ACURA TAR Final Analysis V1.83
Russell Chan
18 Dec 2021
##Set working directory
setwd("C:/Users/RussellChan/OneDrive - University of Twente/2021_Oscillatory EXP 3 Paper 4 Analysis/Current Analysis")##Packages
library(lme4)
library(effects)
library(lattice)
library(car)
library(ggplot2)
library(knitr)
library(reshape2)
library(dplyr)
library(forcats)
library(DHARMa)
library(Hmisc)
library(phia)
library(lsmeans)
library(emmeans)
library(multcomp)
library(plotly)
library(lmerTest)
library(readxl)##Import dataset
d.Pos<-read.table("mpd_6_Posterior_1.csv", sep = ",", header = T, stringsAsFactors = F)
d.Fro2<-read.table("mpd_6_Frontal_WO2.csv", sep = ",", header = T, stringsAsFactors = F)#Factors
#Create Factors
d.Fro2$Subject <- factor(d.Fro2$Subject)
d.Fro2$Group <- factor(d.Fro2$Group)
d.Fro2$Session <- factor(d.Fro2$Session, levels=c('1', '2', '3'))
d.Fro2$Time <-as.factor(d.Fro2$Time)
d.Pos$Subject <- factor(d.Pos$Subject)
d.Pos$Group <- factor(d.Pos$Group)
d.Pos$Session <- factor(d.Pos$Session, levels=c('1', '2', '3'))
d.Pos$Time <-as.factor(d.Pos$Time)#Alpha Models
#Model 1: Alpha Frontal without AF sites.
m.AF_WO <- lmer(ERDSAlpha ~ Group * Session * Time + (1 | Subject), data=d.Fro2, REML = FALSE)
Anova(m.AF_WO)#m.AFtest <- as(m.AF,"lmerModLmerTest")
#summary(m.AF_WO, ddf="Satterthwaite")
#m.AFmpd <- lmer(Alpha ~ Group * Session * Time + (1 | Subject), data=d.Fro2, REML = FALSE)
#Anova(m.AFmpd)
######################
#Model 2: Alpha Posterior
m.AP <- lmer(ERDSAlpha ~ Group * Session * Time + (1 | Subject), data=d.Pos, REML = FALSE)
Anova(m.AP)#m.AFtest <- as(m.AF,"lmerModLmerTest")
#summary(m.AP, ddf="Satterthwaite")#Alpha Posthocs to determine exact locus
#Frontal Alpha WO AF
emmeans(m.AF_WO, pairwise ~ Group | Session)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15480' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15480)' or larger];
## but be warned that this may result in large computation time and memory use.## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15480' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15480)' or larger];
## but be warned that this may result in large computation time and memory use.## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 1.395 13.3 Inf -24.64 27.4
## MED1 -7.404 12.5 Inf -31.95 17.1
## MED21 11.885 10.8 Inf -9.37 33.1
##
## Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -0.089 13.3 Inf -26.12 25.9
## MED1 -1.029 12.5 Inf -25.57 23.5
## MED21 -0.558 10.9 Inf -21.88 20.8
##
## Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -3.794 13.3 Inf -29.83 22.2
## MED1 -5.835 12.5 Inf -30.38 18.7
## MED21 23.299 10.8 Inf 2.04 44.6
##
## Results are averaged over the levels of: Time
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 8.800 18.3 Inf 0.482 0.8798
## Control - MED21 -10.490 17.1 Inf -0.612 0.8137
## MED1 - MED21 -19.290 16.6 Inf -1.164 0.4746
##
## Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 0.940 18.3 Inf 0.051 0.9985
## Control - MED21 0.469 17.2 Inf 0.027 0.9996
## MED1 - MED21 -0.471 16.6 Inf -0.028 0.9996
##
## Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 2.040 18.3 Inf 0.112 0.9931
## Control - MED21 -27.094 17.1 Inf -1.580 0.2543
## MED1 - MED21 -29.134 16.6 Inf -1.759 0.1837
##
## Results are averaged over the levels of: Time
## Degrees-of-freedom method: asymptotic
## P value adjustment: tukey method for comparing a family of 3 estimatesm.AF_WO <- lmer(ERDSAlpha ~ Group * Session + (1 | Subject), data=d.Fro2, REML = FALSE)
emmeans(m.AF_WO, pairwise ~ Group | Session)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15480' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15480)' or larger];
## but be warned that this may result in large computation time and memory use.## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15480' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15480)' or larger];
## but be warned that this may result in large computation time and memory use.## $emmeans
## Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 1.395 13.3 Inf -24.64 27.4
## MED1 -7.404 12.5 Inf -31.95 17.1
## MED21 11.885 10.8 Inf -9.37 33.1
##
## Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -0.089 13.3 Inf -26.13 25.9
## MED1 -1.029 12.5 Inf -25.58 23.5
## MED21 -0.557 10.9 Inf -21.89 20.8
##
## Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -3.794 13.3 Inf -29.83 22.2
## MED1 -5.835 12.5 Inf -30.38 18.7
## MED21 23.299 10.8 Inf 2.04 44.6
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 8.800 18.3 Inf 0.482 0.8799
## Control - MED21 -10.490 17.2 Inf -0.612 0.8138
## MED1 - MED21 -19.290 16.6 Inf -1.164 0.4747
##
## Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 0.940 18.3 Inf 0.051 0.9985
## Control - MED21 0.468 17.2 Inf 0.027 0.9996
## MED1 - MED21 -0.471 16.6 Inf -0.028 0.9996
##
## Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 2.040 18.3 Inf 0.112 0.9931
## Control - MED21 -27.094 17.2 Inf -1.580 0.2544
## MED1 - MED21 -29.134 16.6 Inf -1.758 0.1838
##
## Degrees-of-freedom method: asymptotic
## P value adjustment: tukey method for comparing a family of 3 estimatesemmeans(m.AF_WO, pairwise ~ Session | Group)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15480' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15480)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15480' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15480)' or larger];
## but be warned that this may result in large computation time and memory use.## $emmeans
## Group = Control:
## Session emmean SE df asymp.LCL asymp.UCL
## 1 1.395 13.3 Inf -24.64 27.4
## 2 -0.089 13.3 Inf -26.13 25.9
## 3 -3.794 13.3 Inf -29.83 22.2
##
## Group = MED1:
## Session emmean SE df asymp.LCL asymp.UCL
## 1 -7.404 12.5 Inf -31.95 17.1
## 2 -1.029 12.5 Inf -25.58 23.5
## 3 -5.835 12.5 Inf -30.38 18.7
##
## Group = MED21:
## Session emmean SE df asymp.LCL asymp.UCL
## 1 11.885 10.8 Inf -9.37 33.1
## 2 -0.557 10.9 Inf -21.89 20.8
## 3 23.299 10.8 Inf 2.04 44.6
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Group = Control:
## contrast estimate SE df z.ratio p.value
## 1 - 2 1.48 4.15 Inf 0.357 0.9321
## 1 - 3 5.19 4.15 Inf 1.249 0.4242
## 2 - 3 3.71 4.15 Inf 0.892 0.6454
##
## Group = MED1:
## contrast estimate SE df z.ratio p.value
## 1 - 2 -6.38 3.92 Inf -1.628 0.2339
## 1 - 3 -1.57 3.92 Inf -0.401 0.9153
## 2 - 3 4.81 3.92 Inf 1.227 0.4372
##
## Group = MED21:
## contrast estimate SE df z.ratio p.value
## 1 - 2 12.44 3.50 Inf 3.550 0.0011
## 1 - 3 -11.41 3.39 Inf -3.365 0.0022
## 2 - 3 -23.86 3.50 Inf -6.806 <.0001
##
## Degrees-of-freedom method: asymptotic
## P value adjustment: tukey method for comparing a family of 3 estimates#Posterior Alpha
emmeans(m.AP, pairwise ~ Group | Time | Session)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15410' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15410)' or larger];
## but be warned that this may result in large computation time and memory use.## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15410' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15410)' or larger];
## but be warned that this may result in large computation time and memory use.## $emmeans
## Time = 1, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -0.709 2.86 Inf -6.31 4.892
## MED1 0.555 2.69 Inf -4.72 5.834
## MED21 -4.465 2.33 Inf -9.04 0.105
##
## Time = 2, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -0.706 2.86 Inf -6.31 4.895
## MED1 -1.422 2.69 Inf -6.70 3.857
## MED21 -3.293 2.33 Inf -7.86 1.278
##
## Time = 3, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -2.448 2.86 Inf -8.05 3.153
## MED1 -9.540 2.69 Inf -14.82 -4.261
## MED21 -5.329 2.33 Inf -9.90 -0.759
##
## Time = 4, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -3.713 2.86 Inf -9.31 1.888
## MED1 -10.293 2.69 Inf -15.57 -5.014
## MED21 -5.954 2.33 Inf -10.52 -1.384
##
## Time = 5, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -4.632 2.86 Inf -10.23 0.969
## MED1 -13.101 2.69 Inf -18.38 -7.822
## MED21 -6.207 2.33 Inf -10.78 -1.637
##
## Time = 6, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -4.457 2.86 Inf -10.06 1.144
## MED1 -12.710 2.69 Inf -17.99 -7.431
## MED21 -6.597 2.33 Inf -11.17 -2.027
##
## Time = 7, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -2.673 2.86 Inf -8.27 2.928
## MED1 -11.128 2.69 Inf -16.41 -5.849
## MED21 -8.087 2.33 Inf -12.66 -3.517
##
## Time = 8, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -1.786 2.86 Inf -7.39 3.815
## MED1 -12.274 2.69 Inf -17.55 -6.995
## MED21 -6.793 2.33 Inf -11.36 -2.223
##
## Time = 9, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -1.889 2.86 Inf -7.49 3.711
## MED1 -11.161 2.69 Inf -16.44 -5.882
## MED21 -4.259 2.33 Inf -8.83 0.311
##
## Time = 10, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -2.448 2.86 Inf -8.05 3.153
## MED1 -10.697 2.69 Inf -15.98 -5.418
## MED21 -8.088 2.33 Inf -12.66 -3.518
##
## Time = 1, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.642 2.86 Inf -4.96 6.240
## MED1 -1.920 2.69 Inf -7.20 3.357
## MED21 -2.517 2.34 Inf -7.10 2.069
##
## Time = 2, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -0.275 2.86 Inf -5.87 5.323
## MED1 -6.435 2.69 Inf -11.71 -1.159
## MED21 -4.230 2.34 Inf -8.82 0.355
##
## Time = 3, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -0.694 2.86 Inf -6.29 4.904
## MED1 -7.905 2.69 Inf -13.18 -2.629
## MED21 -5.167 2.34 Inf -9.75 -0.582
##
## Time = 4, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -1.162 2.86 Inf -6.76 4.436
## MED1 -9.698 2.69 Inf -14.97 -4.422
## MED21 -4.225 2.34 Inf -8.81 0.361
##
## Time = 5, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -0.510 2.86 Inf -6.11 5.088
## MED1 -12.537 2.69 Inf -17.81 -7.260
## MED21 -6.986 2.34 Inf -11.57 -2.401
##
## Time = 6, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -1.573 2.86 Inf -7.17 4.025
## MED1 -17.625 2.69 Inf -22.90 -12.348
## MED21 -8.374 2.34 Inf -12.96 -3.789
##
## Time = 7, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -2.055 2.86 Inf -7.65 3.543
## MED1 -15.715 2.69 Inf -20.99 -10.438
## MED21 -8.133 2.34 Inf -12.72 -3.548
##
## Time = 8, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -2.784 2.86 Inf -8.38 2.813
## MED1 -13.434 2.69 Inf -18.71 -8.157
## MED21 -6.155 2.34 Inf -10.74 -1.570
##
## Time = 9, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -3.743 2.86 Inf -9.34 1.855
## MED1 -16.360 2.69 Inf -21.64 -11.083
## MED21 -6.072 2.34 Inf -10.66 -1.487
##
## Time = 10, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -3.749 2.86 Inf -9.35 1.848
## MED1 -13.297 2.69 Inf -18.57 -8.020
## MED21 -5.050 2.34 Inf -9.64 -0.465
##
## Time = 1, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -0.617 2.86 Inf -6.21 4.980
## MED1 -2.746 2.69 Inf -8.02 2.530
## MED21 -1.722 2.33 Inf -6.29 2.848
##
## Time = 2, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -1.060 2.86 Inf -6.66 4.537
## MED1 -4.143 2.69 Inf -9.42 1.133
## MED21 -3.020 2.33 Inf -7.59 1.550
##
## Time = 3, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -1.741 2.86 Inf -7.34 3.855
## MED1 -6.605 2.69 Inf -11.88 -1.329
## MED21 -6.190 2.33 Inf -10.76 -1.620
##
## Time = 4, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -1.819 2.86 Inf -7.42 3.777
## MED1 -7.092 2.69 Inf -12.37 -1.816
## MED21 -7.126 2.33 Inf -11.70 -2.557
##
## Time = 5, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -2.342 2.86 Inf -7.94 3.255
## MED1 -11.586 2.69 Inf -16.86 -6.310
## MED21 -6.403 2.33 Inf -10.97 -1.834
##
## Time = 6, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -1.262 2.86 Inf -6.86 4.334
## MED1 -12.102 2.69 Inf -17.38 -6.825
## MED21 -9.578 2.33 Inf -14.15 -5.008
##
## Time = 7, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -1.962 2.86 Inf -7.56 3.635
## MED1 -13.654 2.69 Inf -18.93 -8.377
## MED21 -11.753 2.33 Inf -16.32 -7.183
##
## Time = 8, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -3.606 2.86 Inf -9.20 1.990
## MED1 -10.834 2.69 Inf -16.11 -5.557
## MED21 -10.387 2.33 Inf -14.96 -5.818
##
## Time = 9, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -2.596 2.86 Inf -8.19 3.001
## MED1 -8.847 2.69 Inf -14.12 -3.570
## MED21 -10.620 2.33 Inf -15.19 -6.051
##
## Time = 10, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -2.018 2.86 Inf -7.61 3.579
## MED1 -11.662 2.69 Inf -16.94 -6.385
## MED21 -9.116 2.33 Inf -13.69 -4.546
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Time = 1, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -1.2638 3.93 Inf -0.322 0.9445
