Behavioural Results V1.0
Russell Chan & Daphne Titsing
17/11/2021
#Set Working Directory
setwd("C:/Users/RussellChan/OneDrive - University of Twente/2020_MSCA_IF/1_Master_thesis/1_ Daphne_2020_ML_Expertise/Behavioural Results")#Load packages
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## lowess#Load DF
## Importing the dataset
d.ML<-read.table("FinalBehavDF26_211117.csv", sep = ",", header = T, stringsAsFactors = F)## Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
## EOF within quoted string## Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
## number of items read is not a multiple of the number of columnsd.ML2<-read.table("FinalBehavDF26_Keypress_211117.csv", sep = ",", header = T, stringsAsFactors = F)## Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
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## number of items read is not a multiple of the number of columnsd.MSL <- read_excel("FinalBehavDF26_211117.xlsx")
d.MSL2 <- read_excel("FinalBehavDF26_Keypress_211117.xlsx")
d.MSL3 <- read_excel("6Key_Trial_ACC_Hand_211118.xlsx")#Create Factors
#Factors for blocks
d.MSL$Subject <- factor(d.MSL$Subject)
d.MSL$Block <- factor(d.MSL$Block)
d.MSL$Trial.ACC <- factor(d.MSL$Trial.ACC)
d.MSL$Hand.indicator <- factor(d.MSL$Hand.indicator)
#For finger positioning
d.MSL2$Position <- factor(d.MSL2$Position)
d.MSL2$Block <- factor(d.MSL2$Block)
d.MSL2$Subject <- factor(d.MSL2$Subject)
#For ACC
d.MSL3$Block <- factor(d.MSL3$Block)
d.MSL3$Subject <- factor(d.MSL3$Subject)#Models
#Checking ACC
m.MSL.ACCper <- lmer(ACC.Per ~ Block + (1|Subject), data = d.MSL3)
Anova(m.MSL.ACCper)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ACC.Per
## Chisq Df Pr(>Chisq)
## Block 168.59 4 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m.MSL.ACCper)## Linear mixed model fit by REML ['lmerMod']
## Formula: ACC.Per ~ Block + (1 | Subject)
## Data: d.MSL3
##
## REML criterion at convergence: 1042.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9429 -0.5207 0.0537 0.5705 3.5144
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 327.7 18.10
## Residual 126.7 11.25
## Number of obs: 130, groups: Subject, 26
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 44.872 4.181 10.733
## Block2 23.478 3.122 7.521
## Block3 30.369 3.122 9.729
## Block4 34.135 3.122 10.935
## Block5 34.135 3.122 10.935
##
## Correlation of Fixed Effects:
## (Intr) Block2 Block3 Block4
## Block2 -0.373
## Block3 -0.373 0.500
## Block4 -0.373 0.500 0.500
## Block5 -0.373 0.500 0.500 0.500#Overall Trial and ACC
m.MSL.RTACC <- lmer(AvgTrial.RT ~ Block * Trial.ACC + (1|Subject), data = d.MSL)
Anova(m.MSL.RTACC)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: AvgTrial.RT
## Chisq Df Pr(>Chisq)
## Block 312.990 4 < 2.2e-16 ***
## Trial.ACC 293.977 1 < 2.2e-16 ***
## Block:Trial.ACC 21.326 4 0.0002729 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m.MSL.RTACC)## Linear mixed model fit by REML ['lmerMod']
## Formula: AvgTrial.RT ~ Block * Trial.ACC + (1 | Subject)
## Data: d.MSL
##
## REML criterion at convergence: 92376.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.000 -0.295 -0.079 0.151 55.044
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 20781 144.2
## Residual 157266 396.6
## Number of obs: 6240, groups: Subject, 26
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 498.85 32.97 15.132
