ERDS Long-Format Final
Russell Chan & Daphne Titsing
04/11/2021
setwd("C:/Users/RussellChan/OneDrive - University of Twente/2020_MSCA_IF/Conferences/2021_Conferences/Dutch Brain and Cognition/new")##Packages##
#Import datasets and set factors for long-format of all datasets
#Russell's DFs
feedback_ALL <- read.csv("feedback_ALL.csv", sep = ",")
prep_ALL <- read.csv("prep_ALL.csv", sep = ",")
motor_ALL <- read.csv("motor_ALL.csv", sep = ",")
#Factor for prep
prep_ALL$Participant <- factor(prep_ALL$Participant)
prep_ALL$Time <- factor(prep_ALL$Time)
prep_ALL$Block <- factor(prep_ALL$Block)
prep_ALL$Channel <- factor(prep_ALL$Channel)
prep_ALL$Beta <- factor(prep_ALL$Beta)
#prep_ALL$RT <- factor(prep_ALL$RT)
#Factor for motor
motor_ALL$Participant <- factor(motor_ALL$Participant)
motor_ALL$Time <- factor(motor_ALL$Time)
motor_ALL$Block <- factor(motor_ALL$Block)
motor_ALL$Channel <- factor(motor_ALL$Channel)
motor_ALL$Beta <- factor(motor_ALL$Beta)
#motor_ALL$RT <- factor(motor_ALL$RT)
#Factor for Lpress
feedback_ALL$Participant <- factor(feedback_ALL$Participant)
feedback_ALL$Time <- factor(feedback_ALL$Time)
feedback_ALL$Block <- factor(feedback_ALL$Block)
feedback_ALL$Channel <- factor(feedback_ALL$Channel)
feedback_ALL$Beta <- factor(feedback_ALL$Beta)
#feedback_ALL$RT <- factor(feedback_ALL$RT)#Filter frequencies
#Fliter Lpress Beta into 3
f_ALL_beta1 <- feedback_ALL %>% filter(Beta %in% c("beta1"))
f_ALL_beta2 <- feedback_ALL %>% filter(Beta %in% c("beta2"))
f_ALL_beta3 <- feedback_ALL %>% filter(Beta %in% c("beta3"))
#Fliter Prep Beta into 3
p_ALL_beta1 <- prep_ALL %>% filter(Beta %in% c("beta1"))
p_ALL_beta2 <- prep_ALL %>% filter(Beta %in% c("beta2"))
p_ALL_beta3 <- prep_ALL %>% filter(Beta %in% c("beta3"))
#Fliter Motor Beta into 3
m_ALL_beta1 <- motor_ALL %>% filter(Beta %in% c("beta1"))
m_ALL_beta2 <- motor_ALL %>% filter(Beta %in% c("beta2"))
m_ALL_beta3 <- motor_ALL %>% filter(Beta %in% c("beta3"))#Prep models for each Beta frequency
m.p_ALL_beta1 <- lmer(ERDS ~ Time * Channel * Block + (1|Participant), data = p_ALL_beta1)
Anova(m.p_ALL_beta1)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ERDS
## Chisq Df Pr(>Chisq)
## Time 127.5310 25 1.019e-15 ***
## Channel 8.9164 3 0.03042 *
## Block 20.6969 1 5.380e-06 ***
## Time:Channel 36.1055 75 0.99996
## Time:Block 61.2863 25 6.936e-05 ***
## Channel:Block 28.3364 3 3.087e-06 ***
## Time:Channel:Block 18.2424 75 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#summary(m.f_ALL_beta1)
m.p_ALL_beta2 <- lmer(ERDS ~ Time * Channel * Block + (1|Participant), data = p_ALL_beta2)
Anova(m.p_ALL_beta2)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ERDS
## Chisq Df Pr(>Chisq)
## Time 109.296 25 1.632e-12 ***
## Channel 3.752 3 0.2895188
## Block 14.541 1 0.0001371 ***
## Time:Channel 31.128 75 0.9999983
## Time:Block 97.691 25 1.532e-10 ***
## Channel:Block 15.225 3 0.0016342 **
## Time:Channel:Block 21.434 75 1.0000000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#summary(m.f_ALL_beta2)
m.p_ALL_beta3 <- lmer(ERDS ~ Time * Channel * Block + (1|Participant), data = p_ALL_beta3)
Anova(m.p_ALL_beta3)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ERDS
## Chisq Df Pr(>Chisq)
## Time 84.7964 25 1.984e-08 ***
## Channel 1.7740 3 0.62060
## Block 5.3598 1 0.02061 *
## Time:Channel 31.2173 75 1.00000
## Time:Block 89.9871 25 2.875e-09 ***
## Channel:Block 8.7163 3 0.03331 *
## Time:Channel:Block 26.2913 75 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#summary(m2.f_ALL_beta3)#Prep Model check
AIC(m.p_ALL_beta1,m.p_ALL_beta2,m.p_ALL_beta3)## df AIC
## m.p_ALL_beta1 210 45918.48
## m.p_ALL_beta2 210 47140.77
## m.p_ALL_beta3 210 50242.20#Motor models for each Beta frequency
m.m_ALL_beta1 <- lmer(ERDS ~ Time * Channel * Block + (1|Participant), data = m_ALL_beta1)
Anova(m.m_ALL_beta1)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ERDS
## Chisq Df Pr(>Chisq)
## Time 144.9790 30 < 2.2e-16 ***
## Channel 4.4947 3 0.2128
## Block 71.9097 1 < 2.2e-16 ***
## Time:Channel 19.5761 90 1.0000
## Time:Block 119.2036 30 1.387e-12 ***
## Channel:Block 5.4756 3 0.1401
## Time:Channel:Block 15.6662 90 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#summary(m.f_ALL_beta1)
m.m_ALL_beta2 <- lmer(ERDS ~ Time * Channel * Block + (1|Participant), data = m_ALL_beta2)
Anova(m.m_ALL_beta2)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ERDS
## Chisq Df Pr(>Chisq)
## Time 115.2816 30 6.231e-12 ***
## Channel 1.5738 3 0.6654
## Block 52.3544 1 4.634e-13 ***
## Time:Channel 12.5035 90 1.0000
## Time:Block 107.4090 30 1.213e-10 ***
## Channel:Block 1.6005 3 0.6593
## Time:Channel:Block 10.4964 90 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#summary(m.f_ALL_beta2)
m.m_ALL_beta3 <- lmer(ERDS ~ Time * Channel * Block + (1|Participant), data = m_ALL_beta3)
Anova(m.m_ALL_beta3)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ERDS
## Chisq Df Pr(>Chisq)
## Time 76.8803 30 5.494e-06 ***
## Channel 4.2239 3 0.2383
## Block 67.5398 1 < 2.2e-16 ***
## Time:Channel 18.9725 90 1.0000
## Time:Block 72.3251 30 2.354e-05 ***
## Channel:Block 3.9613 3 0.2657
## Time:Channel:Block 18.3983 90 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#summary(m.f_ALL_beta3)#Motor Model check
AIC(m.m_ALL_beta1, m.m_ALL_beta2, m.m_ALL_beta3)## df AIC
## m.m_ALL_beta1 250 69544.37
## m.m_ALL_beta2 250 76272.33
## m.m_ALL_beta3 250 75372.07#Lpress for each Beta frequency
m.f_ALL_beta1 <- lmer(ERDS ~ Time * Channel * Block + (1|Participant), data = f_ALL_beta1)
Anova(m.f_ALL_beta1)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ERDS
## Chisq Df Pr(>Chisq)
## Time 423.7751 30 < 2.2e-16 ***
## Channel 4.8075 3 0.1864450
## Block 36.5778 1 1.467e-09 ***
## Time:Channel 11.8638 90 1.0000000
## Time:Block 61.3760 30 0.0006253 ***
## Channel:Block 3.4187 3 0.3314552
## Time:Channel:Block 11.3242 90 1.0000000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#summary(m.f_ALL_beta1)
m.f_ALL_beta2 <- lmer(ERDS ~ Time * Channel * Block + (1|Participant), data = f_ALL_beta2)