## Control - MED21 3.7565 3.69 Inf 1.018 0.5651
## MED1 - MED21 5.0203 3.56 Inf 1.409 0.3361
##
## Time = 2, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 0.7157 3.93 Inf 0.182 0.9819
## Control - MED21 2.5868 3.69 Inf 0.701 0.7627
## MED1 - MED21 1.8711 3.56 Inf 0.525 0.8590
##
## Time = 3, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 7.0924 3.93 Inf 1.806 0.1675
## Control - MED21 2.8813 3.69 Inf 0.781 0.7146
## MED1 - MED21 -4.2111 3.56 Inf -1.182 0.4639
##
## Time = 4, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 6.5794 3.93 Inf 1.676 0.2146
## Control - MED21 2.2412 3.69 Inf 0.608 0.8159
## MED1 - MED21 -4.3383 3.56 Inf -1.218 0.4426
##
## Time = 5, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 8.4692 3.93 Inf 2.157 0.0788
## Control - MED21 1.5757 3.69 Inf 0.427 0.9043
## MED1 - MED21 -6.8935 3.56 Inf -1.935 0.1289
##
## Time = 6, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 8.2527 3.93 Inf 2.102 0.0895
## Control - MED21 2.1399 3.69 Inf 0.580 0.8307
## MED1 - MED21 -6.1129 3.56 Inf -1.716 0.1992
##
## Time = 7, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 8.4554 3.93 Inf 2.153 0.0795
## Control - MED21 5.4144 3.69 Inf 1.468 0.3064
## MED1 - MED21 -3.0410 3.56 Inf -0.854 0.6696
##
## Time = 8, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 10.4885 3.93 Inf 2.671 0.0207
## Control - MED21 5.0074 3.69 Inf 1.358 0.3634
## MED1 - MED21 -5.4811 3.56 Inf -1.539 0.2729
##
## Time = 9, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 9.2711 3.93 Inf 2.361 0.0479
## Control - MED21 2.3697 3.69 Inf 0.642 0.7966
## MED1 - MED21 -6.9014 3.56 Inf -1.937 0.1283
##
## Time = 10, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 8.2491 3.93 Inf 2.101 0.0897
## Control - MED21 5.6403 3.69 Inf 1.529 0.2772
## MED1 - MED21 -2.6088 3.56 Inf -0.732 0.7443
##
## Time = 1, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 2.5617 3.92 Inf 0.653 0.7908
## Control - MED21 3.1586 3.69 Inf 0.856 0.6684
## MED1 - MED21 0.5969 3.57 Inf 0.167 0.9847
##
## Time = 2, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 6.1601 3.92 Inf 1.569 0.2589
## Control - MED21 3.9548 3.69 Inf 1.071 0.5320
## MED1 - MED21 -2.2052 3.57 Inf -0.618 0.8101
##
## Time = 3, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 7.2112 3.92 Inf 1.837 0.1575
## Control - MED21 4.4730 3.69 Inf 1.212 0.4463
## MED1 - MED21 -2.7382 3.57 Inf -0.768 0.7228
##
## Time = 4, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 8.5364 3.92 Inf 2.175 0.0755
## Control - MED21 3.0626 3.69 Inf 0.830 0.6847
## MED1 - MED21 -5.4739 3.57 Inf -1.535 0.2746
##
## Time = 5, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 12.0262 3.92 Inf 3.064 0.0062
## Control - MED21 6.4754 3.69 Inf 1.754 0.1854
## MED1 - MED21 -5.5508 3.57 Inf -1.556 0.2648
##
## Time = 6, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 16.0520 3.92 Inf 4.090 0.0001
## Control - MED21 6.8014 3.69 Inf 1.842 0.1559
## MED1 - MED21 -9.2506 3.57 Inf -2.594 0.0257
##
## Time = 7, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 13.6601 3.92 Inf 3.480 0.0015
## Control - MED21 6.0783 3.69 Inf 1.646 0.2262
## MED1 - MED21 -7.5817 3.57 Inf -2.126 0.0847
##
## Time = 8, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 10.6491 3.92 Inf 2.713 0.0183
## Control - MED21 3.3703 3.69 Inf 0.913 0.6321
## MED1 - MED21 -7.2788 3.57 Inf -2.041 0.1026
##
## Time = 9, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 12.6162 3.92 Inf 3.214 0.0037
## Control - MED21 2.3287 3.69 Inf 0.631 0.8032
## MED1 - MED21 -10.2875 3.57 Inf -2.884 0.0109
##
## Time = 10, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 9.5474 3.92 Inf 2.432 0.0398
## Control - MED21 1.3008 3.69 Inf 0.352 0.9339
## MED1 - MED21 -8.2466 3.57 Inf -2.312 0.0541
##
## Time = 1, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 2.1296 3.92 Inf 0.543 0.8502
## Control - MED21 1.1046 3.69 Inf 0.300 0.9517
## MED1 - MED21 -1.0249 3.56 Inf -0.288 0.9554
##
## Time = 2, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 3.0833 3.92 Inf 0.786 0.7119
## Control - MED21 1.9602 3.69 Inf 0.532 0.8557
## MED1 - MED21 -1.1231 3.56 Inf -0.315 0.9467
##
## Time = 3, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 4.8637 3.92 Inf 1.239 0.4299
## Control - MED21 4.4483 3.69 Inf 1.207 0.4492
## MED1 - MED21 -0.4154 3.56 Inf -0.117 0.9925
##
## Time = 4, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 5.2730 3.92 Inf 1.344 0.3710
## Control - MED21 5.3068 3.69 Inf 1.440 0.3206
## MED1 - MED21 0.0338 3.56 Inf 0.009 1.0000
##
## Time = 5, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 9.2446 3.92 Inf 2.356 0.0485
## Control - MED21 4.0615 3.69 Inf 1.102 0.5130
## MED1 - MED21 -5.1831 3.56 Inf -1.455 0.3126
##
## Time = 6, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 10.8393 3.92 Inf 2.762 0.0158
## Control - MED21 8.3152 3.69 Inf 2.256 0.0622
## MED1 - MED21 -2.5242 3.56 Inf -0.709 0.7583
##
## Time = 7, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 11.6924 3.92 Inf 2.979 0.0081
## Control - MED21 9.7913 3.69 Inf 2.656 0.0216
## MED1 - MED21 -1.9010 3.56 Inf -0.534 0.8547
##
## Time = 8, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 7.2273 3.92 Inf 1.842 0.1561
## Control - MED21 6.7807 3.69 Inf 1.839 0.1568
## MED1 - MED21 -0.4465 3.56 Inf -0.125 0.9914
##
## Time = 9, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 6.2509 3.92 Inf 1.593 0.2487
## Control - MED21 8.0246 3.69 Inf 2.177 0.0752
## MED1 - MED21 1.7737 3.56 Inf 0.498 0.8722
##
## Time = 10, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 9.6437 3.92 Inf 2.457 0.0373
## Control - MED21 7.0980 3.69 Inf 1.925 0.1315
## MED1 - MED21 -2.5458 3.56 Inf -0.715 0.7547
##
## Degrees-of-freedom method: asymptotic
## P value adjustment: tukey method for comparing a family of 3 estimatesm.AP2 <- lmer(ERDSAlpha ~ Group * Session + (1 | Subject), data=d.Pos, REML = FALSE)#Alpha models effects plot
#Front Alpha effects WO AF
ae.m.AF_WO <- allEffects(m.AF_WO)
ae.m.df.AF_WO <- as.data.frame(ae.m.AF_WO[[1]])
#Ordering timepoint if needed
#ae.m.df.AF2$Time <- factor(ae.m.df.AF2$Time, levels=c('1', '2', '3', '4', '5', '6', '7', '8', '9', '10'))
#Ordering Timepoint
ae.m.df.AF_WO$Session <- as.character(ae.m.df.AF_WO$Session)
ae.m.df.AF_WO$Session <- as.numeric(ae.m.df.AF_WO$Session)
##########################
#Posterior Alpha effects
ae.m.AP <- allEffects(m.AP)
ae.m.df.AP <- as.data.frame(ae.m.AP[[1]])
#Ordering RoI
ae.m.df.AP$Time <- factor(ae.m.df.AP$Time, levels=c('1', '2', '3', '4', '5', '6', '7', '8', '9', '10'))
#Ordering Timepoint
ae.m.df.AP$Session <- as.character(ae.m.df.AP$Session)
ae.m.df.AP$Session <- as.numeric(ae.m.df.AP$Session)
ae.m.df.AP$Time <- as.character(ae.m.df.AP$Time)
ae.m.df.AP$Time <- as.numeric(ae.m.df.AP$Time)#Alpha models effects plot
#Some ggplot2 customization to implement if needed.
#limits=c(0.75,3.25)
#breaks=c(1,2,3,4,5,6,7,8,9,10)
#+facet_grid(Session~.)
#geom_line(aes(color=Group),size=1)
#geom_ribbon(aes(ymin=fit-se, ymax=fit+se, fill=Group), alpha=0.2)
#geom_smooth(aes(color=Group),size=1)
#geom_line(aes(color=Group),size=1)
#Frontal Alpha WO Model plot
ae.AF_WO <- ggplot(ae.m.df.AF_WO, aes(x=Session,y=fit, color=Group))+
geom_line(aes(color=Group),size=1) +
geom_path(aes(x=Session, y=fit, color=Group)) +
geom_errorbar(aes(ymin=fit-se, ymax=fit+se), width=.1) +
geom_point(aes(color = Group), size=3)+
ylab("Frontal Alpha ERDS (%)")+
scale_x_continuous(name="Session", breaks=c(1,2,3))+
ggtitle("Frontal Alpha ERDS (%): Group x Session")+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"))
plot(ae.AF_WO)
#Geombar
ae.AF_WO_bar <- ggplot(ae.m.df.AF_WO, aes(fill= Group, y = fit, x = Session))+
geom_col(position = "dodge")+
geom_errorbar(aes(ymin=fit-se, ymax=fit+se),
position = position_dodge(0.9), width = .3)+
scale_x_discrete(name = "Frontal Alpha ERDS (%)", label = c("Session 1", "Session 2" ,"Session 3"))+
ylab("Frontal Alpha ERDS (%)")+
ggsci::scale_fill_jco(name = "Group")+
ggpubr::theme_pubclean()+
theme(axis.title.x = element_blank(), legend.position = "right")+
coord_cartesian(expand = FALSE)
plot(ae.AF_WO_bar)
#Posterior Alpha model plot
ae.AP <- ggplot(ae.m.df.AP, aes(x=Time, y=fit, group=Group)) +
geom_line(aes(color=Group),size=1) +
geom_ribbon(aes(ymin=fit-se, ymax=fit+se, fill=Group), alpha=0.2) +
geom_point(aes(color = Group, shape = Group), size=3)+
ylab("Alpha ERDS (%)")+
scale_x_continuous(name="Time", breaks=c(1,2,3,4,5,6,7,8,9,10))+
ggtitle("Posterior Alpha ERDS % Group x Session x Time")+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"), axis.text = element_text(size = 25))+
facet_grid(.~Session)
plot(ae.AP)
#Theta models
#Model 1: Theta Frontal
m.TF_WO <- lmer(ERDSTheta ~ Group * Session *Time + (1 | Subject), data=d.Fro2, control=lmerControl(optimizer="bobyqa"), REML = FALSE)
Anova(m.TF_WO)#m.AFtest <- as(m.AF,"lmerModLmerTest")
#########################
#Model 3: Theta Posterior
m.TP <- lmer(ERDSTheta ~ Session * Group * Time + (1 | Subject), data=d.Pos, control=lmerControl(optimizer="bobyqa"), REML = FALSE)
Anova(m.TP)#m.AFtest <- as(m.AF,"lmerModLmerTest")
#summary(m.TP, ddf="Satterthwaite")#Theta Posthocs to determine exact locus
#Frontal Theta WO
emmeans(m.TF_WO, pairwise ~ Group | Session)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15480' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15480)' or larger];
## but be warned that this may result in large computation time and memory use.## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15480' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15480)' or larger];
## but be warned that this may result in large computation time and memory use.## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 22.9170 10.11 Inf 3.10 42.7
## MED1 0.0698 9.53 Inf -18.62 18.8
## MED21 22.7481 8.26 Inf 6.57 38.9
##
## Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 3.4295 10.11 Inf -16.39 23.2
## MED1 14.1246 9.53 Inf -4.56 32.8
## MED21 22.1236 8.28 Inf 5.89 38.4
##
## Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control -3.2588 10.11 Inf -23.08 16.6
## MED1 3.4655 9.53 Inf -15.22 22.2
## MED21 11.3798 8.26 Inf -4.80 27.6
##
## Results are averaged over the levels of: Time
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 22.847 13.9 Inf 1.644 0.2273
## Control - MED21 0.169 13.1 Inf 0.013 0.9999
## MED1 - MED21 -22.678 12.6 Inf -1.798 0.1702
##
## Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -10.695 13.9 Inf -0.770 0.7217
## Control - MED21 -18.694 13.1 Inf -1.430 0.3253
## MED1 - MED21 -7.999 12.6 Inf -0.633 0.8017
##
## Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -6.724 13.9 Inf -0.484 0.8790
## Control - MED21 -14.639 13.1 Inf -1.121 0.5009
## MED1 - MED21 -7.914 12.6 Inf -0.627 0.8050
##
## Results are averaged over the levels of: Time
## Degrees-of-freedom method: asymptotic
## P value adjustment: tukey method for comparing a family of 3 estimatesemmeans(m.TF_WO, pairwise ~ Session | Group)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15480' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15480)' or larger];
## but be warned that this may result in large computation time and memory use.## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15480' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15480)' or larger];
## but be warned that this may result in large computation time and memory use.## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## Group = Control:
## Session emmean SE df asymp.LCL asymp.UCL
## 1 22.9170 10.11 Inf 3.10 42.7
## 2 3.4295 10.11 Inf -16.39 23.2
## 3 -3.2588 10.11 Inf -23.08 16.6
##
## Group = MED1:
## Session emmean SE df asymp.LCL asymp.UCL
## 1 0.0698 9.53 Inf -18.62 18.8
## 2 14.1246 9.53 Inf -4.56 32.8
## 3 3.4655 9.53 Inf -15.22 22.2
##
## Group = MED21:
## Session emmean SE df asymp.LCL asymp.UCL
## 1 22.7481 8.26 Inf 6.57 38.9
## 2 22.1236 8.28 Inf 5.89 38.4
## 3 11.3798 8.26 Inf -4.80 27.6
##
## Results are averaged over the levels of: Time
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Group = Control:
## contrast estimate SE df z.ratio p.value
## 1 - 2 19.488 3.06 Inf 6.376 <.0001
## 1 - 3 26.176 3.06 Inf 8.564 <.0001
## 2 - 3 6.688 3.06 Inf 2.188 0.0732
##
## Group = MED1:
## contrast estimate SE df z.ratio p.value
## 1 - 2 -14.055 2.88 Inf -4.877 <.0001
## 1 - 3 -3.396 2.88 Inf -1.178 0.4661
## 2 - 3 10.659 2.88 Inf 3.699 0.0006
##
## Group = MED21:
## contrast estimate SE df z.ratio p.value
## 1 - 2 0.624 2.58 Inf 0.242 0.9682
## 1 - 3 11.368 2.50 Inf 4.555 <.0001
## 2 - 3 10.744 2.58 Inf 4.166 0.0001
##
## Results are averaged over the levels of: Time
## Degrees-of-freedom method: asymptotic
## P value adjustment: tukey method for comparing a family of 3 estimatesm.TF_WO <- lmer(ERDSTheta ~ Group * Session + (1 | Subject), data=d.Fro2, REML = FALSE)
#########################
#Posterior Theta
emmeans(m.TP, pairwise ~ Group | Session | Time)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15410' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15410)' or larger];