## Block2 -127.82 21.63 -5.910
## Block3 -176.64 21.25 -8.314
## Block4 -192.26 21.08 -9.121
## Block5 -207.95 21.10 -9.857
## Trial.ACCIncorrect 288.37 23.06 12.508
## Block2:Trial.ACCIncorrect -102.13 33.25 -3.071
## Block3:Trial.ACCIncorrect -149.49 34.74 -4.303
## Block4:Trial.ACCIncorrect -99.63 35.83 -2.781
## Block5:Trial.ACCIncorrect -56.25 35.98 -1.563
##
## Correlation of Fixed Effects:
## (Intr) Block2 Block3 Block4 Block5 T.ACCI B2:T.A B3:T.A B4:T.A
## Block2 -0.398
## Block3 -0.406 0.617
## Block4 -0.411 0.622 0.634
## Block5 -0.411 0.621 0.634 0.641
## Trl.ACCIncr -0.386 0.574 0.586 0.596 0.596
## Blc2:T.ACCI 0.256 -0.653 -0.402 -0.404 -0.403 -0.665
## Blc3:T.ACCI 0.244 -0.379 -0.614 -0.387 -0.386 -0.634 0.453
## Blc4:T.ACCI 0.240 -0.368 -0.376 -0.590 -0.378 -0.623 0.436 0.420
## Blc5:T.ACCI 0.240 -0.366 -0.374 -0.378 -0.590 -0.623 0.432 0.418 0.407#RT * Hand * ACC
m.MSL.RTACCHand <- lmer(AvgTrial.RT ~ Block * Trial.ACC * Hand.indicator + (1|Subject), data = d.MSL)
Anova(m.MSL.RTACCHand)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: AvgTrial.RT
## Chisq Df Pr(>Chisq)
## Block 312.6479 4 < 2.2e-16 ***
## Trial.ACC 293.0182 1 < 2.2e-16 ***
## Hand.indicator 1.0287 1 0.3104548
## Block:Trial.ACC 20.9217 4 0.0003282 ***
## Block:Hand.indicator 2.1289 4 0.7120680
## Trial.ACC:Hand.indicator 1.5804 1 0.2087086
## Block:Trial.ACC:Hand.indicator 1.7269 4 0.7858234
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m.MSL.RTACCHand)## Linear mixed model fit by REML ['lmerMod']
## Formula: AvgTrial.RT ~ Block * Trial.ACC * Hand.indicator + (1 | Subject)
## Data: d.MSL
##
## REML criterion at convergence: 92280
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.999 -0.296 -0.078 0.153 54.940
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 20733 144.0
## Residual 157331 396.6
## Number of obs: 6240, groups: Subject, 26
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 500.556 36.710 13.635
## Block2 -134.220 30.266 -4.435
## Block3 -170.634 29.646 -5.756
## Block4 -196.886 29.480 -6.679
## Block5 -209.927 29.401 -7.140
## Trial.ACCIncorrect 248.618 32.160 7.731
## Hand.indicatorRight -3.636 33.591 -0.108
## Block2:Trial.ACCIncorrect -70.811 46.914 -1.509
## Block3:Trial.ACCIncorrect -110.416 49.272 -2.241
## Block4:Trial.ACCIncorrect -72.316 50.294 -1.438
## Block5:Trial.ACCIncorrect -17.182 51.232 -0.335
## Block2:Hand.indicatorRight 12.919 43.210 0.299
## Block3:Hand.indicatorRight -12.074 42.419 -0.285
## Block4:Hand.indicatorRight 9.387 42.044 0.223
## Block5:Hand.indicatorRight 4.090 42.047 0.097
## Trial.ACCIncorrect:Hand.indicatorRight 77.882 45.286 1.720
## Block2:Trial.ACCIncorrect:Hand.indicatorRight -60.692 66.261 -0.916
## Block3:Trial.ACCIncorrect:Hand.indicatorRight -75.445 69.079 -1.092
## Block4:Trial.ACCIncorrect:Hand.indicatorRight -51.929 71.510 -0.726
## Block5:Trial.ACCIncorrect:Hand.indicatorRight -76.142 71.540 -1.064##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it#Finger press
#Model for concatenation
m.MSL.position <- lmer(feedback.RT ~ Position * Block + (1|Subject), data = d.MSL2)
Anova(m.MSL.position)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: feedback.RT
## Chisq Df Pr(>Chisq)
## Position 1190.457 5 < 2.2e-16 ***
## Block 907.916 4 < 2.2e-16 ***
## Position:Block 53.748 20 6.303e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m.MSL.position)## Linear mixed model fit by REML ['lmerMod']
## Formula: feedback.RT ~ Position * Block + (1 | Subject)
## Data: d.MSL2
##
## REML criterion at convergence: 604031.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.688 -0.267 -0.105 0.047 96.365
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 26851 163.9
## Residual 597114 772.7
## Number of obs: 37440, groups: Subject, 26
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1044.549 38.874 26.870
