TMS B&W_ JointAction
Giovanna Cuomo
2024-08-29
This script contains the analyses conducted on data related to participants who took part in our study “TMS B&W” for the Joint Action task
First, we load packages we need for analyses #PACKAGES
library (tidyverse) #for data handling## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0 ✔ purrr 0.3.5
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.3 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(readxl) #to read excel files
library(writexl) #to export data as excel file ## Warning: il pacchetto 'writexl' è stato creato con R versione 4.2.3library(lme4) ## Caricamento del pacchetto richiesto: Matrix
##
## Caricamento pacchetto: 'Matrix'
##
## I seguenti oggetti sono mascherati da 'package:tidyr':
##
## expand, pack, unpacklibrary(emmeans) ## Welcome to emmeans.
## Caution: You lose important information if you filter this package's results.
## See '? untidy'library(ggplot2)
library(rstatix)## Warning: il pacchetto 'rstatix' è stato creato con R versione 4.2.3##
## Caricamento pacchetto: 'rstatix'
##
## Il seguente oggetto è mascherato da 'package:stats':
##
## filterlibrary(stats)
library(dplyr)
library(yarrr)## Warning: il pacchetto 'yarrr' è stato creato con R versione 4.2.3## Caricamento del pacchetto richiesto: jpeg
## Caricamento del pacchetto richiesto: BayesFactor## Warning: il pacchetto 'BayesFactor' è stato creato con R versione 4.2.3## Caricamento del pacchetto richiesto: coda
## ************
## Welcome to BayesFactor 0.9.12-4.4. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
##
## Type BFManual() to open the manual.
## ************
## Caricamento del pacchetto richiesto: circlize## Warning: il pacchetto 'circlize' è stato creato con R versione 4.2.3## ========================================
## circlize version 0.4.15
## CRAN page: https://cran.r-project.org/package=circlize
## Github page: https://github.com/jokergoo/circlize
## Documentation: https://jokergoo.github.io/circlize_book/book/
##
## If you use it in published research, please cite:
## Gu, Z. circlize implements and enhances circular visualization
## in R. Bioinformatics 2014.
##
## This message can be suppressed by:
## suppressPackageStartupMessages(library(circlize))
## ========================================
##
## yarrr v0.1.5. Citation info at citation('yarrr'). Package guide at yarrr.guide()
## Email me at Nathaniel.D.Phillips.is@gmail.com
##
## Caricamento pacchetto: 'yarrr'
##
## Il seguente oggetto è mascherato da 'package:ggplot2':
##
## diamondslibrary(xlsx)## Warning: il pacchetto 'xlsx' è stato creato con R versione 4.2.3options(scipen=999)##STUDIO 1 & 3
dati_tms = read.xlsx("dati_tms.xlsx", sheetIndex = 1)Specifico i fattori
dati_tms$sito = as.factor(dati_tms$sito)
dati_tms$bw = as.factor(dati_tms$bw)
dati_tms$Movement = as.factor(dati_tms$Movement)
dati_tms$Grasp = as.factor(dati_tms$Grasp)
dati_tms$Subject = as.factor(dati_tms$Subject)#Grasping Asynchrony
ASY_model = lmer(Asynchrony ~ Exp * bw * sito * Movement +
(1+ sito*bw|Subject), control = lmerControl(optimizer = "bobyqa"),
data = dati_tms)
summary(ASY_model)## Linear mixed model fit by REML ['lmerMod']
## Formula: Asynchrony ~ Exp * bw * sito * Movement + (1 + sito * bw | Subject)
## Data: dati_tms
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 102650.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7291 -0.7364 -0.1646 0.5507 7.3289
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Subject (Intercept) 990.4 31.47
## sitoVertex 304.5 17.45 -0.07
## bwWhite 317.9 17.83 0.01 0.31
## sitoVertex:bwWhite 476.4 21.83 0.09 0.06 -0.35
## Residual 12524.8 111.91
## Number of obs: 8356, groups: Subject, 40
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 169.541 9.648 17.573
## Exp3 -20.120 12.420 -1.620
## bwWhite -4.599 9.017 -0.510
## sitoVertex -13.803 8.938 -1.544
## MovementImitative -14.893 7.778 -1.915
## Exp3:bwWhite 1.815 11.601 0.156
## Exp3:sitoVertex 11.244 11.568 0.972
## bwWhite:sitoVertex 7.650 12.284 0.623
## Exp3:MovementImitative 2.804 10.023 0.280
## bwWhite:MovementImitative -2.507 11.005 -0.228
## sitoVertex:MovementImitative -3.650 10.926 -0.334
## Exp3:bwWhite:sitoVertex 5.431 15.871 0.342
## Exp3:bwWhite:MovementImitative 5.742 14.170 0.405
## Exp3:sitoVertex:MovementImitative 8.799 14.179 0.621
## bwWhite:sitoVertex:MovementImitative 1.259 15.491 0.081
## Exp3:bwWhite:sitoVertex:MovementImitative -3.908 20.004 -0.195##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need itisSingular(ASY_model)## [1] FALSEAnova(ASY_model)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Asynchrony
