      cond
subj   101 102 103 104 109 110
  cl01  23  23  23  23  23  23
  cl02  23  23  23  23  23  23
  cl04  23  23  23  23  23  23
  cl09  23  23  23  23  23  23
  cl10  23  23  23  23  23  23
  cl11  23  23  23  23  23  23
  cl12  23  23  23  23  23  23
  cl16  23  23  23  23  23  23
  cl17  23  23  23  23  23  23
  cl19  23  23  23  23  23  23
  cl22  23  23  23  23  23  23
  cl24  23  23  23  23  23  23
  cl26  23  23  23  23  23  23
  cl27  23  23  23  23  23  23
  cl80  23  23  23  23  23  23
  cl82  23  23  23  23  23  23
          mean       sd   n
101 -2.6499284 2.860180 368
102 -4.4274064 3.715506 368
103 -4.6318740 3.071350 368
104 -2.6464185 3.168678 368
109 -0.2637559 3.646217 368
110 -2.4166731 5.340042 368
  subj cond chan        win     mean
1 cl01  101   F7 +400..+548 -0.61781
2 cl01  101   F3 +400..+548 -0.39076
3 cl01  101   FZ +400..+548 -1.87650
4 cl01  101   F4 +400..+548 -0.72785
5 cl01  101   F8 +400..+548 -0.18351
6 cl01  101  FC5 +400..+548  1.14680
      subj       cond          chan              win            mean         
 cl01   : 138   101:368   C3     :  96   +400..+548:2208   Min.   :-17.7300  
 cl02   : 138   102:368   C4     :  96                     1st Qu.: -5.4326  
 cl04   : 138   103:368   CP5    :  96                     Median : -2.8439  
 cl09   : 138   104:368   CP6    :  96                     Mean   : -2.8393  
 cl10   : 138   109:368   CPZ    :  96                     3rd Qu.: -0.5888  
 cl11   : 138   110:368   CZ     :  96                     Max.   : 17.0690  
 (Other):1380             (Other):1632                                       
'data.frame':	2208 obs. of  5 variables:
 $ subj: Factor w/ 16 levels "cl01","cl02",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ cond: Factor w/ 6 levels "101","102","103",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ chan: Factor w/ 23 levels "C3","C4","CP5",..: 9 7 14 8 10 11 13 12 1 6 ...
 $ win : Factor w/ 1 level "+400..+548": 1 1 1 1 1 1 1 1 1 1 ...
 $ mean: num  -0.618 -0.391 -1.877 -0.728 -0.184 ...
   subj cond chan        win      mean
2  cl01  101   F3 +400..+548 -0.390760
3  cl01  101   FZ +400..+548 -1.876500
4  cl01  101   F4 +400..+548 -0.727850
9  cl01  101   C3 +400..+548 -0.059591
10 cl01  101   CZ +400..+548 -0.495390
11 cl01  101   C4 +400..+548 -4.468000
'data.frame':	864 obs. of  5 variables:
 $ subj: Factor w/ 16 levels "cl01","cl02",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ cond: Factor w/ 6 levels "101","102","103",..: 1 1 1 1 1 1 1 1 1 2 ...
 $ chan: Factor w/ 9 levels "C3","C4","CZ",..: 4 6 5 1 3 2 7 9 8 4 ...
 $ win : Factor w/ 1 level "+400..+548": 1 1 1 1 1 1 1 1 1 1 ...
 $ mean: num  -0.3908 -1.8765 -0.7278 -0.0596 -0.4954 ...
'data.frame':	864 obs. of  6 variables:
 $ subj: Factor w/ 16 levels "cl01","cl02",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ cond: Factor w/ 6 levels "101","102","103",..: 1 1 1 1 1 1 1 1 1 2 ...
 $ chan: Factor w/ 9 levels "C3","C4","CZ",..: 4 6 5 1 3 2 7 9 8 4 ...
 $ win : Factor w/ 1 level "+400..+548": 1 1 1 1 1 1 1 1 1 1 ...
 $ mean: num  -0.3908 -1.8765 -0.7278 -0.0596 -0.4954 ...
 $ roi : Factor w/ 3 levels "ant","mid","pos": 1 1 1 2 2 2 3 3 3 1 ...
          mean       sd   n
101 -2.9527143 2.600372 144
102 -4.9070765 3.627501 144
103 -4.9982821 3.043058 144
104 -2.7896026 2.957440 144
109  0.1261222 3.671147 144
110 -2.1944657 5.605356 144
         mean       sd   n
ant -2.694557 4.483019 288
mid -3.242074 3.931589 288
pos -2.921379 3.825592 288
           mean       sd  n
101 -3.13442350 2.299165 48
102 -4.74751563 3.334857 48
103 -5.44224917 3.567277 48
104 -3.00186137 3.342830 48
109  0.17521654 3.971627 48
110 -0.01650599 6.212680 48
           mean       sd  n
101 -3.31941731 2.413312 48
102 -5.45112252 3.891309 48
103 -5.13440000 2.919682 48
104 -2.98621583 2.598499 48
109  0.05007042 3.712050 48
110 -2.61135917 4.917059 48
          mean       sd  n
101 -2.4043022 2.997516 48
102 -4.5225912 3.646015 48
103 -4.4181972 2.522058 48
104 -2.3807306 2.903209 48
109  0.1530796 3.382781 48
110 -3.9555319 4.963157 48
      subj      cond          chan             win           mean         
 cl01   : 54   101:144   C3     : 96   +400..+548:864   Min.   :-14.7130  
 cl02   : 54   102:144   C4     : 96                    1st Qu.: -5.6515  
 cl04   : 54   103:144   CZ     : 96                    Median : -2.9484  
 cl09   : 54   104:144   F3     : 96                    Mean   : -2.9527  
 cl10   : 54   109:144   F4     : 96                    3rd Qu.: -0.8285  
 cl11   : 54   110:144   FZ     : 96                    Max.   : 17.0690  
 (Other):540             (Other):288                                      
  roi            AB           CD           IJ    
 ant:288   Min.   :-1   Min.   :-1   Min.   :-1  
 mid:288   1st Qu.: 0   1st Qu.: 0   1st Qu.: 0  
 pos:288   Median : 0   Median : 0   Median : 0  
           Mean   : 0   Mean   : 0   Mean   : 0  
           3rd Qu.: 0   3rd Qu.: 0   3rd Qu.: 0  
           Max.   : 1   Max.   : 1   Max.   : 1  
                                                 
