- [[220504_103514 predict other stuff with parameters]] # parameter bivariate correlations - correlate within country, transform r to z within each country, then average z across countries, then transform z back to r - bound correlates with non-decision time at 0.6 - drift correlates with bias at 0.4 ```r param bound bias drift ndt 1: bound 1.000 0.045 -0.091 0.624 2: bias 0.045 1.000 0.391 0.000 3: drift -0.091 0.391 1.000 -0.010 4: ndt 0.624 0.000 -0.010 1.000 ``` # crt ~ parameters - [[220419_151703 fold0 results|versus parameter ~ crt models (4 models, 1 for each parameter)]] ```r m_crt <- brm(crt ~ boundz + biasz + driftz + ndtz + bound_countryz + bias_countryz + drift_countryz + ndt_countryz + (1 + boundz + biasz + driftz + ndtz | Country), d1) Population-Level Effects: Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS Intercept 1.51 0.06 1.39 1.62 1.00 6725 2938 boundz 0.12 0.08 -0.04 0.28 1.00 4813 3028 biasz -0.02 0.07 -0.16 0.11 1.00 5811 3019 driftz 0.31 0.07 0.16 0.44 1.00 4675 3110 ndtz -0.01 0.08 -0.17 0.15 1.00 5208 2947 bound_countryz 0.00 0.06 -0.12 0.12 1.00 5452 3475 bias_countryz -0.01 0.07 -0.14 0.12 1.00 5646 2914 drift_countryz -0.00 0.07 -0.14 0.14 1.00 5155 2969 ndt_countryz 0.02 0.08 -0.15 0.17 1.00 4584 2998 ``` ![[fold0_bayesian_intervals_crt-params.png]] # extra stuff correlations within countries ```r country param bound bias drift ndt 1: Argentina bound 1.000 0.162 -0.022 0.661 # 2: Argentina bias 0.162 1.000 0.199 0.159 3: Argentina drift -0.022 0.199 1.000 -0.255 4: Argentina ndt 0.661 0.159 -0.255 1.000 5: Australia bound 1.000 0.248 0.029 0.860 6: Australia bias 0.248 1.000 0.695 0.261 7: Australia drift 0.029 0.695 1.000 0.065 8: Australia ndt 0.860 0.261 0.065 1.000 9: Brazil bound 1.000 -0.109 -0.145 0.747 10: Brazil bias -0.109 1.000 0.475 -0.418 11: Brazil drift -0.145 0.475 1.000 0.008 12: Brazil ndt 0.747 -0.418 0.008 1.000 13: China bound 1.000 -0.270 -0.259 0.607 14: China bias -0.270 1.000 0.295 -0.059 15: China drift -0.259 0.295 1.000 0.035 16: China ndt 0.607 -0.059 0.035 1.000 17: Egypt bound 1.000 -0.080 0.010 0.594 18: Egypt bias -0.080 1.000 0.368 -0.231 19: Egypt drift 0.010 0.368 1.000 -0.108 20: Egypt ndt 0.594 -0.231 -0.108 1.000 21: India bound 1.000 0.170 0.191 0.713 22: India bias 0.170 1.000 0.284 0.091 23: India drift 0.191 0.284 1.000 0.190 24: India ndt 0.713 0.091 0.190 1.000 25: Italy bound 1.000 -0.230 0.082 0.635 26: Italy bias -0.230 1.000 0.440 0.024 27: Italy drift 0.082 0.440 1.000 0.271 28: Italy ndt 0.635 0.024 0.271 1.000 29: Mexico bound 1.000 0.050 -0.012 0.709 30: Mexico bias 0.050 1.000 0.328 0.096 31: Mexico drift -0.012 0.328 1.000 0.061 32: Mexico ndt 0.709 0.096 0.061 1.000 33: Nigeria bound 1.000 -0.319 -0.155 0.282 34: Nigeria bias -0.319 1.000 0.468 0.153 35: Nigeria drift -0.155 0.468 1.000 0.217 36: Nigeria ndt 0.282 0.153 0.217 1.000 37: Philippines bound 1.000 -0.042 -0.218 0.790 38: Philippines bias -0.042 1.000 0.320 -0.017 39: Philippines drift -0.218 0.320 1.000 -0.052 40: Philippines ndt 0.790 -0.017 -0.052 1.000 41: Russia bound 1.000 0.315 -0.242 0.492 42: Russia bias 0.315 1.000 0.258 -0.228 43: Russia drift -0.242 0.258 1.000 -0.153 44: Russia ndt 0.492 -0.228 -0.153 1.000 45: Saudi Arabia bound 1.000 0.070 -0.280 0.395 46: Saudi Arabia bias 0.070 1.000 0.447 -0.056 47: Saudi Arabia drift -0.280 0.447 1.000 0.104 48: Saudi Arabia ndt 0.395 -0.056 0.104 1.000 49: South Africa bound 1.000 0.027 -0.216 0.261 50: South Africa bias 0.027 1.000 0.676 -0.121 51: South Africa drift -0.216 0.676 1.000 -0.263 52: South Africa ndt 0.261 -0.121 -0.263 1.000 53: Spain bound 1.000 0.035 0.132 0.428 54: Spain bias 0.035 1.000 0.498 0.349 55: Spain drift 0.132 0.498 1.000 0.007 56: Spain ndt 0.428 0.349 0.007 1.000 57: UK bound 1.000 0.375 -0.300 0.548 58: UK bias 0.375 1.000 0.019 -0.011 59: UK drift -0.300 0.019 1.000 -0.156 60: UK ndt 0.548 -0.011 -0.156 1.000 61: USA bound 1.000 0.296 -0.030 0.791 62: USA bias 0.296 1.000 0.252 0.023 63: USA drift -0.030 0.252 1.000 -0.132 64: USA ndt 0.791 0.023 -0.132 1.000 country param bound bias drift ndt ```