## but be warned that this may result in large computation time and memory use.## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15410' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15410)' or larger];
## but be warned that this may result in large computation time and memory use.## $emmeans
## Session = 1, Time = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 28.06 249 Inf -460.13 516
## MED1 35.23 234 Inf -424.00 494
## MED21 7.16 203 Inf -389.74 404
##
## Session = 2, Time = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 138.36 248 Inf -348.03 625
## MED1 126.23 234 Inf -331.52 584
## MED21 3.11 207 Inf -402.94 409
##
## Session = 3, Time = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 14.24 248 Inf -471.28 500
## MED1 3.41 234 Inf -454.35 461
## MED21 11.48 202 Inf -384.95 408
##
## Session = 1, Time = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 58.13 249 Inf -430.06 546
## MED1 65.94 234 Inf -393.29 525
## MED21 96.52 203 Inf -300.39 493
##
## Session = 2, Time = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 146.89 248 Inf -339.50 633
## MED1 300.61 234 Inf -157.14 758
## MED21 33.92 207 Inf -372.13 440
##
## Session = 3, Time = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 38.46 248 Inf -447.06 524
## MED1 77.59 234 Inf -380.16 535
## MED21 494.16 202 Inf 97.73 891
##
## Session = 1, Time = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 77.44 249 Inf -410.76 566
## MED1 108.52 234 Inf -350.71 568
## MED21 181.47 203 Inf -215.43 578
##
## Session = 2, Time = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 227.45 248 Inf -258.94 714
## MED1 347.52 234 Inf -110.24 805
## MED21 114.11 207 Inf -291.94 520
##
## Session = 3, Time = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 119.22 248 Inf -366.30 605
## MED1 121.83 234 Inf -335.93 580
## MED21 550.43 202 Inf 154.01 947
##
## Session = 1, Time = 4:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 83.07 249 Inf -405.12 571
## MED1 123.47 234 Inf -335.76 583
## MED21 264.95 203 Inf -131.95 662
##
## Session = 2, Time = 4:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 105.69 248 Inf -380.71 592
## MED1 462.86 234 Inf 5.11 921
## MED21 76.38 207 Inf -329.68 482
##
## Session = 3, Time = 4:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 156.68 248 Inf -328.84 642
## MED1 145.74 234 Inf -312.01 603
## MED21 716.07 202 Inf 319.64 1112
##
## Session = 1, Time = 5:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 76.65 249 Inf -411.55 565
## MED1 183.86 234 Inf -275.37 643
## MED21 299.09 203 Inf -97.81 696
##
## Session = 2, Time = 5:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 206.46 248 Inf -279.94 693
## MED1 584.74 234 Inf 126.99 1042
## MED21 173.59 207 Inf -232.46 580
##
## Session = 3, Time = 5:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 192.67 248 Inf -292.85 678
## MED1 204.67 234 Inf -253.08 662
## MED21 650.66 202 Inf 254.24 1047
##
## Session = 1, Time = 6:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 65.99 249 Inf -422.20 554
## MED1 205.26 234 Inf -253.97 664
## MED21 381.03 203 Inf -15.87 778
##
## Session = 2, Time = 6:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 288.49 248 Inf -197.91 775
## MED1 666.97 234 Inf 209.21 1125
## MED21 123.21 207 Inf -282.85 529
##
## Session = 3, Time = 6:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 135.46 248 Inf -350.06 621
## MED1 238.48 234 Inf -219.27 696
## MED21 832.94 202 Inf 436.52 1229
##
## Session = 1, Time = 7:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 51.84 249 Inf -436.35 540
## MED1 204.30 234 Inf -254.93 664
## MED21 430.41 203 Inf 33.51 827
##
## Session = 2, Time = 7:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 296.28 248 Inf -190.12 783
## MED1 497.52 234 Inf 39.77 955
## MED21 118.09 207 Inf -287.96 524
##
## Session = 3, Time = 7:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 162.91 248 Inf -322.61 648
## MED1 262.10 234 Inf -195.66 720
## MED21 883.29 202 Inf 486.86 1280
##
## Session = 1, Time = 8:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 52.14 249 Inf -436.06 540
## MED1 212.35 234 Inf -246.88 672
## MED21 378.53 203 Inf -18.37 775
##
## Session = 2, Time = 8:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 241.09 248 Inf -245.31 727
## MED1 608.45 234 Inf 150.70 1066
## MED21 153.12 207 Inf -252.94 559
##
## Session = 3, Time = 8:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 326.80 248 Inf -158.72 812
## MED1 193.79 234 Inf -263.96 652
## MED21 812.13 202 Inf 415.71 1209
##
## Session = 1, Time = 9:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 44.72 249 Inf -443.47 533
## MED1 217.97 234 Inf -241.26 677
## MED21 275.05 203 Inf -121.86 672
##
## Session = 2, Time = 9:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 310.60 248 Inf -175.80 797
## MED1 573.41 234 Inf 115.66 1031
## MED21 308.17 207 Inf -97.88 714
##
## Session = 3, Time = 9:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 290.58 248 Inf -194.94 776
## MED1 110.92 234 Inf -346.84 569
## MED21 793.70 202 Inf 397.27 1190
##
## Session = 1, Time = 10:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 71.26 249 Inf -416.93 559
## MED1 232.86 234 Inf -226.37 692
## MED21 405.98 203 Inf 9.08 803
##
## Session = 2, Time = 10:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 297.14 248 Inf -189.25 784
## MED1 487.43 234 Inf 29.67 945
## MED21 495.45 207 Inf 89.39 901
##
## Session = 3, Time = 10:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 135.36 248 Inf -350.16 621
## MED1 158.82 234 Inf -298.94 617
## MED21 768.86 202 Inf 372.43 1165
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Session = 1, Time = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -7.17 342 Inf -0.021 0.9998
## Control - MED21 20.90 321 Inf 0.065 0.9977
## MED1 - MED21 28.07 310 Inf 0.091 0.9955
##
## Session = 2, Time = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 12.13 341 Inf 0.036 0.9993
## Control - MED21 135.26 323 Inf 0.418 0.9080
## MED1 - MED21 123.12 312 Inf 0.394 0.9178
##
## Session = 3, Time = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 10.83 340 Inf 0.032 0.9994
## Control - MED21 2.76 320 Inf 0.009 1.0000
## MED1 - MED21 -8.07 309 Inf -0.026 0.9996
##
## Session = 1, Time = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -7.81 342 Inf -0.023 0.9997
## Control - MED21 -38.38 321 Inf -0.120 0.9921
## MED1 - MED21 -30.57 310 Inf -0.099 0.9946
##
## Session = 2, Time = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -153.72 341 Inf -0.451 0.8939
## Control - MED21 112.97 323 Inf 0.349 0.9349
## MED1 - MED21 266.70 312 Inf 0.854 0.6692
##
## Session = 3, Time = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -39.14 340 Inf -0.115 0.9927
## Control - MED21 -455.70 320 Inf -1.425 0.3280
## MED1 - MED21 -416.57 309 Inf -1.348 0.3684
##
## Session = 1, Time = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -31.08 342 Inf -0.091 0.9955
## Control - MED21 -104.03 321 Inf -0.324 0.9438
## MED1 - MED21 -72.95 310 Inf -0.236 0.9699
##
## Session = 2, Time = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -120.06 341 Inf -0.352 0.9339
## Control - MED21 113.34 323 Inf 0.351 0.9345
## MED1 - MED21 233.40 312 Inf 0.748 0.7351
##
## Session = 3, Time = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -2.61 340 Inf -0.008 1.0000
## Control - MED21 -431.21 320 Inf -1.348 0.3684
## MED1 - MED21 -428.61 309 Inf -1.387 0.3476
##
## Session = 1, Time = 4:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -40.40 342 Inf -0.118 0.9923
## Control - MED21 -181.88 321 Inf -0.567 0.8379
## MED1 - MED21 -141.48 310 Inf -0.457 0.8913
##
## Session = 2, Time = 4:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -357.18 341 Inf -1.048 0.5465
## Control - MED21 29.31 323 Inf 0.091 0.9955
## MED1 - MED21 386.49 312 Inf 1.238 0.4307
##
## Session = 3, Time = 4:
## contrast estimate SE df z.ratio p.value
## Control - MED1 10.94 340 Inf 0.032 0.9994
## Control - MED21 -559.39 320 Inf -1.749 0.1871
## MED1 - MED21 -570.33 309 Inf -1.846 0.1548
##
## Session = 1, Time = 5:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -107.21 342 Inf -0.314 0.9473
## Control - MED21 -222.44 321 Inf -0.693 0.7676
## MED1 - MED21 -115.23 310 Inf -0.372 0.9265
##
## Session = 2, Time = 5:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -378.28 341 Inf -1.110 0.5078
## Control - MED21 32.86 323 Inf 0.102 0.9943
## MED1 - MED21 411.15 312 Inf 1.317 0.3857
##
## Session = 3, Time = 5:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -12.00 340 Inf -0.035 0.9993
## Control - MED21 -457.99 320 Inf -1.432 0.3243
## MED1 - MED21 -445.99 309 Inf -1.444 0.3186
##
## Session = 1, Time = 6:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -139.27 342 Inf -0.407 0.9126
## Control - MED21 -315.04 321 Inf -0.981 0.5887
## MED1 - MED21 -175.77 310 Inf -0.568 0.8374
##
## Session = 2, Time = 6:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -378.48 341 Inf -1.111 0.5075
## Control - MED21 165.28 323 Inf 0.511 0.8659
## MED1 - MED21 543.76 312 Inf 1.742 0.1897
##
## Session = 3, Time = 6:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -103.03 340 Inf -0.303 0.9508
## Control - MED21 -697.49 320 Inf -2.181 0.0744
## MED1 - MED21 -594.46 309 Inf -1.924 0.1319
##
## Session = 1, Time = 7:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -152.46 342 Inf -0.446 0.8962
## Control - MED21 -378.57 321 Inf -1.179 0.4655
## MED1 - MED21 -226.11 310 Inf -0.730 0.7456
##
## Session = 2, Time = 7:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -201.24 341 Inf -0.591 0.8252
## Control - MED21 178.19 323 Inf 0.551 0.8459
## MED1 - MED21 379.43 312 Inf 1.215 0.4440
##
## Session = 3, Time = 7:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -99.18 340 Inf -0.291 0.9543
## Control - MED21 -720.37 320 Inf -2.253 0.0627
## MED1 - MED21 -621.19 309 Inf -2.011 0.1096
##
## Session = 1, Time = 8:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -160.21 342 Inf -0.469 0.8861
## Control - MED21 -326.39 321 Inf -1.017 0.5662
## MED1 - MED21 -166.18 310 Inf -0.537 0.8533
##
## Session = 2, Time = 8:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -367.36 341 Inf -1.078 0.5278
## Control - MED21 87.97 323 Inf 0.272 0.9600
## MED1 - MED21 455.33 312 Inf 1.458 0.3111
##
## Session = 3, Time = 8:
## contrast estimate SE df z.ratio p.value
## Control - MED1 133.00 340 Inf 0.391 0.9193
## Control - MED21 -485.34 320 Inf -1.518 0.2826
## MED1 - MED21 -618.34 309 Inf -2.001 0.1119
##
## Session = 1, Time = 9:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -173.25 342 Inf -0.507 0.8681
## Control - MED21 -230.32 321 Inf -0.717 0.7531
## MED1 - MED21 -57.07 310 Inf -0.184 0.9814
##
## Session = 2, Time = 9:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -262.81 341 Inf -0.771 0.7207
## Control - MED21 2.42 323 Inf 0.008 1.0000
## MED1 - MED21 265.24 312 Inf 0.850 0.6721
##
## Session = 3, Time = 9:
## contrast estimate SE df z.ratio p.value
## Control - MED1 179.66 340 Inf 0.528 0.8578
## Control - MED21 -503.12 320 Inf -1.573 0.2573
## MED1 - MED21 -682.78 309 Inf -2.210 0.0695
##
## Session = 1, Time = 10:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -161.60 342 Inf -0.473 0.8842
## Control - MED21 -334.71 321 Inf -1.043 0.5499
## MED1 - MED21 -173.12 310 Inf -0.559 0.8418
##
## Session = 2, Time = 10:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -190.28 341 Inf -0.558 0.8422
## Control - MED21 -198.30 323 Inf -0.613 0.8128
## MED1 - MED21 -8.02 312 Inf -0.026 0.9996
##
## Session = 3, Time = 10:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -23.46 340 Inf -0.069 0.9974
## Control - MED21 -633.50 320 Inf -1.981 0.1169
## MED1 - MED21 -610.04 309 Inf -1.974 0.1185
##
## Degrees-of-freedom method: asymptotic
## P value adjustment: tukey method for comparing a family of 3 estimatesm.TP2 <- lmer(ERDSTheta ~ Group * Session + (1 | Subject), data=d.Pos, REML = FALSE)#Theta models effects plot
#Frontal Theta effects
#ae.m.TF2 <- allEffects(m.TF2)
#ae.m.df.TF2 <- as.data.frame(ae.m.TF2[[1]])
#Ordering timepoint if needed
#ae.m.df.AF2$Time <- factor(ae.m.df.AF2$Time, levels=c('1', '2', '3', '4', '5', '6', '7', '8', '9', '10'))
#Ordering Session
#ae.m.df.TF2$Session <- as.character(ae.m.df.TF2$Session)
#ae.m.df.TF2$Session <- as.numeric(ae.m.df.TF2$Session)
#################################
#Frontal Theta effects WO
ae.m.TF_WO <- allEffects(m.TF_WO)
ae.m.df.TF_WO <- as.data.frame(ae.m.TF_WO[[1]])
#Ordering timepoint if needed
#ae.m.df.AF2$Time <- factor(ae.m.df.AF2$Time, levels=c('1', '2', '3', '4', '5', '6', '7', '8', '9', '10'))
#Ordering Session
ae.m.df.TF_WO$Session <- as.character(ae.m.df.TF_WO$Session)
ae.m.df.TF_WO$Session <- as.numeric(ae.m.df.TF_WO$Session)
##########################
#Posterior Theta effects
ae.m.TP2 <- allEffects(m.TP2)
ae.m.df.TP2 <- as.data.frame(ae.m.TP2[[1]])
#Ordering Timepoint
ae.m.df.TP2$Session <- as.character(ae.m.df.TP2$Session)
ae.m.df.TP2$Session <- as.numeric(ae.m.df.TP2$Session)
#Posterior 3 Way
ae.m.TP <- allEffects(m.TP)
ae.m.df.TP <- as.data.frame(ae.m.TP[[1]])
#Ordering RoI
ae.m.df.TP$Time <- factor(ae.m.df.TP$Time, levels=c('1', '2', '3', '4', '5', '6', '7', '8', '9', '10'))
#Ordering Timepoint
ae.m.df.TP$Session <- as.character(ae.m.df.TP$Session)
ae.m.df.TP$Session <- as.numeric(ae.m.df.TP$Session)
ae.m.df.TP$Time <- as.character(ae.m.df.TP$Time)
ae.m.df.TP$Time <- as.numeric(ae.m.df.TP$Time)#Theta models effects plot
#Some ggplot 2 settings to implement if needed.
#limits=c(0.75,3.25)
#breaks=c(1,2,3,4,5,6,7,8,9,10)
#+facet_grid(Session~.)