Linear mixed model fit by REML 
Formula: mean ~ AB + (1 | subj) 
   Data: datan 
  AIC  BIC logLik deviance REMLdev
 4667 4686  -2330     4659    4659
Random effects:
 Groups   Name        Variance Std.Dev.
 subj     (Intercept)  4.5327  2.1290  
 Residual             12.1828  3.4904  
Number of obs: 864, groups: subj, 16

Fixed effects:
            Estimate Std. Error t value
(Intercept)  -2.9527     0.5453  -5.415
AB           -0.9772     0.2057  -4.751

Correlation of Fixed Effects:
   (Intr)
AB 0.000 
Linear mixed model fit by REML 
Formula: mean ~ CD + (1 | subj) 
   Data: datan 
  AIC  BIC logLik deviance REMLdev
 4661 4680  -2327     4652    4653
Random effects:
 Groups   Name        Variance Std.Dev.
 subj     (Intercept)  4.5344  2.1294  
 Residual             12.0928  3.4775  
Number of obs: 864, groups: subj, 16

Fixed effects:
            Estimate Std. Error t value
(Intercept)  -2.9527     0.5452  -5.415
CD            1.1043     0.2049   5.389

Correlation of Fixed Effects:
   (Intr)
CD 0.000 
Linear mixed model fit by REML 
Formula: mean ~ IJ + (1 | subj) 
   Data: datan 
  AIC  BIC logLik deviance REMLdev
 4658 4677  -2325     4649    4650
Random effects:
 Groups   Name        Variance Std.Dev.
 subj     (Intercept)  4.5352  2.1296  
 Residual             12.0498  3.4713  
Number of obs: 864, groups: subj, 16

Fixed effects:
            Estimate Std. Error t value
(Intercept)  -2.9527     0.5452  -5.415
IJ           -1.1603     0.2045  -5.673

Correlation of Fixed Effects:
   (Intr)
IJ 0.000 
Linear mixed model fit by REML 
Formula: mean ~ AB + (1 | subj) 
   Data: subset(datan, roi == "ant") 
  AIC  BIC logLik deviance REMLdev
 1633 1648 -812.7     1626    1625
Random effects:
 Groups   Name        Variance Std.Dev.
 subj     (Intercept)  5.2913  2.3003  
 Residual             14.9541  3.8671  
Number of obs: 288, groups: subj, 16

Fixed effects:
            Estimate Std. Error t value
(Intercept)  -2.6946     0.6185  -4.357
AB           -0.8065     0.3947  -2.044

Correlation of Fixed Effects:
   (Intr)
AB 0.000 
Linear mixed model fit by REML 
Formula: mean ~ CD + (1 | subj) 
   Data: subset(datan, roi == "ant") 
  AIC  BIC logLik deviance REMLdev
 1628 1643   -810     1621    1620
Random effects:
 Groups   Name        Variance Std.Dev.
 subj     (Intercept)  5.3078  2.3039  
 Residual             14.6571  3.8285  
Number of obs: 288, groups: subj, 16

Fixed effects:
            Estimate Std. Error t value
(Intercept)  -2.6946     0.6185  -4.357
CD            1.2202     0.3907   3.123

