Test whether learning ET predicts test ET within and across timepoints

T1

# T1.base <- lmer(meanTargetDT_test_target ~ meanFaceDT_learning + meanTargetDT_learning + (1|CID), filter(ET_Lang_SocialDev_scaled, Timepoint==1))
T1.1 <- lmer(meanTargetDT_test_target ~ meanFaceDT_learning + meanTargetDT_learning + blocktype_centered*Bilingual_centered +
               (1|CID), filter(ET_Lang_SocialDev_scaled, Timepoint==1))

# anova(T1.base,T1.1) #T1.1 better fit
summary(T1.1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## meanTargetDT_test_target ~ meanFaceDT_learning + meanTargetDT_learning +  
##     blocktype_centered * Bilingual_centered + (1 | CID)
##    Data: filter(ET_Lang_SocialDev_scaled, Timepoint == 1)
## 
## REML criterion at convergence: 244.6
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.46473 -0.49927  0.04532  0.48068  2.23773 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  CID      (Intercept) 0.1414   0.3761  
##  Residual             0.7147   0.8454  
## Number of obs: 89, groups:  CID, 45
## 
## Fixed effects:
##                                       Estimate Std. Error       df t value
## (Intercept)                            1.09377    0.26642 78.59540   4.105
## meanFaceDT_learning                    0.13702    0.03719 82.26235   3.684
## meanTargetDT_learning                  0.47941    0.11105 82.97273   4.317
## blocktype_centered                     1.02914    0.18908 46.05866   5.443
## Bilingual_centered                    -0.08589    0.21657 43.57270  -0.397
## blocktype_centered:Bilingual_centered  0.14637    0.36393 43.00323   0.402
##                                       Pr(>|t|)    
## (Intercept)                           9.80e-05 ***
## meanFaceDT_learning                    0.00041 ***
## meanTargetDT_learning                 4.35e-05 ***
## blocktype_centered                    1.96e-06 ***
## Bilingual_centered                     0.69360    
## blocktype_centered:Bilingual_centered  0.68954    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) mnFDT_ mnTDT_ blckt_ Blngl_
## mnFcDT_lrnn -0.726                            
## mnTrgtDT_lr -0.560 -0.002                     
## blcktyp_cnt  0.150  0.041 -0.313              
## Blngl_cntrd -0.175  0.081  0.197 -0.048       
## blcktyp_:B_  0.108 -0.164  0.029 -0.024  0.003

T2

# T2.base <- lmer(meanTargetDT_test_target ~ meanFaceDT_learning + meanTargetDT_learning + (1|CID), filter(ET_Lang_SocialDev_scaled, Timepoint==2))
T2.1 <- lmer(meanTargetDT_test_target ~ meanFaceDT_learning + meanTargetDT_learning + blocktype_centered*Bilingual_centered + 
               (1|CID), filter(ET_Lang_SocialDev_scaled, Timepoint==2))
## singular fit
# anova(T2.base,T2.1) #T2.1 better fit
summary(T2.1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## meanTargetDT_test_target ~ meanFaceDT_learning + meanTargetDT_learning +  
##     blocktype_centered * Bilingual_centered + (1 | CID)
##    Data: filter(ET_Lang_SocialDev_scaled, Timepoint == 2)
## 
## REML criterion at convergence: 137.3
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.98653 -0.49466  0.03916  0.57499  2.21284 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  CID      (Intercept) 0.000    0.0000  
##  Residual             0.802    0.8956  
## Number of obs: 51, groups:  CID, 26
## 
## Fixed effects:
##                                       Estimate Std. Error       df t value
## (Intercept)                            0.50636    0.38874 45.00000   1.303
## meanFaceDT_learning                    0.27593    0.06114 45.00000   4.513
## meanTargetDT_learning                  0.55461    0.12477 45.00000   4.445
## blocktype_centered                     1.31395    0.25505 45.00000   5.152
## Bilingual_centered                    -0.20844    0.25768 45.00000  -0.809
## blocktype_centered:Bilingual_centered  0.53990    0.50369 45.00000   1.072
##                                       Pr(>|t|)    
## (Intercept)                              0.199    
## meanFaceDT_learning                   4.56e-05 ***
## meanTargetDT_learning                 5.68e-05 ***
## blocktype_centered                    5.54e-06 ***
## Bilingual_centered                       0.423    
## blocktype_centered:Bilingual_centered    0.289    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) mnFDT_ mnTDT_ blckt_ Blngl_
## mnFcDT_lrnn -0.786                            
## mnTrgtDT_lr -0.600  0.096                     
## blcktyp_cnt  0.014  0.081 -0.151              
## Blngl_cntrd -0.083 -0.051  0.215 -0.020       
## blcktyp_:B_ -0.072  0.065  0.059 -0.023 -0.011
## convergence code: 0
## singular fit

