Pilot
2023-12-08
Demographics Stuff
Plots
Bayes
Do mass and count superordinate nouns behave similarly across contexts that emphasize individual objects vs functionality?
We only look at English participants for this question.
Base model: Function response ~ 1 + (1|Participant) + (1|Item)
## Family: bernoulli
## Links: mu = logit
## Formula: func_response ~ 1 + (1 | subject_id) + (1 | trial_item)
## Data: df.trial_en_goals (Number of observations: 1680)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~subject_id (Number of levels: 140)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 3.43 0.40 2.74 4.30 1.00 767 1291
##
## ~trial_item (Number of levels: 12)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.76 0.44 1.12 2.81 1.00 814 1441
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -3.78 0.66 -5.11 -2.53 1.01 464 1042
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).This following model is not preregistered.
Function response ~ Syntax + (1|Participant) + (1|Item)
## Family: bernoulli
## Links: mu = logit
## Formula: func_response ~ 1 + eng_syntax + (1 | subject_id) + (1 | trial_item)
## Data: df.trial_en_goals (Number of observations: 1680)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~subject_id (Number of levels: 140)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 3.40 0.40 2.72 4.25 1.00 783 1592
##
## ~trial_item (Number of levels: 12)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.83 0.48 1.12 3.01 1.00 1338 1816
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -3.83 0.89 -5.70 -2.11 1.01 786 1336
## eng_syntaxmass 0.14 1.09 -2.06 2.26 1.00 881 1610
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## model term df1 df2 F.ratio Chisq p.value
## eng_syntax 1 Inf 0.016 0.016 0.8999## Sampling priors, please wait...## Bayes Factor (Savage-Dickey density ratio)
##
## Parameter | BF
## -----------------------
## (Intercept) | 212.63
## eng_syntaxmass | 0.103
##
## * Evidence Against The Null: 0## Computation of Marginal Likelihood: estimating marginal likelihood,
## please wait...## Computation of Marginal Likelihood: estimating marginal likelihood,
## please wait...## Bayes Factors for Model Comparison
##
## Model BF
## [1] 1 + eng_syntax + (1 | subject_id) + (1 | trial_item) 0.084
##
## * Against Denominator: [2] 1 + (1 | subject_id) + (1 | trial_item)
## * Bayes Factor Type: marginal likelihoods (bridgesampling)Function response ~ Context + Syntax + (1|Participant) + (1|Item)
## Family: bernoulli
## Links: mu = logit
## Formula: func_response ~ 1 + context + eng_syntax + (1 | subject_id) + (1 | trial_item)
## Data: df.trial_en_goals (Number of observations: 1680)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~subject_id (Number of levels: 140)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 3.38 0.38 2.69 4.23 1.00 830 1360
##
## ~trial_item (Number of levels: 12)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.84 0.48 1.15 3.01 1.00 1100 1810
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -3.23 0.93 -5.08 -1.36 1.00 649 1354
## contextindividual -1.31 0.64 -2.56 -0.05 1.01 555 1054
## eng_syntaxmass 0.18 1.13 -2.14 2.45 1.00 759 1400
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## Sampling priors, please wait...## Bayes Factor (Savage-Dickey density ratio)
##
## Parameter | BF
## -------------------------
## (Intercept) | 13.32
## contextindividual | 0.574
## eng_syntaxmass | 0.111
##
## * Evidence Against The Null: 0## Computation of Marginal Likelihood: estimating marginal likelihood,
## please wait...## Computation of Marginal Likelihood: estimating marginal likelihood,
## please wait...