Anova(m.f_ALL_beta2)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ERDS
## Chisq Df Pr(>Chisq)
## Time 332.4229 30 < 2.2e-16 ***
## Channel 6.7017 3 0.08204 .
## Block 34.2174 1 4.929e-09 ***
## Time:Channel 13.1626 90 1.00000
## Time:Block 75.7815 30 7.838e-06 ***
## Channel:Block 3.1737 3 0.36562
## Time:Channel:Block 10.0737 90 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#summary(m.f_ALL_beta2)
m.f_ALL_beta3 <- lmer(ERDS ~ Time * Channel * Block + (1|Participant), data = f_ALL_beta3)
Anova(m.f_ALL_beta3)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ERDS
## Chisq Df Pr(>Chisq)
## Time 328.1116 30 < 2.2e-16 ***
## Channel 5.2988 3 0.1512
## Block 40.8472 1 1.646e-10 ***
## Time:Channel 16.7959 90 1.0000
## Time:Block 71.5348 30 3.016e-05 ***
## Channel:Block 3.1486 3 0.3693
## Time:Channel:Block 9.9736 90 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#summary(m.f_ALL_beta3)#Lpress Model check
AIC(m.f_ALL_beta1, m.f_ALL_beta2, m.f_ALL_beta3)## df AIC
## m.f_ALL_beta1 250 76705.56
## m.f_ALL_beta2 250 79912.85
## m.f_ALL_beta3 250 78618.39#Post hocs for prep models
#Feedback Posthocs
lsmeans(m.p_ALL_beta1, pairwise ~ Block | Time) #Here we can directly isolate the timepoints that are interacting## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5408' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5408)' 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 = 5408' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5408)' 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## $lsmeans
## Time = -1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -14.08 2.58 Inf -19.1 -9.02
## B5 -19.77 2.58 Inf -24.8 -14.71
##
## Time = -0.9:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -13.94 2.58 Inf -19.0 -8.88
## B5 -18.71 2.58 Inf -23.8 -13.65
##
## Time = -0.8:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -14.33 2.58 Inf -19.4 -9.27
## B5 -19.66 2.58 Inf -24.7 -14.60
##
## Time = -0.7:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -12.91 2.58 Inf -18.0 -7.84
## B5 -17.62 2.58 Inf -22.7 -12.55
##
## Time = -0.6:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -14.65 2.58 Inf -19.7 -9.58
## B5 -16.52 2.58 Inf -21.6 -11.46
##
## Time = -0.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -14.09 2.58 Inf -19.1 -9.02
## B5 -17.09 2.58 Inf -22.2 -12.03
##
## Time = -0.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -17.71 2.58 Inf -22.8 -12.65
## B5 -15.90 2.58 Inf -21.0 -10.83
##
## Time = -0.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -15.31 2.58 Inf -20.4 -10.25
## B5 -14.51 2.58 Inf -19.6 -9.45
##
## Time = -0.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -15.57 2.58 Inf -20.6 -10.51
## B5 -18.68 2.58 Inf -23.7 -13.62
##
## Time = -0.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -19.67 2.58 Inf -24.7 -14.61
## B5 -16.70 2.58 Inf -21.8 -11.64
##
## Time = 0:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -19.97 2.58 Inf -25.0 -14.91
## B5 -18.81 2.58 Inf -23.9 -13.75
##
## Time = 0.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.58 2.58 Inf -21.6 -11.52
## B5 -17.90 2.58 Inf -23.0 -12.84
##
## Time = 0.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -17.05 2.58 Inf -22.1 -11.99
## B5 -15.99 2.58 Inf -21.0 -10.92
##
## Time = 0.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -21.57 2.58 Inf -26.6 -16.51
## B5 -18.87 2.58 Inf -23.9 -13.81
##
## Time = 0.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -19.54 2.58 Inf -24.6 -14.48
## B5 -16.04 2.58 Inf -21.1 -10.98
##
## Time = 0.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -18.39 2.58 Inf -23.5 -13.33
## B5 -17.15 2.58 Inf -22.2 -12.09
##
## Time = 0.6:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -18.86 2.58 Inf -23.9 -13.80
## B5 -17.98 2.58 Inf -23.0 -12.92
##
## Time = 0.7:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -17.24 2.58 Inf -22.3 -12.18
## B5 -17.95 2.58 Inf -23.0 -12.89
##
## Time = 0.8:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -12.33 2.58 Inf -17.4 -7.26
## B5 -12.64 2.58 Inf -17.7 -7.58
##
## Time = 0.9:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -9.67 2.58 Inf -14.7 -4.61
## B5 -13.85 2.58 Inf -18.9 -8.79
##
## Time = 1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -12.18 2.58 Inf -17.2 -7.11
## B5 -14.00 2.58 Inf -19.1 -8.94
##
## Time = 1.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -9.54 2.58 Inf -14.6 -4.47
## B5 -18.80 2.58 Inf -23.9 -13.74
##
## Time = 1.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -9.06 2.58 Inf -14.1 -4.00
## B5 -18.30 2.58 Inf -23.4 -13.23
##
## Time = 1.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -10.18 2.58 Inf -15.2 -5.11
## B5 -18.47 2.58 Inf -23.5 -13.41
##
## Time = 1.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -7.45 2.58 Inf -12.5 -2.38
## B5 -15.88 2.58 Inf -20.9 -10.81
##
## Time = 1.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -6.81 2.58 Inf -11.9 -1.74
## B5 -8.23 2.58 Inf -13.3 -3.16
##
## Results are averaged over the levels of: Channel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Time = -1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 5.688 2.47 Inf 2.301 0.0214
##
## Time = -0.9:
## contrast estimate SE df z.ratio p.value
## B1 - B5 4.770 2.47 Inf 1.929 0.0537
##
## Time = -0.8:
## contrast estimate SE df z.ratio p.value
## B1 - B5 5.327 2.47 Inf 2.155 0.0312
##
## Time = -0.7:
## contrast estimate SE df z.ratio p.value
## B1 - B5 4.712 2.47 Inf 1.906 0.0567
##
## Time = -0.6:
## contrast estimate SE df z.ratio p.value
## B1 - B5 1.874 2.47 Inf 0.758 0.4485
##
## Time = -0.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 3.005 2.47 Inf 1.215 0.2242
##
## Time = -0.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -1.817 2.47 Inf -0.735 0.4625
##
## Time = -0.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -0.800 2.47 Inf -0.324 0.7462
##
## Time = -0.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 3.113 2.47 Inf 1.259 0.2080
##
## Time = -0.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -2.973 2.47 Inf -1.202 0.2292
##
## Time = 0:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -1.158 2.47 Inf -0.468 0.6395
##
## Time = 0.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 1.321 2.47 Inf 0.534 0.5930
##
## Time = 0.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -1.064 2.47 Inf -0.431 0.6668
##
## Time = 0.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -2.697 2.47 Inf -1.091 0.2754
##
## Time = 0.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -3.502 2.47 Inf -1.417 0.1566
##
## Time = 0.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -1.236 2.47 Inf -0.500 0.6171
##
## Time = 0.6:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -0.883 2.47 Inf -0.357 0.7210
##
## Time = 0.7:
## contrast estimate SE df z.ratio p.value
## B1 - B5 0.714 2.47 Inf 0.289 0.7727
##
## Time = 0.8:
## contrast estimate SE df z.ratio p.value
## B1 - B5 0.317 2.47 Inf 0.128 0.8981
##
## Time = 0.9:
## contrast estimate SE df z.ratio p.value
## B1 - B5 4.177 2.47 Inf 1.690 0.0911
##
## Time = 1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 1.821 2.47 Inf 0.736 0.4615
##
## Time = 1.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 9.266 2.47 Inf 3.748 0.0002
##
## Time = 1.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 9.232 2.47 Inf 3.734 0.0002
##
## Time = 1.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 8.299 2.47 Inf 3.357 0.0008
##
## Time = 1.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 8.428 2.47 Inf 3.409 0.0007
##
## Time = 1.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 1.420 2.47 Inf 0.574 0.5657
##
## Results are averaged over the levels of: Channel
## Degrees-of-freedom method: asymptotic#lsmeans(m.f_ALL_beta1, pairwise ~ Block | Channel) #this is expected but not the focus of the study
lsmeans(m.p_ALL_beta2, pairwise ~ Block | Time) ## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5408' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5408)' 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 = 5408' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5408)' 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## $lsmeans
## Time = -1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -15.99 2.95 Inf -21.8 -10.1981
## B5 -18.93 2.95 Inf -24.7 -13.1378
##
## Time = -0.9:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -12.57 2.95 Inf -18.4 -6.7812
## B5 -16.51 2.95 Inf -22.3 -10.7213
##
## Time = -0.8:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -14.36 2.95 Inf -20.1 -8.5693
## B5 -22.51 2.95 Inf -28.3 -16.7244
##
## Time = -0.7:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -15.74 2.95 Inf -21.5 -9.9563
## B5 -20.95 2.95 Inf -26.7 -15.1614
##
## Time = -0.6:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -19.00 2.95 Inf -24.8 -13.2071
## B5 -17.86 2.95 Inf -23.6 -12.0704
##
## Time = -0.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -13.62 2.95 Inf -19.4 -7.8332
## B5 -17.24 2.95 Inf -23.0 -11.4517
##
## Time = -0.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -21.12 2.95 Inf -26.9 -15.3318
## B5 -15.75 2.95 Inf -21.5 -9.9633
##
## Time = -0.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -15.47 2.95 Inf -21.3 -9.6773
## B5 -13.43 2.95 Inf -19.2 -7.6403
##
## Time = -0.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.30 2.95 Inf -22.1 -10.5147
## B5 -17.50 2.95 Inf -23.3 -11.7084
##
## Time = -0.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -18.56 2.95 Inf -24.3 -12.7693
## B5 -14.66 2.95 Inf -20.5 -8.8755
##
## Time = 0:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.46 2.95 Inf -22.2 -10.6698
## B5 -16.81 2.95 Inf -22.6 -11.0261
##
## Time = 0.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -13.62 2.95 Inf -19.4 -7.8323
## B5 -16.97 2.95 Inf -22.8 -11.1822
##
## Time = 0.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -20.49 2.95 Inf -26.3 -14.7018
## B5 -14.70 2.95 Inf -20.5 -8.9125
##
## Time = 0.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -21.16 2.95 Inf -27.0 -15.3764
## B5 -16.92 2.95 Inf -22.7 -11.1324
##
## Time = 0.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.68 2.95 Inf -22.5 -10.8899
## B5 -15.17 2.95 Inf -21.0 -9.3844
##
## Time = 0.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -18.55 2.95 Inf -24.3 -12.7602
## B5 -13.30 2.95 Inf -19.1 -7.5148
##
## Time = 0.6:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -18.67 2.95 Inf -24.5 -12.8864
## B5 -14.15 2.95 Inf -19.9 -8.3642
##
## Time = 0.7:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.20 2.95 Inf -22.0 -10.4162
## B5 -18.89 2.95 Inf -24.7 -13.1013
##
## Time = 0.8:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -10.62 2.95 Inf -16.4 -4.8333
## B5 -12.61 2.95 Inf -18.4 -6.8261
##
## Time = 0.9:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -5.76 2.95 Inf -11.5 0.0298
## B5 -17.19 2.95 Inf -23.0 -11.4025
##
## Time = 1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -9.65 2.95 Inf -15.4 -3.8573
## B5 -17.25 2.95 Inf -23.0 -11.4651
##
## Time = 1.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -8.98 2.95 Inf -14.8 -3.1939
## B5 -20.49 2.95 Inf -26.3 -14.7031
##
## Time = 1.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -4.82 2.95 Inf -10.6 0.9658
## B5 -16.86 2.95 Inf -22.6 -11.0727
##
## Time = 1.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -11.34 2.95 Inf -17.1 -5.5524
## B5 -18.09 2.95 Inf -23.9 -12.2999
##
## Time = 1.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -11.12 2.95 Inf -16.9 -5.3364
## B5 -17.97 2.95 Inf -23.8 -12.1798
##
## Time = 1.