## Chisq Df Pr(>Chisq)
## Exp 1.7769 1 0.18253
## bw 0.1392 1 0.70911
## sito 0.5381 1 0.46320
## Movement 24.8172 1 0.0000006303 ***
## Exp:bw 0.6884 1 0.40670
## Exp:sito 4.2156 1 0.04005 *
## bw:sito 2.9614 1 0.08528 .
## Exp:Movement 3.3220 1 0.06836 .
## bw:Movement 0.0067 1 0.93494
## sito:Movement 0.0475 1 0.82747
## Exp:bw:sito 0.0797 1 0.77767
## Exp:bw:Movement 0.1429 1 0.70546
## Exp:sito:Movement 0.4671 1 0.49434
## bw:sito:Movement 0.0122 1 0.91189
## Exp:bw:sito:Movement 0.0382 1 0.84510
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1controllo la distribuzione dei residui del modello
res = residuals(ASY_model)
plot(density(res))
plot(ASY_model)
qqnorm(residuals(ASY_model))
qqline(residuals(ASY_model))
Svolgo i posthoc delle interazioni significative o quasi
#emmeans/posthoc
emmeans_interaction <- emmeans(ASY_model, ~ Exp * sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 8356' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 8356)' 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 = 8356' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 8356)' 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 interactionsemmeans_interaction1 <- emmeans(ASY_model, ~ bw * sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 8356' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 8356)' 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 = 8356' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 8356)' 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 interactionsemmeans_interaction2 <- emmeans(ASY_model, ~ Movement * Exp)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 8356' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 8356)' 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 = 8356' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 8356)' 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 interactionsposthoc_interaction <- pairs(emmeans_interaction, adjust = "bonferroni")
posthoc_interaction1 <- pairs(emmeans_interaction1, adjust = "bonferroni")
posthoc_interaction2 <- pairs(emmeans_interaction2, adjust = "bonferroni")
summary(posthoc_interaction)## contrast estimate SE df z.ratio p.value
## Exp1 MPFC - Exp3 MPFC 16.37 11.17 Inf 1.467 0.8549
## Exp1 MPFC - Exp1 Vertex 11.49 6.56 Inf 1.752 0.4783
## Exp1 MPFC - Exp3 Vertex 10.48 12.00 Inf 0.873 1.0000
## Exp3 MPFC - Exp1 Vertex -4.89 12.38 Inf -0.395 1.0000
## Exp3 MPFC - Exp3 Vertex -5.89 5.36 Inf -1.099 1.0000
## Exp1 Vertex - Exp3 Vertex -1.01 13.14 Inf -0.077 1.0000
##
## Results are averaged over the levels of: bw, Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_interaction1)## contrast estimate SE df z.ratio p.value
## Black MPFC - White MPFC 3.51 4.57 Inf 0.769 1.0000
## Black MPFC - Black Vertex 7.81 4.54 Inf 1.720 0.5127
## Black MPFC - White Vertex 1.30 6.35 Inf 0.204 1.0000
## White MPFC - Black Vertex 4.30 4.88 Inf 0.881 1.0000
## White MPFC - White Vertex -2.21 5.84 Inf -0.379 1.0000
## Black Vertex - White Vertex -6.51 5.12 Inf -1.271 1.0000
##
## Results are averaged over the levels of: Exp, Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_interaction2)## contrast estimate SE df z.ratio p.value
## Complementary Exp1 - Imitative Exp1 17.66 3.87 Inf 4.559 <.0001
## Complementary Exp1 - Complementary Exp3 12.23 11.71 Inf 1.045 1.0000
## Complementary Exp1 - Imitative Exp3 20.79 11.71 Inf 1.776 0.4545
## Imitative Exp1 - Complementary Exp3 -5.42 11.70 Inf -0.463 1.0000
## Imitative Exp1 - Imitative Exp3 3.14 11.70 Inf 0.268 1.0000
## Complementary Exp3 - Imitative Exp3 8.56 3.16 Inf 2.705 0.0410
##
## Results are averaged over the levels of: bw, sito
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testsplotto i dati completi
pirateplot(formula = Asynchrony ~ bw + sito + Exp, #dependent variable ~ independent
data = dati_tms, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Grasping Asynchrony", xlab = "",#titles for x and y axes
)
plotto solo il main effect di Movement
pirateplot(formula = Asynchrony ~ Movement, #dependent variable ~ independent
data = dati_tms, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Grasping Asynchrony", xlab = "",#titles for x and y axes
)
##Alternativa
medie_TMS_BW = dati_tms%>%
group_by(Subject, sito, bw, Movement, Exp)%>%
summarise(Asynchrony = mean(Asynchrony, na.rm=T), MovTime = mean(MovTime, na.rm=T), Start = mean(Start, na.rm = T)) # factors means## `summarise()` has grouped output by 'Subject', 'sito', 'bw', 'Movement'. You
## can override using the `.groups` argument.media_ds = medie_TMS_BW %>%
group_by(Subject)%>%
summarise(media = mean(Asynchrony),
devst = sd(Asynchrony))
medie_TMS_BW_2 = merge(medie_TMS_BW, media_ds, all=T)
medie_TMS_BW_2 = medie_TMS_BW_2 %>%
mutate(Asy_z = (Asynchrony - media)/devst)
check_z = medie_TMS_BW_2 %>%
group_by(bw, sito, Movement) %>%
shapiro_test(Asy_z)
view(check_z)runno LMM con punteggi z
ASY_model2 = lmer(Asy_z ~ Exp * bw * sito * Movement +
(1|Subject), control = lmerControl(optimizer = "bobyqa"),
data = medie_TMS_BW_2)## boundary (singular) fit: see help('isSingular')summary(ASY_model2)## Linear mixed model fit by REML ['lmerMod']
## Formula: Asy_z ~ Exp * bw * sito * Movement + (1 | Subject)
## Data: medie_TMS_BW_2
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 845
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.34931 -0.71981 -0.05213 0.65433 2.49708
##
## Random effects:
## Groups Name Variance
## Subject (Intercept) 0.00000000000000000000000000000009078
## Residual 0.82102512640444746949697218951769173
## Std.Dev.
## 0.0000000000000003013
## 0.9061043683839337115
## Number of obs: 318, groups: Subject, 40
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.69819 0.22653 3.082
## Exp3 -0.50367 0.29244 -1.722
## bwWhite -0.38170 0.32036 -1.191
## sitoVertex -0.70643 0.32036 -2.205
## MovementImitative -0.56265 0.32036 -1.756
## Exp3:bwWhite 0.16080 0.41358 0.389
## Exp3:sitoVertex 0.71164 0.41537 1.713
## bwWhite:sitoVertex 0.54256 0.45305 1.198
## Exp3:MovementImitative 0.05984 0.41358 0.145
## bwWhite:MovementImitative -0.08985 0.45305 -0.198
## sitoVertex:MovementImitative 0.00526 0.45305 0.012
## Exp3:bwWhite:sitoVertex -0.12493 0.58616 -0.213
## Exp3:bwWhite:MovementImitative 0.38272 0.58489 0.654
## Exp3:sitoVertex:MovementImitative 0.05657 0.58742 0.096
## bwWhite:sitoVertex:MovementImitative 0.10161 0.64071 0.159
## Exp3:bwWhite:sitoVertex:MovementImitative -0.33023 0.82895 -0.398##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')isSingular(ASY_model2)## [1] TRUEAnova(ASY_model2)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Asy_z
## Chisq Df Pr(>Chisq)
## Exp 0.0002 1 0.989532
## bw 0.0050 1 0.943789
## sito 0.2491 1 0.617680
## Movement 20.6242 1 0.000005589 ***
## Exp:bw 1.0051 1 0.316081
## Exp:sito 8.2233 1 0.004136 **
## bw:sito 4.2711 1 0.038765 *
## Exp:Movement 0.9062 1 0.341127
## bw:Movement 0.2035 1 0.651924
## sito:Movement 0.0022 1 0.962637
## Exp:bw:sito 0.4897 1 0.484059
## Exp:bw:Movement 0.2774 1 0.598382
## Exp:sito:Movement 0.0695 1 0.792076
## bw:sito:Movement 0.0554 1 0.813938
## Exp:bw:sito:Movement 0.1587 1 0.690354
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1controllo la distribuzione dei residui del modello
res = residuals(ASY_model2)
plot(density(res))
plot(ASY_model2)
qqnorm(residuals(ASY_model2))
qqline(residuals(ASY_model2))
#Movement Time
MT_model = lmer(MovTime ~ Exp * bw * sito * Movement +
(1+sito*bw|Subject), control = lmerControl(optimizer = "bobyqa"),
data = dati_tms)
summary(MT_model)## Linear mixed model fit by REML ['lmerMod']
## Formula: MovTime ~ Exp * bw * sito * Movement + (1 + sito * bw | Subject)
## Data: dati_tms
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 112493.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0744 -0.6967 -0.0999 0.6334 5.6873
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Subject (Intercept) 14791 121.62
## sitoVertex 6987 83.59 -0.44
## bwWhite 2820 53.10 -0.13 0.59
## sitoVertex:bwWhite 7247 85.13 0.41 -0.66 -0.71
## Residual 36526 191.12
## Number of obs: 8415, groups: Subject, 40
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1281.4345 31.8410 40.245
## Exp3 -69.6006 41.0854 -1.694
## bwWhite -19.7585 18.7861 -1.052
## sitoVertex -4.2750 24.7532 -0.173
## MovementImitative -23.7644 13.2235 -1.797
## Exp3:bwWhite 28.5220 24.2157 1.178
## Exp3:sitoVertex 0.3497 32.0491 0.011
## bwWhite:sitoVertex 37.8366 28.3325 1.335
## Exp3:MovementImitative 8.2832 17.0579 0.486
## bwWhite:MovementImitative 6.3111 18.6848 0.338
## sitoVertex:MovementImitative 14.3453 18.5935 0.772
## Exp3:bwWhite:sitoVertex -44.5937 36.6565 -1.217
## Exp3:bwWhite:MovementImitative 1.9502 24.0864 0.081
## Exp3:sitoVertex:MovementImitative -6.1128 24.1637 -0.253
## bwWhite:sitoVertex:MovementImitative -8.6684 26.3108 -0.329
## Exp3:bwWhite:sitoVertex:MovementImitative -0.7008 34.0238 -0.021##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need itisSingular(MT_model)## [1] FALSEAnova(MT_model)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: MovTime