Timepoint as predictor

# allTime.base <- lmer(meanTargetDT_test_target ~ meanFaceDT_learning + meanTargetDT_learning + Timepoint_centered +
                       # (1|CID), ET_Lang_SocialDev_scaled)
allTime.1 <- lmer(meanTargetDT_test_target ~ meanFaceDT_learning + meanTargetDT_learning + 
                    Timepoint_centered*blocktype_centered*Bilingual_centered + (1|CID), ET_Lang_SocialDev_scaled)

# anova(allTime.base,allTime.1) #allTime.1 better fit
summary(allTime.1) #timepoint doesn't matter at the trial level
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## meanTargetDT_test_target ~ meanFaceDT_learning + meanTargetDT_learning +  
##     Timepoint_centered * blocktype_centered * Bilingual_centered +  
##     (1 | CID)
##    Data: ET_Lang_SocialDev_scaled
## 
## REML criterion at convergence: 380.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2018 -0.4762  0.0390  0.5278  2.5066 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  CID      (Intercept) 0.06166  0.2483  
##  Residual             0.78671  0.8870  
## Number of obs: 140, groups:  CID, 47
## 
## Fixed effects:
##                                                           Estimate
## (Intercept)                                                0.88609
## meanFaceDT_learning                                        0.17331
## meanTargetDT_learning                                      0.49482
## Timepoint_centered                                         0.20206
## blocktype_centered                                         1.02863
## Bilingual_centered                                        -0.05846
## Timepoint_centered:blocktype_centered                      0.26101
## Timepoint_centered:Bilingual_centered                     -0.15253
## blocktype_centered:Bilingual_centered                      0.09010
## Timepoint_centered:blocktype_centered:Bilingual_centered   0.39210
##                                                          Std. Error
## (Intercept)                                                 0.22931
## meanFaceDT_learning                                         0.03220
## meanTargetDT_learning                                       0.08457
## Timepoint_centered                                          0.16188
## blocktype_centered                                          0.19355
## Bilingual_centered                                          0.20523
## Timepoint_centered:blocktype_centered                       0.31238
## Timepoint_centered:Bilingual_centered                       0.31956
## blocktype_centered:Bilingual_centered                       0.37991
## Timepoint_centered:blocktype_centered:Bilingual_centered    0.62765
##                                                                 df t value
## (Intercept)                                              106.09920   3.864
## meanFaceDT_learning                                      116.30748   5.382
## meanTargetDT_learning                                    122.28948   5.851
## Timepoint_centered                                       117.52423   1.248
## blocktype_centered                                        85.61263   5.315
## Bilingual_centered                                        73.94915  -0.285
## Timepoint_centered:blocktype_centered                     81.42741   0.836
## Timepoint_centered:Bilingual_centered                    118.73744  -0.477
## blocktype_centered:Bilingual_centered                     82.79589   0.237
## Timepoint_centered:blocktype_centered:Bilingual_centered  82.84213   0.625
##                                                          Pr(>|t|)    
## (Intercept)                                              0.000192 ***
## meanFaceDT_learning                                      3.88e-07 ***
## meanTargetDT_learning                                    4.18e-08 ***
## Timepoint_centered                                       0.214444    
## blocktype_centered                                       8.37e-07 ***
## Bilingual_centered                                       0.776542    
## Timepoint_centered:blocktype_centered                    0.405857    
## Timepoint_centered:Bilingual_centered                    0.634016    
## blocktype_centered:Bilingual_centered                    0.813122    
## Timepoint_centered:blocktype_centered:Bilingual_centered 0.533880    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) mnFDT_ mnTDT_ Tmpnt_ blckt_ Blngl_ Tmp_:_ Tm_:B_ bl_:B_
## mnFcDT_lrnn -0.747                                                        
## mnTrgtDT_lr -0.520  0.029                                                 
## Tmpnt_cntrd -0.255  0.113 -0.137                                          
## blcktyp_cnt  0.101  0.027 -0.232  0.030                                   
## Blngl_cntrd -0.140  0.078  0.157 -0.013 -0.024                            
## Tmpnt_cnt:_ -0.045  0.020  0.055 -0.015 -0.598  0.003                     
## Tmpnt_cn:B_  0.052 -0.081  0.016 -0.020 -0.012 -0.548  0.016              
## blcktyp_:B_  0.096 -0.136  0.016 -0.024 -0.018  0.003  0.004  0.005       
## Tmpnt_:_:B_ -0.095  0.110  0.022  0.027  0.004  0.005 -0.014 -0.018 -0.608