## Position2 -464.724 30.934 -15.023
## Position3 -511.147 30.934 -16.524
## Position4 -459.536 30.934 -14.855
## Position5 -447.877 30.934 -14.478
## Position6 -437.054 30.934 -14.129
## Block2 -331.837 30.934 -10.727
## Block3 -418.853 30.934 -13.540
## Block4 -430.942 30.934 -13.931
## Block5 -399.487 30.934 -12.914
## Position2:Block2 122.889 43.747 2.809
## Position3:Block2 202.824 43.747 4.636
## Position4:Block2 173.731 43.747 3.971
## Position5:Block2 113.039 43.747 2.584
## Position6:Block2 11.473 43.747 0.262
## Position2:Block3 163.530 43.747 3.738
## Position3:Block3 195.252 43.747 4.463
## Position4:Block3 173.803 43.747 3.973
## Position5:Block3 122.696 43.747 2.805
## Position6:Block3 50.469 43.747 1.154
## Position2:Block4 168.374 43.747 3.849
## Position3:Block4 195.320 43.747 4.465
## Position4:Block4 183.142 43.747 4.186
## Position5:Block4 122.687 43.747 2.804
## Position6:Block4 46.493 43.747 1.063
## Position2:Block5 123.251 43.747 2.817
## Position3:Block5 143.081 43.747 3.271
## Position4:Block5 140.810 43.747 3.219
## Position5:Block5 76.185 43.747 1.741
## Position6:Block5 4.442 43.747 0.102##
## Correlation matrix not shown by default, as p = 30 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it#Posthocs
#ACC Percentage
lsmeans(m.MSL.ACCper, pairwise ~ Block )## $lsmeans
## Block lsmean SE df lower.CL upper.CL
## 1 44.9 4.18 40.6 36.4 53.3
## 2 68.3 4.18 40.6 59.9 76.8
## 3 75.2 4.18 40.6 66.8 83.7
## 4 79.0 4.18 40.6 70.6 87.5
## 5 79.0 4.18 40.6 70.6 87.5
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## 1 - 2 -23.48 3.12 100 -7.521 <.0001
## 1 - 3 -30.37 3.12 100 -9.729 <.0001
## 1 - 4 -34.13 3.12 100 -10.935 <.0001
## 1 - 5 -34.13 3.12 100 -10.935 <.0001
## 2 - 3 -6.89 3.12 100 -2.208 0.1854
## 2 - 4 -10.66 3.12 100 -3.414 0.0081
## 2 - 5 -10.66 3.12 100 -3.414 0.0081
## 3 - 4 -3.77 3.12 100 -1.206 0.7476
## 3 - 5 -3.77 3.12 100 -1.206 0.7476
## 4 - 5 0.00 3.12 100 0.000 1.0000
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 5 estimates#RT ACC Posthocs
lsmeans(m.MSL.RTACC, pairwise ~ Block | Trial.ACC) #Block_ACC## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 6240' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 6240)' 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 = 6240' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 6240)' or larger];
## but be warned that this may result in large computation time and memory use.## $lsmeans
## Trial.ACC = Correct:
## Block lsmean SE df asymp.LCL asymp.UCL
## 1 499 33.0 Inf 434 563
## 2 371 31.4 Inf 309 433
## 3 322 31.1 Inf 261 383
## 4 307 31.0 Inf 246 367
## 5 291 31.0 Inf 230 352
##
## Trial.ACC = Incorrect:
## Block lsmean SE df asymp.LCL asymp.UCL
## 1 787 32.1 Inf 724 850
## 2 557 34.8 Inf 489 626
## 3 461 36.5 Inf 389 533
## 4 495 37.6 Inf 422 569
## 5 523 37.7 Inf 449 597
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Trial.ACC = Correct:
## contrast estimate SE df z.ratio p.value
## 1 - 2 127.8 21.6 Inf 5.910 <.0001
## 1 - 3 176.6 21.2 Inf 8.314 <.0001
## 1 - 4 192.3 21.1 Inf 9.121 <.0001
## 1 - 5 208.0 21.1 Inf 9.857 <.0001
## 2 - 3 48.8 18.8 Inf 2.601 0.0702
## 2 - 4 64.4 18.6 Inf 3.469 0.0047
## 2 - 5 80.1 18.6 Inf 4.309 0.0002
## 3 - 4 15.6 18.1 Inf 0.863 0.9105
## 3 - 5 31.3 18.1 Inf 1.728 0.4166
## 4 - 5 15.7 17.9 Inf 0.878 0.9050
##
## Trial.ACC = Incorrect:
## contrast estimate SE df z.ratio p.value
## 1 - 2 229.9 25.2 Inf 9.130 <.0001
## 1 - 3 326.1 27.4 Inf 11.891 <.0001
## 1 - 4 291.9 28.9 Inf 10.086 <.0001
## 1 - 5 264.2 29.0 Inf 9.098 <.0001
## 2 - 3 96.2 30.2 Inf 3.186 0.0126
## 2 - 4 61.9 31.7 Inf 1.956 0.2877
## 2 - 5 34.3 31.8 Inf 1.077 0.8183
## 3 - 4 -34.2 33.4 Inf -1.026 0.8433
## 3 - 5 -61.9 33.5 Inf -1.849 0.3452
## 4 - 5 -27.7 34.7 Inf -0.797 0.9315
##
## Degrees-of-freedom method: asymptotic
## P value adjustment: tukey method for comparing a family of 5 estimates#Between blocks for accurate trials, differences were predicated mainly in 1st block RT and rest of the blocks and some of Block 2.
