051 | ||
m7b <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + respTime.log.c + (s1_1 + s2_1) +
(1|face) +
(1 | participant),
family = binomial("logit"),
data = d2)
summary(m7b)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + respTime.log.c + (s1_1 + s2_1) +
## (1 | face) + (1 | participant)
## Data: d2
##
## AIC BIC logLik deviance df.resid
## 55889.4 55949.7 -27937.7 55875.4 40823
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7559 -0.9767 0.4740 0.9578 1.7665
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 0.170703 0.4132
## face (Intercept) 0.002431 0.0493
## Number of obs: 40830, groups: participant, 825; face, 141
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.019645 0.033244 -0.591 0.5546
## trustNo_0 0.081927 0.040315 2.032 0.0421 *
## respTime.log.c -0.023591 0.016957 -1.391 0.1641
## s1_1 0.002789 0.048119 0.058 0.9538
## s2_1 0.040273 0.042473 0.948 0.3430
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsN_0 rspT.. s1_1
## trustNo_0 -0.634
## respTm.lg.c -0.104 0.086
## s1_1 -0.161 -0.390 0.119
## s2_1 -0.445 -0.013 -0.016 0.317tab_model(m7b)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.98 | 0.92 – 1.05 | 0.555 |
| trustNo_0 | 1.09 | 1.00 – 1.17 | 0.042 |
| respTime.log.c | 0.98 | 0.94 – 1.01 | 0.164 |
| s1_1 | 1.00 | 0.91 – 1.10 | 0.954 |
| s2_1 | 1.04 | 0.96 – 1.13 | 0.343 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 0.17 | ||
| τ00 face | 0.00 | ||
| ICC | 0.05 | ||
| N face | 141 | ||
| N participant | 825 | ||
| Observations | 40830 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.051 | ||
aggregate(d$respTime, list(d$condition, d$study), mean)m7 <- lmer(respTime.log ~ trust_.5 + (S1v23 + S2v3) +
(trustworthy|face) +
(1|participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)),
data = d2)
summary(m7)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: respTime.log ~ trust_.5 + (S1v23 + S2v3) + (trustworthy | face) +
## (1 | participant)
## Data: d2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 65107.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.1031 -0.6625 -0.0621 0.6174 7.1222
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 0.2676450 0.51734
## face (Intercept) 0.0039562 0.06290
## trustworthy 0.0006534 0.02556 -0.77
## Residual 0.2691035 0.51875
## Number of obs: 40447, groups: participant, 825; face, 140
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.11136 0.02066 923.64006 53.797 < 2e-16 ***
## trust_.5 -0.22335 0.03650 1435.96520 -6.118 1.22e-09 ***
## S1v23 0.35085 0.04609 881.81764 7.612 6.94e-14 ***
## S2v3 -0.04009 0.04384 825.05746 -0.914 0.361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 S1v23
## trust_.5 -0.326
## S1v23 -0.257 0.375
## S2v3 -0.206 0.012 -0.132m7 <- lmer(respTime.log ~ trustYes_0 + (S1v23 + S2v3) +
(trustworthy|face) +
(1|participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)),
data = d2)
summary(m7)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: respTime.log ~ trustYes_0 + (S1v23 + S2v3) + (trustworthy | face) +
## (1 | participant)
## Data: d2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 65107.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.1031 -0.6625 -0.0621 0.6174 7.1222
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 0.2676455 0.51734
## face (Intercept) 0.0039541 0.06288
## trustworthy 0.0006532 0.02556 -0.77
## Residual 0.2691035 0.51875
## Number of obs: 40447, groups: participant, 825; face, 140
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.99969 0.02267 992.30259 44.101 < 2e-16 ***
## trustYes_0 0.22335 0.03650 1435.96291 6.118 1.22e-09 ***
## S1v23 0.35085 0.04609 881.81555 7.612 6.94e-14 ***
## S2v3 -0.04009 0.04384 825.05546 -0.914 0.361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 S1v23
## trustYes_0 -0.508
## S1v23 0.067 -0.375
## S2v3 -0.179 -0.012 -0.132m7 <- lmer(respTime.log ~ trustNo_0 + (S1v23 + S2v3) +
(trustworthy|face) +
(1|participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)),