#Frontal Theta: Grp * Session plot
#ae.TF2 <- ggplot(ae.m.df.TF2, aes(x=Session,y=fit, color=Group))+
# geom_line() +
# geom_path(aes(x=Session, y=fit, color=Group)) +
# geom_errorbar(aes(ymin=fit-se, ymax=fit+se), width=.1) +
# geom_point(aes(color = Group), size=3)+
# ylab("Frontal Theta ERDS (%)")+
# scale_x_continuous(name="Session", breaks=c(1,2,3))+
# ggtitle("Frontal Theta ERDS (%): Group x Session")+
# theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"))
#plot(ae.TF2)
#Frontal Theta WO: Grp * Session plot
ae.TF_WO <- ggplot(ae.m.df.TF_WO, aes(x=Session,y=fit, color=Group))+
geom_line() +
geom_path(aes(x=Session, y=fit, color=Group)) +
geom_errorbar(aes(ymin=fit-se, ymax=fit+se), width=.1) +
geom_point(aes(color = Group), size=3)+
ylab("Frontal Theta ERDS (%)")+
scale_x_continuous(name="Session", breaks=c(1,2,3))+
ggtitle("Frontal Theta ERDS (%): Group x Session")+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"))
plot(ae.TF_WO)
#Posterior Theta: Grp * Session plot
ae.TP2 <- ggplot(ae.m.df.TP2, aes(x=Session,y=fit, color=Group))+
geom_line() +
geom_path(aes(x=Session, y=fit, color=Group)) +
geom_errorbar(aes(ymin=fit-se, ymax=fit+se), width=.1) +
geom_point(aes(color = Group), size=3)+
ylab("Posterior Theta ERDS (%)")+
scale_x_continuous(name="Session", breaks=c(1,2,3))+
ggtitle("Posterior Theta ERDS (%): Group x Session")+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"))
plot(ae.TP2)
#Posterior 3-Way model plot
ae.TP <- ggplot(ae.m.df.TP, aes(x=Time, y=fit, group=Group))+
geom_ribbon(aes(ymin=fit-se, ymax=fit+se, fill=Group), alpha=0.2) +
geom_line(aes(color=Group),size=1) +
geom_point(aes(color = Group, shape = Group), size=3)+
ylab("Theta ERDS (%)")+
scale_x_continuous(name="Time", breaks=c(1,2,3,4,5,6,7,8,9,10))+
ggtitle("Posterior Theta ERDS % Group x Session x Time")+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"), axis.text = element_text(size = 25))+
facet_grid(.~Session)
plot(ae.TP)
#Theta to Alpha Models
#Model 1: TAR Frontal
m.TARF_WO <- lmer(TAR2 ~ Group * Session * Time + (1 | Subject), data=d.Fro2, REML = FALSE)
Anova(m.TARF_WO)#m.AFtest <- as(m.AF,"lmerModLmerTest")
#summary(m.TARF_WO, ddf="Satterthwaite")
#Model 3: TAR Posterior
m.TARP <- lmer(TAR ~ Group * Session * Time + (1 | Subject), data=d.Pos, REML = FALSE)
Anova(m.TARP)#m.AFtest <- as(m.AF,"lmerModLmerTest")
#summary(m.TARP, ddf="Satterthwaite")#TAR Posthocs to determine exact locus
#Frontal TAR
emmeans(m.TARF_WO, pairwise ~ Group| Time | Session)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15480' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15480)' or larger];
## but be warned that this may result in large computation time and memory use.## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15480' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15480)' or larger];
## but be warned that this may result in large computation time and memory use.## $emmeans
## Time = 1, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.974 0.0320 Inf 0.911 1.036
## MED1 1.045 0.0302 Inf 0.986 1.104
## MED21 1.063 0.0261 Inf 1.011 1.114
##
## Time = 2, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.972 0.0320 Inf 0.910 1.035
## MED1 1.061 0.0302 Inf 1.002 1.120
## MED21 1.061 0.0261 Inf 1.010 1.112
##
## Time = 3, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.987 0.0320 Inf 0.924 1.050
## MED1 1.091 0.0302 Inf 1.032 1.150
## MED21 1.074 0.0261 Inf 1.023 1.125
##
## Time = 4, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.998 0.0320 Inf 0.935 1.060
## MED1 1.083 0.0302 Inf 1.024 1.142
## MED21 1.109 0.0261 Inf 1.058 1.161
##
## Time = 5, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.984 0.0320 Inf 0.921 1.047
## MED1 1.089 0.0302 Inf 1.030 1.148
## MED21 1.081 0.0261 Inf 1.030 1.132
##
## Time = 6, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.977 0.0320 Inf 0.914 1.040
## MED1 1.089 0.0302 Inf 1.030 1.148
## MED21 1.083 0.0261 Inf 1.032 1.134
##
## Time = 7, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.990 0.0320 Inf 0.928 1.053
## MED1 1.081 0.0302 Inf 1.022 1.140
## MED21 1.084 0.0261 Inf 1.033 1.136
##
## Time = 8, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.963 0.0320 Inf 0.900 1.025
## MED1 1.071 0.0302 Inf 1.012 1.130
## MED21 1.061 0.0261 Inf 1.010 1.112
##
## Time = 9, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.966 0.0320 Inf 0.903 1.029
## MED1 1.074 0.0302 Inf 1.015 1.134
## MED21 1.049 0.0261 Inf 0.997 1.100
##
## Time = 10, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.974 0.0320 Inf 0.912 1.037
## MED1 1.061 0.0302 Inf 1.001 1.120
## MED21 1.070 0.0261 Inf 1.019 1.121
##
## Time = 1, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.940 0.0320 Inf 0.877 1.002
## MED1 1.080 0.0302 Inf 1.021 1.139
## MED21 1.065 0.0262 Inf 1.013 1.116
##
## Time = 2, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.941 0.0320 Inf 0.878 1.004
## MED1 1.070 0.0302 Inf 1.011 1.129
## MED21 1.075 0.0262 Inf 1.024 1.126
##
## Time = 3, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.964 0.0320 Inf 0.901 1.027
## MED1 1.067 0.0302 Inf 1.008 1.126
## MED21 1.074 0.0262 Inf 1.023 1.125
##
## Time = 4, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.955 0.0320 Inf 0.892 1.018
## MED1 1.074 0.0302 Inf 1.014 1.133
## MED21 1.093 0.0262 Inf 1.042 1.144
##
## Time = 5, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.935 0.0320 Inf 0.872 0.998
## MED1 1.082 0.0302 Inf 1.023 1.141
## MED21 1.102 0.0262 Inf 1.051 1.153
##
## Time = 6, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.936 0.0320 Inf 0.873 0.998
## MED1 1.129 0.0302 Inf 1.070 1.188
## MED21 1.105 0.0262 Inf 1.053 1.156
##
## Time = 7, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.938 0.0320 Inf 0.876 1.001
## MED1 1.110 0.0302 Inf 1.051 1.169
## MED21 1.082 0.0262 Inf 1.031 1.134
##
## Time = 8, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.938 0.0320 Inf 0.875 1.001
## MED1 1.089 0.0302 Inf 1.030 1.148
## MED21 1.060 0.0262 Inf 1.008 1.111
##
## Time = 9, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.932 0.0320 Inf 0.869 0.994
## MED1 1.105 0.0302 Inf 1.046 1.164
## MED21 1.062 0.0262 Inf 1.010 1.113
##
## Time = 10, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.940 0.0320 Inf 0.877 1.002
## MED1 1.072 0.0302 Inf 1.012 1.131
## MED21 1.069 0.0262 Inf 1.018 1.121
##
## Time = 1, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.948 0.0320 Inf 0.885 1.010
## MED1 1.055 0.0302 Inf 0.996 1.114
## MED21 1.038 0.0261 Inf 0.987 1.089
##
## Time = 2, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.942 0.0320 Inf 0.879 1.005
## MED1 1.060 0.0302 Inf 1.001 1.119
## MED21 1.063 0.0261 Inf 1.012 1.114
##
## Time = 3, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.961 0.0320 Inf 0.898 1.024
## MED1 1.071 0.0302 Inf 1.012 1.130
## MED21 1.064 0.0261 Inf 1.012 1.115
##
## Time = 4, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.948 0.0320 Inf 0.885 1.011
## MED1 1.097 0.0302 Inf 1.038 1.156
## MED21 1.085 0.0261 Inf 1.034 1.137
##
## Time = 5, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.948 0.0320 Inf 0.885 1.011
## MED1 1.107 0.0302 Inf 1.048 1.166
## MED21 1.085 0.0261 Inf 1.034 1.136
##
## Time = 6, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.937 0.0320 Inf 0.874 1.000
## MED1 1.099 0.0302 Inf 1.040 1.158
## MED21 1.077 0.0261 Inf 1.026 1.128
##
## Time = 7, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.943 0.0320 Inf 0.881 1.006
## MED1 1.096 0.0302 Inf 1.036 1.155
## MED21 1.087 0.0261 Inf 1.036 1.138
##
## Time = 8, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.971 0.0320 Inf 0.908 1.033
## MED1 1.097 0.0302 Inf 1.038 1.156
## MED21 1.055 0.0261 Inf 1.004 1.106
##
## Time = 9, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.967 0.0320 Inf 0.905 1.030
## MED1 1.071 0.0302 Inf 1.012 1.130
## MED21 1.053 0.0261 Inf 1.002 1.104
##
## Time = 10, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.938 0.0320 Inf 0.875 1.001
## MED1 1.093 0.0302 Inf 1.034 1.152
## MED21 1.060 0.0261 Inf 1.009 1.111
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Time = 1, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.071080 0.0440 Inf -1.617 0.2385
## Control - MED21 -0.088837 0.0413 Inf -2.151 0.0799
## MED1 - MED21 -0.017757 0.0399 Inf -0.445 0.8966
##
## Time = 2, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.088262 0.0440 Inf -2.007 0.1104
## Control - MED21 -0.088626 0.0413 Inf -2.146 0.0808
## MED1 - MED21 -0.000363 0.0399 Inf -0.009 1.0000
##
## Time = 3, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.103717 0.0440 Inf -2.359 0.0481
## Control - MED21 -0.087081 0.0413 Inf -2.108 0.0881
## MED1 - MED21 0.016636 0.0399 Inf 0.417 0.9086
##
## Time = 4, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.085559 0.0440 Inf -1.946 0.1260
## Control - MED21 -0.111558 0.0413 Inf -2.701 0.0189
## MED1 - MED21 -0.025999 0.0399 Inf -0.652 0.7915
##
## Time = 5, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.105517 0.0440 Inf -2.400 0.0433
## Control - MED21 -0.097385 0.0413 Inf -2.358 0.0482
## MED1 - MED21 0.008132 0.0399 Inf 0.204 0.9774
##
## Time = 6, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.111786 0.0440 Inf -2.542 0.0296
## Control - MED21 -0.106243 0.0413 Inf -2.572 0.0273
## MED1 - MED21 0.005543 0.0399 Inf 0.139 0.9894
##
## Time = 7, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.090634 0.0440 Inf -2.061 0.0980
## Control - MED21 -0.093947 0.0413 Inf -2.275 0.0594
## MED1 - MED21 -0.003313 0.0399 Inf -0.083 0.9962
##
## Time = 8, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.107932 0.0440 Inf -2.455 0.0375
## Control - MED21 -0.098129 0.0413 Inf -2.376 0.0461
## MED1 - MED21 0.009803 0.0399 Inf 0.246 0.9673
##
## Time = 9, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.108466 0.0440 Inf -2.467 0.0363
## Control - MED21 -0.082718 0.0413 Inf -2.003 0.1115
## MED1 - MED21 0.025749 0.0399 Inf 0.645 0.7950
##
## Time = 10, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.086330 0.0440 Inf -1.963 0.1214
## Control - MED21 -0.095686 0.0413 Inf -2.317 0.0535
## MED1 - MED21 -0.009356 0.0399 Inf -0.234 0.9701
##
## Time = 1, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.140166 0.0440 Inf -3.188 0.0041
## Control - MED21 -0.125163 0.0413 Inf -3.027 0.0070
## MED1 - MED21 0.015002 0.0399 Inf 0.376 0.9252
##
## Time = 2, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.129395 0.0440 Inf -2.943 0.0091
## Control - MED21 -0.134262 0.0413 Inf -3.247 0.0033
## MED1 - MED21 -0.004867 0.0399 Inf -0.122 0.9919
##
## Time = 3, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.103239 0.0440 Inf -2.348 0.0495
## Control - MED21 -0.109801 0.0413 Inf -2.656 0.0216
## MED1 - MED21 -0.006562 0.0399 Inf -0.164 0.9852
##
## Time = 4, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.118649 0.0440 Inf -2.699 0.0191
## Control - MED21 -0.138203 0.0413 Inf -3.342 0.0024
## MED1 - MED21 -0.019555 0.0399 Inf -0.489 0.8763
##
## Time = 5, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.147096 0.0440 Inf -3.346 0.0024
## Control - MED21 -0.167114 0.0413 Inf -4.042 0.0002
## MED1 - MED21 -0.020018 0.0399 Inf -0.501 0.8708
##
## Time = 6, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.193575 0.0440 Inf -4.403 <.0001
## Control - MED21 -0.168961 0.0413 Inf -4.086 0.0001
## MED1 - MED21 0.024614 0.0399 Inf 0.616 0.8113
##
## Time = 7, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.171738 0.0440 Inf -3.906 0.0003
## Control - MED21 -0.143901 0.0413 Inf -3.480 0.0015
## MED1 - MED21 0.027836 0.0399 Inf 0.697 0.7654
##
## Time = 8, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.151400 0.0440 Inf -3.443 0.0017
## Control - MED21 -0.121812 0.0413 Inf -2.946 0.0090
## MED1 - MED21 0.029587 0.0399 Inf 0.741 0.7393
##
## Time = 9, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.173267 0.0440 Inf -3.941 0.0002
## Control - MED21 -0.130156 0.0413 Inf -3.148 0.0047
## MED1 - MED21 0.043111 0.0399 Inf 1.079 0.5270
##
## Time = 10, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.132013 0.0440 Inf -3.002 0.0075
## Control - MED21 -0.129837 0.0413 Inf -3.140 0.0048
## MED1 - MED21 0.002176 0.0399 Inf 0.054 0.9984
##
## Time = 1, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.107417 0.0440 Inf -2.443 0.0387
## Control - MED21 -0.090478 0.0413 Inf -2.191 0.0727
## MED1 - MED21 0.016939 0.0399 Inf 0.425 0.9054
##
## Time = 2, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.117647 0.0440 Inf -2.676 0.0204
## Control - MED21 -0.120789 0.0413 Inf -2.925 0.0097
## MED1 - MED21 -0.003142 0.0399 Inf -0.079 0.9966
##
## Time = 3, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.110034 0.0440 Inf -2.503 0.0330
## Control - MED21 -0.102415 0.0413 Inf -2.480 0.0351
## MED1 - MED21 0.007618 0.0399 Inf 0.191 0.9801
##
## Time = 4, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.148660 0.0440 Inf -3.381 0.0021
## Control - MED21 -0.137247 0.0413 Inf -3.323 0.0026
## MED1 - MED21 0.011413 0.0399 Inf 0.286 0.9559
##
## Time = 5, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.159164 0.0440 Inf -3.620 0.0009
## Control - MED21 -0.136786 0.0413 Inf -3.312 0.0027
## MED1 - MED21 0.022378 0.0399 Inf 0.561 0.8409
##
## Time = 6, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.161784 0.0440 Inf -3.680 0.0007
## Control - MED21 -0.139596 0.0413 Inf -3.380 0.0021
## MED1 - MED21 0.022188 0.0399 Inf 0.556 0.8433
##
## Time = 7, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.152095 0.0440 Inf -3.459 0.0016
## Control - MED21 -0.143355 0.0413 Inf -3.471 0.0015
## MED1 - MED21 0.008740 0.0399 Inf 0.219 0.9739
##
## Time = 8, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.126623 0.0440 Inf -2.880 0.0111
## Control - MED21 -0.084455 0.0413 Inf -2.045 0.1017
## MED1 - MED21 0.042168 0.0399 Inf 1.057 0.5410
##
## Time = 9, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.103928 0.0440 Inf -2.364 0.0475
## Control - MED21 -0.085462 0.0413 Inf -2.069 0.0963
## MED1 - MED21 0.018466 0.0399 Inf 0.463 0.8887
##
## Time = 10, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.155006 0.0440 Inf -3.525 0.0012
## Control - MED21 -0.121926 0.0413 Inf -2.952 0.0089
## MED1 - MED21 0.033080 0.0399 Inf 0.829 0.6850
##
## Degrees-of-freedom method: asymptotic
## P value adjustment: tukey method for comparing a family of 3 estimates#Posterior TAR
emmeans(m.TARP, pairwise ~ Group | Time | Session)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15410' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15410)' or larger];