#For inaccuarate trials, differences were between block 1 and rest of blocks and between block 2 and 3. Rest were not significant.
#ACC_Block
lsmeans(m.MSL.RTACC, pairwise ~ Trial.ACC | Block) ## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 6240' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 6240)' 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 = 6240' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 6240)' or larger];
## but be warned that this may result in large computation time and memory use.## $lsmeans
## Block = 1:
## Trial.ACC lsmean SE df asymp.LCL asymp.UCL
## Correct 499 33.0 Inf 434 563
## Incorrect 787 32.1 Inf 724 850
##
## Block = 2:
## Trial.ACC lsmean SE df asymp.LCL asymp.UCL
## Correct 371 31.4 Inf 309 433
## Incorrect 557 34.8 Inf 489 626
##
## Block = 3:
## Trial.ACC lsmean SE df asymp.LCL asymp.UCL
## Correct 322 31.1 Inf 261 383
## Incorrect 461 36.5 Inf 389 533
##
## Block = 4:
## Trial.ACC lsmean SE df asymp.LCL asymp.UCL
## Correct 307 31.0 Inf 246 367
## Incorrect 495 37.6 Inf 422 569
##
## Block = 5:
## Trial.ACC lsmean SE df asymp.LCL asymp.UCL
## Correct 291 31.0 Inf 230 352
## Incorrect 523 37.7 Inf 449 597
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Block = 1:
## contrast estimate SE df z.ratio p.value
## Correct - Incorrect -288 23.1 Inf -12.508 <.0001
##
## Block = 2:
## contrast estimate SE df z.ratio p.value
## Correct - Incorrect -186 24.9 Inf -7.489 <.0001
##
## Block = 3:
## contrast estimate SE df z.ratio p.value
## Correct - Incorrect -139 26.9 Inf -5.165 <.0001
##
## Block = 4:
## contrast estimate SE df z.ratio p.value
## Correct - Incorrect -189 28.0 Inf -6.734 <.0001
##
## Block = 5:
## contrast estimate SE df z.ratio p.value
## Correct - Incorrect -232 28.2 Inf -8.245 <.0001
##
## Degrees-of-freedom method: asymptotic#Across all blocks, accurate trials were faster than inaccurate trials
#Quick plots
emmip(m.MSL.RTACC, Trial.ACC ~ Block)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 6240' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 6240)' 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 = 6240' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 6240)' or larger];
## but be warned that this may result in large computation time and memory use.
#Concetanation
summary(glht(m.MSL.position, linfct = mcp(Block = "Tukey")), test = adjusted("holm"))## Warning in mcp2matrix(model, linfct = linfct): covariate interactions found --
## default contrast might be inappropriate##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = feedback.RT ~ Position * Block + (1 | Subject),
## data = d.MSL2)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## 2 - 1 == 0 -331.84 30.93 -10.727 < 2e-16 ***
## 3 - 1 == 0 -418.85 30.93 -13.540 < 2e-16 ***
## 4 - 1 == 0 -430.94 30.93 -13.931 < 2e-16 ***
## 5 - 1 == 0 -399.49 30.93 -12.914 < 2e-16 ***
## 3 - 2 == 0 -87.02 30.93 -2.813 0.02454 *
## 4 - 2 == 0 -99.10 30.93 -3.204 0.00814 **
## 5 - 2 == 0 -67.65 30.93 -2.187 0.11499
## 4 - 3 == 0 -12.09 30.93 -0.391 1.00000
## 5 - 3 == 0 19.37 30.93 0.626 1.00000
## 5 - 4 == 0 31.45 30.93 1.017 0.92772
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- holm method)lsmeans(m.MSL.position, pairwise ~ Block | Position)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 37440' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 37440)' 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 = 37440' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 37440)' or larger];