data = d2)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00227424 (tol = 0.002, component 1)summary(m7)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: respTime.log ~ trustNo_0 + (S1v23 + S2v3) + (trustworthy | face) +
## (1 | participant)
## Data: d2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 65107.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.1031 -0.6625 -0.0621 0.6174 7.1222
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 0.2676450 0.51734
## face (Intercept) 0.0039509 0.06286
## trustworthy 0.0006529 0.02555 -0.77
## Residual 0.2691035 0.51875
## Number of obs: 40447, groups: participant, 825; face, 140
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.22303 0.03172 1185.29447 38.561 < 2e-16 ***
## trustNo_0 -0.22335 0.03650 1435.96542 -6.118 1.22e-09 ***
## S1v23 0.35085 0.04609 881.81784 7.612 6.94e-14 ***
## S2v3 -0.04009 0.04384 825.05767 -0.914 0.361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsN_0 S1v23
## trustNo_0 -0.788
## S1v23 -0.383 0.375
## S2v3 -0.141 0.012 -0.132
## optimizer (bobyqa) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00227424 (tol = 0.002, component 1)m7 <- lmer(respTime.log ~ trust_.5 + (s2_1 + s3_1) +
(trustworthy|face) +
(1|participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)),
data = d2)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00244687 (tol = 0.002, component 1)summary(m7)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: respTime.log ~ trust_.5 + (s2_1 + s3_1) + (trustworthy | face) +
## (1 | participant)
## Data: d2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 65107.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.1031 -0.6625 -0.0621 0.6174 7.1222
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 0.2676449 0.51734
## face (Intercept) 0.0039610 0.06294
## trustworthy 0.0006539 0.02557 -0.77
## Residual 0.2691035 0.51875
## Number of obs: 40447, groups: participant, 825; face, 140
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.87746 0.04120 925.60796 21.299 < 2e-16 ***
## trust_.5 -0.22335 0.03650 1435.96561 -6.118 1.22e-09 ***
## s2_1 0.37089 0.05359 863.67551 6.921 8.74e-12 ***
## s3_1 0.33081 0.04835 879.94080 6.841 1.47e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 s2_1
## trust_.5 -0.443
## s2_1 -0.750 0.318
## s3_1 -0.836 0.362 0.634
## optimizer (bobyqa) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00244687 (tol = 0.002, component 1)m7 <- lmer(respTime.log ~ trustYes_0 + (s2_1 + s3_1) +
(trustworthy|face) +
(1|participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)),
data = d2)
summary(m7)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: respTime.log ~ trustYes_0 + (s2_1 + s3_1) + (trustworthy | face) +
## (1 | participant)
## Data: d2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 65107.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.1031 -0.6625 -0.0621 0.6174 7.1222
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 0.2676452 0.51734
## face (Intercept) 0.0039530 0.06287
## trustworthy 0.0006531 0.02556 -0.77
## Residual 0.2691035 0.51875
## Number of obs: 40447, groups: participant, 825; face, 140
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.658e-01 3.693e-02 8.419e+02 20.734 < 2e-16 ***
## trustYes_0 2.233e-01 3.650e-02 1.436e+03 6.118 1.22e-09 ***
## s2_1 3.709e-01 5.359e-02 8.637e+02 6.921 8.74e-12 ***
## s3_1 3.308e-01 4.835e-02 8.799e+02 6.841 1.47e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 s2_1
## trustYes_0 0.000
## s2_1 -0.680 -0.318
## s3_1 -0.754 -0.362 0.634m7 <- lmer(respTime.log ~ trustNo_0 + (s2_1 + s3_1) +
(trustworthy|face) +
(1|participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)),
data = d2)
summary(m7)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: respTime.log ~ trustNo_0 + (s2_1 + s3_1) + (trustworthy | face) +
## (1 | participant)
## Data: d2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 65107.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.1031 -0.6625 -0.0621 0.6174 7.1222
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 0.267645 0.51734