## but be warned that this may result in large computation time and memory use.## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15410' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15410)' or larger];
## but be warned that this may result in large computation time and memory use.## $emmeans
## Time = 1, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.288 0.0619 Inf 0.167 0.410
## MED1 0.378 0.0583 Inf 0.264 0.492
## MED21 0.489 0.0505 Inf 0.390 0.588
##
## Time = 2, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.325 0.0619 Inf 0.204 0.446
## MED1 0.454 0.0583 Inf 0.340 0.568
## MED21 0.507 0.0505 Inf 0.408 0.606
##
## Time = 3, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.363 0.0619 Inf 0.242 0.484
## MED1 0.617 0.0583 Inf 0.502 0.731
## MED21 0.554 0.0505 Inf 0.455 0.653
##
## Time = 4, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.385 0.0619 Inf 0.263 0.506
## MED1 0.635 0.0583 Inf 0.521 0.749
## MED21 0.575 0.0505 Inf 0.476 0.673
##
## Time = 5, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.411 0.0619 Inf 0.289 0.532
## MED1 0.700 0.0583 Inf 0.586 0.814
## MED21 0.585 0.0505 Inf 0.486 0.684
##
## Time = 6, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.406 0.0619 Inf 0.284 0.527
## MED1 0.679 0.0583 Inf 0.565 0.793
## MED21 0.618 0.0505 Inf 0.519 0.717
##
## Time = 7, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.332 0.0619 Inf 0.211 0.454
## MED1 0.678 0.0583 Inf 0.564 0.792
## MED21 0.688 0.0505 Inf 0.589 0.787
##
## Time = 8, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.331 0.0619 Inf 0.209 0.452
## MED1 0.699 0.0583 Inf 0.585 0.813
## MED21 0.635 0.0505 Inf 0.536 0.734
##
## Time = 9, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.341 0.0619 Inf 0.220 0.463
## MED1 0.692 0.0583 Inf 0.577 0.806
## MED21 0.618 0.0505 Inf 0.519 0.717
##
## Time = 10, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.388 0.0619 Inf 0.267 0.509
## MED1 0.675 0.0583 Inf 0.560 0.789
## MED21 0.715 0.0505 Inf 0.616 0.814
##
## Time = 1, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.302 0.0618 Inf 0.181 0.423
## MED1 0.425 0.0583 Inf 0.311 0.539
## MED21 0.494 0.0507 Inf 0.394 0.593
##
## Time = 2, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.318 0.0618 Inf 0.196 0.439
## MED1 0.545 0.0583 Inf 0.430 0.659
## MED21 0.566 0.0507 Inf 0.466 0.665
##
## Time = 3, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.355 0.0618 Inf 0.234 0.476
## MED1 0.604 0.0583 Inf 0.490 0.718
## MED21 0.605 0.0507 Inf 0.505 0.704
##
## Time = 4, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.334 0.0618 Inf 0.213 0.455
## MED1 0.633 0.0583 Inf 0.519 0.747
## MED21 0.586 0.0507 Inf 0.487 0.685
##
## Time = 5, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.360 0.0618 Inf 0.239 0.481
## MED1 0.665 0.0583 Inf 0.550 0.779
## MED21 0.644 0.0507 Inf 0.545 0.744
##
## Time = 6, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.389 0.0618 Inf 0.268 0.510
## MED1 0.834 0.0583 Inf 0.720 0.949
## MED21 0.657 0.0507 Inf 0.558 0.757
##
## Time = 7, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.404 0.0618 Inf 0.283 0.525
## MED1 0.790 0.0583 Inf 0.675 0.904
## MED21 0.670 0.0507 Inf 0.570 0.769
##
## Time = 8, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.410 0.0618 Inf 0.289 0.531
## MED1 0.759 0.0583 Inf 0.644 0.873
## MED21 0.634 0.0507 Inf 0.535 0.734
##
## Time = 9, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.425 0.0618 Inf 0.303 0.546
## MED1 0.812 0.0583 Inf 0.698 0.926
## MED21 0.630 0.0507 Inf 0.530 0.729
##
## Time = 10, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.459 0.0618 Inf 0.338 0.580
## MED1 0.715 0.0583 Inf 0.601 0.830
## MED21 0.635 0.0507 Inf 0.536 0.735
##
## Time = 1, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.335 0.0618 Inf 0.214 0.456
## MED1 0.304 0.0583 Inf 0.190 0.419
## MED21 0.443 0.0505 Inf 0.344 0.542
##
## Time = 2, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.352 0.0618 Inf 0.231 0.474
## MED1 0.406 0.0583 Inf 0.292 0.520
## MED21 0.565 0.0505 Inf 0.466 0.664
##
## Time = 3, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.394 0.0618 Inf 0.273 0.515
## MED1 0.493 0.0583 Inf 0.379 0.607
## MED21 0.604 0.0505 Inf 0.505 0.703
##
## Time = 4, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.396 0.0618 Inf 0.275 0.517
## MED1 0.497 0.0583 Inf 0.383 0.611
## MED21 0.672 0.0505 Inf 0.574 0.771
##
## Time = 5, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.412 0.0618 Inf 0.291 0.533
## MED1 0.628 0.0583 Inf 0.514 0.742
## MED21 0.679 0.0505 Inf 0.580 0.778
##
## Time = 6, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.378 0.0618 Inf 0.257 0.499
## MED1 0.634 0.0583 Inf 0.520 0.749
## MED21 0.737 0.0505 Inf 0.638 0.836
##
## Time = 7, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.415 0.0618 Inf 0.294 0.536
## MED1 0.655 0.0583 Inf 0.541 0.769
## MED21 0.766 0.0505 Inf 0.667 0.865
##
## Time = 8, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.454 0.0618 Inf 0.333 0.575
## MED1 0.592 0.0583 Inf 0.478 0.706
## MED21 0.758 0.0505 Inf 0.659 0.857
##
## Time = 9, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.411 0.0618 Inf 0.290 0.532
## MED1 0.527 0.0583 Inf 0.413 0.641
## MED21 0.761 0.0505 Inf 0.662 0.860
##
## Time = 10, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.380 0.0618 Inf 0.259 0.501
## MED1 0.625 0.0583 Inf 0.510 0.739
## MED21 0.744 0.0505 Inf 0.645 0.843
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Time = 1, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.089455 0.0850 Inf -1.052 0.5438
## Control - MED21 -0.200611 0.0798 Inf -2.513 0.0321
## MED1 - MED21 -0.111156 0.0771 Inf -1.442 0.3195
##
## Time = 2, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.128908 0.0850 Inf -1.517 0.2831
## Control - MED21 -0.181941 0.0798 Inf -2.279 0.0588
## MED1 - MED21 -0.053032 0.0771 Inf -0.688 0.7706
##
## Time = 3, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.253678 0.0850 Inf -2.984 0.0080
## Control - MED21 -0.191389 0.0798 Inf -2.397 0.0436
## MED1 - MED21 0.062289 0.0771 Inf 0.808 0.6981
##
## Time = 4, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.250596 0.0850 Inf -2.948 0.0090
## Control - MED21 -0.189939 0.0798 Inf -2.379 0.0457
## MED1 - MED21 0.060656 0.0771 Inf 0.787 0.7112
##
## Time = 5, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.289480 0.0850 Inf -3.406 0.0019
## Control - MED21 -0.174636 0.0798 Inf -2.188 0.0733
## MED1 - MED21 0.114844 0.0771 Inf 1.489 0.2959
##
## Time = 6, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.273243 0.0850 Inf -3.215 0.0037
## Control - MED21 -0.212258 0.0798 Inf -2.659 0.0214
## MED1 - MED21 0.060985 0.0771 Inf 0.791 0.7086
##
## Time = 7, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.345411 0.0850 Inf -4.064 0.0001
## Control - MED21 -0.355492 0.0798 Inf -4.453 <.0001
## MED1 - MED21 -0.010081 0.0771 Inf -0.131 0.9906
##
## Time = 8, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.368499 0.0850 Inf -4.335 <.0001
## Control - MED21 -0.304380 0.0798 Inf -3.813 0.0004
## MED1 - MED21 0.064118 0.0771 Inf 0.832 0.6834
##
## Time = 9, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.350118 0.0850 Inf -4.119 0.0001
## Control - MED21 -0.276234 0.0798 Inf -3.460 0.0016
## MED1 - MED21 0.073884 0.0771 Inf 0.958 0.6033
##
## Time = 10, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.286532 0.0850 Inf -3.371 0.0022
## Control - MED21 -0.326714 0.0798 Inf -4.092 0.0001
## MED1 - MED21 -0.040181 0.0771 Inf -0.521 0.8610
##
## Time = 1, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.122756 0.0849 Inf -1.445 0.3177
## Control - MED21 -0.191487 0.0800 Inf -2.395 0.0438
## MED1 - MED21 -0.068731 0.0772 Inf -0.890 0.6467
##
## Time = 2, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.227015 0.0849 Inf -2.673 0.0206
## Control - MED21 -0.248194 0.0800 Inf -3.104 0.0054
## MED1 - MED21 -0.021179 0.0772 Inf -0.274 0.9594
##
## Time = 3, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.249059 0.0849 Inf -2.932 0.0094
## Control - MED21 -0.249540 0.0800 Inf -3.121 0.0051
## MED1 - MED21 -0.000481 0.0772 Inf -0.006 1.0000
##
## Time = 4, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.298874 0.0849 Inf -3.519 0.0013
## Control - MED21 -0.252041 0.0800 Inf -3.152 0.0046
## MED1 - MED21 0.046833 0.0772 Inf 0.606 0.8166
##
## Time = 5, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.304693 0.0849 Inf -3.587 0.0010
## Control - MED21 -0.284496 0.0800 Inf -3.558 0.0011
## MED1 - MED21 0.020197 0.0772 Inf 0.262 0.9630
##
## Time = 6, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.445492 0.0849 Inf -5.245 <.0001
## Control - MED21 -0.268269 0.0800 Inf -3.355 0.0023
## MED1 - MED21 0.177223 0.0772 Inf 2.295 0.0565
##
## Time = 7, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.385852 0.0849 Inf -4.543 <.0001
## Control - MED21 -0.265986 0.0800 Inf -3.327 0.0025
## MED1 - MED21 0.119866 0.0772 Inf 1.552 0.2668
##
## Time = 8, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.348655 0.0849 Inf -4.105 0.0001
## Control - MED21 -0.224361 0.0800 Inf -2.806 0.0139
## MED1 - MED21 0.124294 0.0772 Inf 1.609 0.2416
##
## Time = 9, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.387203 0.0849 Inf -4.559 <.0001
## Control - MED21 -0.205114 0.0800 Inf -2.565 0.0278
## MED1 - MED21 0.182089 0.0772 Inf 2.358 0.0483
##
## Time = 10, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.256528 0.0849 Inf -3.020 0.0071
## Control - MED21 -0.176432 0.0800 Inf -2.207 0.0700
## MED1 - MED21 0.080096 0.0772 Inf 1.037 0.5534
##
## Time = 1, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 0.030452 0.0849 Inf 0.359 0.9316
## Control - MED21 -0.108114 0.0798 Inf -1.355 0.3646
## MED1 - MED21 -0.138566 0.0771 Inf -1.798 0.1702
##
## Time = 2, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.053673 0.0849 Inf -0.632 0.8025
## Control - MED21 -0.212457 0.0798 Inf -2.663 0.0211
## MED1 - MED21 -0.158784 0.0771 Inf -2.060 0.0982
##
## Time = 3, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.099030 0.0849 Inf -1.166 0.4735
## Control - MED21 -0.210053 0.0798 Inf -2.633 0.0230
## MED1 - MED21 -0.111023 0.0771 Inf -1.441 0.3200
##
## Time = 4, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.100785 0.0849 Inf -1.187 0.4610
## Control - MED21 -0.276325 0.0798 Inf -3.464 0.0015
## MED1 - MED21 -0.175540 0.0771 Inf -2.278 0.0589
##
## Time = 5, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.216083 0.0849 Inf -2.544 0.0295
## Control - MED21 -0.266941 0.0798 Inf -3.346 0.0024
## MED1 - MED21 -0.050857 0.0771 Inf -0.660 0.7867
##
## Time = 6, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.256159 0.0849 Inf -3.016 0.0072
## Control - MED21 -0.358416 0.0798 Inf -4.493 <.0001
## MED1 - MED21 -0.102258 0.0771 Inf -1.327 0.3802
##
## Time = 7, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.239468 0.0849 Inf -2.820 0.0133
## Control - MED21 -0.350837 0.0798 Inf -4.398 <.0001
## MED1 - MED21 -0.111369 0.0771 Inf -1.445 0.3178
##
## Time = 8, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.138570 0.0849 Inf -1.632 0.2322
## Control - MED21 -0.304252 0.0798 Inf -3.814 0.0004
## MED1 - MED21 -0.165682 0.0771 Inf -2.150 0.0801
##
## Time = 9, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.115976 0.0849 Inf -1.366 0.3590
## Control - MED21 -0.350299 0.0798 Inf -4.391 <.0001
## MED1 - MED21 -0.234323 0.0771 Inf -3.041 0.0067
##
## Time = 10, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.244776 0.0849 Inf -2.882 0.0110
## Control - MED21 -0.364148 0.0798 Inf -4.565 <.0001
## MED1 - MED21 -0.119373 0.0771 Inf -1.549 0.2681
##
## Degrees-of-freedom method: asymptotic
## P value adjustment: tukey method for comparing a family of 3 estimatesemmeans(m.TARP, pairwise ~ Session | Group) #Within the group across sessions MED21 is continually increasing## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15410' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15410)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15410' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15410)' or larger];
## but be warned that this may result in large computation time and memory use.## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## Group = Control:
## Session emmean SE df asymp.LCL asymp.UCL
## 1 0.357 0.0587 Inf 0.242 0.472
## 2 0.376 0.0587 Inf 0.260 0.491
## 3 0.393 0.0587 Inf 0.278 0.508
##
## Group = MED1:
## Session emmean SE df asymp.LCL asymp.UCL
## 1 0.621 0.0554 Inf 0.512 0.729
## 2 0.678 0.0554 Inf 0.570 0.787
## 3 0.536 0.0554 Inf 0.428 0.645
##
## Group = MED21:
## Session emmean SE df asymp.LCL asymp.UCL
## 1 0.598 0.0480 Inf 0.504 0.692
## 2 0.612 0.0480 Inf 0.518 0.706
## 3 0.673 0.0479 Inf 0.579 0.767
##
## Results are averaged over the levels of: Time
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Group = Control:
## contrast estimate SE df z.ratio p.value
## 1 - 2 -0.0185 0.00913 Inf -2.030 0.1051
## 1 - 3 -0.0357 0.00911 Inf -3.922 0.0003
## 2 - 3 -0.0172 0.00908 Inf -1.895 0.1400
##
## Group = MED1:
## contrast estimate SE df z.ratio p.value
## 1 - 2 -0.0575 0.00857 Inf -6.716 <.0001
## 1 - 3 0.0845 0.00857 Inf 9.858 <.0001
## 2 - 3 0.1420 0.00854 Inf 16.626 <.0001
##
## Group = MED21:
## contrast estimate SE df z.ratio p.value
## 1 - 2 -0.0138 0.00765 Inf -1.798 0.1704
## 1 - 3 -0.0745 0.00741 Inf -10.067 <.0001
## 2 - 3 -0.0608 0.00764 Inf -7.952 <.0001
##
## Results are averaged over the levels of: Time
## Degrees-of-freedom method: asymptotic
## P value adjustment: tukey method for comparing a family of 3 estimatesemmeans(m.TARP, pairwise ~ Time | Group * Session)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15410' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15410)' or larger];
## but be warned that this may result in large computation time and memory use.## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15410' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15410)' or larger];
## but be warned that this may result in large computation time and memory use.## $emmeans
## Group = Control, Session = 1:
## Time emmean SE df asymp.LCL asymp.UCL
## 1 0.288 0.0619 Inf 0.167 0.410
## 2 0.325 0.0619 Inf 0.204 0.446
## 3 0.363 0.0619 Inf 0.242 0.484
## 4 0.385 0.0619 Inf 0.263 0.506
## 5 0.411 0.0619 Inf 0.289 0.532
## 6 0.406 0.0619 Inf 0.284 0.527
## 7 0.332 0.0619 Inf 0.211 0.454
## 8 0.331 0.0619 Inf 0.209 0.452
## 9 0.341 0.0619 Inf 0.220 0.463
## 10 0.388 0.0619 Inf 0.267 0.509
##
## Group = MED1, Session = 1:
## Time emmean SE df asymp.LCL asymp.UCL
## 1 0.378 0.0583 Inf 0.264 0.492
## 2 0.454 0.0583 Inf 0.340 0.568
## 3 0.617 0.0583 Inf 0.502 0.731
## 4 0.635 0.0583 Inf 0.521 0.749
## 5 0.700 0.0583 Inf 0.586 0.814
## 6 0.679 0.0583 Inf 0.565 0.793
## 7 0.678 0.0583 Inf 0.564 0.792
## 8 0.699 0.0583 Inf 0.585 0.813
## 9 0.692 0.0583 Inf 0.577 0.806
## 10 0.675 0.0583 Inf 0.560 0.789
##
## Group = MED21, Session = 1:
## Time emmean SE df asymp.LCL asymp.UCL
## 1 0.489 0.0505 Inf 0.390 0.588
## 2 0.507 0.0505 Inf 0.408 0.606
## 3 0.554 0.0505 Inf 0.455 0.653
## 4 0.575 0.0505 Inf 0.476 0.673
## 5 0.585 0.0505 Inf 0.486 0.684
## 6 0.618 0.0505 Inf 0.519 0.717
## 7 0.688 0.0505 Inf 0.589 0.787
## 8 0.635 0.0505 Inf 0.536 0.734
## 9 0.618 0.0505 Inf 0.519 0.717
## 10 0.715 0.0505 Inf 0.616 0.814
##
## Group = Control, Session = 2:
## Time emmean SE df asymp.LCL asymp.UCL
## 1 0.302 0.0618 Inf 0.181 0.423
## 2 0.318 0.0618 Inf 0.196 0.439
## 3 0.355 0.0618 Inf 0.234 0.476
## 4 0.334 0.0618 Inf 0.213 0.455
## 5 0.360 0.0618 Inf 0.239 0.481
## 6 0.389 0.0618 Inf 0.268 0.510
## 7 0.404 0.0618 Inf 0.283 0.525
## 8 0.410 0.0618 Inf 0.289 0.531
## 9 0.425 0.0618 Inf 0.303 0.546
## 10 0.459 0.0618 Inf 0.338 0.580
##
## Group = MED1, Session = 2:
## Time emmean SE df asymp.LCL asymp.UCL
## 1 0.425 0.0583 Inf 0.311 0.539
## 2 0.545 0.0583 Inf 0.430 0.659
## 3 0.604 0.0583 Inf 0.490 0.718
## 4 0.633 0.0583 Inf 0.519 0.747
## 5 0.665 0.0583 Inf 0.550 0.779
## 6 0.834 0.0583 Inf 0.720 0.949
## 7 0.790 0.0583 Inf 0.675 0.904
## 8 0.759 0.0583 Inf 0.644 0.873
## 9 0.812 0.0583 Inf 0.698 0.926
## 10 0.715 0.0583 Inf 0.601 0.830
##
## Group = MED21, Session = 2:
## Time emmean SE df asymp.LCL asymp.UCL
## 1 0.494 0.0507 Inf 0.394 0.593
## 2 0.566 0.0507 Inf 0.466 0.665
## 3 0.605 0.0507 Inf 0.505 0.704
## 4 0.586 0.0507 Inf 0.487 0.685
## 5 0.644 0.0507 Inf 0.545 0.744
## 6 0.657 0.0507 Inf 0.558 0.757
## 7 0.670 0.0507 Inf 0.570 0.769
## 8 0.634 0.0507 Inf 0.535 0.734
## 9 0.630 0.0507 Inf 0.530 0.729
## 10 0.635 0.0507 Inf 0.536 0.735
##
## Group = Control, Session = 3:
## Time emmean SE df asymp.LCL asymp.UCL
## 1 0.335 0.0618 Inf 0.214 0.456
## 2 0.352 0.0618 Inf 0.231 0.474
## 3 0.394 0.0618 Inf 0.273 0.515
## 4 0.396 0.0618 Inf 0.275 0.517
## 5 0.412 0.0618 Inf 0.291 0.533
## 6 0.378 0.0618 Inf 0.257 0.499
## 7 0.415 0.0618 Inf 0.294 0.536
## 8 0.454 0.0618 Inf 0.333 0.575
## 9 0.411 0.0618 Inf 0.290 0.532
## 10 0.380 0.0618 Inf 0.259 0.501
##
## Group = MED1, Session = 3:
## Time emmean SE df asymp.LCL asymp.UCL
## 1 0.304 0.0583 Inf 0.190 0.419
## 2 0.406 0.0583 Inf 0.292 0.520
## 3 0.493 0.0583 Inf 0.379 0.607
## 4 0.497 0.0583 Inf 0.383 0.611
## 5 0.628 0.0583 Inf 0.514 0.742
## 6 0.634 0.0583 Inf 0.520 0.749
## 7 0.655 0.0583 Inf 0.541 0.769
## 8 0.592 0.0583 Inf 0.478 0.706
## 9 0.527 0.0583 Inf 0.413 0.641
## 10 0.625 0.0583 Inf 0.510 0.739
##
## Group = MED21, Session = 3:
## Time emmean SE df asymp.LCL asymp.UCL
## 1 0.443 0.0505 Inf 0.344 0.542
## 2 0.565 0.0505 Inf 0.466 0.664
## 3 0.604 0.0505 Inf 0.505 0.703
## 4 0.672 0.0505 Inf 0.574 0.771
## 5 0.679 0.0505 Inf 0.580 0.778
## 6 0.737 0.0505 Inf 0.638 0.836
## 7 0.766 0.0505 Inf 0.667 0.865
## 8 0.758 0.0505 Inf 0.659 0.857
## 9 0.761 0.0505 Inf 0.662 0.860
## 10 0.744 0.0505 Inf 0.645 0.843
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Group = Control, Session = 1:
## contrast estimate SE df z.ratio p.value
## 1 - 2 -0.036571 0.0290 Inf -1.263 0.9616
## 1 - 3 -0.074388 0.0290 Inf -2.569 0.2322
## 1 - 4 -0.096072 0.0290 Inf -3.318 0.0309
## 1 - 5 -0.122030 0.0290 Inf -4.215 0.0010
## 1 - 6 -0.117106 0.0290 Inf -4.045 0.0021
## 1 - 7 -0.043975 0.0290 Inf -1.519 0.8850
## 1 - 8 -0.042078 0.0290 Inf -1.453 0.9101
## 1 - 9 -0.052938 0.0290 Inf -1.829 0.7171
## 1 - 10 -0.099690 0.0290 Inf -3.443 0.0204
## 2 - 3 -0.037817 0.0290 Inf -1.306 0.9525
## 2 - 4 -0.059501 0.0290 Inf -2.055 0.5595
## 2 - 5 -0.085458 0.0290 Inf -2.952 0.0918
## 2 - 6 -0.080535 0.0290 Inf -2.782 0.1425
## 2 - 7 -0.007404 0.0290 Inf -0.256 1.0000
## 2 - 8 -0.005507 0.0290 Inf -0.190 1.0000
## 2 - 9 -0.016367 0.0290 Inf -0.565 0.9999
## 2 - 10 -0.063119 0.0290 Inf -2.180 0.4705
## 3 - 4 -0.021685 0.0290 Inf -0.749 0.9992
## 3 - 5 -0.047642 0.0290 Inf -1.646 0.8256
## 3 - 6 -0.042718 0.0290 Inf -1.476 0.9020
## 3 - 7 0.030413 0.0290 Inf 1.050 0.9891
## 3 - 8 0.032310 0.0290 Inf 1.116 0.9832
## 3 - 9 0.021449 0.0290 Inf 0.741 0.9992
## 3 - 10 -0.025302 0.0290 Inf -0.874 0.9972
## 4 - 5 -0.025957 0.0290 Inf -0.897 0.9966
## 4 - 6 -0.021034 0.0290 Inf -0.727 0.9994
## 4 - 7 0.052098 0.0290 Inf 1.800 0.7358
## 4 - 8 0.053995 0.0290 Inf 1.865 0.6929
## 4 - 9 0.043134 0.0290 Inf 1.490 0.8966
## 4 - 10 -0.003618 0.0290 Inf -0.125 1.0000
## 5 - 6 0.004924 0.0290 Inf 0.170 1.0000
## 5 - 7 0.078055 0.0290 Inf 2.696 0.1750
## 5 - 8 0.079952 0.0290 Inf 2.762 0.1497
## 5 - 9 0.069091 0.0290 Inf 2.386 0.3338
## 5 - 10 0.022339 0.0290 Inf 0.772 0.9989
## 6 - 7 0.073131 0.0290 Inf 2.526 0.2543
## 6 - 8 0.075028 0.0290 Inf 2.592 0.2214
## 6 - 9 0.064168 0.0290 Inf 2.216 0.4453
## 6 - 10 0.017416 0.0290 Inf 0.602 0.9999
## 7 - 8 0.001897 0.0290 Inf 0.066 1.0000
## 7 - 9 -0.008964 0.0290 Inf -0.310 1.0000
## 7 - 10 -0.055716 0.0290 Inf -1.924 0.6523
## 8 - 9 -0.010861 0.0290 Inf -0.375 1.0000
## 8 - 10 -0.057612 0.0290 Inf -1.990 0.6061
## 9 - 10 -0.046752 0.0290 Inf -1.615 0.8413
##
## Group = MED1, Session = 1:
## contrast estimate SE df z.ratio p.value
## 1 - 2 -0.076025 0.0272 Inf -2.797 0.1372
## 1 - 3 -0.238610 0.0272 Inf -8.780 <.0001
## 1 - 4 -0.257213 0.0272 Inf -9.464 <.0001
## 1 - 5 -0.322055 0.0272 Inf -11.850 <.0001
## 1 - 6 -0.300894 0.0272 Inf -11.071 <.0001
## 1 - 7 -0.299930 0.0272 Inf -11.036 <.0001
## 1 - 8 -0.321121 0.0272 Inf -11.816 <.0001
## 1 - 9 -0.313601 0.0272 Inf -11.539 <.0001
## 1 - 10 -0.296768 0.0272 Inf -10.920 <.0001
## 2 - 3 -0.162586 0.0272 Inf -5.982 <.0001
## 2 - 4 -0.181188 0.0272 Inf -6.667 <.0001
## 2 - 5 -0.246030 0.0272 Inf -9.053 <.0001
## 2 - 6 -0.224869 0.0272 Inf -8.274 <.0001
## 2 - 7 -0.223906 0.0272 Inf -8.239 <.0001
## 2 - 8 -0.245097 0.0272 Inf -9.018 <.0001
## 2 - 9 -0.237576 0.0272 Inf -8.742 <.0001
## 2 - 10 -0.220743 0.0272 Inf -8.122 <.0001
## 3 - 4 -0.018603 0.0272 Inf -0.684 0.9996
## 3 - 5 -0.083445 0.0272 Inf -3.070 0.0659
## 3 - 6 -0.062284 0.0272 Inf -2.292 0.3943
## 3 - 7 -0.061320 0.0272 Inf -2.256 0.4180
## 3 - 8 -0.082511 0.0272 Inf -3.036 0.0726
## 3 - 9 -0.074991 0.0272 Inf -2.759 0.1506
## 3 - 10 -0.058157 0.0272 Inf -2.140 0.4990
## 4 - 5 -0.064842 0.0272 Inf -2.386 0.3342
## 4 - 6 -0.043681 0.0272 Inf -1.607 0.8451
## 4 - 7 -0.042718 0.0272 Inf -1.572 0.8620
## 4 - 8 -0.063908 0.0272 Inf -2.352 0.3556
## 4 - 9 -0.056388 0.0272 Inf -2.075 0.5455
## 4 - 10 -0.039555 0.0272 Inf -1.455 0.9094
## 5 - 6 0.021161 0.0272 Inf 0.779 0.9989
## 5 - 7 0.022124 0.0272 Inf 0.814 0.9984
## 5 - 8 0.000934 0.0272 Inf 0.034 1.0000
## 5 - 9 0.008454 0.0272 Inf 0.311 1.0000
## 5 - 10 0.025287 0.0272 Inf 0.930 0.9955
## 6 - 7 0.000964 0.0272 Inf 0.035 1.0000
## 6 - 8 -0.020227 0.0272 Inf -0.744 0.9992
## 6 - 9 -0.012707 0.0272 Inf -0.468 1.0000
## 6 - 10 0.004126 0.0272 Inf 0.152 1.0000
## 7 - 8 -0.021191 0.0272 Inf -0.780 0.9989
## 7 - 9 -0.013671 0.0272 Inf -0.503 1.0000
## 7 - 10 0.003163 0.0272 Inf 0.116 1.0000
## 8 - 9 0.007520 0.0272 Inf 0.277 1.0000
## 8 - 10 0.024354 0.0272 Inf 0.896 0.9966
## 9 - 10 0.016834 0.0272 Inf 0.619 0.9998
##
## Group = MED21, Session = 1:
## contrast estimate SE df z.ratio p.value
## 1 - 2 -0.017901 0.0234 Inf -0.764 0.9990
## 1 - 3 -0.065165 0.0234 Inf -2.779 0.1433
## 1 - 4 -0.085400 0.0234 Inf -3.643 0.0101
## 1 - 5 -0.096055 0.0234 Inf -4.097 0.0017
## 1 - 6 -0.128752 0.0234 Inf -5.492 <.0001
## 1 - 7 -0.198855 0.0234 Inf -8.482 <.0001
## 1 - 8 -0.145847 0.0234 Inf -6.221 <.0001
## 1 - 9 -0.128561 0.0234 Inf -5.483 <.0001
## 1 - 10 -0.225793 0.0234 Inf -9.631 <.0001
## 2 - 3 -0.047265 0.0234 Inf -2.016 0.5876
## 2 - 4 -0.067500 0.0234 Inf -2.879 0.1114
## 2 - 5 -0.078154 0.0234 Inf -3.333 0.0294
## 2 - 6 -0.110852 0.0234 Inf -4.728 0.0001
## 2 - 7 -0.180955 0.0234 Inf -7.718 <.0001
## 2 - 8 -0.127946 0.0234 Inf -5.457 <.0001
## 2 - 9 -0.110661 0.0234 Inf -4.720 0.0001
## 2 - 10 -0.207892 0.0234 Inf -8.867 <.0001
## 3 - 4 -0.020235 0.0234 Inf -0.863 0.9975
## 3 - 5 -0.030890 0.0234 Inf -1.318 0.9499
## 3 - 6 -0.063587 0.0234 Inf -2.712 0.1686
## 3 - 7 -0.133690 0.0234 Inf -5.702 <.0001
## 3 - 8 -0.080681 0.0234 Inf -3.441 0.0206
## 3 - 9 -0.063396 0.0234 Inf -2.704 0.1718
## 3 - 10 -0.160627 0.0234 Inf -6.851 <.0001
## 4 - 5 -0.010655 0.0234 Inf -0.454 1.0000
## 4 - 6 -0.043352 0.0234 Inf -1.849 0.7035
## 4 - 7 -0.113455 0.0234 Inf -4.839 0.0001
## 4 - 8 -0.060447 0.0234 Inf -2.578 0.2279
## 4 - 9 -0.043161 0.0234 Inf -1.841 0.7089
## 4 - 10 -0.140392 0.0234 Inf -5.988 <.0001
## 5 - 6 -0.032698 0.0234 Inf -1.395 0.9293
## 5 - 7 -0.102800 0.0234 Inf -4.385 0.0005
## 5 - 8 -0.049792 0.0234 Inf -2.124 0.5104
## 5 - 9 -0.032506 0.0234 Inf -1.386 0.9317
## 5 - 10 -0.129738 0.0234 Inf -5.534 <.0001
## 6 - 7 -0.070103 0.0234 Inf -2.990 0.0826
## 6 - 8 -0.017094 0.0234 Inf -0.729 0.9993
## 6 - 9 0.000191 0.0234 Inf 0.008 1.0000
## 6 - 10 -0.097040 0.0234 Inf -4.139 0.0014
## 7 - 8 0.053009 0.0234 Inf 2.261 0.4148
## 7 - 9 0.070294 0.0234 Inf 2.998 0.0808
## 7 - 10 -0.026937 0.0234 Inf -1.149 0.9795
## 8 - 9 0.017285 0.0234 Inf 0.737 0.9993
## 8 - 10 -0.079946 0.0234 Inf -3.410 0.0228
## 9 - 10 -0.097231 0.0234 Inf -4.147 0.0014
##
## Group = Control, Session = 2:
## contrast estimate SE df z.ratio p.value
## 1 - 2 -0.015324 0.0287 Inf -0.533 1.0000
## 1 - 3 -0.052768 0.0287 Inf -1.836 0.7125
## 1 - 4 -0.031665 0.0287 Inf -1.101 0.9847
## 1 - 5 -0.057647 0.0287 Inf -2.005 0.5953
## 1 - 6 -0.086685 0.0287 Inf -3.015 0.0770
## 1 - 7 -0.101446 0.0287 Inf -3.529 0.0152
## 1 - 8 -0.107714 0.0287 Inf -3.747 0.0069
## 1 - 9 -0.122261 