#Linear Mixed-Effects Model Summary.

## Linear mixed model fit by REML ['lmerMod']
## Formula: zscore_snr ~ DutyCycle + (1 | SubjectID)
##    Data: long_data
## 
## REML criterion at convergence: 373.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.9991 -0.5829 -0.1092  0.5969  2.6220 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  SubjectID (Intercept) 2.995    1.731   
##  Residual              1.778    1.333   
## Number of obs: 96, groups:  SubjectID, 24
## 
## Fixed effects:
##              Estimate Std. Error t value
## (Intercept)    4.4049     0.4459   9.878
## DutyCycle50    1.0231     0.3849   2.658
## DutyCycle75    1.9401     0.3849   5.041
## DutyCycle100   0.5726     0.3849   1.488
## 
## Correlation of Fixed Effects:
##             (Intr) DtyC50 DtyC75
## DutyCycle50 -0.432              
## DutyCycle75 -0.432  0.500       
## DutyCycl100 -0.432  0.500  0.500

#ANOVA Results.

ANOVA Results for SNR by Duty Cycle
F Df Df.res Pr(>F)
DutyCycle 9.059458 3 69 3.9e-05

#Post-hoc Pairwise Comparisons

Post-hoc Pairwise Comparisons of Duty Cycles
group1 group2 estimate SE df t.ratio p.value y.position stars
Duty_Cycle25 Duty_Cycle50 -1.0230848 0.3848844 69 -2.658161 0.0585394 8 ns
Duty_Cycle25 Duty_Cycle75 -1.9400788 0.3848844 69 -5.040679 0.0000214 10 ***
Duty_Cycle25 Duty_Cycle100 -0.5726399 0.3848844 69 -1.487823 0.8481215 8 ns
Duty_Cycle50 Duty_Cycle75 -0.9169941 0.3848844 69 -2.382518 0.1197289 8 ns
Duty_Cycle50 Duty_Cycle100 0.4504448 0.3848844 69 1.170338 1.0000000 8 ns
Duty_Cycle75 Duty_Cycle100 1.3674389 0.3848844 69 3.552856 0.0041542 11 **

Marginal and Conditional R².

## Marginal R²: 0.0963
## Conditional R²: 0.6634

SNR Plot with Significance Bars.

#Correlation Analysis between EEG and SNR

Spearman Correlation between EEG and SNR Z-scores by Duty Cycle
DutyCycle correlation p_value
25 -0.1478261 0.4889180
50 -0.0513043 0.8118785
75 0.1713043 0.4217527
100 -0.4304348 0.0369066

#EEG vs. SNR Correlation Plot

## `geom_smooth()` using formula = 'y ~ x'