## Chisq Df Pr(>Chisq)
## Exp 3.3224 1 0.068341 .
## bw 0.3259 1 0.568096
## sito 0.2354 1 0.627531
## Movement 8.2647 1 0.004042 **
## Exp:bw 0.3626 1 0.547069
## Exp:sito 1.8662 1 0.171910
## bw:sito 0.1763 1 0.674577
## Exp:Movement 0.5018 1 0.478726
## bw:Movement 0.1241 1 0.724625
## sito:Movement 0.5317 1 0.465878
## Exp:bw:sito 1.9240 1 0.165418
## Exp:bw:Movement 0.0088 1 0.925112
## Exp:sito:Movement 0.1445 1 0.703851
## bw:sito:Movement 0.2968 1 0.585925
## Exp:bw:sito:Movement 0.0004 1 0.983567
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1controllo la distribuzione dei residui del modello
res = residuals(MT_model)
plot(density(res))
plot(MT_model)
qqnorm(residuals(MT_model))
qqline(residuals(MT_model))
Non ci sono post-hoc da svolgere, quindi plotto i dati completi
pirateplot(formula = MovTime ~ bw + sito + Movement, #dependent variable ~ independent
data = dati_tms, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Movement Time", xlab = "",#titles for x and y axes
)
plotto i dati del main effect
pirateplot(formula = MovTime ~ Movement, #dependent variable ~ independent
data = dati_tms, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Movement Time", xlab = "",#titles for x and y axes
)
Calcolo medie e ds
library(dplyr)
summary_stats <- dati_tms %>%
group_by(Movement) %>%
summarise(
mean_MovTime = mean(MovTime, na.rm = TRUE), # Calcola la media
sd_MovTime = sd(MovTime, na.rm = TRUE) # Calcola la deviazione standard
)#Start - RT
RT_model = lmer(Start ~ Exp * bw * sito * Movement +
(1+sito*bw|Subject), control = lmerControl(optimizer = "bobyqa"),
data = dati_tms)
summary(RT_model)## Linear mixed model fit by REML ['lmerMod']
## Formula: Start ~ Exp * bw * sito * Movement + (1 + sito * bw | Subject)
## Data: dati_tms
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 103746
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6279 -0.6037 -0.0732 0.5382 5.1626
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Subject (Intercept) 10971 104.74
## sitoVertex 6497 80.60 -0.35
## bwWhite 2913 53.97 -0.25 0.48
## sitoVertex:bwWhite 5080 71.27 0.03 -0.40 -0.57
## Residual 14541 120.58
## Number of obs: 8324, groups: Subject, 40
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 395.3584 26.8647 14.717
## Exp3 -5.4322 34.6725 -0.157
## bwWhite -24.4025 15.9225 -1.533
## sitoVertex -6.5585 21.8341 -0.300
## MovementImitative -12.5495 8.3804 -1.497
## Exp3:bwWhite 0.2307 20.5363 0.011
## Exp3:sitoVertex -3.3633 28.2844 -0.119
## bwWhite:sitoVertex -17.8724 21.4117 -0.835
## Exp3:MovementImitative 16.9921 10.8140 1.571
## bwWhite:MovementImitative 14.8005 11.8656 1.247
## sitoVertex:MovementImitative -3.7189 11.7818 -0.316
## Exp3:bwWhite:sitoVertex 38.7627 27.7364 1.398
## Exp3:bwWhite:MovementImitative -18.7756 15.2894 -1.228
## Exp3:sitoVertex:MovementImitative 8.8443 15.3182 0.577
## bwWhite:sitoVertex:MovementImitative 2.7282 16.6904 0.163
## Exp3:bwWhite:sitoVertex:MovementImitative -6.8240 21.5863 -0.316##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need itisSingular(RT_model)## [1] FALSEAnova(RT_model)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Start
## Chisq Df Pr(>Chisq)
## Exp 0.1131 1 0.736630
## bw 7.3784 1 0.006601 **
## sito 0.2118 1 0.645338
## Movement 0.0075 1 0.930968
## Exp:bw 0.1641 1 0.685370
## Exp:sito 0.5092 1 0.475499
## bw:sito 0.1305 1 0.717898
## Exp:Movement 3.5998 1 0.057786 .
## bw:Movement 0.3036 1 0.581622
## sito:Movement 0.0266 1 0.870349
## Exp:bw:sito 1.9170 1 0.166191
## Exp:bw:Movement 4.2304 1 0.039707 *
## Exp:sito:Movement 0.2511 1 0.616320
## bw:sito:Movement 0.0163 1 0.898407
## Exp:bw:sito:Movement 0.0999 1 0.751908
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1controllo la distribuzione dei residui del modello
res = residuals(RT_model)
plot(density(res))
plot(RT_model)
qqnorm(residuals(RT_model))
qqline(residuals(RT_model))
Svolgo i posthoc per le interazioni significative o quasi
#emmeans/posthoc
emmeans_interaction1 <- emmeans(RT_model, ~ Exp * Movement)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 8324' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 8324)' 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 = 8324' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 8324)' 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 interactionsemmeans_interaction2 <- emmeans(RT_model, ~ Movement * Exp * bw)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 8324' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 8324)' 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 = 8324' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 8324)' 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 interactionsposthoc_interaction1 <- pairs(emmeans_interaction1, adjust = "bonferroni")
posthoc_interaction2 <- pairs(emmeans_interaction2, adjust = "bonferroni")
summary(posthoc_interaction1)## contrast estimate SE df z.ratio p.value
## Exp1 Complementary - Exp3 Complementary -2.69 31.48 Inf -0.086 1.0000
## Exp1 Complementary - Exp1 Imitative 6.33 4.17 Inf 1.516 0.7767
## Exp1 Complementary - Exp3 Imitative -6.69 31.47 Inf -0.212 1.0000
## Exp3 Complementary - Exp1 Imitative 9.02 31.47 Inf 0.287 1.0000
## Exp3 Complementary - Exp3 Imitative -3.99 3.42 Inf -1.167 1.0000
## Exp1 Imitative - Exp3 Imitative -13.01 31.47 Inf -0.413 1.0000
##
## Results are averaged over the levels of: bw, sito
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_interaction2)## contrast estimate SE df z.ratio
## Complementary Exp1 Black - Imitative Exp1 Black 14.409 5.89 Inf 2.446
## Complementary Exp1 Black - Complementary Exp3 Black 7.114 32.21 Inf 0.221
## Complementary Exp1 Black - Imitative Exp3 Black 0.109 32.21 Inf 0.003
## Complementary Exp1 Black - Complementary Exp1 White 33.339 12.68 Inf 2.629
## Complementary Exp1 Black - Imitative Exp1 White 31.583 12.67 Inf 2.492
## Complementary Exp1 Black - Complementary Exp3 White 20.840 32.45 Inf 0.642
## Complementary Exp1 Black - Imitative Exp3 White 19.858 32.45 Inf 0.612
## Imitative Exp1 Black - Complementary Exp3 Black -7.295 32.20 Inf -0.227
## Imitative Exp1 Black - Imitative Exp3 Black -14.300 32.20 Inf -0.444
## Imitative Exp1 Black - Complementary Exp1 White 18.930 12.66 Inf 1.496
## Imitative Exp1 Black - Imitative Exp1 White 17.174 12.65 Inf 1.358
## Imitative Exp1 Black - Complementary Exp3 White 6.431 32.44 Inf 0.198
## Imitative Exp1 Black - Imitative Exp3 White 5.449 32.44 Inf 0.168
## Complementary Exp3 Black - Imitative Exp3 Black -7.005 4.89 Inf -1.431
## Complementary Exp3 Black - Complementary Exp1 White 26.225 32.60 Inf 0.804
## Complementary Exp3 Black - Imitative Exp1 White 24.469 32.60 Inf 0.751
## Complementary Exp3 Black - Complementary Exp3 White 13.727 10.42 Inf 1.318
## Complementary Exp3 Black - Imitative Exp3 White 12.744 10.41 Inf 1.224
## Imitative Exp3 Black - Complementary Exp1 White 33.230 32.60 Inf 1.019
## Imitative Exp3 Black - Imitative Exp1 White 31.475 32.60 Inf 0.965
## Imitative Exp3 Black - Complementary Exp3 White 20.732 10.41 Inf 1.991
## Imitative Exp3 Black - Imitative Exp3 White 19.750 10.41 Inf 1.898
## Complementary Exp1 White - Imitative Exp1 White -1.756 5.91 Inf -0.297
## Complementary Exp1 White - Complementary Exp3 White -12.498 32.84 Inf -0.381
## Complementary Exp1 White - Imitative Exp3 White -13.480 32.84 Inf -0.411
## Imitative Exp1 White - Complementary Exp3 White -10.743 32.84 Inf -0.327
## Imitative Exp1 White - Imitative Exp3 White -11.725 32.83 Inf -0.357
## Complementary Exp3 White - Imitative Exp3 White -0.982 4.78 Inf -0.205
## p.value
## 0.4045
## 1.0000
## 1.0000
## 0.2396
## 0.3553
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