## Bayes Factors for Model Comparison
##
## Model BF
## [1] 1 + context + eng_syntax + (1 | subject_id) + (1 | trial_item) 0.033
##
## * Against Denominator: [2] 1 + (1 | subject_id) + (1 | trial_item)
## * Bayes Factor Type: marginal likelihoods (bridgesampling)Function response ~ Context * Syntax + (1|Participant) + (1|Item)
## Family: bernoulli
## Links: mu = logit
## Formula: func_response ~ context * eng_syntax + (1 | subject_id) + (1 | trial_item)
## Data: df.trial_en_goals (Number of observations: 1680)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~subject_id (Number of levels: 140)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 3.42 0.41 2.68 4.31 1.00 876 1696
##
## ~trial_item (Number of levels: 12)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.90 0.51 1.17 3.13 1.01 1202 1806
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## Intercept -3.37 1.00 -5.33 -1.41 1.00
## contextindividual -1.04 0.72 -2.48 0.33 1.01
## eng_syntaxmass 0.44 1.22 -1.94 2.84 1.00
## contextindividual:eng_syntaxmass -0.53 0.38 -1.30 0.21 1.00
## Bulk_ESS Tail_ESS
## Intercept 819 1117
## contextindividual 600 1378
## eng_syntaxmass 703 1055
## contextindividual:eng_syntaxmass 4572 2869
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## model term df1 df2 F.ratio Chisq p.value
## context 1 Inf 3.539 3.539 0.0599
## eng_syntax 1 Inf 0.021 0.021 0.8837
## context:eng_syntax 1 Inf 1.901 1.901 0.1680## Sampling priors, please wait...## Bayes Factor (Savage-Dickey density ratio)
##
## Parameter | BF
## ----------------------------------------
## (Intercept) | 11.29
## contextindividual | 0.214
## eng_syntaxmass | 0.116
## contextindividual:eng_syntaxmass | 0.098
##
## * Evidence Against The Null: 0## Computation of Marginal Likelihood: estimating marginal likelihood,
## please wait...## Computation of Marginal Likelihood: estimating marginal likelihood,
## please wait...## Bayes Factors for Model Comparison
##
## Model BF
## [1] context * eng_syntax + (1 | subject_id) + (1 | trial_item) 0.004
##
## * Against Denominator: [2] 1 + (1 | subject_id) + (1 | trial_item)
## * Bayes Factor Type: marginal likelihoods (bridgesampling)This set of models suggest that there may be a small effect of context, but the best model is still the base model that contains no predictors.
Do English and French speakers use different quantificational bases when making comparisons for superordinate nouns that differ in syntactic status across the two languages?
For this, we only look at nouns that are mass in English and count in French (e.g., furniture). In these models, Language is a proxy for Syntax.
Base model: Function response ~ 1 + (1|Participant) + (1|Item)
## Family: bernoulli
## Links: mu = logit
## Formula: func_response ~ 1 + (1 | subject_id) + (1 | trial_item)
## Data: df.trial_goals %>% filter(eng_syntax == "mass") (Number of observations: 1680)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~subject_id (Number of levels: 280)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 2.74 0.26 2.26 3.29 1.00 1330 2373
##
## ~trial_item (Number of levels: 6)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.95 0.76 0.99 3.98 1.00 1297 1667
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -2.79 0.86 -4.41 -0.95 1.00 934 1585
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).Function response ~ Language + (1|Participant) + (1|Item)
## Family: bernoulli
## Links: mu = logit
## Formula: func_response ~ lang + (1 | subject_id) + (1 | trial_item)
## Data: df.trial_goals %>% filter(eng_syntax == "mass") (Number of observations: 1680)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~subject_id (Number of levels: 280)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 2.76 0.26 2.29 3.32 1.00 1258 2312
##
## ~trial_item (Number of levels: 6)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.91 0.74 1.01 3.64 1.00 1772 1538
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -3.06 0.86 -4.73 -1.27 1.00 1460 1775
## langfrench 0.43 0.40 -0.35 1.25 1.00 1540 1986
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## Computation of Marginal Likelihood: estimating marginal likelihood,