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -7.16 2.95 Inf -12.9 -1.3726
## B5 -5.35 2.95 Inf -11.1 0.4379
##
## Results are averaged over the levels of: Channel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Time = -1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 2.940 2.78 Inf 1.057 0.2904
##
## Time = -0.9:
## contrast estimate SE df z.ratio p.value
## B1 - B5 3.940 2.78 Inf 1.417 0.1565
##
## Time = -0.8:
## contrast estimate SE df z.ratio p.value
## B1 - B5 8.155 2.78 Inf 2.933 0.0034
##
## Time = -0.7:
## contrast estimate SE df z.ratio p.value
## B1 - B5 5.205 2.78 Inf 1.872 0.0612
##
## Time = -0.6:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -1.137 2.78 Inf -0.409 0.6827
##
## Time = -0.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 3.618 2.78 Inf 1.301 0.1931
##
## Time = -0.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -5.369 2.78 Inf -1.931 0.0535
##
## Time = -0.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -2.037 2.78 Inf -0.733 0.4638
##
## Time = -0.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 1.194 2.78 Inf 0.429 0.6677
##
## Time = -0.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -3.894 2.78 Inf -1.400 0.1614
##
## Time = 0:
## contrast estimate SE df z.ratio p.value
## B1 - B5 0.356 2.78 Inf 0.128 0.8980
##
## Time = 0.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 3.350 2.78 Inf 1.205 0.2283
##
## Time = 0.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -5.789 2.78 Inf -2.082 0.0373
##
## Time = 0.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -4.244 2.78 Inf -1.526 0.1269
##
## Time = 0.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -1.506 2.78 Inf -0.541 0.5882
##
## Time = 0.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -5.245 2.78 Inf -1.887 0.0592
##
## Time = 0.6:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -4.522 2.78 Inf -1.626 0.1038
##
## Time = 0.7:
## contrast estimate SE df z.ratio p.value
## B1 - B5 2.685 2.78 Inf 0.966 0.3342
##
## Time = 0.8:
## contrast estimate SE df z.ratio p.value
## B1 - B5 1.993 2.78 Inf 0.717 0.4735
##
## Time = 0.9:
## contrast estimate SE df z.ratio p.value
## B1 - B5 11.432 2.78 Inf 4.112 <.0001
##
## Time = 1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 7.608 2.78 Inf 2.736 0.0062
##
## Time = 1.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 11.509 2.78 Inf 4.139 <.0001
##
## Time = 1.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 12.038 2.78 Inf 4.330 <.0001
##
## Time = 1.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 6.747 2.78 Inf 2.427 0.0152
##
## Time = 1.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 6.843 2.78 Inf 2.461 0.0138
##
## Time = 1.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -1.810 2.78 Inf -0.651 0.5149
##
## Results are averaged over the levels of: Channel
## Degrees-of-freedom method: asymptotic#lsmeans(m.f_ALL_beta2, pairwise ~ Block | Channel)
lsmeans(m.p_ALL_beta3, pairwise ~ Block | Time) ## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5408' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5408)' 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 = 5408' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5408)' 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## $lsmeans
## Time = -1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.844 3.53 Inf -23.76 -9.930
## B5 -18.928 3.53 Inf -25.84 -12.014
##
## Time = -0.9:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -15.180 3.53 Inf -22.09 -8.267
## B5 -13.684 3.53 Inf -20.60 -6.770
##
## Time = -0.8:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -15.201 3.53 Inf -22.11 -8.288
## B5 -19.016 3.53 Inf -25.93 -12.102
##
## Time = -0.7:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.463 3.53 Inf -23.38 -9.549
## B5 -18.666 3.53 Inf -25.58 -11.753
##
## Time = -0.6:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -18.313 3.53 Inf -25.23 -11.400
## B5 -15.282 3.53 Inf -22.20 -8.369
##
## Time = -0.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -13.757 3.53 Inf -20.67 -6.843
## B5 -14.543 3.53 Inf -21.46 -7.630
##
## Time = -0.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -20.240 3.53 Inf -27.15 -13.327
## B5 -13.985 3.53 Inf -20.90 -7.071
##
## Time = -0.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -13.358 3.53 Inf -20.27 -6.444
## B5 -11.628 3.53 Inf -18.54 -4.715
##
## Time = -0.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -15.876 3.53 Inf -22.79 -8.962
## B5 -14.018 3.53 Inf -20.93 -7.104
##
## Time = -0.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -14.390 3.53 Inf -21.30 -7.476
## B5 -14.106 3.53 Inf -21.02 -7.192
##
## Time = 0:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -14.686 3.53 Inf -21.60 -7.773
## B5 -13.769 3.53 Inf -20.68 -6.855
##
## Time = 0.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -12.426 3.53 Inf -19.34 -5.513
## B5 -15.909 3.53 Inf -22.82 -8.995
##
## Time = 0.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -19.441 3.53 Inf -26.35 -12.527
## B5 -10.026 3.53 Inf -16.94 -3.112
##
## Time = 0.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -18.721 3.53 Inf -25.63 -11.807
## B5 -11.618 3.53 Inf -18.53 -4.704
##
## Time = 0.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -12.773 3.53 Inf -19.69 -5.860
## B5 -10.590 3.53 Inf -17.50 -3.677
##
## Time = 0.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -15.109 3.53 Inf -22.02 -8.195
## B5 -6.269 3.53 Inf -13.18 0.645
##
## Time = 0.6:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -14.344 3.53 Inf -21.26 -7.430
## B5 -8.519 3.53 Inf -15.43 -1.605
##
## Time = 0.7:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -12.175 3.53 Inf -19.09 -5.261
## B5 -16.877 3.53 Inf -23.79 -9.963
##
## Time = 0.8:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 1.641 3.53 Inf -5.27 8.554
## B5 -13.213 3.53 Inf -20.13 -6.300
##
## Time = 0.9:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -2.603 3.53 Inf -9.52 4.310
## B5 -15.240 3.53 Inf -22.15 -8.327
##
## Time = 1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -3.075 3.53 Inf -9.99 3.839
## B5 -15.611 3.53 Inf -22.52 -8.697
##
## Time = 1.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -9.105 3.53 Inf -16.02 -2.192
## B5 -17.121 3.53 Inf -24.03 -10.208
##
## Time = 1.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 0.489 3.53 Inf -6.42 7.403
## B5 -13.652 3.53 Inf -20.57 -6.739
##
## Time = 1.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -5.823 3.53 Inf -12.74 1.091
## B5 -15.690 3.53 Inf -22.60 -8.776
##
## Time = 1.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -9.898 3.53 Inf -16.81 -2.985
## B5 -17.484 3.53 Inf -24.40 -10.570
##
## Time = 1.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -8.031 3.53 Inf -14.94 -1.118
## B5 -4.535 3.53 Inf -11.45 2.378
##
## Results are averaged over the levels of: Channel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Time = -1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 2.084 3.75 Inf 0.556 0.5784
##
## Time = -0.9:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -1.497 3.75 Inf -0.399 0.6899
##
## Time = -0.8:
## contrast estimate SE df z.ratio p.value
## B1 - B5 3.815 3.75 Inf 1.017 0.3091
##
## Time = -0.7:
## contrast estimate SE df z.ratio p.value
## B1 - B5 2.204 3.75 Inf 0.588 0.5569
##
## Time = -0.6:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -3.031 3.75 Inf -0.808 0.4190
##
## Time = -0.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 0.787 3.75 Inf 0.210 0.8339
##
## Time = -0.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -6.255 3.75 Inf -1.668 0.0954
##
## Time = -0.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -1.730 3.75 Inf -0.461 0.6447
##
## Time = -0.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -1.858 3.75 Inf -0.495 0.6204
##
## Time = -0.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -0.284 3.75 Inf -0.076 0.9397
##
## Time = 0:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -0.918 3.75 Inf -0.245 0.8067
##
## Time = 0.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 3.482 3.75 Inf 0.928 0.3532
##
## Time = 0.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -9.415 3.75 Inf -2.510 0.0121
##
## Time = 0.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -7.103 3.75 Inf -1.894 0.0583
##
## Time = 0.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -2.183 3.75 Inf -0.582 0.5606
##
## Time = 0.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -8.840 3.75 Inf -2.357 0.0184
##
## Time = 0.6:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -5.825 3.75 Inf -1.553 0.1204
##
## Time = 0.7:
## contrast estimate SE df z.ratio p.value
## B1 - B5 4.702 3.75 Inf 1.254 0.2100
##
## Time = 0.8:
## contrast estimate SE df z.ratio p.value
## B1 - B5 14.854 3.75 Inf 3.960 0.0001
##
## Time = 0.9:
## contrast estimate SE df z.ratio p.value
## B1 - B5 12.637 3.75 Inf 3.369 0.0008
##
## Time = 1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 12.536 3.75 Inf 3.342 0.0008
##
## Time = 1.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 8.016 3.75 Inf 2.137 0.0326
##
## Time = 1.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 14.141 3.75 Inf 3.770 0.0002
##
## Time = 1.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 9.867 3.75 Inf 2.631 0.0085
##
## Time = 1.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 7.586 3.75 Inf 2.022 0.0431
##
## Time = 1.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -3.496 3.75 Inf -0.932 0.3513
##
## Results are averaged over the levels of: Channel
## Degrees-of-freedom method: asymptotic#lsmeans(m.f_ALL_beta3, pairwise ~ Block | Channel)#Post hocs for motor models
#Feedback Posthocs
lsmeans(m.m_ALL_beta1, pairwise ~ Block | Time) #Here we can directly isolate the timepoints that are interacting## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 6448' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 6448)' 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 = 6448' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 6448)' 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## $lsmeans