##
## Results are averaged over the levels of: sito
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 28 testsCalcolo medie e ds
summary_statsRT <- dati_tms %>%
group_by(bw) %>%
summarise(
mean_MovTime = mean(Start, na.rm = TRUE), # Calcola la media
sd_MovTime = sd(Start, na.rm = TRUE) # Calcola la deviazione standard
)plotto i dati completi
pirateplot(formula = Start ~ bw + sito + Movement, #dependent variable ~ independent
data = dati_tms, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Reaction Times", xlab = "",#titles for x and y axes
)
plotto il main effect significativo
pirateplot(formula = Start ~ bw , #dependent variable ~ independent
data = dati_tms, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Reaction Times", xlab = "",#titles for x and y axes
)
##TUTTI gli STUDI
dataset_2 = read.xlsx("dataset_2.xlsx", sheetIndex = 1)Specifico i fattori
dataset_2$sito = as.factor(dataset_2$sito)
dataset_2$bw = as.factor(dataset_2$bw)
dataset_2$Movement = as.factor(dataset_2$Movement)
dataset_2$Grasp = as.factor(dataset_2$Grasp)
dataset_2$Subject = as.factor(dataset_2$Subject)#Grasping Asynchrony
ASY_model_3studi = lmer (Asynchrony ~ Exp + bw * sito * Movement +
(1+bw+sito|Subject), control = lmerControl(optimizer = "bobyqa"),
data = dataset_2)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?#summary(ASY_model_3studi)
isSingular(ASY_model_3studi)## [1] FALSEanova(ASY_model_3studi)## Analysis of Variance Table
## npar Sum Sq Mean Sq F value
## Exp 2 35419 17709 1.3942
## bw 1 274 274 0.0216
## sito 2 10412 5206 0.4099
## Movement 1 587096 587096 46.2205
## bw:sito 2 29297 14649 1.1532
## bw:Movement 1 4164 4164 0.3278
## sito:Movement 2 9642 4821 0.3795
## bw:sito:Movement 2 3905 1952 0.1537Svolgo i posthoc
#emmeans/posthoc
emmeans_interaction <- emmeans(ASY_model_3studi, ~ bw * sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 11516' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 11516)' 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 = 11516' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 11516)' 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 interactionsemmeans_interaction1 <- emmeans(ASY_model_3studi, ~ bw * Movement)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 11516' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 11516)' 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 = 11516' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 11516)' 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 interactionsemmeans_interaction2 <- emmeans(ASY_model_3studi, ~ Movement * sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 11516' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 11516)' 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 = 11516' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 11516)' 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 interactionsemmeans_sito <- emmeans(ASY_model_3studi, ~ sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 11516' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 11516)' 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 = 11516' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 11516)' 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 interactionsemmeans_bw <- emmeans(ASY_model_3studi, ~ bw)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 11516' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 11516)' 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 = 11516' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 11516)' 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 interactionsemmeans_Movement <- emmeans(ASY_model_3studi, ~ Movement)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 11516' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 11516)' 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 = 11516' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 11516)' 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 interactionsposthoc_interaction <- pairs(emmeans_interaction, adjust = "bonferroni")
posthoc_interaction1 <- pairs(emmeans_interaction1, adjust = "bonferroni")
posthoc_interaction2 <- pairs(emmeans_interaction2, adjust = "bonferroni")
posthoc_sito <- contrast(emmeans_sito, adjust = "bonferroni")
posthoc_bw <- contrast(emmeans_bw, adjust = "bonferroni")
posthoc_Movement <- contrast(emmeans_Movement, adjust = "bonferroni")
summary(posthoc_interaction)## contrast estimate SE df z.ratio p.value
## Black MPFC - White MPFC 4.45 4.14 Inf 1.074 1.0000
## Black MPFC - Black Vertex 5.47 4.88 Inf 1.120 1.0000
## Black MPFC - White Vertex 2.96 5.73 Inf 0.516 1.0000
## Black MPFC - Black VPM -6.46 13.53 Inf -0.477 1.0000
## Black MPFC - White VPM -4.65 13.82 Inf -0.337 1.0000
## White MPFC - Black Vertex 1.02 4.57 Inf 0.223 1.0000
## White MPFC - White Vertex -1.49 4.71 Inf -0.316 1.0000
## White MPFC - Black VPM -10.91 13.31 Inf -0.820 1.0000
## White MPFC - White VPM -9.10 13.51 Inf -0.673 1.0000
## Black Vertex - White Vertex -2.51 3.59 Inf -0.699 1.0000
## Black Vertex - Black VPM -11.93 12.69 Inf -0.940 1.0000
## Black Vertex - White VPM -10.12 13.04 Inf -0.776 1.0000
## White Vertex - Black VPM -9.42 12.77 Inf -0.737 1.0000
## White Vertex - White VPM -7.61 12.82 Inf -0.594 1.0000
## Black VPM - White VPM 1.81 6.58 Inf 0.275 1.0000
##
## Results are averaged over the levels of: Exp, Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 15 testssummary(posthoc_interaction1)## contrast estimate SE df z.ratio p.value
## Black Complementary - White Complementary 1.885 4.01 Inf 0.470 1.0000
## Black Complementary - Black Imitative 15.816 3.43 Inf 4.607 <.0001
## Black Complementary - White Imitative 16.430 4.00 Inf 4.104 0.0002
## White Complementary - Black Imitative 13.931 4.01 Inf 3.471 0.0031
## White Complementary - White Imitative 14.545 3.44 Inf 4.228 0.0001
## Black Imitative - White Imitative 0.614 4.00 Inf 0.153 1.0000
##
## Results are averaged over the levels of: Exp, sito
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_interaction2)## contrast estimate SE df z.ratio p.value
## Complementary MPFC - Imitative MPFC 12.72 3.49 Inf 3.645 0.0040
## Complementary MPFC - Complementary Vertex 1.21 4.77 Inf 0.253 1.0000
## Complementary MPFC - Imitative Vertex 15.49 4.77 Inf 3.251 0.0173
## Complementary MPFC - Complementary VPM -10.69 13.40 Inf -0.798 1.0000
## Complementary MPFC - Imitative VPM 7.85 13.40 Inf 0.586 1.0000
## Imitative MPFC - Complementary Vertex -11.51 4.76 Inf -2.419 0.2337
## Imitative MPFC - Imitative Vertex 2.77 4.75 Inf 0.582 1.0000
## Imitative MPFC - Complementary VPM -23.41 13.40 Inf -1.748 1.0000
## Imitative MPFC - Imitative VPM -4.87 13.39 Inf -0.364 1.0000
## Complementary Vertex - Imitative Vertex 14.28 2.97 Inf 4.807 <.0001
## Complementary Vertex - Complementary VPM -11.90 12.68 Inf -0.938 1.0000
## Complementary Vertex - Imitative VPM 6.64 12.68 Inf 0.524 1.0000
## Imitative Vertex - Complementary VPM -26.18 12.68 Inf -2.064 0.5850
## Imitative Vertex - Imitative VPM -7.64 12.68 Inf -0.603 1.0000
## Complementary VPM - Imitative VPM 18.54 5.67 Inf 3.270 0.0161
##
## Results are averaged over the levels of: Exp, bw
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 15 testssummary(posthoc_sito)## contrast estimate SE df z.ratio p.value