## but be warned that this may result in large computation time and memory use.## $lsmeans
## Position = 1:
## Block lsmean SE df asymp.LCL asymp.UCL
## 1 1045 38.9 Inf 968 1121
## 2 713 38.9 Inf 637 789
## 3 626 38.9 Inf 550 702
## 4 614 38.9 Inf 537 690
## 5 645 38.9 Inf 569 721
##
## Position = 2:
## Block lsmean SE df asymp.LCL asymp.UCL
## 1 580 38.9 Inf 504 656
## 2 371 38.9 Inf 295 447
## 3 325 38.9 Inf 248 401
## 4 317 38.9 Inf 241 393
## 5 304 38.9 Inf 227 380
##
## Position = 3:
## Block lsmean SE df asymp.LCL asymp.UCL
## 1 533 38.9 Inf 457 610
## 2 404 38.9 Inf 328 481
## 3 310 38.9 Inf 234 386
## 4 298 38.9 Inf 222 374
## 5 277 38.9 Inf 201 353
##
## Position = 4:
## Block lsmean SE df asymp.LCL asymp.UCL
## 1 585 38.9 Inf 509 661
## 2 427 38.9 Inf 351 503
## 3 340 38.9 Inf 264 416
## 4 337 38.9 Inf 261 413
## 5 326 38.9 Inf 250 403
##
## Position = 5:
## Block lsmean SE df asymp.LCL asymp.UCL
## 1 597 38.9 Inf 520 673
## 2 378 38.9 Inf 302 454
## 3 301 38.9 Inf 224 377
## 4 288 38.9 Inf 212 365
## 5 273 38.9 Inf 197 350
##
## Position = 6:
## Block lsmean SE df asymp.LCL asymp.UCL
## 1 607 38.9 Inf 531 684
## 2 287 38.9 Inf 211 363
## 3 239 38.9 Inf 163 315
## 4 223 38.9 Inf 147 299
## 5 212 38.9 Inf 136 289
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Position = 1:
## contrast estimate SE df z.ratio p.value
## 1 - 2 331.84 30.9 Inf 10.727 <.0001
## 1 - 3 418.85 30.9 Inf 13.540 <.0001
## 1 - 4 430.94 30.9 Inf 13.931 <.0001
## 1 - 5 399.49 30.9 Inf 12.914 <.0001
## 2 - 3 87.02 30.9 Inf 2.813 0.0394
## 2 - 4 99.11 30.9 Inf 3.204 0.0118
## 2 - 5 67.65 30.9 Inf 2.187 0.1846
## 3 - 4 12.09 30.9 Inf 0.391 0.9951
## 3 - 5 -19.37 30.9 Inf -0.626 0.9709
## 4 - 5 -31.45 30.9 Inf -1.017 0.8477
##
## Position = 2:
## contrast estimate SE df z.ratio p.value
## 1 - 2 208.95 30.9 Inf 6.755 <.0001
## 1 - 3 255.32 30.9 Inf 8.254 <.0001
## 1 - 4 262.57 30.9 Inf 8.488 <.0001
## 1 - 5 276.24 30.9 Inf 8.930 <.0001
## 2 - 3 46.38 30.9 Inf 1.499 0.5630
## 2 - 4 53.62 30.9 Inf 1.733 0.4133
## 2 - 5 67.29 30.9 Inf 2.175 0.1891
## 3 - 4 7.24 30.9 Inf 0.234 0.9993
## 3 - 5 20.91 30.9 Inf 0.676 0.9616
## 4 - 5 13.67 30.9 Inf 0.442 0.9921
##
## Position = 3:
## contrast estimate SE df z.ratio p.value
## 1 - 2 129.01 30.9 Inf 4.171 0.0003
## 1 - 3 223.60 30.9 Inf 7.228 <.0001
## 1 - 4 235.62 30.9 Inf 7.617 <.0001
## 1 - 5 256.41 30.9 Inf 8.289 <.0001
## 2 - 3 94.59 30.9 Inf 3.058 0.0189
## 2 - 4 106.61 30.9 Inf 3.446 0.0052
## 2 - 5 127.39 30.9 Inf 4.118 0.0004
## 3 - 4 12.02 30.9 Inf 0.389 0.9952
## 3 - 5 32.80 30.9 Inf 1.060 0.8268
## 4 - 5 20.78 30.9 Inf 0.672 0.9625
##
## Position = 4:
## contrast estimate SE df z.ratio p.value
## 1 - 2 158.11 30.9 Inf 5.111 <.0001
## 1 - 3 245.05 30.9 Inf 7.922 <.0001
## 1 - 4 247.80 30.9 Inf 8.011 <.0001
## 1 - 5 258.68 30.9 Inf 8.362 <.0001
## 2 - 3 