0.0287 Inf -4.253 0.0009
## 1 - 10 -0.156610 0.0287 Inf -5.448 <.0001
## 2 - 3 -0.037444 0.0287 Inf -1.302 0.9534
## 2 - 4 -0.016341 0.0287 Inf -0.568 0.9999
## 2 - 5 -0.042322 0.0287 Inf -1.472 0.9033
## 2 - 6 -0.071361 0.0287 Inf -2.482 0.2779
## 2 - 7 -0.086121 0.0287 Inf -2.996 0.0813
## 2 - 8 -0.092390 0.0287 Inf -3.214 0.0429
## 2 - 9 -0.106937 0.0287 Inf -3.720 0.0076
## 2 - 10 -0.141286 0.0287 Inf -4.915 <.0001
## 3 - 4 0.021103 0.0287 Inf 0.734 0.9993
## 3 - 5 -0.004879 0.0287 Inf -0.170 1.0000
## 3 - 6 -0.033917 0.0287 Inf -1.180 0.9755
## 3 - 7 -0.048678 0.0287 Inf -1.693 0.7997
## 3 - 8 -0.054946 0.0287 Inf -1.911 0.6614
## 3 - 9 -0.069493 0.0287 Inf -2.417 0.3152
## 3 - 10 -0.103842 0.0287 Inf -3.612 0.0113
## 4 - 5 -0.025982 0.0287 Inf -0.904 0.9964
## 4 - 6 -0.055020 0.0287 Inf -1.914 0.6596
## 4 - 7 -0.069781 0.0287 Inf -2.427 0.3093
## 4 - 8 -0.076049 0.0287 Inf -2.645 0.1966
## 4 - 9 -0.090596 0.0287 Inf -3.151 0.0519
## 4 - 10 -0.124945 0.0287 Inf -4.346 0.0006
## 5 - 6 -0.029038 0.0287 Inf -1.010 0.9918
## 5 - 7 -0.043799 0.0287 Inf -1.524 0.8831
## 5 - 8 -0.050068 0.0287 Inf -1.742 0.7716
## 5 - 9 -0.064615 0.0287 Inf -2.248 0.4239
## 5 - 10 -0.098963 0.0287 Inf -3.442 0.0205
## 6 - 7 -0.014760 0.0287 Inf -0.513 1.0000
## 6 - 8 -0.021029 0.0287 Inf -0.731 0.9993
## 6 - 9 -0.035576 0.0287 Inf -1.238 0.9664
## 6 - 10 -0.069925 0.0287 Inf -2.432 0.3063
## 7 - 8 -0.006269 0.0287 Inf -0.218 1.0000
## 7 - 9 -0.020816 0.0287 Inf -0.724 0.9994
## 7 - 10 -0.055164 0.0287 Inf -1.919 0.6561
## 8 - 9 -0.014547 0.0287 Inf -0.506 1.0000
## 8 - 10 -0.048896 0.0287 Inf -1.701 0.7954
## 9 - 10 -0.034349 0.0287 Inf -1.195 0.9733
##
## Group = MED1, Session = 2:
## contrast estimate SE df z.ratio p.value
## 1 - 2 -0.119583 0.0270 Inf -4.427 0.0004
## 1 - 3 -0.179072 0.0270 Inf -6.630 <.0001
## 1 - 4 -0.207783 0.0270 Inf -7.693 <.0001
## 1 - 5 -0.239584 0.0270 Inf -8.870 <.0001
## 1 - 6 -0.409421 0.0270 Inf -15.158 <.0001
## 1 - 7 -0.364542 0.0270 Inf -13.497 <.0001
## 1 - 8 -0.333614 0.0270 Inf -12.352 <.0001
## 1 - 9 -0.386709 0.0270 Inf -14.318 <.0001
## 1 - 10 -0.290382 0.0270 Inf -10.751 <.0001
## 2 - 3 -0.059488 0.0270 Inf -2.202 0.4549
## 2 - 4 -0.088199 0.0270 Inf -3.265 0.0365
## 2 - 5 -0.120001 0.0270 Inf -4.443 0.0004
## 2 - 6 -0.289838 0.0270 Inf -10.731 <.0001
## 2 - 7 -0.244958 0.0270 Inf -9.069 <.0001
## 2 - 8 -0.214030 0.0270 Inf -7.924 <.0001
## 2 - 9 -0.267125 0.0270 Inf -9.890 <.0001
## 2 - 10 -0.170799 0.0270 Inf -6.324 <.0001
## 3 - 4 -0.028711 0.0270 Inf -1.063 0.9881
## 3 - 5 -0.060512 0.0270 Inf -2.240 0.4288
## 3 - 6 -0.230350 0.0270 Inf -8.528 <.0001
## 3 - 7 -0.185470 0.0270 Inf -6.867 <.0001
## 3 - 8 -0.154542 0.0270 Inf -5.722 <.0001
## 3 - 9 -0.207637 0.0270 Inf -7.688 <.0001
## 3 - 10 -0.111310 0.0270 Inf -4.121 0.0015
## 4 - 5 -0.031801 0.0270 Inf -1.177 0.9758
## 4 - 6 -0.201639 0.0270 Inf -7.465 <.0001
## 4 - 7 -0.156759 0.0270 Inf -5.804 <.0001
## 4 - 8 -0.125831 0.0270 Inf -4.659 0.0001
## 4 - 9 -0.178926 0.0270 Inf -6.625 <.0001
## 4 - 10 -0.082599 0.0270 Inf -3.058 0.0682
## 5 - 6 -0.169837 0.0270 Inf -6.288 <.0001
## 5 - 7 -0.124958 0.0270 Inf -4.626 0.0002
## 5 - 8 -0.094030 0.0270 Inf -3.481 0.0179
## 5 - 9 -0.147124 0.0270 Inf -5.447 <.0001
## 5 - 10 -0.050798 0.0270 Inf -1.881 0.6823
## 6 - 7 0.044880 0.0270 Inf 1.662 0.8171
## 6 - 8 0.075808 0.0270 Inf 2.807 0.1340
## 6 - 9 0.022713 0.0270 Inf 0.841 0.9979
## 6 - 10 0.119040 0.0270 Inf 4.407 0.0004
## 7 - 8 0.030928 0.0270 Inf 1.145 0.9800
## 7 - 9 -0.022167 0.0270 Inf -0.821 0.9983
## 7 - 10 0.074160 0.0270 Inf 2.746 0.1556
## 8 - 9 -0.053095 0.0270 Inf -1.966 0.6233
## 8 - 10 0.043232 0.0270 Inf 1.601 0.8483
## 9 - 10 0.096326 0.0270 Inf 3.566 0.0133
##
## Group = MED21, Session = 2:
## contrast estimate SE df z.ratio p.value
## 1 - 2 -0.072031 0.0244 Inf -2.948 0.0926
## 1 - 3 -0.110821 0.0244 Inf -4.536 0.0002
## 1 - 4 -0.092218 0.0244 Inf -3.775 0.0062
## 1 - 5 -0.150656 0.0244 Inf -6.167 <.0001
## 1 - 6 -0.163468 0.0244 Inf -6.691 <.0001
## 1 - 7 -0.175944 0.0244 Inf -7.202 <.0001
## 1 - 8 -0.140589 0.0244 Inf -5.755 <.0001
## 1 - 9 -0.135888 0.0244 Inf -5.562 <.0001
## 1 - 10 -0.141554 0.0244 Inf -5.794 <.0001
## 2 - 3 -0.038790 0.0244 Inf -1.588 0.8545
## 2 - 4 -0.020187 0.0244 Inf -0.826 0.9982
## 2 - 5 -0.078625 0.0244 Inf -3.218 0.0423
## 2 - 6 -0.091436 0.0244 Inf -3.743 0.0070
## 2 - 7 -0.103913 0.0244 Inf -4.253 0.0009
## 2 - 8 -0.068557 0.0244 Inf -2.806 0.1342
## 2 - 9 -0.063857 0.0244 Inf -2.614 0.2109
## 2 - 10 -0.069523 0.0244 Inf -2.846 0.1214
## 3 - 4 0.018603 0.0244 Inf 0.761 0.9991
## 3 - 5 -0.039835 0.0244 Inf -1.631 0.8334
## 3 - 6 -0.052646 0.0244 Inf -2.155 0.4883
## 3 - 7 -0.065123 0.0244 Inf -2.666 0.1878
## 3 - 8 -0.029767 0.0244 Inf -1.218 0.9696
## 3 - 9 -0.025067 0.0244 Inf -1.026 0.9908
## 3 - 10 -0.030733 0.0244 Inf -1.258 0.9626
## 4 - 5 -0.058438 0.0244 Inf -2.392 0.3305
## 4 - 6 -0.071249 0.0244 Inf -2.916 0.1009
## 4 - 7 -0.083726 0.0244 Inf -3.427 0.0216
## 4 - 8 -0.048370 0.0244 Inf -1.980 0.6133
## 4 - 9 -0.043670 0.0244 Inf -1.787 0.7434
## 4 - 10 -0.049336 0.0244 Inf -2.019 0.5852
## 5 - 6 -0.012812 0.0244 Inf -0.524 1.0000
## 5 - 7 -0.025288 0.0244 Inf -1.035 0.9902
## 5 - 8 0.010068 0.0244 Inf 0.412 1.0000
## 5 - 9 0.014768 0.0244 Inf 0.604 0.9999
## 5 - 10 0.009102 0.0244 Inf 0.373 1.0000
## 6 - 7 -0.012477 0.0244 Inf -0.511 1.0000
## 6 - 8 0.022879 0.0244 Inf 0.936 0.9953
## 6 - 9 0.027579 0.0244 Inf 1.129 0.9819
## 6 - 10 0.021913 0.0244 Inf 0.897 0.9966
## 7 - 8 0.035356 0.0244 Inf 1.447 0.9123
## 7 - 9 0.040056 0.0244 Inf 1.640 0.8287
## 7 - 10 0.034390 0.0244 Inf 1.408 0.9253
## 8 - 9 0.004700 0.0244 Inf 0.192 1.0000
## 8 - 10 -0.000966 0.0244 Inf -0.040 1.0000
## 9 - 10 -0.005666 0.0244 Inf -0.232 1.0000
##
## Group = Control, Session = 3:
## contrast estimate SE df z.ratio p.value
## 1 - 2 -0.017614 0.0286 Inf -0.615 0.9998
## 1 - 3 -0.058889 0.0286 Inf -2.056 0.5592
## 1 - 4 -0.061243 0.0286 Inf -2.138 0.5004
## 1 - 5 -0.077159 0.0286 Inf -2.693 0.1761
## 1 - 6 -0.043401 0.0286 Inf -1.515 0.8866
## 1 - 7 -0.080465 0.0286 Inf -2.809 0.1333
## 1 - 8 -0.118916 0.0286 Inf -4.151 0.0014
## 1 - 9 -0.076159 0.0286 Inf -2.658 0.1909
## 1 - 10 -0.045025 0.0286 Inf -1.572 0.8620
## 2 - 3 -0.041275 0.0286 Inf -1.441 0.9145
## 2 - 4 -0.043630 0.0286 Inf -1.523 0.8833
## 2 - 5 -0.059545 0.0286 Inf -2.079 0.5428
## 2 - 6 -0.025787 0.0286 Inf -0.900 0.9965
## 2 - 7 -0.062851 0.0286 Inf -2.194 0.4609
## 2 - 8 -0.101302 0.0286 Inf -3.536 0.0148
## 2 - 9 -0.058545 0.0286 Inf -2.044 0.5678
## 2 - 10 -0.027411 0.0286 Inf -0.957 0.9945
## 3 - 4 -0.002354 0.0286 Inf -0.082 1.0000
## 3 - 5 -0.018270 0.0286 Inf -0.638 0.9998
## 3 - 6 0.015488 0.0286 Inf 0.541 0.9999
## 3 - 7 -0.021576 0.0286 Inf -0.753 0.9991
## 3 - 8 -0.060027 0.0286 Inf -2.095 0.5307
## 3 - 9 -0.017270 0.0286 Inf -0.603 0.9999
## 3 - 10 0.013864 0.0286 Inf 0.484 1.0000
## 4 - 5 -0.015915 0.0286 Inf -0.556 0.9999
## 4 - 6 0.017842 0.0286 Inf 0.623 0.9998
## 4 - 7 -0.019222 0.0286 Inf -0.671 0.9997
## 4 - 8 -0.057673 0.0286 Inf -2.013 0.5896
## 4 - 9 -0.014915 0.0286 Inf -0.521 1.0000
## 4 - 10 0.016218 0.0286 Inf 0.566 0.9999
## 5 - 6 0.033758 0.0286 Inf 1.178 0.9757
## 5 - 7 -0.003306 0.0286 Inf -0.115 1.0000
## 5 - 8 -0.041757 0.0286 Inf -1.458 0.9086
## 5 - 9 0.001000 0.0286 Inf 0.035 1.0000
## 5 - 10 0.032133 0.0286 Inf 1.122 0.9826
## 6 - 7 -0.037064 0.0286 Inf -1.294 0.9553
## 6 - 8 -0.075515 0.0286 Inf -2.636 0.2008
## 6 - 9 -0.032758 0.0286 Inf -1.143 0.9802
## 6 - 10 -0.001624 0.0286 Inf -0.057 1.0000
## 7 - 8 -0.038451 0.0286 Inf -1.342 0.9439
## 7 - 9 0.004306 0.0286 Inf 0.150 1.0000
## 7 - 10 0.035440 0.0286 Inf 1.237 0.9665
## 8 - 9 0.042757 0.0286 Inf 1.493 0.8956
## 8 - 10 0.073891 0.0286 Inf 2.579 0.2274
## 9 - 10 0.031133 0.0286 Inf 1.087 0.9861
##
## Group = MED1, Session = 3:
## contrast estimate SE df z.ratio p.value
## 1 - 2 -0.101739 0.0270 Inf -3.767 0.0064
## 1 - 3 -0.188371 0.0270 Inf -6.974 <.0001
## 1 - 4 -0.192480 0.0270 Inf -7.126 <.0001
## 1 - 5 -0.323694 0.0270 Inf -11.984 <.0001
## 1 - 6 -0.330012 0.0270 Inf -12.218 <.0001
## 1 - 7 -0.350385 0.0270 Inf -12.973 <.0001
## 1 - 8 -0.287938 0.0270 Inf -10.661 <.0001
## 1 - 9 -0.222587 0.0270 Inf -8.241 <.0001
## 1 - 10 -0.320253 0.0270 Inf -11.857 <.0001
## 2 - 3 -0.086632 0.0270 Inf -3.207 0.0438
## 2 - 4 -0.090741 0.0270 Inf -3.360 0.0270
## 2 - 5 -0.221955 0.0270 Inf -8.218 <.0001
## 2 - 6 -0.228272 0.0270 Inf -8.452 <.0001
## 2 - 7 -0.248646 0.0270 Inf -9.206 <.0001
## 2 - 8 -0.186199 0.0270 Inf -6.894 <.0001
## 2 - 9 -0.120847 0.0270 Inf -4.474 0.0003
## 2 - 10 -0.218514 0.0270 Inf -8.090 <.0001
## 3 - 4 -0.004109 0.0270 Inf -0.152 1.0000
## 3 - 5 -0.135323 0.0270 Inf -5.010 <.0001
## 3 - 6 -0.141641 0.0270 Inf -5.244 <.0001
## 3 - 7 -0.162015 0.0270 Inf -5.998 <.0001
## 3 - 8 -0.099567 0.0270 Inf -3.686 0.0086
## 3 - 9 -0.034216 0.0270 Inf -1.267 0.9609
## 3 - 10 -0.131882 0.0270 Inf -4.883 <.0001
## 4 - 5 -0.131214 0.0270 Inf -4.858 0.0001
## 4 - 6 -0.137531 0.0270 Inf -5.092 <.0001
## 4 - 7 -0.157905 0.0270 Inf -5.846 <.0001
## 4 - 8 -0.095458 0.0270 Inf -3.534 0.0149
## 4 - 9 -0.030106 0.0270 Inf -1.115 0.9834
## 4 - 10 -0.127773 0.0270 Inf -4.731 0.0001
## 5 - 6 -0.006318 0.0270 Inf -0.234 1.0000
## 5 - 7 -0.026691 0.0270 Inf -0.988 0.9930
## 5 - 8 0.035756 0.0270 Inf 1.324 0.9484
## 5 - 9 0.101107 0.0270 Inf 3.743 0.0070
## 5 - 10 0.003441 0.0270 Inf 0.127 1.0000
## 6 - 7 -0.020374 0.0270 Inf -0.754 0.9991
## 6 - 8 0.042073 0.0270 Inf 1.558 0.8683
## 6 - 9 0.107425 0.0270 Inf 3.977 0.0028
## 6 - 10 0.009759 0.0270 Inf 0.361 1.0000
## 7 - 8 0.062447 0.0270 Inf 2.312 0.3809
## 7 - 9 0.127799 0.0270 Inf 4.732 0.0001
## 7 - 10 0.030132 0.0270 Inf 1.116 0.9833
## 8 - 9 0.065352 0.0270 Inf 2.420 0.3139
## 8 - 10 -0.032315 0.0270 Inf -1.196 0.9731
## 9 - 10 -0.097666 0.0270 Inf -3.616 0.0112
##
## Group = MED21, Session = 3:
## contrast estimate SE df z.ratio p.value
## 1 - 2 -0.121958 0.0234 Inf -5.214 <.0001
## 1 - 3 -0.160829 0.0234 Inf -6.876 <.0001
## 1 - 4 -0.229455 0.0234 Inf -9.810 <.0001
## 1 - 5 -0.235986 0.0234 Inf -10.089 <.0001
## 1 - 6 -0.293704 0.0234 Inf -12.556 <.0001
## 1 - 7 -0.323189 0.0234 Inf -13.817 <.0001
## 1 - 8 -0.315055 0.0234 Inf -13.469 <.0001
## 1 - 9 -0.318344 0.0234 Inf -13.610 <.0001
## 1 - 10 -0.301060 0.0234 Inf -12.871 <.0001
## 2 - 3 -0.038871 0.0234 Inf -1.662 0.8170
## 2 - 4 -0.107498 0.0234 Inf -4.596 0.0002
## 2 - 5 -0.114028 0.0234 Inf -4.875 <.0001
## 2 - 6 -0.171746 0.0234 Inf -7.342 <.0001
## 2 - 7 -0.201231 0.0234 Inf -8.603 <.0001
## 2 - 8 -0.193097 0.0234 Inf -8.255 <.0001
## 2 - 9 -0.196386 0.0234 Inf -8.396 <.0001
## 2 - 10 -0.179103 0.0234 Inf -7.657 <.0001
## 3 - 4 -0.068626 0.0234 Inf -2.934 0.0963
## 3 - 5 -0.075157 0.0234 Inf -3.213 0.0430
## 3 - 6 -0.132875 0.0234 Inf -5.681 <.0001
## 3 - 7 -0.162360 0.0234 Inf -6.941 <.0001
## 3 - 8 -0.154226 0.0234 Inf -6.593 <.0001
## 3 - 9 -0.157515 0.0234 Inf -6.734 <.0001
## 3 - 10 -0.140232 0.0234 Inf -5.995 <.0001
## 4 - 5 -0.006531 0.0234 Inf -0.279 1.0000
## 4 - 6 -0.064249 0.0234 Inf -2.747 0.1552
## 4 - 7 -0.093734 0.0234 Inf -4.007 0.0025
## 4 - 8 -0.085599 0.0234 Inf -3.660 0.0095
## 4 - 9 -0.088889 0.0234 Inf -3.800 0.0056
## 4 - 10 -0.071605 0.0234 Inf -3.061 0.0676
## 5 - 6 -0.057718 0.0234 Inf -2.468 0.2861
## 5 - 7 -0.087203 0.0234 Inf -3.728 0.0074
## 5 - 8 -0.079069 0.0234 Inf -3.380 0.0252
## 5 - 9 -0.082358 0.0234 Inf -3.521 0.0156
## 5 - 10 -0.065075 0.0234 Inf -2.782 0.1424
## 6 - 7 -0.029485 0.0234 Inf -1.261 0.9621
## 6 - 8 -0.021351 0.0234 Inf -0.913 0.9961
## 6 - 9 -0.024640 0.0234 Inf -1.053 0.9888
## 6 - 10 -0.007357 0.0234 Inf -0.315 1.0000
## 7 - 8 0.008134 0.0234 Inf 0.348 1.0000
## 7 - 9 0.004845 0.0234 Inf 0.207 1.0000
## 7 - 10 0.022128 0.0234 Inf 0.946 0.9949
## 8 - 9 -0.003289 0.0234 Inf -0.141 1.0000
## 8 - 10 0.013994 0.0234 Inf 0.598 0.9999
## 9 - 10 0.017284 0.0234 Inf 0.739 0.9993
##
## Degrees-of-freedom method: asymptotic
## P value adjustment: tukey method for comparing a family of 10 estimatesemmeans(m.TARP, pairwise ~ Group | Time | Session)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15410' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15410)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15410' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15410)' or larger];
## but be warned that this may result in large computation time and memory use.