## MPFC effect -1.93 4.96 Inf -0.390 1.0000
## Vertex effect -3.92 4.32 Inf -0.908 1.0000
## VPM effect 5.85 8.30 Inf 0.705 1.0000
##
## Results are averaged over the levels of: Exp, bw, Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 3 testssummary(posthoc_bw)## contrast estimate SE df z.ratio p.value
## Black effect 0.625 1.59 Inf 0.392 1.0000
## White effect -0.625 1.59 Inf -0.392 1.0000
##
## Results are averaged over the levels of: Exp, sito, Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 2 testssummary(posthoc_Movement)## contrast estimate SE df z.ratio p.value
## Complementary effect 7.59 1.22 Inf 6.247 <.0001
## Imitative effect -7.59 1.22 Inf -6.247 <.0001
##
## Results are averaged over the levels of: Exp, bw, sito
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 2 testsplotto i dati
pirateplot(formula = Asynchrony ~ bw + sito + Movement, #dependent variable ~ independent
data = dataset_2, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Grasping Asynchrony", xlab = "",#titles for x and y axes
)
#Movement Time
MT_model_3studi = lmer(MovTime ~ Exp * bw * Movement +
(1+bw|Subject), control = lmerControl(optimizer = "bobyqa"),
data = dataset_2)
#summary(MT_model_3studi)
isSingular(MT_model_3studi)## [1] FALSEAnova(MT_model_3studi)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: MovTime
## Chisq Df Pr(>Chisq)
## Exp 8.1235 2 0.01722 *
## bw 0.1436 1 0.70470
## Movement 5.3885 1 0.02027 *
## Exp:bw 0.5771 2 0.74936
## Exp:Movement 3.1673 2 0.20522
## bw:Movement 0.1746 1 0.67608
## Exp:bw:Movement 0.0105 2 0.99476
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1controllo la distribuzione dei residui del modello
res = residuals(MT_model_3studi)
plot(density(res))
plot(MT_model_3studi)
qqnorm(residuals(MT_model_3studi))
qqline(residuals(MT_model_3studi))
Calcolo medie e ds
summary_statsMT_Exp <- dataset_2 %>%
group_by(Exp) %>%
summarise(
mean_MovTime = mean(MovTime, na.rm = TRUE), # Calcola la media
sd_MovTime = sd(MovTime, na.rm = TRUE) # Calcola la deviazione standard
)
summary_statsMT <- dataset_2 %>%
group_by(Movement) %>%
summarise(
mean_MovTime = mean(MovTime, na.rm = TRUE), # Calcola la media
sd_MovTime = sd(MovTime, na.rm = TRUE) # Calcola la deviazione standard
)Svolgo i posthoc
#emmeans/posthoc
emmeans_Experiment <- emmeans(MT_model_3studi, ~ Exp)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 11575' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 11575)' 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 = 11575' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 11575)' 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 interactionsposthoc_Experiment <- contrast(emmeans_Experiment, adjust = "bonferroni")
summary(posthoc_Experiment)## contrast estimate SE df z.ratio p.value
## Exp1 effect 9.6 24.6 Inf 0.389 1.0000
## Exp2 effect 44.6 25.1 Inf 1.778 0.2263
## Exp3 effect -54.2 22.4 Inf -2.419 0.0467
##
## Results are averaged over the levels of: bw, Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 3 testsplotto i dati dei main effect
pirateplot(formula = MovTime ~ Movement, #dependent variable ~ independent
data = dataset_2, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Movement Time", xlab = "",#titles for x and y axes
)
pirateplot(formula = MovTime ~ Exp, #dependent variable ~ independent
data = dataset_2, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Movement Time", xlab = "",#titles for x and y axes
)
#Start - RT
RT_model_3studi = lmer(Start ~ Exp * bw * Movement +
(1+bw|Subject), control = lmerControl(optimizer = "bobyqa"),
data = dataset_2)
#summary(RT_model_3studi)
isSingular(RT_model_3studi)## [1] FALSEAnova(RT_model_3studi)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Start
## Chisq Df Pr(>Chisq)
## Exp 12.9470 2 0.001544 **
## bw 7.2134 1 0.007236 **
## Movement 3.8781 1 0.048919 *
## Exp:bw 2.5901 2 0.273891
## Exp:Movement 13.0285 2 0.001482 **
## bw:Movement 0.0809 1 0.776099
## Exp:bw:Movement 4.2210 2 0.121176
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1controllo la distribuzione dei residui del modello
res = residuals(RT_model_3studi)
plot(density(res))
plot(RT_model_3studi)
qqnorm(residuals(RT_model_3studi))
qqline(residuals(RT_model_3studi))
Svolgo i posthoc
#emmeans/posthoc
RT_emmeans_interaction <- emmeans(RT_model_3studi, ~ Exp * Movement)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 11438' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 11438)' 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 = 11438' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 11438)' 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 interactionsRT_emmeans_Experiment <- emmeans(MT_model_3studi, ~ Exp)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 11575' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 11575)' 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 = 11575' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 11575)' 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 interactionsposthoc_interaction <- pairs(RT_emmeans_interaction, adjust = "bonferroni")
posthoc_Experiment <- contrast(RT_emmeans_Experiment, adjust = "bonferroni")
summary(posthoc_interaction)## contrast estimate SE df z.ratio p.value
## Exp1 Complementary - Exp2 Complementary 90.34 33.90 Inf 2.664 0.1157
## Exp1 Complementary - Exp3 Complementary -2.67 30.45 Inf -0.088 1.0000
## Exp1 Complementary - Exp1 Imitative 6.39 4.24 Inf 1.506 1.0000
## Exp1 Complementary - Exp2 Imitative 106.42 33.90 Inf 3.139 0.0254
## Exp1 Complementary - Exp3 Imitative -6.71 30.45 Inf -0.220 1.0000
## Exp2 Complementary - Exp3 Complementary -93.01 31.05 Inf -2.995 0.0411
## Exp2 Complementary - Exp1 Imitative -83.94 33.90 Inf -2.476 0.1992
## Exp2 Complementary - Exp2 Imitative 16.08 4.40 Inf 3.655 0.0039
## Exp2 Complementary - Exp3 Imitative -97.05 31.05 Inf -3.126 0.0266
## Exp3 Complementary - Exp1 Imitative 9.06 30.44 Inf 0.298 1.0000
## Exp3 Complementary - Exp2 Imitative 109.09 31.05 Inf 3.514 0.0066
## Exp3 Complementary - Exp3 Imitative -4.04 3.48 Inf -1.161 1.0000
## Exp1 Imitative - Exp2 Imitative 100.03 33.90 Inf 2.951 0.0476
## Exp1 Imitative - Exp3 Imitative -13.11 30.44 Inf -0.431 1.0000
## Exp2 Imitative - Exp3 Imitative -113.13 31.05 Inf -3.644 0.0040
##
## Results are averaged over the levels of: bw
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 15 testssummary(posthoc_Experiment)## contrast estimate SE df z.ratio p.value
## Exp1 effect 9.6 24.6 Inf 0.389 1.0000
## Exp2 effect 44.6 25.1 Inf 1.778 0.2263
## Exp3 effect -54.2 22.4 Inf -2.419 0.0467
##
## Results are averaged over the levels of: bw, Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 3 testsCalcolo medie e ds
statsRT_Exp <- dataset_2 %>%