## please wait...
## Computation of Marginal Likelihood: estimating marginal likelihood,
## please wait...## Bayes Factors for Model Comparison
##
## Model BF
## [1] lang + (1 | subject_id) + (1 | trial_item) 0.946
##
## * Against Denominator: [2] 1 + (1 | subject_id) + (1 | trial_item)
## * Bayes Factor Type: marginal likelihoods (bridgesampling)This following model is not preregistered.
Function response ~ Language + Context + (1|Participant) + (1|Item)
## Family: bernoulli
## Links: mu = logit
## Formula: func_response ~ lang + context + (1 | subject_id) + (1 | trial_item)
## Data: df.trial_goals %>% filter(eng_syntax == "mass") (Number of observations: 1680)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~subject_id (Number of levels: 280)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 2.58 0.25 2.11 3.10 1.00 1438 2477
##
## ~trial_item (Number of levels: 6)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.91 0.72 0.99 3.64 1.00 1619 2021
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -2.14 0.87 -3.84 -0.34 1.00 1408 1921
## langfrench 0.42 0.38 -0.34 1.17 1.00 1842 2446
## contextindividual -1.75 0.40 -2.54 -1.00 1.00 1625 2299
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## model term df1 df2 F.ratio Chisq p.value
## lang 1 Inf 1.171 1.171 0.2792
## context 1 Inf 19.289 19.289 <.0001
## (confounded) 1 Inf 0.000 20.400 1.0000## Computation of Marginal Likelihood: estimating marginal likelihood,
## please wait...
## Computation of Marginal Likelihood: estimating marginal likelihood,
## please wait...## Bayes Factors for Model Comparison
##
## Model BF
## [1] lang + context + (1 | subject_id) + (1 | trial_item) 1.96e+04
##
## * Against Denominator: [2] 1 + (1 | subject_id) + (1 | trial_item)
## * Bayes Factor Type: marginal likelihoods (bridgesampling)Function response ~ Language * Context + (1|Participant) + (1|Item)
## Family: bernoulli
## Links: mu = logit
## Formula: func_response ~ lang * context + (1 | subject_id) + (1 | trial_item)
## Data: df.trial_goals %>% filter(eng_syntax == "mass") (Number of observations: 1680)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~subject_id (Number of levels: 280)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 2.58 0.25 2.12 3.10 1.00 1091 1971
##
## ~trial_item (Number of levels: 6)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.92 0.72 1.01 3.74 1.00 1722 2082
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -2.34 0.88 -4.03 -0.52 1.00 1184
## langfrench 0.79 0.52 -0.20 1.82 1.00 1139
## contextindividual -1.34 0.55 -2.47 -0.29 1.01 1148
## langfrench:contextindividual -0.84 0.77 -2.39 0.60 1.00 1159
## Tail_ESS
## Intercept 1604
## langfrench 1690
## contextindividual 1982
## langfrench:contextindividual 1820
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## model term df1 df2 F.ratio Chisq p.value
## lang 1 Inf 0.861 0.861 0.3535
## context 1 Inf 19.775 19.775 <.0001
## lang:context 1 Inf 1.193 1.193 0.2747## Computation of Marginal Likelihood: estimating marginal likelihood,
## please wait...
## Computation of Marginal Likelihood: estimating marginal likelihood,
## please wait...## Bayes Factors for Model Comparison
##
## Model BF
## [1] lang * context + (1 | subject_id) + (1 | trial_item) 0.028
##
## * Against Denominator: [2] lang + context + (1 | subject_id) + (1 | trial_item)
## * Bayes Factor Type: marginal likelihoods (bridgesampling)This set of models suggest that there is no effect of language (for these nouns, a proxy for syntax). But there is a main effect of context.
Now I’m just checking the fit of the lang + context model…
Exploratory from here…
Including all the data.