## Time = 0:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -11.05 6.37 Inf -23.536 1.433
## B5 -7.76 6.37 Inf -20.243 4.726
##
## Time = 0.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -9.09 6.37 Inf -21.578 3.391
## B5 -5.26 6.37 Inf -17.747 7.222
##
## Time = 0.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -6.57 6.37 Inf -19.055 5.914
## B5 -7.57 6.37 Inf -20.057 4.912
##
## Time = 0.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -6.12 6.37 Inf -18.604 6.365
## B5 3.49 6.37 Inf -8.990 15.979
##
## Time = 0.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -10.90 6.37 Inf -23.387 1.582
## B5 3.86 6.37 Inf -8.621 16.348
##
## Time = 0.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -12.09 6.37 Inf -24.571 0.398
## B5 -7.02 6.37 Inf -19.506 5.463
##
## Time = 0.6:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -15.36 6.37 Inf -27.844 -2.875
## B5 -7.25 6.37 Inf -19.739 5.230
##
## Time = 0.7:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -15.13 6.37 Inf -27.619 -2.650
## B5 -8.24 6.37 Inf -20.722 4.247
##
## Time = 0.8:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -8.35 6.37 Inf -20.834 4.135
## B5 -9.38 6.37 Inf -21.865 3.104
##
## Time = 0.9:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 1.81 6.37 Inf -10.671 14.298
## B5 -9.58 6.37 Inf -22.060 2.909
##
## Time = 1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -10.46 6.37 Inf -22.946 2.023
## B5 -10.59 6.37 Inf -23.073 1.896
##
## Time = 1.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -15.34 6.37 Inf -27.824 -2.855
## B5 -6.47 6.37 Inf -18.951 6.017
##
## Time = 1.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.79 6.37 Inf -29.279 -4.310
## B5 -10.13 6.37 Inf -22.619 2.350
##
## Time = 1.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -13.78 6.37 Inf -26.268 -1.299
## B5 -12.84 6.37 Inf -25.325 -0.357
##
## Time = 1.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -13.99 6.37 Inf -26.473 -1.504
## B5 -10.33 6.37 Inf -22.818 2.151
##
## Time = 1.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -17.54 6.37 Inf -30.023 -5.054
## B5 -9.69 6.37 Inf -22.171 2.798
##
## Time = 1.6:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.10 6.37 Inf -28.583 -3.614
## B5 -11.14 6.37 Inf -23.625 1.344
##
## Time = 1.7:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -14.49 6.37 Inf -26.973 -2.004
## B5 -4.73 6.37 Inf -17.213 7.756
##
## Time = 1.8:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -14.02 6.37 Inf -26.509 -1.540
## B5 -5.67 6.37 Inf -18.151 6.818
##
## Time = 1.9:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -17.82 6.37 Inf -30.309 -5.340
## B5 -5.33 6.37 Inf -17.817 7.152
##
## Time = 2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -18.72 6.37 Inf -31.203 -6.234
## B5 -3.13 6.37 Inf -15.617 9.352
##
## Time = 2.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -15.26 6.37 Inf -27.749 -2.780
## B5 -3.60 6.37 Inf -16.080 8.888
##
## Time = 2.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -14.92 6.37 Inf -27.407 -2.438
## B5 -1.43 6.37 Inf -13.910 11.059
##
## Time = 2.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.00 6.37 Inf -28.485 -3.516
## B5 9.99 6.37 Inf -2.498 22.471
##
## Time = 2.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.99 6.37 Inf -29.477 -4.508
## B5 1.60 6.37 Inf -10.888 14.080
##
## Time = 2.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -14.04 6.37 Inf -26.528 -1.560
## B5 2.54 6.37 Inf -9.944 15.025
##
## Time = 2.6:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -14.81 6.37 Inf -27.294 -2.325
## B5 11.88 6.37 Inf -0.602 24.367
##
## Time = 2.7:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -13.21 6.37 Inf -25.690 -0.721
## B5 12.06 6.37 Inf -0.424 24.545
##
## Time = 2.8:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -12.09 6.37 Inf -24.578 0.391
## B5 11.89 6.37 Inf -0.596 24.372
##
## Time = 2.9:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -3.70 6.37 Inf -16.180 8.789
## B5 17.33 6.37 Inf 4.846 29.815
##
## Time = 3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -8.65 6.37 Inf -21.130 3.838
## B5 77.64 6.37 Inf 65.160 90.129
##
## Results are averaged over the levels of: Channel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Time = 0:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -3.292 8.19 Inf -0.402 0.6877
##
## Time = 0.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -3.830 8.19 Inf -0.468 0.6400
##
## Time = 0.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 1.001 8.19 Inf 0.122 0.9027
##
## Time = 0.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -9.614 8.19 Inf -1.174 0.2405
##
## Time = 0.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -14.766 8.19 Inf -1.803 0.0714
##
## Time = 0.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -5.065 8.19 Inf -0.618 0.5364
##
## Time = 0.6:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -8.105 8.19 Inf -0.990 0.3224
##
## Time = 0.7:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -6.897 8.19 Inf -0.842 0.3998
##
## Time = 0.8:
## contrast estimate SE df z.ratio p.value
## B1 - B5 1.031 8.19 Inf 0.126 0.8999
##
## Time = 0.9:
## contrast estimate SE df z.ratio p.value
## B1 - B5 11.389 8.19 Inf 1.390 0.1644
##
## Time = 1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 0.127 8.19 Inf 0.016 0.9876
##
## Time = 1.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -8.873 8.19 Inf -1.083 0.2787
##
## Time = 1.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -6.660 8.19 Inf -0.813 0.4162
##
## Time = 1.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -0.942 8.19 Inf -0.115 0.9084
##
## Time = 1.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -3.655 8.19 Inf -0.446 0.6554
##
## Time = 1.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -7.852 8.19 Inf -0.959 0.3378
##
## Time = 1.6:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -4.958 8.19 Inf -0.605 0.5450
##
## Time = 1.7:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -9.760 8.19 Inf -1.192 0.2335
##
## Time = 1.8:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -8.358 8.19 Inf -1.020 0.3076
##
## Time = 1.9:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -12.492 8.19 Inf -1.525 0.1272
##
## Time = 2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -15.586 8.19 Inf -1.903 0.0571
##
## Time = 2.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -11.668 8.19 Inf -1.425 0.1543
##
## Time = 2.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -13.497 8.19 Inf -1.648 0.0994
##
## Time = 2.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -25.987 8.19 Inf -3.173 0.0015
##
## Time = 2.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -18.589 8.19 Inf -2.269 0.0232
##
## Time = 2.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -16.585 8.19 Inf -2.025 0.0429
##
## Time = 2.6:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -26.692 8.19 Inf -3.259 0.0011
##
## Time = 2.7:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -25.267 8.19 Inf -3.085 0.0020
##
## Time = 2.8:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -23.982 8.19 Inf -2.928 0.0034
##
## Time = 2.9:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -21.026 8.19 Inf -2.567 0.0103
##
## Time = 3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -86.291 8.19 Inf -10.535 <.0001
##
## Results are averaged over the levels of: Channel
## Degrees-of-freedom method: asymptotic#lsmeans(m.f_ALL_beta1, pairwise ~ Block | Channel) #this is expected but not the focus of the study
lsmeans(m.m_ALL_beta2, pairwise ~ Block | Time) ## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 6448' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 6448)' 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 = 6448' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 6448)' 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## $lsmeans
## Time = 0:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -11.2396 10.7 Inf -32.16 9.682
## B5 -9.5934 10.7 Inf -30.51 11.328
##
## Time = 0.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -10.1447 10.7 Inf -31.07 10.777
## B5 -5.9846 10.7 Inf -26.91 14.937
##
## Time = 0.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -12.3109 10.7 Inf -33.23 8.610
## B5 -2.3557 10.7 Inf -23.28 18.566
##
## Time = 0.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -12.7917 10.7 Inf -33.71 8.130
## B5 1.8488 10.7 Inf -19.07 22.770
##
## Time = 0.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.6940 10.7 Inf -37.62 4.227
## B5 28.0407 10.7 Inf 7.12 48.962
##
## Time = 0.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.5523 10.7 Inf -37.47 4.369
## B5 2.8955 10.7 Inf -18.03 23.817
##
## Time = 0.6:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -21.5502 10.7 Inf -42.47 -0.629
## B5 -7.5987 10.7 Inf -28.52 13.323
##
## Time = 0.7:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -19.2451 10.7 Inf -40.17 1.676
## B5 -7.2676 10.7 Inf -28.19 13.654
##
## Time = 0.8:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -13.1635 10.7 Inf -34.08 7.758
## B5 -11.2917 10.7 Inf -32.21 9.630
##
## Time = 0.9:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -0.1849 10.7 Inf -21.11 20.736
## B5 -13.2839 10.7 Inf -34.21 7.638
##
## Time = 1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -13.4845 10.7 Inf -34.41 7.437
## B5 -11.9712 10.7 Inf -32.89 8.950
##
## Time = 1.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -18.7397 10.7 Inf -39.66 2.182
## B5 -9.4383 10.7 Inf -30.36 11.483
##
## Time = 1.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -19.2293 10.7 Inf -40.15 1.692
## B5 -13.9906 10.7 Inf -34.91 6.931
##
## Time = 1.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -14.9465 10.7 Inf -35.87 5.975
## B5 -16.9219 10.7 Inf -37.84 3.999
##
## Time = 1.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -13.4999 10.7 Inf -34.42 7.421
## B5 -13.8596 10.7 Inf -34.78 7.062
##
## Time = 1.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -17.8975 10.7 Inf -38.82 3.024
## B5 -10.2685 10.7 Inf -31.19 10.653
##
## Time = 1.6:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -17.7820 10.7 Inf -38.70 3.139
## B5 -10.7109 10.7 Inf -31.63 10.211
##
## Time = 1.7:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -15.2073 10.7 Inf -36.13 5.714
## B5 -1.8739 10.7 Inf -22.80 19.048
##
## Time = 1.8:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -14.3551 10.7 Inf -35.28 6.566
## B5 -5.8072 10.7 Inf -26.73 15.114
##
## Time = 1.9:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -19.6640 10.7 Inf -40.59 1.257
## B5 -2.5829 10.7 Inf -23.50 18.339
##
## Time = 2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -20.3055 10.7 Inf -41.23 0.616
## B5 0.5913 10.7 Inf -20.33 21.513
##
## Time = 2.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.8437 10.7 Inf -37.77 4.078
## B5 -0.3736 10.7 Inf -21.30 20.548
##
## Time = 2.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.1028 10.7 Inf -37.02 4.819
## B5 -2.2915 10.7 Inf -23.21 18.630
##
## Time = 2.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -17.6077 10.7 Inf -38.53 3.314
## B5 24.6680 10.7 Inf 3.75 45.589
##
## Time = 2.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -17.2848 10.7 Inf -38.21 3.637
## B5 7.2272 10.7 Inf -13.69 28.149
##
## Time = 2.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.7046 10.7 Inf -37.63 4.217
## B5 -0.0380 10.7 Inf -20.96 20.883
##
## Time = 2.6:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -15.1436 10.7 Inf -36.07 5.778
## B5 32.5127 10.7 Inf 11.59 53.434
##
## Time = 2.7:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -15.2187 10.7 Inf -36.14 5.703
## B5 15.8817 10.7 Inf -5.04 36.803
##
## Time = 2.8:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -14.9371 10.7 Inf -35.86 5.984
## B5 7.5206 10.7 Inf -13.40 28.442
##
## Time = 2.9:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -4.6995 10.7 Inf -25.62 16.222
## B5 8.4377 10.7 Inf -12.48 29.359
##
## Time = 3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -10.9848 10.7 Inf -31.91 9.937
## B5 131.4699 10.7 Inf 110.55 152.391
##
## Results are averaged over the levels of: Channel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Time = 0:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -1.65 14.1 Inf -0.117 0.9071
##
## Time = 0.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -4.16 14.1 Inf -0.295 0.7680
##
## Time = 0.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -9.96 14.1 Inf -0.706 0.4802
##
## Time = 0.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -14.64 14.1 Inf -1.038 0.2992
##
## Time = 0.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -44.73 14.1 Inf -3.172 0.0015
##
## Time = 0.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -19.45 14.1 Inf -1.379 0.1679
##
## Time = 0.6:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -13.95 14.1 Inf -0.989 0.3225
##
## Time = 0.7:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -11.98 14.1 Inf -0.849 0.3957
##
## Time = 0.8:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -1.87 14.1 Inf -0.133 0.8944
##
## Time = 0.9:
## contrast estimate SE df z.ratio p.value
## B1 - B5 13.10 14.1 Inf 0.929 0.3529
##
## Time = 1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -1.51 14.1 Inf -0.107 0.9145
##
## Time = 1.