group_by(Exp) %>%
summarise(
mean_MovTime = mean(Start, na.rm = TRUE), # Calcola la media
sd_MovTime = sd(Start, na.rm = TRUE) # Calcola la deviazione standard
)
statsRT_Mov <- dataset_2 %>%
group_by(Movement) %>%
summarise(
mean_MovTime = mean(Start, na.rm = TRUE), # Calcola la media
sd_MovTime = sd(Start, na.rm = TRUE) # Calcola la deviazione standard
)
statsRT_bw <- dataset_2 %>%
group_by(bw) %>%
summarise(
mean_MovTime = mean(Start, na.rm = TRUE), # Calcola la media
sd_MovTime = sd(Start, na.rm = TRUE) # Calcola la deviazione standard
)
statsRT_interaction <- dataset_2 %>%
group_by(Exp, Movement) %>%
summarise(
mean_MovTime = mean(Start, na.rm = TRUE), # Calcola la media
sd_MovTime = sd(Start, na.rm = TRUE) # Calcola la deviazione standard
)## `summarise()` has grouped output by 'Exp'. You can override using the `.groups`
## argument.plotto i dati dei significant effects
pirateplot(formula = Start ~ Movement, #dependent variable ~ independent
data = dataset_2, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Reaction Times", xlab = "",#titles for x and y axes
)
pirateplot(formula = Start ~ Exp, #dependent variable ~ independent
data = dataset_2, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Reaction Times", xlab = "",#titles for x and y axes
)
pirateplot(formula = Start ~ bw, #dependent variable ~ independent
data = dataset_2, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Reaction Times", xlab = "",#titles for x and y axes
)
pirateplot(formula = Start ~ Exp + Movement, #dependent variable ~ independent
data = dataset_2, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Reaction Times", xlab = "",#titles for x and y axes
)
##STUDIO 1
EXP1 = read.xlsx("dati_tms_EXP1.xlsx", sheetIndex = 1)Specifico i fattori
EXP1$sito = as.factor(EXP1$sito)
EXP1$bw = as.factor(EXP1$bw)
EXP1$Grasp= as.factor(EXP1$Grasp)
EXP1$Movement = as.factor(EXP1$Movement)
EXP1$Subject = as.factor (EXP1$Subject)#Grasping Asynchrony
ASY_EXP1 = lmer (Asynchrony ~ bw * sito * Movement +
(1+bw+sito|Subject), control = lmerControl(optimizer = "bobyqa"),
data = EXP1)
isSingular(ASY_EXP1)## [1] FALSEsummary(ASY_EXP1)## Linear mixed model fit by REML ['lmerMod']
## Formula: Asynchrony ~ bw * sito * Movement + (1 + bw + sito | Subject)
## Data: EXP1
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 41534.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0542 -0.7233 -0.1689 0.5170 6.8739
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Subject (Intercept) 1183.8 34.41
## bwWhite 187.1 13.68 0.17
## sitoVertex 401.3 20.03 -0.46 0.11
## Residual 14464.6 120.27
## Number of obs: 3344, groups: Subject, 16
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 169.624 10.487 16.174
## bwWhite -4.722 9.090 -0.520
## sitoVertex -13.919 9.765 -1.425
## MovementImitative -14.917 8.358 -1.785
## bwWhite:sitoVertex 7.722 11.825 0.653
## bwWhite:MovementImitative -2.448 11.826 -0.207
## sitoVertex:MovementImitative -3.614 11.741 -0.308
## bwWhite:sitoVertex:MovementImitative 1.383 16.647 0.083
##
## Correlation of Fixed Effects:
## (Intr) bwWhit stVrtx MvmntI bwWh:V bwW:MI stV:MI
## bwWhite -0.326
## sitoVertex -0.545 0.426
## MovmntImttv -0.410 0.473 0.441
## bwWht:stVrt 0.290 -0.660 -0.608 -0.364
## bwWht:MvmnI 0.290 -0.660 -0.311 -0.707 0.507
## stVrtx:MvmI 0.292 -0.337 -0.613 -0.712 0.506 0.503
## bwWht:sV:MI -0.206 0.469 0.432 0.502 -0.710 -0.710 -0.705Svolgo i posthoc
#emmeans/posthoc
emmeans_interaction <- emmeans(ASY_EXP1, ~ bw * sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3344' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3344)' 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 = 3344' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3344)' 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 interactionsemmeans_interaction1 <- emmeans(ASY_EXP1, ~ bw * Movement)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3344' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3344)' 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 = 3344' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3344)' 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 interactionsemmeans_interaction2 <- emmeans(ASY_EXP1, ~ Movement * sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3344' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3344)' 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 = 3344' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3344)' 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 interactionsemmeans_sito <- emmeans(ASY_EXP1, ~ sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3344' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3344)' 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 = 3344' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3344)' 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 interactionsemmeans_bw <- emmeans(ASY_EXP1, ~ bw)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3344' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3344)' 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 = 3344' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3344)' 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 interactionsemmeans_Movement <- emmeans(ASY_EXP1, ~ Movement)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3344' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3344)' 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 = 3344' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3344)' 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 interactionsposthoc_interaction <- pairs(emmeans_interaction, adjust = "bonferroni")
posthoc_interaction1 <- pairs(emmeans_interaction1, adjust = "bonferroni")
posthoc_interaction2 <- pairs(emmeans_interaction2, adjust = "bonferroni")
posthoc_sito <- contrast(emmeans_sito, adjust = "bonferroni")
posthoc_bw <- contrast(emmeans_bw, adjust = "bonferroni")
posthoc_Movement <- contrast(emmeans_Movement, adjust = "bonferroni")
summary(posthoc_interaction)## contrast estimate SE df z.ratio p.value
## Black MPFC - White MPFC 5.95 6.83 Inf 0.870 1.0000
## Black MPFC - Black Vertex 15.73 7.72 Inf 2.038 0.2495
## Black MPFC - White Vertex 13.26 8.67 Inf 1.528 0.7585
## White MPFC - Black Vertex 9.78 8.22 Inf 1.189 1.0000
## White MPFC - White Vertex 7.31 7.74 Inf 0.945 1.0000
## Black Vertex - White Vertex -2.47 6.78 Inf -0.364 1.0000
##
## Results are averaged over the levels of: Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_interaction1)## contrast estimate SE df z.ratio p.value
## Black Complementary - White Complementary 0.861 6.83 Inf 0.126 1.0000
## Black Complementary - Black Imitative 16.724 5.87 Inf 2.848 0.0264
## Black Complementary - White Imitative 19.342 6.83 Inf 2.831 0.0279
## White Complementary - Black Imitative 15.863 6.78 Inf 2.339 0.1160
## White Complementary - White Imitative 18.480 5.90 Inf 3.132 0.0104
## Black Imitative - White Imitative 2.618 6.78 Inf 0.386 1.0000
##
## Results are averaged over the levels of: sito
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_interaction2)## contrast estimate SE df z.ratio p.value