## face (Intercept) 0.003952 0.06287
## trustworthy 0.000653 0.02555 -0.77
## Residual 0.269103 0.51875
## Number of obs: 40447, groups: participant, 825; face, 140
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.98913 0.05193 1078.04546 19.047 < 2e-16 ***
## trustNo_0 -0.22335 0.03650 1435.96538 -6.118 1.22e-09 ***
## s2_1 0.37089 0.05359 863.67526 6.921 8.74e-12 ***
## s3_1 0.33081 0.04835 879.94056 6.841 1.47e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsN_0 s2_1
## trustNo_0 -0.703
## s2_1 -0.707 0.318
## s3_1 -0.791 0.362 0.634m7 <- lmer(respTime.log ~ trust_.5 + (s1_1 + s3_1) +
(trustworthy|face) +
(1|participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)),
data = d2)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00244469 (tol = 0.002, component 1)summary(m7)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: respTime.log ~ trust_.5 + (s1_1 + s3_1) + (trustworthy | face) +
## (1 | participant)
## Data: d2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 65107.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.1031 -0.6625 -0.0621 0.6174 7.1222
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 0.2676448 0.51734
## face (Intercept) 0.0039579 0.06291
## trustworthy 0.0006536 0.02556 -0.77
## Residual 0.2691035 0.51875
## Number of obs: 40447, groups: participant, 825; face, 140
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.24835 0.03544 843.96425 35.224 < 2e-16 ***
## trust_.5 -0.22335 0.03650 1435.96637 -6.118 1.22e-09 ***
## s1_1 -0.37089 0.05359 863.67616 -6.921 8.74e-12 ***
## s3_1 -0.04009 0.04384 825.05850 -0.914 0.361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 s1_1
## trust_.5 -0.035
## s1_1 -0.640 -0.318
## s3_1 -0.796 0.012 0.523
## optimizer (bobyqa) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00244469 (tol = 0.002, component 1)m7 <- lmer(respTime.log ~ trustYes_0 + (s1_1 + s3_1) +
(trustworthy|face) +
(1|participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)),
data = d2)
summary(m7)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: respTime.log ~ trustYes_0 + (s1_1 + s3_1) + (trustworthy | face) +
## (1 | participant)
## Data: d2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 65107.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.1031 -0.6625 -0.0621 0.6174 7.1222
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 0.2676457 0.51734
## face (Intercept) 0.0039557 0.06289
## trustworthy 0.0006533 0.02556 -0.77
## Residual 0.2691034 0.51875
## Number of obs: 40447, groups: participant, 825; face, 140
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.13668 0.03929 923.18199 28.928 < 2e-16 ***
## trustYes_0 0.22335 0.03650 1435.96168 6.118 1.22e-09 ***
## s1_1 -0.37089 0.05359 863.67200 -6.921 8.74e-12 ***
## s3_1 -0.04009 0.04384 825.05442 -0.914 0.361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 s1_1
## trustYes_0 -0.433
## s1_1 -0.725 0.318
## s3_1 -0.713 -0.012 0.523m7 <- lmer(respTime.log ~ trustNo_0 + (s1_1 + s3_1) +
(trustworthy|face) +
(1|participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)),
data = d2)
summary(m7)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: respTime.log ~ trustNo_0 + (s1_1 + s3_1) + (trustworthy | face) +
## (1 | participant)
## Data: d2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 65107.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.1031 -0.6625 -0.0621 0.6174 7.1222
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 0.267645 0.51734
## face (Intercept) 0.003952 0.06287
## trustworthy 0.000653 0.02555 -0.77
## Residual 0.269103 0.51875
## Number of obs: 40447, groups: participant, 825; face, 140
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.36003 0.04043 943.75058 33.642 < 2e-16 ***