86.94 30.9 Inf 2.811 0.0397
## 2 - 4 89.69 30.9 Inf 2.900 0.0307
## 2 - 5 100.57 30.9 Inf 3.251 0.0101
## 3 - 4 2.75 30.9 Inf 0.089 1.0000
## 3 - 5 13.63 30.9 Inf 0.441 0.9922
## 4 - 5 10.88 30.9 Inf 0.352 0.9967
##
## Position = 5:
## contrast estimate SE df z.ratio p.value
## 1 - 2 218.80 30.9 Inf 7.073 <.0001
## 1 - 3 296.16 30.9 Inf 9.574 <.0001
## 1 - 4 308.25 30.9 Inf 9.965 <.0001
## 1 - 5 323.30 30.9 Inf 10.451 <.0001
## 2 - 3 77.36 30.9 Inf 2.501 0.0904
## 2 - 4 89.46 30.9 Inf 2.892 0.0314
## 2 - 5 104.50 30.9 Inf 3.378 0.0065
## 3 - 4 12.10 30.9 Inf 0.391 0.9951
## 3 - 5 27.14 30.9 Inf 0.878 0.9053
## 4 - 5 15.05 30.9 Inf 0.486 0.9886
##
## Position = 6:
## contrast estimate SE df z.ratio p.value
## 1 - 2 320.36 30.9 Inf 10.356 <.0001
## 1 - 3 368.38 30.9 Inf 11.909 <.0001
## 1 - 4 384.45 30.9 Inf 12.428 <.0001
## 1 - 5 395.05 30.9 Inf 12.771 <.0001
## 2 - 3 48.02 30.9 Inf 1.552 0.5282
## 2 - 4 64.08 30.9 Inf 2.072 0.2324
## 2 - 5 74.68 30.9 Inf 2.414 0.1114
## 3 - 4 16.06 30.9 Inf 0.519 0.9855
## 3 - 5 26.66 30.9 Inf 0.862 0.9108
## 4 - 5 10.60 30.9 Inf 0.343 0.9970
##
## Degrees-of-freedom method: asymptotic
## P value adjustment: tukey method for comparing a family of 5 estimateslsmeans(m.MSL.position, pairwise ~ Position | Block)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 37440' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 37440)' 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 = 37440' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 37440)' or larger];
## but be warned that this may result in large computation time and memory use.## $lsmeans
## Block = 1:
## Position lsmean SE df asymp.LCL asymp.UCL
## 1 1045 38.9 Inf 968 1121
## 2 580 38.9 Inf 504 656
## 3 533 38.9 Inf 457 610
## 4 585 38.9 Inf 509 661
## 5 597 38.9 Inf 520 673
## 6 607 38.9 Inf 531 684
##
## Block = 2:
## Position lsmean SE df asymp.LCL asymp.UCL
## 1 713 38.9 Inf 637 789
## 2 371 38.9 Inf 295 447
## 3 404 38.9 Inf 328 481
## 4 427 38.9 Inf 351 503
## 5 378 38.9 Inf 302 454
## 6 287 38.9 Inf 211 363
##
## Block = 3:
## Position lsmean SE df asymp.LCL asymp.UCL
## 1 626 38.9 Inf 550 702
## 2 325 38.9 Inf 248 401
## 3 310 38.9 Inf 234 386
## 4 340 38.9 Inf 264 416
## 5 301 38.9 Inf 224 377
## 6 239 38.9 Inf 163 315
##
## Block = 4:
## Position lsmean SE df asymp.LCL asymp.UCL
## 1 614 38.9 Inf 537 690
## 2 317 38.9 Inf 241 393
## 3 298 38.9 Inf 222 374
## 4 337 38.9 Inf 261 413
## 5 288 38.9 Inf 212 365
## 6 223 38.9 Inf 147 299
##
## Block = 5:
## Position lsmean SE df asymp.LCL asymp.UCL
## 1 645 38.9 Inf 569 721
## 2 304 38.9 Inf 227 380
## 3 277 38.9 Inf 201 353
## 4 326 38.9 Inf 250 403
## 5 273 38.9 Inf 197 350
## 6 212 38.9 Inf 136 289
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## Block = 1:
## contrast estimate SE df z.ratio p.value
## 1 - 2 464.72 30.9 Inf 15.023 <.0001
## 1 - 3 511.15 30.9 Inf 16.524 <.0001
## 1 - 4 459.54 30.9 Inf 14.855 <.0001
## 1 - 5 447.88 30.9 Inf 14.478 <.0001
## 1 - 6 437.05 30.9 Inf 14.129 <.0001
## 2 - 3 46.42 30.9 Inf 1.501 0.6639
## 2 - 4 -5.19 30.9 Inf -0.168 1.0000
## 2 - 5 -16.85 30.9 Inf -0.545 0.9943
## 2 - 6 -27.67 30.9 Inf -0.894 0.9480
## 3 - 4 -51.61 30.9 Inf -1.668 0.5528
## 3 - 5 -63.27 30.9 Inf -2.045 0.3166
## 3 - 6 -74.09 30.9 Inf -2.395 0.1578
## 4 - 5 -11.66 30.9 Inf -0.377 0.9990
## 4 - 6 -22.48 30.9 Inf -0.727 0.9787
## 5 - 6 -10.82 30.9 Inf -0.350 0.9993
##
## Block = 2:
## contrast estimate SE df z.ratio p.value
## 1 - 2 341.84 30.9 Inf 11.050 <.0001
## 1 - 3 308.32 30.9 Inf 9.967 <.0001
## 1 - 4 285.81 30.9 Inf 9.239 <.0001
## 1 - 5 334.84 30.9 Inf 10.824 <.0001
## 1 - 6 425.58 30.9 Inf 13.758 <.0001
## 2 - 3 -33.51 30.9 Inf -1.083 0.8882
## 2 - 4 -56.03 30.9 Inf -1.811 0.4583
## 2 - 5 -7.00 30.9 Inf -0.226 0.9999
## 2 - 6 83.75 30.9 Inf 2.707 0.0738
## 3 - 4 -22.52 30.9 Inf -0.728 0.9786
## 3 - 5 26.52 30.9 Inf 0.857 0.9565
## 3 - 6 117.26 30.9 Inf 3.791 0.0021
## 4 - 5 49.03 30.9 Inf 1.585 0.6085
## 4 - 6 139.78 30.9 Inf 4.519 0.0001
## 5 - 6 90.74 30.9 Inf 2.933 0.0393
##
## Block = 3:
## contrast estimate SE df z.ratio p.value
## 1 - 2 301.19 30.9 Inf 9.737 <.0001
## 1 - 3 315.89 30.9 Inf 10.212 <.0001
## 1 - 4 285.73 30.9 Inf 9.237 <.0001
## 1 - 5 325.18 30.9 Inf 10.512 <.0001
## 1 - 6 386.59 30.9 Inf 12.497 <.0001
## 2 - 3 14.70 30.9 Inf 0.475 0.9970
## 2 - 4 -15.46 30.9 Inf -0.500 0.9962
## 2 - 5 23.99 30.9 Inf 0.775 0.9717
## 2 - 6 85.39 30.9 Inf 2.760 0.0640
## 3 - 4 -30.16 30.9 Inf -0.975 0.9260
## 3 - 5 9.29 30.9 Inf 0.300 0.9997
## 3 - 6 70.69 30.9 Inf 2.285 0.2000
## 4 - 5 39.45 30.9 Inf 1.275 0.7987
## 4 - 6 100.85 30.9 Inf 3.260 0.0142
## 5 - 6 61.40 30.9 Inf 1.985 0.3509
##
## Block = 4:
## contrast estimate SE df z.ratio p.value
## 1 - 2 296.35 30.9 Inf 9.580 <.0001
## 1 - 3 315.83 30.9 Inf 10.210 <.0001
## 1 - 4 276.39 30.9 Inf 8.935 <.0001
## 1 - 5 325.19 30.9 Inf 10.512 <.0001
## 1 - 6 390.56 30.9 Inf 12.626 <.0001
## 2 - 3 19.48 30.9 Inf 0.630 0.9889
## 2 - 4 -19.96 30.9 Inf -0.645 0.9875
## 2 - 5 28.84 30.9 Inf 0.932 0.9383
## 2 - 6 94.21 30.9 Inf 3.046 0.0281
## 3 - 4 -39.43 30.9 Inf -1.275 0.7990
## 3 - 5 9.36 30.9 Inf 0.303 0.9997
## 3 - 6 74.73 30.9 Inf 2.416 0.1506
## 4 - 5 48.80 30.9 Inf 1.577 0.6135
## 4 - 6 114.17 30.9 Inf 3.691 0.0031
## 5 - 6 65.37 30.9 Inf 2.113 0.2803
##
## Block = 5:
## contrast estimate SE df z.ratio p.value
## 1 - 2 341.47 30.9 Inf 11.039 <.0001
## 1 - 3 368.07 30.9 Inf 11.898 <.0001
## 1 - 4 318.73 30.9 Inf 10.303 <.0001
## 1 - 5 371.69 30.9 Inf 12.016 <.0001
## 1 - 6 432.61 30.9 Inf 13.985 <.0001
## 2 - 3 26.59 30.9 Inf 0.860 0.9559
## 2 - 4 -22.75 30.9 Inf -0.735 0.9776
## 2 - 5 30.22 30.9 Inf 0.977 0.9254
## 2 - 6 91.14 30.9 Inf 2.946 0.0379
## 3 - 4 -49.34 30.9 Inf -1.595 0.6019
## 3 - 5 3.63 30.9 Inf 0.117 1.0000
## 3 - 6 64.55 30.9 Inf 2.087 0.2942
## 4 - 5 52.97 30.9 Inf 1.712 0.5236
## 4 - 6 113.89 30.9 Inf 3.682 0.0032
## 5 - 6 60.92 30.9 Inf 1.969 0.3600
##
## Degrees-of-freedom method: asymptotic
## P value adjustment: tukey method for comparing a family of 6 estimates#Quick Plots position
emmip(m.MSL.position, Block ~ Position)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 37440' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 37440)' 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 = 37440' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 37440)' or larger];