## Family: bernoulli
## Links: mu = logit
## Formula: func_response ~ 1 + (1 | subject_id) + (1 | trial_item)
## Data: df.trial_goals (Number of observations: 3360)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~subject_id (Number of levels: 280)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 3.13 0.24 2.68 3.63 1.00 843 1603
##
## ~trial_item (Number of levels: 12)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.82 0.44 1.17 2.89 1.00 799 1512
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -3.34 0.61 -4.49 -2.10 1.00 665 977
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## Sampling priors, please wait...## Bayes Factor (Savage-Dickey density ratio)
##
## Parameter | BF
## --------------------
## (Intercept) | 731.92
##
## * Evidence Against The Null: 0## Family: bernoulli
## Links: mu = logit
## Formula: func_response ~ 1 + lang + eng_syntax + (1 | subject_id) + (1 | trial_item)
## Data: df.trial_goals (Number of observations: 3360)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~subject_id (Number of levels: 280)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 3.13 0.25 2.69 3.64 1.00 923 1690
##
## ~trial_item (Number of levels: 12)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.92 0.49 1.21 3.08 1.00 1860 2599
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -3.68 0.88 -5.47 -1.90 1.00 1512 2226
## langfrench 0.58 0.43 -0.27 1.44 1.00 825 1697
## eng_syntaxmass 0.11 1.13 -2.17 2.29 1.00 1387 2139
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## Sampling priors, please wait...## Bayes Factor (Savage-Dickey density ratio)
##
## Parameter | BF
## ----------------------
## (Intercept) | 54.32
## langfrench | 0.114
## eng_syntaxmass | 0.106
##
## * Evidence Against The Null: 0## Computation of Marginal Likelihood: estimating marginal likelihood,
## please wait...## Computation of Marginal Likelihood: estimating marginal likelihood,
## please wait...## Bayes Factors for Model Comparison
##
## Model BF
## [1] 1 + lang + eng_syntax + (1 | subject_id) + (1 | trial_item) 0.126
##
## * Against Denominator: [2] 1 + (1 | subject_id) + (1 | trial_item)
## * Bayes Factor Type: marginal likelihoods (bridgesampling)## Family: bernoulli
## Links: mu = logit
## Formula: func_response ~ 1 + lang + context + eng_syntax + (1 | subject_id) + (1 | trial_item)
## Data: df.trial_goals (Number of observations: 3360)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~subject_id (Number of levels: 280)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 2.98 0.23 2.56 3.46 1.01 898 2109
##
## ~trial_item (Number of levels: 12)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.92 0.50 1.21 3.09 1.00 920 1332
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -2.76 0.90 -4.55 -0.96 1.00 763 1274
## langfrench 0.57 0.41 -0.22 1.40 1.01 600 973
## contextindividual -1.80 0.42 -2.66 -0.99 1.01 576 1207
## eng_syntaxmass 0.14 1.16 -2.09 2.49 1.00 746 1174
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## Sampling priors, please wait...## Bayes Factor (Savage-Dickey density ratio)
##
## Parameter | BF
## -------------------------
## (Intercept) | 7.68
## langfrench | 0.101
## contextindividual | 67.26
## eng_syntaxmass | 0.103
##
## * Evidence Against The Null: 0## Computation of Marginal Likelihood: estimating marginal likelihood,
## please wait...
## Computation of Marginal Likelihood: estimating marginal likelihood,
## please wait...## Bayes Factors for Model Comparison
##
## Model BF
## [1] 1 + lang + context + eng_syntax + (1 | subject_id) + (1 | trial_item) 0.215
##
## * Against Denominator: [2] 1 + (1 | subject_id) + (1 | trial_item)
## * Bayes Factor Type: marginal likelihoods (bridgesampling)## Family: bernoulli
## Links: mu = logit
## Formula: func_response ~ 1 + lang * eng_syntax + (1 | subject_id) + (1 | trial_item)
## Data: df.trial_goals (Number of observations: 3360)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~subject_id (Number of levels: 280)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 3.13 0.24 2.69 3.66 1.00 948 1748
##
## ~trial_item (Number of levels: 12)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.91 0.48 1.21 3.06 1.00 1564 2320
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -3.78 0.88 -5.54 -2.06 1.00 1543
## langfrench 0.70 0.45 -0.19 1.59 1.01 939
## eng_syntaxmass 0.21 1.16 -2.11 2.54 1.00 1570
## langfrench:eng_syntaxmass -0.20 0.25 -0.70 0.29 1.01 10094
## Tail_ESS
## Intercept 1684
## langfrench 2023
## eng_syntaxmass 1888
## langfrench:eng_syntaxmass 2099
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## model term df1 df2 F.ratio Chisq p.value
## lang 1 Inf 1.971 1.971 0.1603
## eng_syntax 1 Inf 0.009 0.009 0.9226
## lang:eng_syntax 1 Inf 0.606 0.606 0.4363## Sampling priors, please wait...## Bayes Factor (Savage-Dickey density ratio)