Plot MCDI change over time by child

## Warning: Removed 30 rows containing missing values (geom_point).
## Warning: Removed 25 rows containing missing values (geom_path).

Predict MCDI from ET, total social tasks and previous MCDI

T1 (T1 predictors)

T1CDI.full <- lm(MCDI_total_T1 ~ Social_total_T1 + meanTargetDT_test_target_NS_T1 + meanTargetDT_test_target_S_T1 +
                   meanFaceDT_learning_S_T1 + meanFaceDT_learning_NS_T1 + Bilingual_CS, wideData)
summary(T1CDI.full)
## 
## Call:
## lm(formula = MCDI_total_T1 ~ Social_total_T1 + meanTargetDT_test_target_NS_T1 + 
##     meanTargetDT_test_target_S_T1 + meanFaceDT_learning_S_T1 + 
##     meanFaceDT_learning_NS_T1 + Bilingual_CS, data = wideData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -297.13 -153.81  -61.85  117.76  592.34 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)
## (Intercept)                       9.076    153.165   0.059    0.953
## Social_total_T1                  21.473     12.964   1.656    0.106
## meanTargetDT_test_target_NS_T1    9.291     46.960   0.198    0.844
## meanTargetDT_test_target_S_T1     5.437     30.169   0.180    0.858
## meanFaceDT_learning_S_T1          4.792     16.304   0.294    0.770
## meanFaceDT_learning_NS_T1        12.269     12.027   1.020    0.314
## Bilingual_CS                     60.064     71.955   0.835    0.409
## 
## Residual standard error: 218.7 on 37 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.1122, Adjusted R-squared:  -0.03179 
## F-statistic: 0.7792 on 6 and 37 DF,  p-value: 0.5915

T2 (T2 predictors)

T2CDI.full <- lm(MCDI_total_T2 ~ Social_total_T2 + meanTargetDT_test_target_NS_T2 + meanTargetDT_test_target_S_T2 +
                   meanFaceDT_learning_S_T2 + meanFaceDT_learning_NS_T2 + Bilingual_CS, wideData)
summary(T2CDI.full)
## 
## Call:
## lm(formula = MCDI_total_T2 ~ Social_total_T2 + meanTargetDT_test_target_NS_T2 + 
##     meanTargetDT_test_target_S_T2 + meanFaceDT_learning_S_T2 + 
##     meanFaceDT_learning_NS_T2 + Bilingual_CS, data = wideData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -435.39 -181.13  -19.62  182.68  552.53 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)
## (Intercept)                     245.636    238.358   1.031    0.317
## Social_total_T2                   3.760     23.287   0.161    0.874
## meanTargetDT_test_target_NS_T2    7.272     87.330   0.083    0.935
## meanTargetDT_test_target_S_T2     7.930     57.979   0.137    0.893
## meanFaceDT_learning_S_T2          7.151     34.955   0.205    0.840
## meanFaceDT_learning_NS_T2        20.869     38.143   0.547    0.591
## Bilingual_CS                     23.405    131.768   0.178    0.861
## 
## Residual standard error: 292.2 on 17 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.0722, Adjusted R-squared:  -0.2553 
## F-statistic: 0.2205 on 6 and 17 DF,  p-value: 0.9648

T2 (T1 predictors)