## Complementary MPFC - Imitative MPFC 16.14 5.91 Inf 2.730 0.0380
## Complementary MPFC - Complementary Vertex 10.06 7.75 Inf 1.298 1.0000
## Complementary MPFC - Imitative Vertex 29.12 7.74 Inf 3.762 0.0010
## Imitative MPFC - Complementary Vertex -6.08 7.72 Inf -0.788 1.0000
## Imitative MPFC - Imitative Vertex 12.98 7.71 Inf 1.684 0.5529
## Complementary Vertex - Imitative Vertex 19.06 5.86 Inf 3.254 0.0068
##
## Results are averaged over the levels of: bw
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_sito)## contrast estimate SE df z.ratio p.value
## MPFC effect 5.76 3.26 Inf 1.769 0.1539
## Vertex effect -5.76 3.26 Inf -1.769 0.1539
##
## Results are averaged over the levels of: bw, Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 2 testssummary(posthoc_bw)## contrast estimate SE df z.ratio p.value
## Black effect 0.87 2.69 Inf 0.323 1.0000
## White effect -0.87 2.69 Inf -0.323 1.0000
##
## Results are averaged over the levels of: sito, Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 2 testssummary(posthoc_Movement)## contrast estimate SE df z.ratio p.value
## Complementary effect 8.8 2.08 Inf 4.229 <.0001
## Imitative effect -8.8 2.08 Inf -4.229 <.0001
##
## Results are averaged over the levels of: bw, sito
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 2 testsplotto i dati
pirateplot(formula = Asynchrony ~ bw + sito + Movement, #dependent variable ~ independent
data = EXP1, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Grasping Asynchrony", xlab = "Experiment 1",#titles for x and y axes
)
#Movement Time
MT_EXP1 = lmer(MovTime ~ bw * sito * Movement +
(1+sito*bw|Subject), control = lmerControl(optimizer = "bobyqa"),
data = EXP1)
summary(MT_EXP1)## Linear mixed model fit by REML ['lmerMod']
## Formula: MovTime ~ bw * sito * Movement + (1 + sito * bw | Subject)
## Data: EXP1
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 45479.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9061 -0.6882 -0.0853 0.6300 5.3459
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Subject (Intercept) 13895 117.88
## sitoVertex 4375 66.15 -0.63
## bwWhite 1855 43.07 -0.36 0.23
## sitoVertex:bwWhite 8527 92.34 0.47 -0.60 -0.77
## Residual 39909 199.77
## Number of obs: 3382, groups: Subject, 16
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1281.888 31.080 41.245
## bwWhite -20.251 17.572 -1.152
## sitoVertex -4.756 21.579 -0.220
## MovementImitative -24.061 13.818 -1.741
## bwWhite:sitoVertex 38.344 30.247 1.268
## bwWhite:MovementImitative 6.474 19.528 0.332
## sitoVertex:MovementImitative 14.590 19.432 0.751
## bwWhite:sitoVertex:MovementImitative -8.886 27.499 -0.323
##
## Correlation of Fixed Effects:
## (Intr) bwWhit stVrtx MvmntI bwWh:V bwW:MI stV:MI
## bwWhite -0.388
## sitoVertex -0.600 0.366
## MovmntImttv -0.227 0.401 0.327
## bwWht:stVrt 0.443 -0.722 -0.647 -0.233
## bwWht:MvmnI 0.161 -0.562 -0.231 -0.708 0.326
## stVrtx:MvmI 0.161 -0.285 -0.458 -0.711 0.327 0.503
## bwWht:sV:MI -0.114 0.399 0.324 0.502 -0.459 -0.710 -0.707isSingular(MT_EXP1)## [1] FALSESvolgo i posthoc
#emmeans/posthoc
emmeans_interaction <- emmeans(MT_EXP1, ~ bw * sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3382' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3382)' 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 = 3382' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3382)' 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 interactionsemmeans_interaction1 <- emmeans(MT_EXP1, ~ bw * Movement)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3382' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3382)' 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 = 3382' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3382)' 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 interactionsemmeans_interaction2 <- emmeans(MT_EXP1, ~ Movement * sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3382' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3382)' 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 = 3382' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3382)' 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 interactionsemmeans_sito <- emmeans(MT_EXP1, ~ sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3382' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3382)' 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 = 3382' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3382)' 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 interactionsemmeans_bw <- emmeans(MT_EXP1, ~ bw)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3382' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3382)' 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 = 3382' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3382)' 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 interactionsemmeans_Movement <- emmeans(MT_EXP1, ~ Movement)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3382' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3382)' 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 = 3382' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3382)' 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 interactionsposthoc_interaction <- pairs(emmeans_interaction, adjust = "bonferroni")
posthoc_interaction1 <- pairs(emmeans_interaction1, adjust = "bonferroni")
posthoc_interaction2 <- pairs(emmeans_interaction2, adjust = "bonferroni")
posthoc_sito <- contrast(emmeans_sito, adjust = "bonferroni")
posthoc_bw <- contrast(emmeans_bw, adjust = "bonferroni")
posthoc_Movement <- contrast(emmeans_Movement, adjust = "bonferroni")
summary(posthoc_interaction)## contrast estimate SE df z.ratio p.value
## Black MPFC - White MPFC 17.01 14.5 Inf 1.170 1.0000
## Black MPFC - Black Vertex -2.54 19.2 Inf -0.132 1.0000
## Black MPFC - White Vertex -19.43 16.1 Inf -1.210 1.0000
## White MPFC - Black Vertex -19.55 20.0 Inf -0.977 1.0000
## White MPFC - White Vertex -36.44 21.0 Inf -1.736 0.4958
## Black Vertex - White Vertex -16.89 19.0 Inf -0.889 1.0000
##
## Results are averaged over the levels of: Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_interaction1)## contrast estimate SE df z.ratio p.value
## Black Complementary - White Complementary 1.079 12.40 Inf 0.087 1.0000
## Black Complementary - Black Imitative 16.767 9.72 Inf 1.726 0.5065
## Black Complementary - White Imitative 15.815 12.39 Inf 1.277 1.0000
## White Complementary - Black Imitative 15.688 12.33 Inf 1.272 1.0000
## White Complementary - White Imitative 14.736 9.73 Inf 1.515 0.7791
## Black Imitative - White Imitative -0.952 12.32 Inf -0.077 1.0000
##
## Results are averaged over the levels of: sito
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_interaction2)## contrast estimate SE df z.ratio p.value
## Complementary MPFC - Imitative MPFC 20.82 9.76 Inf 2.133 0.1976
## Complementary MPFC - Complementary Vertex -14.42 16.49 Inf -0.874 1.0000
## Complementary MPFC - Imitative Vertex -3.74 16.47 Inf -0.227 1.0000
## Imitative MPFC - Complementary Vertex -35.24 16.46 Inf -2.141 0.1937
## Imitative MPFC - Imitative Vertex -24.56 16.44 Inf -1.494 0.8104
## Complementary Vertex - Imitative Vertex 10.68 9.68 Inf 1.103 1.0000
##
## Results are averaged over the levels of: bw
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_sito)## contrast estimate SE df z.ratio p.value