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -9.30 14.1 Inf -0.660 0.5095
##
## Time = 1.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -5.24 14.1 Inf -0.371 0.7103
##
## Time = 1.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 1.98 14.1 Inf 0.140 0.8886
##
## Time = 1.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 0.36 14.1 Inf 0.026 0.9797
##
## Time = 1.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -7.63 14.1 Inf -0.541 0.5885
##
## Time = 1.6:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -7.07 14.1 Inf -0.501 0.6161
##
## Time = 1.7:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -13.33 14.1 Inf -0.946 0.3444
##
## Time = 1.8:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -8.55 14.1 Inf -0.606 0.5444
##
## Time = 1.9:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -17.08 14.1 Inf -1.211 0.2258
##
## Time = 2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -20.90 14.1 Inf -1.482 0.1384
##
## Time = 2.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -16.47 14.1 Inf -1.168 0.2428
##
## Time = 2.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -13.81 14.1 Inf -0.979 0.3274
##
## Time = 2.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -42.28 14.1 Inf -2.998 0.0027
##
## Time = 2.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -24.51 14.1 Inf -1.738 0.0822
##
## Time = 2.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -16.67 14.1 Inf -1.182 0.2373
##
## Time = 2.6:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -47.66 14.1 Inf -3.379 0.0007
##
## Time = 2.7:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -31.10 14.1 Inf -2.205 0.0274
##
## Time = 2.8:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -22.46 14.1 Inf -1.593 0.1113
##
## Time = 2.9:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -13.14 14.1 Inf -0.932 0.3515
##
## Time = 3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -142.45 14.1 Inf -10.102 <.0001
##
## Results are averaged over the levels of: Channel
## Degrees-of-freedom method: asymptotic#lsmeans(m.f_ALL_beta2, pairwise ~ Block | Channel)
lsmeans(m.m_ALL_beta3, pairwise ~ Block | Time) ## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 6448' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 6448)' 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 = 6448' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 6448)' 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## $lsmeans
## Time = 0:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -11.969 10.3 Inf -32.25 8.311
## B5 -12.602 10.3 Inf -32.88 7.678
##
## Time = 0.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -11.111 10.3 Inf -31.39 9.169
## B5 -11.146 10.3 Inf -31.43 9.133
##
## Time = 0.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -14.407 10.3 Inf -34.69 5.873
## B5 -1.426 10.3 Inf -21.71 18.853
##
## Time = 0.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -13.067 10.3 Inf -33.35 7.213
## B5 18.839 10.3 Inf -1.44 39.119
##
## Time = 0.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.962 10.3 Inf -37.24 3.318
## B5 60.442 10.3 Inf 40.16 80.722
##
## Time = 0.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -17.181 10.3 Inf -37.46 3.098
## B5 13.557 10.3 Inf -6.72 33.837
##
## Time = 0.6:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -21.130 10.3 Inf -41.41 -0.851
## B5 5.314 10.3 Inf -14.97 25.593
##
## Time = 0.7:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -21.535 10.3 Inf -41.81 -1.255
## B5 6.290 10.3 Inf -13.99 26.570
##
## Time = 0.8:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.609 10.3 Inf -36.89 3.671
## B5 -10.051 10.3 Inf -30.33 10.228
##
## Time = 0.9:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -8.660 10.3 Inf -28.94 11.619
## B5 -14.196 10.3 Inf -34.48 6.084
##
## Time = 1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -12.970 10.3 Inf -33.25 7.310
## B5 -11.593 10.3 Inf -31.87 8.687
##
## Time = 1.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.101 10.3 Inf -36.38 4.178
## B5 -10.809 10.3 Inf -31.09 9.471
##
## Time = 1.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -18.624 10.3 Inf -38.90 1.656
## B5 -7.318 10.3 Inf -27.60 12.962
##
## Time = 1.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.995 10.3 Inf -37.27 3.284
## B5 -14.788 10.3 Inf -35.07 5.492
##
## Time = 1.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -10.821 10.3 Inf -31.10 9.458
## B5 -13.555 10.3 Inf -33.83 6.725
##
## Time = 1.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -18.605 10.3 Inf -38.89 1.674
## B5 -10.228 10.3 Inf -30.51 10.051
##
## Time = 1.6:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -19.291 10.3 Inf -39.57 0.988
## B5 -10.166 10.3 Inf -30.45 10.114
##
## Time = 1.7:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -15.561 10.3 Inf -35.84 4.718
## B5 -7.010 10.3 Inf -27.29 13.269
##
## Time = 1.8:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -14.932 10.3 Inf -35.21 5.348
## B5 -6.398 10.3 Inf -26.68 13.882
##
## Time = 1.9:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.774 10.3 Inf -37.05 3.505
## B5 -1.979 10.3 Inf -22.26 18.300
##
## Time = 2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -19.906 10.3 Inf -40.19 0.374
## B5 3.422 10.3 Inf -16.86 23.702
##
## Time = 2.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -19.572 10.3 Inf -39.85 0.708
## B5 4.492 10.3 Inf -15.79 24.772
##
## Time = 2.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -16.893 10.3 Inf -37.17 3.386
## B5 0.772 10.3 Inf -19.51 21.051
##
## Time = 2.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -18.264 10.3 Inf -38.54 2.016
## B5 12.050 10.3 Inf -8.23 32.330
##
## Time = 2.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -15.828 10.3 Inf -36.11 4.452
## B5 14.234 10.3 Inf -6.05 34.514
##
## Time = 2.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -17.028 10.3 Inf -37.31 3.251
## B5 -1.439 10.3 Inf -21.72 18.840
##
## Time = 2.6:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -13.688 10.3 Inf -33.97 6.592
## B5 26.444 10.3 Inf 6.16 46.723
##
## Time = 2.7:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -15.214 10.3 Inf -35.49 5.065
## B5 15.573 10.3 Inf -4.71 35.853
##
## Time = 2.8:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -15.056 10.3 Inf -35.34 5.224
## B5 3.078 10.3 Inf -17.20 23.358
##
## Time = 2.9:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -5.320 10.3 Inf -25.60 14.960
## B5 5.027 10.3 Inf -15.25 25.307
##
## Time = 3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -9.505 10.3 Inf -29.78 10.775
## B5 75.072 10.3 Inf 54.79 95.352
##
## Results are averaged over the levels of: Channel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Time = 0:
## contrast estimate SE df z.ratio p.value
## B1 - B5 0.633 13.1 Inf 0.048 0.9615
##
## Time = 0.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 0.036 13.1 Inf 0.003 0.9978
##
## Time = 0.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -12.981 13.1 Inf -0.991 0.3218
##
## Time = 0.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -31.906 13.1 Inf -2.435 0.0149
##
## Time = 0.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -77.403 13.1 Inf -5.908 <.0001
##
## Time = 0.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -30.738 13.1 Inf -2.346 0.0190
##
## Time = 0.6:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -26.444 13.1 Inf -2.018 0.0435
##
## Time = 0.7:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -27.825 13.1 Inf -2.124 0.0337
##
## Time = 0.8:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -6.558 13.1 Inf -0.501 0.6167
##
## Time = 0.9:
## contrast estimate SE df z.ratio p.value
## B1 - B5 5.536 13.1 Inf 0.423 0.6726
##
## Time = 1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -1.377 13.1 Inf -0.105 0.9163
##
## Time = 1.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -5.293 13.1 Inf -0.404 0.6862
##
## Time = 1.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -11.306 13.1 Inf -0.863 0.3882
##
## Time = 1.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -2.207 13.1 Inf -0.168 0.8662
##
## Time = 1.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 2.733 13.1 Inf 0.209 0.8347
##
## Time = 1.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -8.377 13.1 Inf -0.639 0.5226
##
## Time = 1.6:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -9.126 13.1 Inf -0.697 0.4861
##
## Time = 1.7:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -8.551 13.1 Inf -0.653 0.5140
##
## Time = 1.8:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -8.533 13.1 Inf -0.651 0.5148
##
## Time = 1.9:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -14.795 13.1 Inf -1.129 0.2588
##
## Time = 2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -23.328 13.1 Inf -1.781 0.0750
##
## Time = 2.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -24.063 13.1 Inf -1.837 0.0663
##
## Time = 2.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -17.665 13.1 Inf -1.348 0.1776
##
## Time = 2.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -30.314 13.1 Inf -2.314 0.0207
##
## Time = 2.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -30.062 13.1 Inf -2.295 0.0218
##
## Time = 2.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -15.589 13.1 Inf -1.190 0.2341
##
## Time = 2.6:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -40.132 13.1 Inf -3.063 0.0022
##
## Time = 2.7:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -30.788 13.1 Inf -2.350 0.0188
##
## Time = 2.8:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -18.134 13.1 Inf -1.384 0.1663
##
## Time = 2.9:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -10.347 13.1 Inf -0.790 0.4296
##
## Time = 3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -84.577 13.1 Inf -6.456 <.0001
##
## Results are averaged over the levels of: Channel
## Degrees-of-freedom method: asymptotic#lsmeans(m.f_ALL_beta3, pairwise ~ Block | Channel)#Post hocs for Lpress models
#Feedback Posthocs
lsmeans(m.f_ALL_beta1, pairwise ~ Block | Time) #Here we can directly isolate the timepoints that are interacting## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 6448' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 6448)' 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 = 6448' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 6448)' 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## $lsmeans