T2CDI.fromT1 <- lm(MCDI_total_T2 ~ MCDI_total_T1 + Social_total_T1 + meanTargetDT_test_target_NS_T1 +
                     meanTargetDT_test_target_S_T1 + meanFaceDT_learning_S_T1 + meanFaceDT_learning_NS_T1 + 
                     Bilingual_CS, wideData)
summary(T2CDI.fromT1)
## 
## Call:
## lm(formula = MCDI_total_T2 ~ MCDI_total_T1 + Social_total_T1 + 
##     meanTargetDT_test_target_NS_T1 + meanTargetDT_test_target_S_T1 + 
##     meanFaceDT_learning_S_T1 + meanFaceDT_learning_NS_T1 + Bilingual_CS, 
##     data = wideData)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -277.116  -33.962    4.077   64.666  171.519 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                     28.1980   141.3856   0.199    0.844    
## MCDI_total_T1                    0.9902     0.1383   7.161 2.26e-06 ***
## Social_total_T1                  5.1710    11.5325   0.448    0.660    
## meanTargetDT_test_target_NS_T1  22.2641    46.0617   0.483    0.635    
## meanTargetDT_test_target_S_T1   33.3415    30.3760   1.098    0.289    
## meanFaceDT_learning_S_T1        -9.9915    17.1829  -0.581    0.569    
## meanFaceDT_learning_NS_T1       -4.9334    16.1568  -0.305    0.764    
## Bilingual_CS                     9.5873    64.7424   0.148    0.884    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 126.3 on 16 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.7907, Adjusted R-squared:  0.6992 
## F-statistic: 8.636 on 7 and 16 DF,  p-value: 0.0001956

T3 (T1 and T2 predictors)

T3CDI.1 <- lm(MCDI_total_T3 ~ MCDI_total_T2 + Social_total_T1 + meanTargetDT_test_target_NS_T1 + 
                meanTargetDT_test_target_S_T1, filter(wideData, CID %in% MCDI_T3_growth$CID))
summary(T3CDI.1)
## 
## Call:
## lm(formula = MCDI_total_T3 ~ MCDI_total_T2 + Social_total_T1 + 
##     meanTargetDT_test_target_NS_T1 + meanTargetDT_test_target_S_T1, 
##     data = filter(wideData, CID %in% MCDI_T3_growth$CID))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -154.59  -79.65  -30.60   48.66  370.92 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                    508.8866   184.1890   2.763   0.0185 *
## MCDI_total_T2                    0.6475     0.2541   2.549   0.0271 *
## Social_total_T1                -21.7652    16.0648  -1.355   0.2026  
## meanTargetDT_test_target_NS_T1 -27.0624    54.8157  -0.494   0.6312  
## meanTargetDT_test_target_S_T1    1.8910    31.9615   0.059   0.9539  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 145 on 11 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:  0.3783, Adjusted R-squared:  0.1522 
## F-statistic: 1.673 on 4 and 11 DF,  p-value: 0.2256

Any relations between vocab and individual social tasks?

## Warning: package 'corrplot' was built under R version 3.5.3
## corrplot 0.84 loaded

Monolinguals

Bilinguals

Predict language outcomes from individual social tasks

T2 MCDI (raw)

T2CDI_indivSocial <- lm(MCDI_total_T2 ~ MCDI_total_T1 + StickerTotal_T1 + ToyNameTotal_T1 +
                          GazeFollow_FirstVoc_T1 + Bilingual_CS, wideData)
summary(T2CDI_indivSocial)
## 
## Call:
## lm(formula = MCDI_total_T2 ~ MCDI_total_T1 + StickerTotal_T1 + 
##     ToyNameTotal_T1 + GazeFollow_FirstVoc_T1 + Bilingual_CS, 
##     data = wideData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -169.45  -64.48  -11.71   75.49  149.80 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             66.7307    90.9061   0.734   0.4735    
## MCDI_total_T1            0.9477     0.1203   7.876 6.78e-07 ***
## StickerTotal_T1        -41.4996    22.6707  -1.831   0.0859 .  
## ToyNameTotal_T1         61.1053    30.5396   2.001   0.0627 .  
## GazeFollow_FirstVoc_T1  30.0394    21.5159   1.396   0.1817    
## Bilingual_CS            36.3302    48.3115   0.752   0.4630    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 106.8 on 16 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:  0.8262, Adjusted R-squared:  0.7719 
## F-statistic: 15.21 on 5 and 16 DF,  p-value: 1.37e-05

T2 MCDI (change from T1)