## but be warned that this may result in large computation time and memory use.
#Effects and plots
#Barplot with accuracy per block (first model)
ae.m.MSL.ACCper <- allEffects(m.MSL.ACCper)
ae.m.df.ACCper <- as.data.frame(ae.m.MSL.ACCper[[1]])
ae.ACCper <- ggplot(ae.m.df.ACCper, aes(x = Block, y = fit, fill=Block)) +
geom_bar(stat = "identity", colour = "black") +
geom_errorbar(aes(ymin=lower, ymax=upper), width=.1) +
ylab("Mean Accuracy (%)")+
xlab("Block")+
ggtitle("Accuracy (%) over training blocks") +
theme_classic()
print (ae.ACCper)
##m.MSL.RTACCHand model
#Need Effects lib
ae.m.MSL.RTACC <- allEffects(m.MSL.RTACC)
ae.m.df.RTACC <- as.data.frame(ae.m.MSL.RTACC[[1]])
#The main plot.
ae.RTACC<-ggplot(ae.m.df.RTACC, aes(x=Block,y=fit, group=Trial.ACC))+
geom_ribbon(aes(ymin=lower, ymax=upper, fill=Trial.ACC), alpha = 0.2) +
geom_line(aes(color=Trial.ACC), size = 1) +
geom_point(aes(color=Trial.ACC, shape=Trial.ACC), size = 3.5)+
ylab("Mean Trial RT (ms)")+
xlab("Block")+
ggtitle("RT ~ Block * Accuracy interactions")
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## $ panel.border : list()
## ..- attr(*, "class")= chr [1:2] "element_blank" "element"
## $ panel.spacing : 'simpleUnit' num 5.5points
## ..- attr(*, "unit")= int 8
## $ panel.spacing.x : NULL
## $ panel.spacing.y : NULL
## $ panel.grid :List of 6
## ..$ colour : chr "grey92"
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## ..$ linetype : NULL
## ..$ lineend : NULL
## ..$ arrow : logi FALSE
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_line" "element"
## $ panel.grid.major : list()
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## $ panel.grid.minor : list()
## ..- attr(*, "class")= chr [1:2] "element_blank" "element"
## $ panel.grid.major.x : NULL
## $ panel.grid.major.y : NULL
## $ panel.grid.minor.x : NULL
## $ panel.grid.minor.y : NULL
## $ panel.ontop : logi FALSE
## $ plot.background :List of 5
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## ..- attr(*, "class")= chr [1:2] "element_text" "element"
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## ..$ inherit.blank: logi TRUE
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## $ strip.background.y : NULL
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## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ strip.switch.pad.grid : 'simpleUnit' num 2.75points
## ..- attr(*, "unit")= int 8
## $ strip.switch.pad.wrap : 'simpleUnit' num 2.75points
## ..- attr(*, "unit")= int 8
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## ..$ face : NULL
## ..$ colour : NULL
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## ..$ vjust : NULL
## ..$ angle : num 90
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## ..$ margin : NULL
## ..$ debug : NULL
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi TRUE
## - attr(*, "validate")= logi TRUEprint (ae.RTACC)
##m.MSL.RTACCHand model
#Need Effects lib
ae.m.MSL.position<-allEffects(m.MSL.position)
ae.m.df.position<-as.data.frame(ae.m.MSL.position[[1]])
#The main polot.
ae.position<-ggplot(ae.m.df.position, aes(x=Position,y=fit, color=Block))+
geom_ribbon(aes(ymin=lower, ymax=upper, fill=Block, group=Block), alpha=0.2) +
#geom_errorbar(aes(ymin=lower, ymax=upper), width=.1) +
geom_line(aes(group=Block), size = 1) +
geom_point(aes(color=Block, shape=Block), size = 3.5) +
ylab("Mean RT (ms)") +
xlab("Key Position") +
ggtitle("RT ~ Position * Block interactions")+
theme_classic()
#Printing Session effects facet
print (ae.position)