## Time = -0.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 5.386 13.4 Inf -20.788 31.6
## B5 -2.528 13.4 Inf -28.702 23.6
##
## Time = -0.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -1.293 13.4 Inf -27.467 24.9
## B5 -1.798 13.4 Inf -27.972 24.4
##
## Time = -0.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -1.101 13.4 Inf -27.275 25.1
## B5 -0.012 13.4 Inf -26.186 26.2
##
## Time = -0.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -4.079 13.4 Inf -30.253 22.1
## B5 0.523 13.4 Inf -25.651 26.7
##
## Time = -0.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -0.636 13.4 Inf -26.810 25.5
## B5 2.092 13.4 Inf -24.081 28.3
##
## Time = 0:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 2.719 13.4 Inf -23.455 28.9
## B5 6.071 13.4 Inf -20.103 32.2
##
## Time = 0.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 2.658 13.4 Inf -23.516 28.8
## B5 15.732 13.4 Inf -10.442 41.9
##
## Time = 0.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 4.706 13.4 Inf -21.468 30.9
## B5 17.619 13.4 Inf -8.555 43.8
##
## Time = 0.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 13.322 13.4 Inf -12.852 39.5
## B5 18.390 13.4 Inf -7.784 44.6
##
## Time = 0.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 15.061 13.4 Inf -11.113 41.2
## B5 19.642 13.4 Inf -6.532 45.8
##
## Time = 0.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 21.511 13.4 Inf -4.663 47.7
## B5 26.563 13.4 Inf 0.389 52.7
##
## Time = 0.6:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 34.564 13.4 Inf 8.390 60.7
## B5 43.722 13.4 Inf 17.548 69.9
##
## Time = 0.7:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 47.470 13.4 Inf 21.296 73.6
## B5 59.647 13.4 Inf 33.473 85.8
##
## Time = 0.8:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 45.679 13.4 Inf 19.505 71.9
## B5 54.469 13.4 Inf 28.295 80.6
##
## Time = 0.9:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 39.505 13.4 Inf 13.331 65.7
## B5 48.080 13.4 Inf 21.906 74.3
##
## Time = 1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 46.276 13.4 Inf 20.102 72.4
## B5 48.554 13.4 Inf 22.380 74.7
##
## Time = 1.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 45.845 13.4 Inf 19.671 72.0
## B5 56.268 13.4 Inf 30.094 82.4
##
## Time = 1.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 38.552 13.4 Inf 12.378 64.7
## B5 61.364 13.4 Inf 35.190 87.5
##
## Time = 1.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 34.766 13.4 Inf 8.592 60.9
## B5 51.422 13.4 Inf 25.248 77.6
##
## Time = 1.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 40.058 13.4 Inf 13.884 66.2
## B5 54.699 13.4 Inf 28.525 80.9
##
## Time = 1.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 41.326 13.4 Inf 15.152 67.5
## B5 109.087 13.4 Inf 82.913 135.3
##
## Time = 1.6:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 49.423 13.4 Inf 23.249 75.6
## B5 79.171 13.4 Inf 52.997 105.3
##
## Time = 1.7:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 48.581 13.4 Inf 22.408 74.8
## B5 74.928 13.4 Inf 48.754 101.1
##
## Time = 1.8:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 50.016 13.4 Inf 23.842 76.2
## B5 73.642 13.4 Inf 47.468 99.8
##
## Time = 1.9:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 49.349 13.4 Inf 23.175 75.5
## B5 70.024 13.4 Inf 43.850 96.2
##
## Time = 2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 54.661 13.4 Inf 28.487 80.8
## B5 63.726 13.4 Inf 37.552 89.9
##
## Time = 2.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 52.203 13.4 Inf 26.029 78.4
## B5 82.935 13.4 Inf 56.761 109.1
##
## Time = 2.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 47.225 13.4 Inf 21.051 73.4
## B5 129.736 13.4 Inf 103.562 155.9
##
## Time = 2.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 49.578 13.4 Inf 23.404 75.8
## B5 110.349 13.4 Inf 84.175 136.5
##
## Time = 2.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 55.706 13.4 Inf 29.532 81.9
## B5 53.658 13.4 Inf 27.484 79.8
##
## Time = 2.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 13.862 13.4 Inf -12.312 40.0
## B5 5.576 13.4 Inf -20.598 31.7
##
## Results are averaged over the levels of: Channel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Time = -0.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 7.914 14.6 Inf 0.543 0.5869
##
## Time = -0.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 0.505 14.6 Inf 0.035 0.9724
##
## Time = -0.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -1.089 14.6 Inf -0.075 0.9404
##
## Time = -0.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -4.601 14.6 Inf -0.316 0.7521
##
## Time = -0.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -2.728 14.6 Inf -0.187 0.8514
##
## Time = 0:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -3.352 14.6 Inf -0.230 0.8180
##
## Time = 0.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -13.074 14.6 Inf -0.898 0.3694
##
## Time = 0.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -12.914 14.6 Inf -0.887 0.3753
##
## Time = 0.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -5.068 14.6 Inf -0.348 0.7279
##
## Time = 0.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -4.581 14.6 Inf -0.315 0.7531
##
## Time = 0.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -5.053 14.6 Inf -0.347 0.7287
##
## Time = 0.6:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -9.158 14.6 Inf -0.629 0.5295
##
## Time = 0.7:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -12.177 14.6 Inf -0.836 0.4031
##
## Time = 0.8:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -8.791 14.6 Inf -0.604 0.5462
##
## Time = 0.9:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -8.575 14.6 Inf -0.589 0.5560
##
## Time = 1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -2.278 14.6 Inf -0.156 0.8757
##
## Time = 1.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -10.424 14.6 Inf -0.716 0.4742
##
## Time = 1.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -22.812 14.6 Inf -1.566 0.1173
##
## Time = 1.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -16.657 14.6 Inf -1.144 0.2528
##
## Time = 1.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -14.641 14.6 Inf -1.005 0.3148
##
## Time = 1.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -67.761 14.6 Inf -4.652 <.0001
##
## Time = 1.6:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -29.748 14.6 Inf -2.042 0.0411
##
## Time = 1.7:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -26.347 14.6 Inf -1.809 0.0705
##
## Time = 1.8:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -23.626 14.6 Inf -1.622 0.1048
##
## Time = 1.9:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -20.675 14.6 Inf -1.419 0.1558
##
## Time = 2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -9.065 14.6 Inf -0.622 0.5337
##
## Time = 2.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -30.732 14.6 Inf -2.110 0.0349
##
## Time = 2.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -82.512 14.6 Inf -5.665 <.0001
##
## Time = 2.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -60.771 14.6 Inf -4.172 <.0001
##
## Time = 2.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 2.048 14.6 Inf 0.141 0.8882
##
## Time = 2.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 8.286 14.6 Inf 0.569 0.5694
##
## Results are averaged over the levels of: Channel
## Degrees-of-freedom method: asymptotic#lsmeans(m.f_ALL_beta1, pairwise ~ Block | Channel) #this is expected but not the focus of the study
lsmeans(m.f_ALL_beta2, pairwise ~ Block | Time) ## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 6448' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 6448)' 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 = 6448' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 6448)' 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## $lsmeans
## Time = -0.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 8.360 17.7 Inf -26.2840 43.0
## B5 2.618 17.7 Inf -32.0264 37.3
##
## Time = -0.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -4.936 17.7 Inf -39.5800 29.7
## B5 -2.752 17.7 Inf -37.3957 31.9
##
## Time = -0.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -1.433 17.7 Inf -36.0769 33.2
## B5 -2.841 17.7 Inf -37.4853 31.8
##
## Time = -0.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -3.757 17.7 Inf -38.4015 30.9
## B5 -1.737 17.7 Inf -36.3812 32.9
##
## Time = -0.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -0.719 17.7 Inf -35.3634 33.9
## B5 2.227 17.7 Inf -32.4173 36.9
##
## Time = 0:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 2.453 17.7 Inf -32.1911 37.1
## B5 12.032 17.7 Inf -22.6117 46.7
##
## Time = 0.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 4.542 17.7 Inf -30.1024 39.2
## B5 20.343 17.7 Inf -14.3012 55.0
##
## Time = 0.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 9.009 17.7 Inf -25.6352 43.7
## B5 23.615 17.7 Inf -11.0291 58.3
##
## Time = 0.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 20.137 17.7 Inf -14.5072 54.8
## B5 26.494 17.7 Inf -8.1497 61.1
##
## Time = 0.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 17.064 17.7 Inf -17.5803 51.7
## B5 23.456 17.7 Inf -11.1881 58.1
##
## Time = 0.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 24.881 17.7 Inf -9.7627 59.5
## B5 28.082 17.7 Inf -6.5622 62.7
##
## Time = 0.6:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 41.638 17.7 Inf 6.9938 76.3
## B5 38.917 17.7 Inf 4.2728 73.6
##
## Time = 0.7:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 40.873 17.7 Inf 6.2289 75.5
## B5 61.519 17.7 Inf 26.8749 96.2
##
## Time = 0.8:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 36.790 17.7 Inf 2.1460 71.4
## B5 58.992 17.7 Inf 24.3483 93.6
##
## Time = 0.9:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 41.405 17.7 Inf 6.7605 76.0
## B5 45.073 17.7 Inf 10.4285 79.7
##
## Time = 1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 43.332 17.7 Inf 8.6882 78.0
## B5 43.561 17.7 Inf 8.9170 78.2
##
## Time = 1.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 48.267 17.7 Inf 13.6225 82.9
## B5 66.807 17.7 Inf 32.1626 101.5
##
## Time = 1.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 39.154 17.7 Inf 4.5099 73.8
## B5 60.038 17.7 Inf 25.3939 94.7
##
## Time = 1.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 34.580 17.7 Inf -0.0645 69.2
## B5 58.330 17.7 Inf 23.6864 93.0
##
## Time = 1.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 50.884 17.7 Inf 16.2395 85.5
## B5 47.977 17.7 Inf 13.3328 82.6
##
## Time = 1.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 47.725 17.7 Inf 13.0813 82.4
## B5 161.092 17.7 Inf 126.4480 195.7
##
## Time = 1.6:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 52.321 17.7 Inf 17.6767 87.0
## B5 107.676 17.7 Inf 73.0319 142.3
##
## Time = 1.7:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 45.135 17.7 Inf 10.4906 79.8
## B5 77.545 17.7 Inf 42.9013 112.2
##
## Time = 1.8:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 48.556 17.7 Inf 13.9120 83.2
## B5 79.398 17.7 Inf 44.7543 114.0
##
## Time = 1.9:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 55.565 17.7 Inf 20.9211 90.2
## B5 59.460 17.7 Inf 24.8155 94.1
##
## Time = 2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 64.082 17.7 Inf 29.4384 98.7
## B5 66.446 17.7 Inf 31.8022 101.1
##
## Time = 2.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 60.928 17.7 Inf 26.2843 95.6
## B5 78.809 17.7 Inf 44.1654 113.5
##
## Time = 2.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 49.793 17.7 Inf 15.1488 84.4
## B5 141.258 17.7 Inf 106.6143 175.9
##
## Time = 2.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 53.920 17.7 Inf 19.2760 88.6
## B5 149.652 17.7 Inf 115.0082 184.3
##
## Time = 2.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 56.536 17.7 Inf 21.8916 91.2
## B5 73.966 17.7 Inf 39.3217 108.6
##
## Time = 2.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 7.433 17.7 Inf -27.2107 42.1
## B5 0.733 17.7 Inf -33.9112 35.4
##
## Results are averaged over the levels of: Channel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Time = -0.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 5.742 18.9 Inf 0.304 0.7608
##
## Time = -0.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -2.184 18.9 Inf -0.116 0.9078
##
## Time = -0.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 1.408 18.9 Inf 0.075 0.9405
##
## Time = -0.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -2.020 18.9 Inf -0.107 0.9147
##
## Time = -0.