T2CDIchange_indivSocial <- lm(MCDI_change_T2 ~ Bilingual_CS + StickerTotal_T1 + ToyNameTotal_T1 +
                          GazeFollow_FirstVoc_T1, wideData)
summary(T2CDIchange_indivSocial)
## 
## Call:
## lm(formula = MCDI_change_T2 ~ Bilingual_CS + StickerTotal_T1 + 
##     ToyNameTotal_T1 + GazeFollow_FirstVoc_T1, data = wideData)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -167.123  -59.787   -7.428   88.017  152.140 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               57.30      86.15   0.665   0.5149  
## Bilingual_CS              34.30      46.92   0.731   0.4748  
## StickerTotal_T1          -42.40      22.03  -1.925   0.0712 .
## ToyNameTotal_T1           59.96      29.69   2.019   0.0595 .
## GazeFollow_FirstVoc_T1    27.90      20.44   1.365   0.1901  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 104.2 on 17 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:  0.2844, Adjusted R-squared:  0.116 
## F-statistic: 1.689 on 4 and 17 DF,  p-value: 0.1989

T3 MCDI (raw)

T3CDI_indivSocial <- lm(MCDI_total_T3 ~ Bilingual_CS + StickerTotal_T2 + ToyNameTotal_T2 +
                          GazeFollow_FirstVoc_T2, wideData)
summary(T3CDI_indivSocial)
## 
## Call:
## lm(formula = MCDI_total_T3 ~ Bilingual_CS + StickerTotal_T2 + 
##     ToyNameTotal_T2 + GazeFollow_FirstVoc_T2, data = wideData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -554.41  -52.82  -15.38   88.02  328.38 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            395.6392   199.4711   1.983   0.0688 .
## Bilingual_CS           -67.4941   134.2784  -0.503   0.6236  
## StickerTotal_T2          0.3419    53.1720   0.006   0.9950  
## ToyNameTotal_T2         64.5496    56.9784   1.133   0.2777  
## GazeFollow_FirstVoc_T2  66.2400    45.4205   1.458   0.1685  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 210.1 on 13 degrees of freedom
##   (31 observations deleted due to missingness)
## Multiple R-squared:  0.2245, Adjusted R-squared:  -0.01408 
## F-statistic: 0.941 on 4 and 13 DF,  p-value: 0.471

T3 MCDI (change from T2)

T3CDIchange_indivSocial <- lm(MCDI_change_T3 ~ Bilingual_CS + StickerTotal_T2 + ToyNameTotal_T2 +
                                GazeFollow_FirstVoc_T2, wideData)
summary(T3CDIchange_indivSocial)
## 
## Call:
## lm(formula = MCDI_change_T3 ~ Bilingual_CS + StickerTotal_T2 + 
##     ToyNameTotal_T2 + GazeFollow_FirstVoc_T2, data = wideData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -406.88  -76.38   -1.61   85.08  580.92 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)
## (Intercept)              213.61     218.43   0.978    0.346
## Bilingual_CS            -147.26     147.04  -1.001    0.335
## StickerTotal_T2          -44.74      58.23  -0.768    0.456
## ToyNameTotal_T2           57.93      62.40   0.928    0.370
## GazeFollow_FirstVoc_T2   -15.66      49.74  -0.315    0.758
## 
## Residual standard error: 230 on 13 degrees of freedom
##   (31 observations deleted due to missingness)
## Multiple R-squared:  0.09561,    Adjusted R-squared:  -0.1827 
## F-statistic: 0.3436 on 4 and 13 DF,  p-value: 0.8438

Look at social dev task performance and MCDI across timepoints

Social dev tasks

(monolingual = 0, bilingual = 1; top row is T1, bottom row is T2)

ggplot(filter(graphData, Timepoint != 3), aes(Bilingual_CS,Performance, color=Bilingual_CS))+
  stat_summary(fun.y=mean, geom='bar')+
  stat_summary(fun.data=mean_se, geom='pointrange')+
  facet_wrap(c("Timepoint", "Task"), nrow=2, ncol=4)+
  theme_classic()
## Warning: Removed 82 rows containing non-finite values (stat_summary).

## Warning: Removed 82 rows containing non-finite values (stat_summary).

# ggplot(filter(graphData, Timepoint==2), aes(Bilingual_CS,Performance))+
#   stat_summary(fun.y=mean, geom='bar')+
#   stat_summary(fun.data=mean_se, geom='pointrange')+
#   facet_wrap('Task')+
#   theme_classic()

MCDI

ggplot(CDIgraph, aes(Timepoint,MCDI,group=CID))+
  geom_line(aes(color=as.factor(Bilingual_CS)))+
  theme_classic()
## Warning: Removed 26 rows containing missing values (geom_path).