##
## Parameter | BF
## ---------------------------------
## (Intercept) | 98.18
## langfrench | 0.152
## eng_syntaxmass | 0.115
## langfrench:eng_syntaxmass | 0.032
##
## * Evidence Against The Null: 0## Computation of Marginal Likelihood: estimating marginal likelihood,
## please wait...
## Computation of Marginal Likelihood: estimating marginal likelihood,
## please wait...## Bayes Factors for Model Comparison
##
## Model BF
## [1] 1 + lang * eng_syntax + (1 | subject_id) + (1 | trial_item) 5.58e-04
##
## * Against Denominator: [2] 1 + (1 | subject_id) + (1 | trial_item)
## * Bayes Factor Type: marginal likelihoods (bridgesampling)## Family: bernoulli
## Links: mu = logit
## Formula: func_response ~ lang * context * eng_syntax + (1 | subject_id) + (1 | trial_item)
## Data: df.trial_goals (Number of observations: 3360)
## Draws: 4 chains, each with iter = 6000; warmup = 3000; thin = 1;
## total post-warmup draws = 12000
##
## Multilevel Hyperparameters:
## ~subject_id (Number of levels: 280)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 2.98 0.24 2.56 3.48 1.00 3515 6212
##
## ~trial_item (Number of levels: 12)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.91 0.48 1.22 3.06 1.00 4362 5791
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI
## Intercept -3.25 0.91 -5.08
## langfrench 1.33 0.58 0.18
## contextindividual -0.94 0.63 -2.20
## eng_syntaxmass 0.47 1.16 -1.83
## langfrench:contextindividual -1.46 0.88 -3.20
## langfrench:eng_syntaxmass -0.45 0.33 -1.10
## contextindividual:eng_syntaxmass -0.51 0.38 -1.24
## langfrench:contextindividual:eng_syntaxmass 0.60 0.52 -0.42
## u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -1.45 1.00 3343 5225
## langfrench 2.49 1.00 2183 4512
## contextindividual 0.28 1.00 2547 5053
## eng_syntaxmass 2.79 1.00 3900 4834
## langfrench:contextindividual 0.28 1.00 2483 4791
## langfrench:eng_syntaxmass 0.21 1.00 13772 9963
## contextindividual:eng_syntaxmass 0.24 1.00 12664 9993
## langfrench:contextindividual:eng_syntaxmass 1.61 1.00 11474 9670
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## model term df1 df2 F.ratio Chisq p.value
## lang 1 Inf 1.644 1.644 0.1998
## context 1 Inf 18.007 18.007 <.0001
## eng_syntax 1 Inf 0.016 0.016 0.8995
## lang:context 1 Inf 1.892 1.892 0.1690
## lang:eng_syntax 1 Inf 0.315 0.315 0.5747
## context:eng_syntax 1 Inf 0.594 0.594 0.4410
## lang:context:eng_syntax 1 Inf 1.329 1.329 0.2489## Sampling priors, please wait...## Bayes Factor (Savage-Dickey density ratio)
##
## Parameter | BF
## ---------------------------------------------------
## (Intercept) | 24.05
## langfrench | 0.708
## contextindividual | 0.195
## eng_syntaxmass | 0.123
## langfrench:contextindividual | 0.353
## langfrench:eng_syntaxmass | 0.082
## contextindividual:eng_syntaxmass | 0.091
## langfrench:contextindividual:eng_syntaxmass | 0.104
##
## * Evidence Against The Null: 0## Computation of Marginal Likelihood: estimating marginal likelihood,
## please wait...
## Computation of Marginal Likelihood: estimating marginal likelihood,
## please wait...## Bayes Factors for Model Comparison
##
## Model BF
## [1] lang * context * eng_syntax + (1 | subject_id) + (1 | trial_item) 3.62e-05
##
## * Against Denominator: [2] 1 + (1 | subject_id) + (1 | trial_item)
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## Iteration: 1000## Iteration: 1## Estimated Bayes factor in favor of fit.lang_context over fit.base: 0.64011
## NOTE: Results may be misleading due to involvement in interactions
More exploratory stuff