## trustNo_0 -0.22335 0.03650 1435.96505 -6.118 1.22e-09 ***
## s1_1 -0.37089 0.05359 863.67495 -6.921 8.74e-12 ***
## s3_1 -0.04009 0.04384 825.05731 -0.914 0.361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsN_0 s1_1
## trustNo_0 -0.482
## s1_1 -0.418 -0.318
## s3_1 -0.703 0.012 0.523m7 <- lmer(respTime.log ~ trust_.5 + (s1_1 + s2_1) +
(trustworthy|face) +
(1|participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)),
data = d2)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00430879 (tol = 0.002, component 1)summary(m7)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: respTime.log ~ trust_.5 + (s1_1 + s2_1) + (trustworthy | face) +
## (1 | participant)
## Data: d2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 65107.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.1031 -0.6625 -0.0621 0.6174 7.1222
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 0.2676454 0.51734
## face (Intercept) 0.0039688 0.06300
## trustworthy 0.0006546 0.02559 -0.77
## Residual 0.2691035 0.51875
## Number of obs: 40447, groups: participant, 825; face, 140
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.20826 0.02654 867.19490 45.528 < 2e-16 ***
## trust_.5 -0.22335 0.03650 1435.96300 -6.118 1.22e-09 ***
## s1_1 -0.33080 0.04835 879.93849 -6.841 1.47e-11 ***
## s2_1 0.04009 0.04384 825.05564 0.914 0.361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 s1_1
## trust_.5 -0.028
## s1_1 -0.524 -0.362
## s2_1 -0.589 -0.012 0.327
## optimizer (bobyqa) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00430879 (tol = 0.002, component 1)m7 <- lmer(respTime.log ~ trustYes_0 + (s1_1 + s2_1) +
(trustworthy|face) +
(1|participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)),
data = d2)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00229484 (tol = 0.002, component 1)summary(m7)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: respTime.log ~ trustYes_0 + (s1_1 + s2_1) + (trustworthy | face) +
## (1 | participant)
## Data: d2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 65107.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.1031 -0.6625 -0.0621 0.6174 7.1222
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 0.2676452 0.51734
## face (Intercept) 0.0039539 0.06288
## trustworthy 0.0006532 0.02556 -0.77
## Residual 0.2691035 0.51875
## Number of obs: 40447, groups: participant, 825; face, 140
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.09659 0.03179 999.89014 34.492 < 2e-16 ***
## trustYes_0 0.22335 0.03650 1435.96453 6.118 1.22e-09 ***
## s1_1 -0.33081 0.04835 879.93978 -6.841 1.47e-11 ***
## s2_1 0.04009 0.04384 825.05687 0.914 0.361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsY_0 s1_1
## trustYes_0 -0.551
## s1_1 -0.646 0.362
## s2_1 -0.498 0.012 0.327
## optimizer (bobyqa) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00229484 (tol = 0.002, component 1)m7 <- lmer(respTime.log ~ trustNo_0 + (s1_1 + s2_1) +
(trustworthy|face) +
(1|participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)),
data = d2)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00479147 (tol = 0.002, component 1)summary(m7)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: respTime.log ~ trustNo_0 + (s1_1 + s2_1) + (trustworthy | face) +
## (1 | participant)
## Data: d2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 65107.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.1031 -0.6625 -0.0621 0.6174 7.1222
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 0.2676443 0.51734
## face (Intercept) 0.0039705 0.06301
## trustworthy 0.0006548 0.02559 -0.77
## Residual 0.2691035 0.51875
## Number of obs: 40447, groups: participant, 825; face, 140
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.31994 0.03262 1017.10085 40.463 < 2e-16 ***
## trustNo_0 -0.22335 0.03650 1435.96916 -6.118 1.22e-09 ***
## s1_1 -0.33080 0.04835 879.94402 -6.841 1.47e-11 ***
## s2_1 0.04009 0.04384 825.06097 0.914 0.361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trsN_0 s1_1
## trustNo_0 -0.582
## s1_1 -0.224 -0.362
## s2_1 -0.473 -0.012 0.327
## optimizer (bobyqa) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00479147 (tol = 0.002, component 1)