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -2.946 18.9 Inf -0.156 0.8759
##
## Time = 0:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -9.579 18.9 Inf -0.508 0.6115
##
## Time = 0.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -15.801 18.9 Inf -0.838 0.4021
##
## Time = 0.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -14.606 18.9 Inf -0.774 0.4387
##
## Time = 0.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -6.358 18.9 Inf -0.337 0.7361
##
## Time = 0.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -6.392 18.9 Inf -0.339 0.7347
##
## Time = 0.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -3.200 18.9 Inf -0.170 0.8653
##
## Time = 0.6:
## contrast estimate SE df z.ratio p.value
## B1 - B5 2.721 18.9 Inf 0.144 0.8853
##
## Time = 0.7:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -20.646 18.9 Inf -1.095 0.2737
##
## Time = 0.8:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -22.202 18.9 Inf -1.177 0.2391
##
## Time = 0.9:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -3.668 18.9 Inf -0.194 0.8458
##
## Time = 1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -0.229 18.9 Inf -0.012 0.9903
##
## Time = 1.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -18.540 18.9 Inf -0.983 0.3256
##
## Time = 1.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -20.884 18.9 Inf -1.107 0.2682
##
## Time = 1.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -23.751 18.9 Inf -1.259 0.2079
##
## Time = 1.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 2.907 18.9 Inf 0.154 0.8775
##
## Time = 1.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -113.367 18.9 Inf -6.011 <.0001
##
## Time = 1.6:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -55.355 18.9 Inf -2.935 0.0033
##
## Time = 1.7:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -32.411 18.9 Inf -1.718 0.0857
##
## Time = 1.8:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -30.842 18.9 Inf -1.635 0.1020
##
## Time = 1.9:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -3.894 18.9 Inf -0.206 0.8364
##
## Time = 2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -2.364 18.9 Inf -0.125 0.9003
##
## Time = 2.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -17.881 18.9 Inf -0.948 0.3431
##
## Time = 2.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -91.465 18.9 Inf -4.850 <.0001
##
## Time = 2.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -95.732 18.9 Inf -5.076 <.0001
##
## Time = 2.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -17.430 18.9 Inf -0.924 0.3554
##
## Time = 2.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 6.701 18.9 Inf 0.355 0.7224
##
## Results are averaged over the levels of: Channel
## Degrees-of-freedom method: asymptotic#lsmeans(m.f_ALL_beta2, pairwise ~ Block | Channel)
lsmeans(m.f_ALL_beta3, pairwise ~ Block | Time) ## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 6448' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 6448)' 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 = 6448' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 6448)' 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## $lsmeans
## Time = -0.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 11.579 17 Inf -21.65 44.8
## B5 8.007 17 Inf -25.22 41.2
##
## Time = -0.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -6.186 17 Inf -39.41 27.0
## B5 -5.318 17 Inf -38.54 27.9
##
## Time = -0.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -3.811 17 Inf -37.04 29.4
## B5 -4.141 17 Inf -37.37 29.1
##
## Time = -0.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -3.286 17 Inf -36.51 29.9
## B5 -3.002 17 Inf -36.23 30.2
##
## Time = -0.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 -2.108 17 Inf -35.33 31.1
## B5 0.642 17 Inf -32.58 33.9
##
## Time = 0:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 1.647 17 Inf -31.58 34.9
## B5 12.059 17 Inf -21.17 45.3
##
## Time = 0.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 2.801 17 Inf -30.42 36.0
## B5 21.063 17 Inf -12.16 54.3
##
## Time = 0.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 7.999 17 Inf -25.23 41.2
## B5 23.365 17 Inf -9.86 56.6
##
## Time = 0.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 13.604 17 Inf -19.62 46.8
## B5 24.221 17 Inf -9.00 57.4
##
## Time = 0.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 11.981 17 Inf -21.24 45.2
## B5 20.351 17 Inf -12.87 53.6
##
## Time = 0.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 15.835 17 Inf -17.39 49.1
## B5 21.726 17 Inf -11.50 55.0
##
## Time = 0.6:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 25.625 17 Inf -7.60 58.9
## B5 23.818 17 Inf -9.41 57.0
##
## Time = 0.7:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 28.392 17 Inf -4.83 61.6
## B5 37.881 17 Inf 4.66 71.1
##
## Time = 0.8:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 24.873 17 Inf -8.35 58.1
## B5 50.670 17 Inf 17.44 83.9
##
## Time = 0.9:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 31.407 17 Inf -1.82 64.6
## B5 40.401 17 Inf 7.18 73.6
##
## Time = 1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 34.333 17 Inf 1.11 67.6
## B5 31.436 17 Inf -1.79 64.7
##
## Time = 1.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 39.167 17 Inf 5.94 72.4
## B5 51.257 17 Inf 18.03 84.5
##
## Time = 1.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 30.151 17 Inf -3.07 63.4
## B5 59.303 17 Inf 26.08 92.5
##
## Time = 1.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 27.141 17 Inf -6.08 60.4
## B5 60.114 17 Inf 26.89 93.3
##
## Time = 1.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 41.773 17 Inf 8.55 75.0
## B5 41.190 17 Inf 7.96 74.4
##
## Time = 1.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 41.967 17 Inf 8.74 75.2
## B5 134.047 17 Inf 100.82 167.3
##
## Time = 1.6:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 42.719 17 Inf 9.49 75.9
## B5 136.825 17 Inf 103.60 170.1
##
## Time = 1.7:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 35.221 17 Inf 2.00 68.4
## B5 66.906 17 Inf 33.68 100.1
##
## Time = 1.8:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 40.751 17 Inf 7.53 74.0
## B5 77.007 17 Inf 43.78 110.2
##
## Time = 1.9:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 46.621 17 Inf 13.40 79.8
## B5 61.695 17 Inf 28.47 94.9
##
## Time = 2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 54.552 17 Inf 21.33 87.8
## B5 57.102 17 Inf 23.88 90.3
##
## Time = 2.1:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 62.160 17 Inf 28.93 95.4
## B5 63.649 17 Inf 30.42 96.9
##
## Time = 2.2:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 56.064 17 Inf 22.84 89.3
## B5 97.049 17 Inf 63.82 130.3
##
## Time = 2.3:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 46.765 17 Inf 13.54 80.0
## B5 120.539 17 Inf 87.31 153.8
##
## Time = 2.4:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 54.115 17 Inf 20.89 87.3
## B5 93.189 17 Inf 59.96 126.4
##
## Time = 2.5:
## Block lsmean SE df asymp.LCL asymp.UCL
## B1 18.198 17 Inf -15.03 51.4
## B5 13.303 17 Inf -19.92 46.5
##
## Results are averaged over the levels of: Channel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Time = -0.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 3.572 17 Inf 0.210 0.8334
##
## Time = -0.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -0.869 17 Inf -0.051 0.9592
##
## Time = -0.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 0.331 17 Inf 0.019 0.9845
##
## Time = -0.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -0.284 17 Inf -0.017 0.9867
##
## Time = -0.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -2.751 17 Inf -0.162 0.8713
##
## Time = 0:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -10.412 17 Inf -0.613 0.5398
##
## Time = 0.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -18.262 17 Inf -1.075 0.2822
##
## Time = 0.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -15.366 17 Inf -0.905 0.3655
##
## Time = 0.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -10.617 17 Inf -0.625 0.5318
##
## Time = 0.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -8.370 17 Inf -0.493 0.6221
##
## Time = 0.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -5.891 17 Inf -0.347 0.7287
##
## Time = 0.6:
## contrast estimate SE df z.ratio p.value
## B1 - B5 1.807 17 Inf 0.106 0.9153
##
## Time = 0.7:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -9.490 17 Inf -0.559 0.5763
##
## Time = 0.8:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -25.798 17 Inf -1.519 0.1287
##
## Time = 0.9:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -8.994 17 Inf -0.530 0.5964
##
## Time = 1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 2.896 17 Inf 0.171 0.8646
##
## Time = 1.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -12.090 17 Inf -0.712 0.4765
##
## Time = 1.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -29.152 17 Inf -1.717 0.0860
##
## Time = 1.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -32.973 17 Inf -1.942 0.0522
##
## Time = 1.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 0.583 17 Inf 0.034 0.9726
##
## Time = 1.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -92.080 17 Inf -5.422 <.0001
##
## Time = 1.6:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -94.105 17 Inf -5.541 <.0001
##
## Time = 1.7:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -31.685 17 Inf -1.866 0.0621
##
## Time = 1.8:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -36.256 17 Inf -2.135 0.0328
##
## Time = 1.9:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -15.074 17 Inf -0.888 0.3747
##
## Time = 2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -2.550 17 Inf -0.150 0.8807
##
## Time = 2.1:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -1.489 17 Inf -0.088 0.9301
##
## Time = 2.2:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -40.985 17 Inf -2.413 0.0158
##
## Time = 2.3:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -73.774 17 Inf -4.344 <.0001
##
## Time = 2.4:
## contrast estimate SE df z.ratio p.value
## B1 - B5 -39.074 17 Inf -2.301 0.0214
##
## Time = 2.5:
## contrast estimate SE df z.ratio p.value
## B1 - B5 4.895 17 Inf 0.288 0.7732
##
## Results are averaged over the levels of: Channel
## Degrees-of-freedom method: asymptotic#lsmeans(m.f_ALL_beta3, pairwise ~ Block | Channel)#Filter out significant timepoints, based on <.05 as Tukey HSD is already adjusted for more conservative CIs
p_ST_beta1 <- p_ALL_beta1 %>% filter(Time %in% c(-1.0, -0.9, -0.8, -0.7, -0.6, 1.1, 1.2, 1.3, 1.4))
p_ST_beta2 <- p_ALL_beta2 %>% filter(Time %in% c(0.9, 1.0, 1.1, 1.2, 1.3, 1.4))
p_ST_beta3 <- p_ALL_beta3 %>% filter(Time %in% c(0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4))
m_ST_beta1 <- m_ALL_beta1 %>% filter(Time %in% c(2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0))
m_ST_beta2 <- m_ALL_beta2 %>% filter(Time %in% c(0.4, 2.3, 2.6, 2.7, 3.0))
m_ST_beta3 <- m_ALL_beta3 %>% filter(Time %in% c(0.3, 0.4, 0.5, 0.6, 0.7, 2.3, 2.4, 2.6, 2.7, 3.0))
lp_ST_beta1 <- f_ALL_beta1 %>% filter(Time %in% c(1.5, 1.6, 2.1, 2.2, 2.3))
lp_ST_beta2 <- f_ALL_beta2 %>% filter(Time %in% c(1.5, 1.6, 2.2, 2.3))
lp_ST_beta3 <- f_ALL_beta3 %>% filter(Time %in% c(1.5, 1.6, 1.8, 2.2, 2.3, 2.4))#Final RT Models prep
#p_ST_beta1 <- read.csv("p_ST_beta1.csv", sep = ",")
#p_ST_beta1$RT <- factor(p_ST_beta1$RT)
#p_ST_beta2$RT <- factor(p_ST_beta2$RT)
#p_ST_beta3$RT <- factor(p_ST_beta3$RT)
#Checking factors
#(l <- sapply(p_ST_beta1, function(x) is.factor(x)))
m.p_ST_beta1 <- lmer(ERDS ~ Block * RT + (1|Participant), data = p_ST_beta1)