Correlation of Fixed Effects:
   (Intr)
CD 0.000 
Linear mixed model fit by REML 
Formula: mean ~ IJ + (1 | subj) 
   Data: subset(datan, roi == "ant") 
  AIC  BIC logLik deviance REMLdev
 1638 1652 -814.8     1630    1630
Random effects:
 Groups   Name        Variance Std.Dev.
 subj     (Intercept)  5.2787  2.2975  
 Residual             15.1813  3.8963  
Number of obs: 288, groups: subj, 16

Fixed effects:
            Estimate Std. Error t value
(Intercept) -2.69456    0.61850  -4.357
IJ          -0.09586    0.39767  -0.241

Correlation of Fixed Effects:
   (Intr)
IJ 0.000 
Linear mixed model fit by REML 
Formula: mean ~ AB + (1 | subj) 
   Data: subset(datan, roi == "mid") 
  AIC  BIC logLik deviance REMLdev
 1547 1562 -769.4     1539    1539
Random effects:
 Groups   Name        Variance Std.Dev.
 subj     (Intercept)  4.3869  2.0945  
 Residual             10.9886  3.3149  
Number of obs: 288, groups: subj, 16

Fixed effects:
            Estimate Std. Error t value
(Intercept)  -3.2421     0.5588  -5.801
AB           -1.0659     0.3383  -3.150

Correlation of Fixed Effects:
   (Intr)
AB 0.000 
Linear mixed model fit by REML 
Formula: mean ~ CD + (1 | subj) 
   Data: subset(datan, roi == "mid") 
  AIC  BIC logLik deviance REMLdev
 1547 1561 -769.4     1539    1539
Random effects:
 Groups   Name        Variance Std.Dev.
 subj     (Intercept)  4.3873  2.0946  
 Residual             10.9823  3.3140  
Number of obs: 288, groups: subj, 16

Fixed effects:
            Estimate Std. Error t value
(Intercept)  -3.2421     0.5588  -5.801
CD            1.0741     0.3382   3.176

Correlation of Fixed Effects:
   (Intr)
CD 0.000 
Linear mixed model fit by REML 
Formula: mean ~ IJ + (1 | subj) 
   Data: subset(datan, roi == "mid") 
  AIC  BIC logLik deviance REMLdev
 1541 1556 -766.6     1534    1533
Random effects:
 Groups   Name        Variance Std.Dev.
 subj     (Intercept)  4.3994  2.0975  
 Residual             10.7637  3.2808  
Number of obs: 288, groups: subj, 16

Fixed effects:
            Estimate Std. Error t value
(Intercept)  -3.2421     0.5588  -5.802
IJ           -1.3307     0.3348  -3.974

Correlation of Fixed Effects:
   (Intr)
IJ 0.000 
Linear mixed model fit by REML 
Formula: mean ~ AB + (1 | subj) 
   Data: subset(datan, roi == "pos") 
  AIC  BIC logLik deviance REMLdev
 1520 1535   -756     1512    1512
Random effects:
 Groups   Name        Variance Std.Dev.
 subj     (Intercept) 4.6346   2.1528  
 Residual             9.9345   3.1519  
Number of obs: 288, groups: subj, 16

Fixed effects:
            Estimate Std. Error t value
(Intercept)  -2.9214     0.5693  -5.132
AB           -1.0591     0.3217  -3.292

Correlation of Fixed Effects:
   (Intr)
AB 0.000 
Linear mixed model fit by REML 
Formula: mean ~ CD + (1 | subj) 
   Data: subset(datan, roi == "pos") 
  AIC  BIC logLik deviance REMLdev
 1521 1536 -756.5     1513    1513
Random effects:
 Groups   Name        Variance Std.Dev.
 subj     (Intercept) 4.6330   2.1524  
 Residual             9.9642   3.1566  
Number of obs: 288, groups: subj, 16

Fixed effects:
            Estimate Std. Error t value
(Intercept)  -2.9214     0.5693  -5.132
CD            1.0187     0.3222   3.162

Correlation of Fixed Effects:
   (Intr)
CD 0.000 
Linear mixed model fit by REML 
Formula: mean ~ IJ + (1 | subj) 
   Data: subset(datan, roi == "pos") 
  AIC  BIC logLik deviance REMLdev
 1488 1503 -740.2     1481    1480
Random effects:
 Groups   Name        Variance Std.Dev.
 subj     (Intercept) 4.6956   2.1669  
 Residual             8.8369   2.9727  
Number of obs: 288, groups: subj, 16

Fixed effects:
            Estimate Std. Error t value
(Intercept)  -2.9214     0.5693  -5.132
IJ           -2.0543     0.3034  -6.771

Correlation of Fixed Effects:
   (Intr)
IJ 0.000 