## $emmeans
## Time = 1, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.288 0.0619 Inf 0.167 0.410
## MED1 0.378 0.0583 Inf 0.264 0.492
## MED21 0.489 0.0505 Inf 0.390 0.588
##
## Time = 2, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.325 0.0619 Inf 0.204 0.446
## MED1 0.454 0.0583 Inf 0.340 0.568
## MED21 0.507 0.0505 Inf 0.408 0.606
##
## Time = 3, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.363 0.0619 Inf 0.242 0.484
## MED1 0.617 0.0583 Inf 0.502 0.731
## MED21 0.554 0.0505 Inf 0.455 0.653
##
## Time = 4, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.385 0.0619 Inf 0.263 0.506
## MED1 0.635 0.0583 Inf 0.521 0.749
## MED21 0.575 0.0505 Inf 0.476 0.673
##
## Time = 5, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.411 0.0619 Inf 0.289 0.532
## MED1 0.700 0.0583 Inf 0.586 0.814
## MED21 0.585 0.0505 Inf 0.486 0.684
##
## Time = 6, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.406 0.0619 Inf 0.284 0.527
## MED1 0.679 0.0583 Inf 0.565 0.793
## MED21 0.618 0.0505 Inf 0.519 0.717
##
## Time = 7, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.332 0.0619 Inf 0.211 0.454
## MED1 0.678 0.0583 Inf 0.564 0.792
## MED21 0.688 0.0505 Inf 0.589 0.787
##
## Time = 8, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.331 0.0619 Inf 0.209 0.452
## MED1 0.699 0.0583 Inf 0.585 0.813
## MED21 0.635 0.0505 Inf 0.536 0.734
##
## Time = 9, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.341 0.0619 Inf 0.220 0.463
## MED1 0.692 0.0583 Inf 0.577 0.806
## MED21 0.618 0.0505 Inf 0.519 0.717
##
## Time = 10, Session = 1:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.388 0.0619 Inf 0.267 0.509
## MED1 0.675 0.0583 Inf 0.560 0.789
## MED21 0.715 0.0505 Inf 0.616 0.814
##
## Time = 1, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.302 0.0618 Inf 0.181 0.423
## MED1 0.425 0.0583 Inf 0.311 0.539
## MED21 0.494 0.0507 Inf 0.394 0.593
##
## Time = 2, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.318 0.0618 Inf 0.196 0.439
## MED1 0.545 0.0583 Inf 0.430 0.659
## MED21 0.566 0.0507 Inf 0.466 0.665
##
## Time = 3, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.355 0.0618 Inf 0.234 0.476
## MED1 0.604 0.0583 Inf 0.490 0.718
## MED21 0.605 0.0507 Inf 0.505 0.704
##
## Time = 4, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.334 0.0618 Inf 0.213 0.455
## MED1 0.633 0.0583 Inf 0.519 0.747
## MED21 0.586 0.0507 Inf 0.487 0.685
##
## Time = 5, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.360 0.0618 Inf 0.239 0.481
## MED1 0.665 0.0583 Inf 0.550 0.779
## MED21 0.644 0.0507 Inf 0.545 0.744
##
## Time = 6, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.389 0.0618 Inf 0.268 0.510
## MED1 0.834 0.0583 Inf 0.720 0.949
## MED21 0.657 0.0507 Inf 0.558 0.757
##
## Time = 7, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.404 0.0618 Inf 0.283 0.525
## MED1 0.790 0.0583 Inf 0.675 0.904
## MED21 0.670 0.0507 Inf 0.570 0.769
##
## Time = 8, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.410 0.0618 Inf 0.289 0.531
## MED1 0.759 0.0583 Inf 0.644 0.873
## MED21 0.634 0.0507 Inf 0.535 0.734
##
## Time = 9, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.425 0.0618 Inf 0.303 0.546
## MED1 0.812 0.0583 Inf 0.698 0.926
## MED21 0.630 0.0507 Inf 0.530 0.729
##
## Time = 10, Session = 2:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.459 0.0618 Inf 0.338 0.580
## MED1 0.715 0.0583 Inf 0.601 0.830
## MED21 0.635 0.0507 Inf 0.536 0.735
##
## Time = 1, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.335 0.0618 Inf 0.214 0.456
## MED1 0.304 0.0583 Inf 0.190 0.419
## MED21 0.443 0.0505 Inf 0.344 0.542
##
## Time = 2, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.352 0.0618 Inf 0.231 0.474
## MED1 0.406 0.0583 Inf 0.292 0.520
## MED21 0.565 0.0505 Inf 0.466 0.664
##
## Time = 3, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.394 0.0618 Inf 0.273 0.515
## MED1 0.493 0.0583 Inf 0.379 0.607
## MED21 0.604 0.0505 Inf 0.505 0.703
##
## Time = 4, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.396 0.0618 Inf 0.275 0.517
## MED1 0.497 0.0583 Inf 0.383 0.611
## MED21 0.672 0.0505 Inf 0.574 0.771
##
## Time = 5, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.412 0.0618 Inf 0.291 0.533
## MED1 0.628 0.0583 Inf 0.514 0.742
## MED21 0.679 0.0505 Inf 0.580 0.778
##
## Time = 6, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.378 0.0618 Inf 0.257 0.499
## MED1 0.634 0.0583 Inf 0.520 0.749
## MED21 0.737 0.0505 Inf 0.638 0.836
##
## Time = 7, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.415 0.0618 Inf 0.294 0.536
## MED1 0.655 0.0583 Inf 0.541 0.769
## MED21 0.766 0.0505 Inf 0.667 0.865
##
## Time = 8, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.454 0.0618 Inf 0.333 0.575
## MED1 0.592 0.0583 Inf 0.478 0.706
## MED21 0.758 0.0505 Inf 0.659 0.857
##
## Time = 9, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.411 0.0618 Inf 0.290 0.532
## MED1 0.527 0.0583 Inf 0.413 0.641
## MED21 0.761 0.0505 Inf 0.662 0.860
##
## Time = 10, Session = 3:
## Group emmean SE df asymp.LCL asymp.UCL
## Control 0.380 0.0618 Inf 0.259 0.501
## MED1 0.625 0.0583 Inf 0.510 0.739
## MED21 0.744 0.0505 Inf 0.645 0.843
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Time = 1, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.089455 0.0850 Inf -1.052 0.5438
## Control - MED21 -0.200611 0.0798 Inf -2.513 0.0321
## MED1 - MED21 -0.111156 0.0771 Inf -1.442 0.3195
##
## Time = 2, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.128908 0.0850 Inf -1.517 0.2831
## Control - MED21 -0.181941 0.0798 Inf -2.279 0.0588
## MED1 - MED21 -0.053032 0.0771 Inf -0.688 0.7706
##
## Time = 3, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.253678 0.0850 Inf -2.984 0.0080
## Control - MED21 -0.191389 0.0798 Inf -2.397 0.0436
## MED1 - MED21 0.062289 0.0771 Inf 0.808 0.6981
##
## Time = 4, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.250596 0.0850 Inf -2.948 0.0090
## Control - MED21 -0.189939 0.0798 Inf -2.379 0.0457
## MED1 - MED21 0.060656 0.0771 Inf 0.787 0.7112
##
## Time = 5, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.289480 0.0850 Inf -3.406 0.0019
## Control - MED21 -0.174636 0.0798 Inf -2.188 0.0733
## MED1 - MED21 0.114844 0.0771 Inf 1.489 0.2959
##
## Time = 6, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.273243 0.0850 Inf -3.215 0.0037
## Control - MED21 -0.212258 0.0798 Inf -2.659 0.0214
## MED1 - MED21 0.060985 0.0771 Inf 0.791 0.7086
##
## Time = 7, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.345411 0.0850 Inf -4.064 0.0001
## Control - MED21 -0.355492 0.0798 Inf -4.453 <.0001
## MED1 - MED21 -0.010081 0.0771 Inf -0.131 0.9906
##
## Time = 8, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.368499 0.0850 Inf -4.335 <.0001
## Control - MED21 -0.304380 0.0798 Inf -3.813 0.0004
## MED1 - MED21 0.064118 0.0771 Inf 0.832 0.6834
##
## Time = 9, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.350118 0.0850 Inf -4.119 0.0001
## Control - MED21 -0.276234 0.0798 Inf -3.460 0.0016
## MED1 - MED21 0.073884 0.0771 Inf 0.958 0.6033
##
## Time = 10, Session = 1:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.286532 0.0850 Inf -3.371 0.0022
## Control - MED21 -0.326714 0.0798 Inf -4.092 0.0001
## MED1 - MED21 -0.040181 0.0771 Inf -0.521 0.8610
##
## Time = 1, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.122756 0.0849 Inf -1.445 0.3177
## Control - MED21 -0.191487 0.0800 Inf -2.395 0.0438
## MED1 - MED21 -0.068731 0.0772 Inf -0.890 0.6467
##
## Time = 2, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.227015 0.0849 Inf -2.673 0.0206
## Control - MED21 -0.248194 0.0800 Inf -3.104 0.0054
## MED1 - MED21 -0.021179 0.0772 Inf -0.274 0.9594
##
## Time = 3, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.249059 0.0849 Inf -2.932 0.0094
## Control - MED21 -0.249540 0.0800 Inf -3.121 0.0051
## MED1 - MED21 -0.000481 0.0772 Inf -0.006 1.0000
##
## Time = 4, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.298874 0.0849 Inf -3.519 0.0013
## Control - MED21 -0.252041 0.0800 Inf -3.152 0.0046
## MED1 - MED21 0.046833 0.0772 Inf 0.606 0.8166
##
## Time = 5, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.304693 0.0849 Inf -3.587 0.0010
## Control - MED21 -0.284496 0.0800 Inf -3.558 0.0011
## MED1 - MED21 0.020197 0.0772 Inf 0.262 0.9630
##
## Time = 6, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.445492 0.0849 Inf -5.245 <.0001
## Control - MED21 -0.268269 0.0800 Inf -3.355 0.0023
## MED1 - MED21 0.177223 0.0772 Inf 2.295 0.0565
##
## Time = 7, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.385852 0.0849 Inf -4.543 <.0001
## Control - MED21 -0.265986 0.0800 Inf -3.327 0.0025
## MED1 - MED21 0.119866 0.0772 Inf 1.552 0.2668
##
## Time = 8, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.348655 0.0849 Inf -4.105 0.0001
## Control - MED21 -0.224361 0.0800 Inf -2.806 0.0139
## MED1 - MED21 0.124294 0.0772 Inf 1.609 0.2416
##
## Time = 9, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.387203 0.0849 Inf -4.559 <.0001
## Control - MED21 -0.205114 0.0800 Inf -2.565 0.0278
## MED1 - MED21 0.182089 0.0772 Inf 2.358 0.0483
##
## Time = 10, Session = 2:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.256528 0.0849 Inf -3.020 0.0071
## Control - MED21 -0.176432 0.0800 Inf -2.207 0.0700
## MED1 - MED21 0.080096 0.0772 Inf 1.037 0.5534
##
## Time = 1, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 0.030452 0.0849 Inf 0.359 0.9316
## Control - MED21 -0.108114 0.0798 Inf -1.355 0.3646
## MED1 - MED21 -0.138566 0.0771 Inf -1.798 0.1702
##
## Time = 2, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.053673 0.0849 Inf -0.632 0.8025
## Control - MED21 -0.212457 0.0798 Inf -2.663 0.0211
## MED1 - MED21 -0.158784 0.0771 Inf -2.060 0.0982
##
## Time = 3, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.099030 0.0849 Inf -1.166 0.4735
## Control - MED21 -0.210053 0.0798 Inf -2.633 0.0230
## MED1 - MED21 -0.111023 0.0771 Inf -1.441 0.3200
##
## Time = 4, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.100785 0.0849 Inf -1.187 0.4610
## Control - MED21 -0.276325 0.0798 Inf -3.464 0.0015
## MED1 - MED21 -0.175540 0.0771 Inf -2.278 0.0589
##
## Time = 5, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.216083 0.0849 Inf -2.544 0.0295
## Control - MED21 -0.266941 0.0798 Inf -3.346 0.0024
## MED1 - MED21 -0.050857 0.0771 Inf -0.660 0.7867
##
## Time = 6, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.256159 0.0849 Inf -3.016 0.0072
## Control - MED21 -0.358416 0.0798 Inf -4.493 <.0001
## MED1 - MED21 -0.102258 0.0771 Inf -1.327 0.3802
##
## Time = 7, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.239468 0.0849 Inf -2.820 0.0133
## Control - MED21 -0.350837 0.0798 Inf -4.398 <.0001
## MED1 - MED21 -0.111369 0.0771 Inf -1.445 0.3178
##
## Time = 8, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.138570 0.0849 Inf -1.632 0.2322
## Control - MED21 -0.304252 0.0798 Inf -3.814 0.0004
## MED1 - MED21 -0.165682 0.0771 Inf -2.150 0.0801
##
## Time = 9, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.115976 0.0849 Inf -1.366 0.3590
## Control - MED21 -0.350299 0.0798 Inf -4.391 <.0001
## MED1 - MED21 -0.234323 0.0771 Inf -3.041 0.0067
##
## Time = 10, Session = 3:
## contrast estimate SE df z.ratio p.value
## Control - MED1 -0.244776 0.0849 Inf -2.882 0.0110
## Control - MED21 -0.364148 0.0798 Inf -4.565 <.0001
## MED1 - MED21 -0.119373 0.0771 Inf -1.549 0.2681
##
## Degrees-of-freedom method: asymptotic
## P value adjustment: tukey method for comparing a family of 3 estimates#TAR models effects plot
#Front TAR effects
ae.m.TARF <- allEffects(m.TARF_WO)
ae.m.df.TARF <- as.data.frame(ae.m.TARF[[1]])
#Ordering timepoint if needed
#ae.m.df.AF2$Time <- factor(ae.m.df.AF2$Time, levels=c('1', '2', '3', '4', '5', '6', '7', '8', '9', '10'))
#Ordering Timepoint
ae.m.df.TARF$Session <- as.character(ae.m.df.TARF$Session)
ae.m.df.TARF$Session <- as.numeric(ae.m.df.TARF$Session)
ae.m.df.TARF$Time <- as.character(ae.m.df.TARF$Time)
ae.m.df.TARF$Time <- as.numeric(ae.m.df.TARF$Time)
##########################
#Posterior TAR effects
ae.m.TARP <- allEffects(m.TARP)
ae.m.df.TARP <- as.data.frame(ae.m.TARP[[1]])
#Ordering RoI
#ae.m.df.TARP$Time <- factor(ae.m.df.TARP$Time, levels=c('1', '2', '3', '4', '5', '6', '7', '8', '9', '10'))
#Ordering Timepoint
ae.m.df.TARP$Session <- as.character(ae.m.df.TARP$Session)
ae.m.df.TARP$Session <- as.numeric(ae.m.df.TARP$Session)
ae.m.df.TARP$Time <- as.character(ae.m.df.TARP$Time)
ae.m.df.TARP$Time <- as.numeric(ae.m.df.TARP$Time)#Frontal TAR Model
ae.TARF <- ggplot(ae.m.df.TARF, aes(x=Time, y=fit, group=Group))+
geom_ribbon(aes(ymin=fit-se, ymax=fit+se, fill=Group), alpha=0.2)+
geom_point(aes(color = Group, shape = Group), size=3)+
ylab("Theta to alpha ratio (n.u.)")+
geom_line(aes(color=Group),size=1)+
scale_x_continuous(name="Time", breaks=c(1,2,3,4,5,6,7,8,9,10))+
ggtitle("Theta to alpha ratio (n.u.)")+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"), axis.text = element_text(size = 25))+
facet_grid(.~Session)
plot(ae.TARF)
#Posterior TAR model
ae.TARP <- ggplot(ae.m.df.TARP, aes(x=Time, y=fit, group=Group))+
geom_ribbon(aes(ymin=lower, ymax=upper, fill=Group), alpha=0.2)+
geom_point(aes(color = Group, shape = Group), size=3)+
ylab("Theta to alpha ratio (n.u.)")+
geom_line(aes(color=Group),size=1)+
scale_x_continuous(name="Time", breaks=c(1,2,3,4,5,6,7,8,9,10))+
ggtitle("Theta to alpha ration (n.u.)")+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"), axis.text = element_text(size = 25))+
facet_grid(.~Session)
plot(ae.TARP)