Anova(m.p_ST_beta1)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ERDS
## Chisq Df Pr(>Chisq)
## Block 32.4677 1 1.212e-08 ***
## RT 2.7465 1 0.09747 .
## Block:RT 33.0413 1 9.022e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#summary(m.f_ALL_beta1)
m.p_ST_beta2 <- lmer(ERDS ~ Block * RT + (1|Participant), data = p_ST_beta2)
Anova(m.p_ST_beta2)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ERDS
## Chisq Df Pr(>Chisq)
## Block 10.6169 1 0.001121 **
## RT 0.1188 1 0.730386
## Block:RT 0.0711 1 0.789764
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#summary(m.f_ALL_beta2)
m.p_ST_beta3 <- lmer(ERDS ~ Block * RT + (1|Participant), data = p_ST_beta3)
Anova(m.p_ST_beta3)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ERDS
## Chisq Df Pr(>Chisq)
## Block 4.5298 1 0.033310 *
## RT 1.8206 1 0.177242
## Block:RT 9.2913 1 0.002302 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#summary(m2.f_ALL_beta3)#Final RT Models motor
m.m_ST_beta1 <- lmer(ERDS ~ Block * RT + (1|Participant), data = m_ST_beta1)
Anova(m.m_ST_beta1)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ERDS
## Chisq Df Pr(>Chisq)
## Block 9.1588 1 0.002475 **
## RT 0.7977 1 0.371775
## Block:RT 48.4232 1 3.435e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#summary(m.f_ALL_beta1)
m.m_ST_beta2 <- lmer(ERDS ~ Block * RT + (1|Participant), data = m_ST_beta2)
Anova(m.m_ST_beta2)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ERDS
## Chisq Df Pr(>Chisq)
## Block 6.9428 1 0.0084159 **
## RT 0.0009 1 0.9765240
## Block:RT 12.8091 1 0.0003449 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#summary(m.f_ALL_beta2)
m.m_ST_beta3 <- lmer(ERDS ~ Block * RT + (1|Participant), data = m_ST_beta3)
Anova(m.m_ST_beta3)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ERDS
## Chisq Df Pr(>Chisq)
## Block 25.6719 1 4.047e-07 ***
## RT 3.8256 1 0.05048 .
## Block:RT 3.1481 1 0.07601 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#summary(m2.f_ALL_beta3)#Final RT Models Last Press
m.lp_ST_beta1 <- lmer(ERDS ~ Block * RT + (1|Participant), data = lp_ST_beta1)
Anova(m.lp_ST_beta1)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ERDS
## Chisq Df Pr(>Chisq)
## Block 14.1532 1 0.0001685 ***
## RT 2.8442 1 0.0917037 .
## Block:RT 2.2904 1 0.1301798
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#summary(m.f_ALL_beta1)
m.lp_ST_beta2 <- lmer(ERDS ~ Block * RT + (1|Participant), data = lp_ST_beta2)
Anova(m.lp_ST_beta2)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ERDS
## Chisq Df Pr(>Chisq)
## Block 9.4327 1 0.002132 **
## RT 0.6039 1 0.437086
## Block:RT 7.3925 1 0.006550 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#summary(m.f_ALL_beta2)
m.lp_ST_beta3 <- lmer(ERDS ~ Block * RT + (1|Participant), data = lp_ST_beta3)
Anova(m.lp_ST_beta3)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ERDS
## Chisq Df Pr(>Chisq)
## Block 11.1977 1 0.0008190 ***
## RT 0.4143 1 0.5197757
## Block:RT 13.7786 1 0.0002057 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#summary(m2.f_ALL_beta3)#PREP beta1 plot
#########
#Need Effects library
#Running effect and preparing a new DF
ae.m.1<-allEffects(m.p_ST_beta1)
ae.m.df.1<-as.data.frame(ae.m.1[[1]])
#Model plot: with 95% CIs
ae.prep.beta1 <- ggplot(na.omit(ae.m.df.1), aes(x=RT, y=fit, color=Block))+
geom_line(aes(group=Block), size=1) +
geom_ribbon(data= ae.m.df.1, aes(ymin=lower, ymax=upper, group=Block, fill=Block), alpha=0.2) +
geom_point(aes(color = Block, shape = Block), size=2.2)+
ylab("ERDS (%)")+
xlab("RT (ms)")+
ggtitle("Prep Beta1 ERDS (%) - Block x RT")+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line =element_line(colour = "black"))
####
plot(ae.prep.beta1)
#PREP beta3 plot
#########
#Need Effects library
#Running effect and preparing a new DF
ae.m.2<-allEffects(m.p_ST_beta3)
ae.m.df.2<-as.data.frame(ae.m.2[[1]])
#Model plot: with 95% CIs
ae.prep.beta3 <- ggplot(na.omit(ae.m.df.2), aes(x=RT, y=fit, color=Block))+
geom_line(aes(group=Block), size=1) +
geom_ribbon(data= ae.m.df.2, aes(ymin=lower, ymax=upper, group=Block, fill=Block), alpha=0.2) +
geom_point(aes(color = Block, shape = Block), size=2.2)+
ylab("ERDS (%)")+
xlab("RT (ms)")+
ggtitle("Prep Beta3 ERDS (%) - Block x RT")+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line =element_line(colour = "black"))
####
plot(ae.prep.beta3)
#Motor beta1 plot
#########
#Need Effects library
#Running effect and preparing a new DF
ae.m.3<-allEffects(m.m_ST_beta1)
ae.m.df.3<-as.data.frame(ae.m.3[[1]])
#Model plot: with 95% CIs
ae.motor.beta1 <- ggplot(na.omit(ae.m.df.3), aes(x=RT, y=fit, color=Block))+
geom_line(aes(group=Block), size=1) +
geom_ribbon(data= ae.m.df.3, aes(ymin=lower, ymax=upper, group=Block, fill=Block), alpha=0.2) +
geom_point(aes(color = Block, shape = Block), size=2.2)+
ylab("ERDS (%)")+
xlab("RT (ms)")+
ggtitle("Motor Beta2 ERDS (%) - Block x RT")+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line =element_line(colour = "black"))
####
plot(ae.motor.beta1)
#Motor beta2 plot
#########
#Need Effects library
#Running effect and preparing a new DF
ae.m.4<-allEffects(m.m_ST_beta2)
ae.m.df.4<-as.data.frame(ae.m.4[[1]])
#Model plot: with 95% CIs
ae.motor.beta2 <- ggplot(na.omit(ae.m.df.4), aes(x=RT, y=fit, color=Block))+
geom_line(aes(group=Block), size=1) +
geom_ribbon(data= ae.m.df.4, aes(ymin=lower, ymax=upper, group=Block, fill=Block), alpha=0.2) +
geom_point(aes(color = Block, shape = Block), size=2.2)+
ylab("ERDS (%)")+
xlab("RT (ms)")+
ggtitle("Motor Beta2 ERDS (%) - Block x RT")+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line =element_line(colour = "black"))
####
plot(ae.motor.beta2)
#Lpress beta2 plot
#########
#Need Effects library
#Running effect and preparing a new DF
ae.m.5<-allEffects(m.lp_ST_beta2)
ae.m.df.5<-as.data.frame(ae.m.5[[1]])
#Model plot: with 95% CIs
ae.lpress.beta2 <- ggplot(na.omit(ae.m.df.5), aes(x=RT, y=fit, color=Block))+
geom_line(aes(group=Block), size=1) +
geom_ribbon(data= ae.m.df.5, aes(ymin=lower, ymax=upper, group=Block, fill=Block), alpha=0.2) +
geom_point(aes(color = Block, shape = Block), size=2.2)+
ylab("ERDS (%)")+
xlab("RT (ms)")+
ggtitle("Last Press Beta2 ERDS (%) - Block x RT")+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line =element_line(colour = "black"))
####
plot(ae.lpress.beta2)
#Lpress beta3 plot
#########
#Need Effects library
#Running effect and preparing a new DF
ae.m.6<-allEffects(m.lp_ST_beta3)
ae.m.df.6<-as.data.frame(ae.m.6[[1]])
#Model plot: with 95% CIs
ae.lpress.beta3 <- ggplot(na.omit(ae.m.df.5), aes(x=RT, y=fit, color=Block))+
geom_line(aes(group=Block), size=1) +
geom_ribbon(data= ae.m.df.6, aes(ymin=lower, ymax=upper, group=Block, fill=Block), alpha=0.2) +
geom_point(aes(color = Block, shape = Block), size=2.2)+
ylab("ERDS (%)")+
xlab("RT (ms)")+
ggtitle("Last Press Beta3 ERDS (%) - Block x RT")+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line =element_line(colour = "black"))
####
plot(ae.lpress.beta3)