==============================================================================
PHASED days x tDCS LME -- mergeNaive (Box-B2 vs Box-A2)
==============================================================================
model per phase: success ~ dayp*tDCS + (1|subject)  [dayp = day - phaseStart]

phase     N  A2slope B2slope  day p     tDCS(lvl) p  interaction p  slopeDiff [95% CI]
--------------------------------------------------------------------------------------------
0-5      11    8.26   15.16  1.6e-10   0.852        0.000         +6.90 [+3.40, +10.41]
6-10     10    5.97    4.83  3.9e-05   0.007        0.584         -1.14 [-5.31, +3.03]
6-13     10    2.83    4.47  0.00071   0.012        0.196         +1.65 [-0.87, +4.17]

Note: the interaction p (and CI) use fitlme observation-level DF and are
ANTICONSERVATIVE at these small subject counts (see tdcs_power_sim). The early
phase carries the Box-B2 faster-acquisition signal; late phases converge.
==============================================================================
FULL MODEL SUMMARY -- phase 0-5: success ~ dayp*tDCS + (1|subject)
==============================================================================

Linear mixed-effects model fit by ML

Model information:
    Number of observations              65
    Fixed effects coefficients           4
    Random effects coefficients         11
    Covariance parameters                2

Formula:
    success ~ 1 + dayp*tDCS + (1 | subject)

Model fit statistics:
    AIC       BIC       LogLikelihood    Deviance
    532.27    545.32    -260.14          520.27  

Fixed effects coefficients (95% CIs):
    Name                   Estimate    SE        tStat     DF    pValue    
    {'(Intercept)'}          9.046     4.8672    1.8586    61      0.067913
    {'dayp'       }         8.2602     1.0773    7.6677    61    1.6422e-10
    {'tDCS'       }        -1.4984     7.9706    -0.188    61        0.8515
    {'dayp:tDCS'  }         6.9041     1.7525    3.9397    61    0.00021248


    Lower       Upper 
    -0.68649    18.779
       6.106    10.414
     -17.437     14.44
      3.3998    10.408

Random effects covariance parameters (95% CIs):
Group: subject (11 Levels)
    Name1                  Name2                  Type           Estimate
    {'(Intercept)'}        {'(Intercept)'}        {'std'}        9.4501  


    Lower     Upper 
    5.5672    16.041

Group: Error
    Name               Estimate    Lower     Upper 
    {'Res Std'}        11.565      9.5751    13.968


==============================================================================
FULL MODEL SUMMARY -- phase 6-10: success ~ dayp*tDCS + (1|subject)
==============================================================================

Linear mixed-effects model fit by ML

Model information:
    Number of observations              50
    Fixed effects coefficients           4
    Random effects coefficients         10
    Covariance parameters                2

Formula:
    success ~ 1 + dayp*tDCS + (1 | subject)

Model fit statistics:
    AIC       BIC       LogLikelihood    Deviance
    404.12    415.59    -196.06          392.12  

Fixed effects coefficients (95% CIs):
    Name                   Estimate    SE        tStat       DF    pValue    
    {'(Intercept)'}           62.1      5.345      11.618    46    2.7953e-15
    {'dayp'       }         5.9667     1.3111      4.5509    46    3.8983e-05
    {'tDCS'       }             24     8.4512      2.8398    46     0.0067003
    {'dayp:tDCS'  }        -1.1417      2.073    -0.55073    46       0.58449


    Lower      Upper 
     51.341    72.859
     3.3276    8.6058
     6.9887    41.011
    -5.3144    3.0311

Random effects covariance parameters (95% CIs):
Group: subject (10 Levels)
    Name1                  Name2                  Type           Estimate
    {'(Intercept)'}        {'(Intercept)'}        {'std'}        10.466  


    Lower    Upper 
    6.207    17.646

Group: Error
    Name               Estimate    Lower     Upper 
    {'Res Std'}        10.156      8.1572    12.644


==============================================================================
FULL MODEL SUMMARY -- phase 6-13: success ~ dayp*tDCS + (1|subject)
==============================================================================

Linear mixed-effects model fit by ML

Model information:
    Number of observations              73
    Fixed effects coefficients           4
    Random effects coefficients         10
    Covariance parameters                2

Formula:
    success ~ 1 + dayp*tDCS + (1 | subject)

Model fit statistics:
    AIC       BIC       LogLikelihood    Deviance
    593.06    606.81    -290.53          581.06  

Fixed effects coefficients (95% CIs):
    Name                   Estimate    SE         tStat     DF    pValue    
    {'(Intercept)'}        66.814       4.8749    13.706    69    2.2545e-21
    {'dayp'       }        2.8256      0.79686    3.5459    69    0.00070793
    {'tDCS'       }        19.873       7.7041    2.5796    69      0.012026
    {'dayp:tDCS'  }         1.648       1.2621    1.3058    69       0.19595


    Lower       Upper 
      57.089    76.539
      1.2359    4.4153
       4.504    35.242
    -0.86972    4.1658

Random effects covariance parameters (95% CIs):
Group: subject (10 Levels)
    Name1                  Name2                  Type           Estimate
    {'(Intercept)'}        {'(Intercept)'}        {'std'}        9.246   


    Lower     Upper 
    5.4692    15.631

Group: Error
    Name               Estimate    Lower    Upper 
    {'Res Std'}        11.498      9.661    13.686