## MPFC effect -9.74 7.48 Inf -1.303 0.3854
## Vertex effect 9.74 7.48 Inf 1.303 0.3854
##
## Results are averaged over the levels of: bw, Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 2 testssummary(posthoc_bw)## contrast estimate SE df z.ratio p.value
## Black effect 0.0316 5.13 Inf 0.006 1.0000
## White effect -0.0316 5.13 Inf -0.006 1.0000
##
## Results are averaged over the levels of: sito, Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 2 testssummary(posthoc_Movement)## contrast estimate SE df z.ratio p.value
## Complementary effect 7.88 3.44 Inf 2.291 0.0439
## Imitative effect -7.88 3.44 Inf -2.291 0.0439
##
## Results are averaged over the levels of: bw, sito
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 2 testsplotto i dati
pirateplot(formula = MovTime ~ bw + sito + Movement, #dependent variable ~ independent
data = EXP1, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Movement Time", xlab = "Experiment 1",#titles for x and y axes
)
#Start - RT
RT_EXP1 = lmer(Start ~ bw * sito * Movement +
(1+sito*bw|Subject), control = lmerControl(optimizer = "bobyqa"),
data = EXP1)
summary(RT_EXP1)## Linear mixed model fit by REML ['lmerMod']
## Formula: Start ~ bw * sito * Movement + (1 + sito * bw | Subject)
## Data: EXP1
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 42331.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2862 -0.6013 -0.0558 0.5439 4.6925
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Subject (Intercept) 10228 101.13
## sitoVertex 6010 77.53 -0.57
## bwWhite 3189 56.47 -0.42 0.43
## sitoVertex:bwWhite 6903 83.08 0.46 -0.56 -0.72
## Residual 17650 132.85
## Number of obs: 3347, groups: Subject, 16
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 395.318 26.133 15.127
## bwWhite -24.387 16.912 -1.442
## sitoVertex -6.504 21.480 -0.303
## MovementImitative -12.505 9.233 -1.354
## bwWhite:sitoVertex -17.874 24.546 -0.728
## bwWhite:MovementImitative 14.794 13.073 1.132
## sitoVertex:MovementImitative -3.782 12.980 -0.291
## bwWhite:sitoVertex:MovementImitative 2.738 18.388 0.149
##
## Correlation of Fixed Effects:
## (Intr) bwWhit stVrtx MvmntI bwWh:V bwW:MI stV:MI
## bwWhite -0.440
## sitoVertex -0.578 0.446
## MovmntImttv -0.181 0.279 0.220
## bwWht:stVrt 0.444 -0.719 -0.593 -0.192
## bwWht:MvmnI 0.128 -0.392 -0.155 -0.706 0.270
## stVrtx:MvmI 0.129 -0.199 -0.307 -0.711 0.269 0.502
## bwWht:sV:MI -0.091 0.279 0.217 0.502 -0.379 -0.711 -0.706isSingular(RT_EXP1)## [1] FALSESvolgo i posthoc
#emmeans/posthoc
emmeans_interaction <- emmeans(RT_EXP1, ~ bw * sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3347)' 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 = 3347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3347)' 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 interactionsemmeans_interaction1 <- emmeans(RT_EXP1, ~ bw * Movement)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3347)' 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 = 3347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3347)' 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 interactionsemmeans_interaction2 <- emmeans(RT_EXP1, ~ Movement * sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3347)' 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 = 3347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3347)' 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 interactionsemmeans_sito <- emmeans(RT_EXP1, ~ sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3347)' 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 = 3347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3347)' 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 interactionsemmeans_bw <- emmeans(RT_EXP1, ~ bw)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3347)' 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 = 3347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3347)' 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 interactionsemmeans_Movement <- emmeans(RT_EXP1, ~ Movement)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3347)' 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 = 3347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3347)' 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 interactionsposthoc_interaction <- pairs(emmeans_interaction, adjust = "bonferroni")
posthoc_interaction1 <- pairs(emmeans_interaction1, adjust = "bonferroni")
posthoc_interaction2 <- pairs(emmeans_interaction2, adjust = "bonferroni")
posthoc_sito <- contrast(emmeans_sito, adjust = "bonferroni")
posthoc_bw <- contrast(emmeans_bw, adjust = "bonferroni")
posthoc_Movement <- contrast(emmeans_Movement, adjust = "bonferroni")
summary(posthoc_interaction)## contrast estimate SE df z.ratio p.value
## Black MPFC - White MPFC 16.99 15.6 Inf 1.092 1.0000
## Black MPFC - Black Vertex 8.39 20.4 Inf 0.411 1.0000
## Black MPFC - White Vertex 41.89 20.2 Inf 2.074 0.2287
## White MPFC - Black Vertex -8.60 19.5 Inf -0.440 1.0000
## White MPFC - White Vertex 24.90 19.9 Inf 1.251 1.0000
## Black Vertex - White Vertex 33.50 15.8 Inf 2.123 0.2026
##
## Results are averaged over the levels of: Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_interaction1)## contrast estimate SE df z.ratio p.value
## Black Complementary - White Complementary 33.32 11.75 Inf 2.835 0.0275
## Black Complementary - Black Imitative 14.40 6.49 Inf 2.218 0.1593
## Black Complementary - White Imitative 31.56 11.74 Inf 2.687 0.0432
## White Complementary - Black Imitative -18.93 11.72 Inf -1.615 0.6383
## White Complementary - White Imitative -1.77 6.51 Inf -0.271 1.0000
## Black Imitative - White Imitative 17.16 11.71 Inf 1.465 0.8572
##
## Results are averaged over the levels of: sito
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_interaction2)## contrast estimate SE df z.ratio p.value
## Complementary MPFC - Imitative MPFC 5.11 6.54 Inf 0.781 1.0000
## Complementary MPFC - Complementary Vertex 15.44 17.31 Inf 0.892 1.0000
## Complementary MPFC - Imitative Vertex 22.96 17.30 Inf 1.328 1.0000
## Imitative MPFC - Complementary Vertex 10.33 17.29 Inf 0.598 1.0000
## Imitative MPFC - Imitative Vertex 17.85 17.28 Inf 1.033 1.0000
## Complementary Vertex - Imitative Vertex 7.52 6.47 Inf 1.163 1.0000
##
## Results are averaged over the levels of: bw
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_sito)## contrast estimate SE df z.ratio p.value
## MPFC effect 8.32 8.34 Inf 0.999 0.6360
## Vertex effect -8.32 8.34 Inf -0.999 0.6360
##
## Results are averaged over the levels of: bw, Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 2 testssummary(posthoc_bw)## contrast estimate SE df z.ratio p.value
## Black effect 12.6 5.4 Inf 2.338 0.0387
## White effect -12.6 5.4 Inf -2.338 0.0387
##
## Results are averaged over the levels of: sito, Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 2 testssummary(posthoc_Movement)## contrast estimate SE df z.ratio p.value
## Complementary effect 3.16 2.3 Inf 1.374 0.3392
## Imitative effect -3.16 2.3 Inf -1.374 0.3392
##
## Results are averaged over the levels of: bw, sito
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 2 testsplotto i dati
pirateplot(formula = Start ~ bw + sito + Movement, #dependent variable ~ independent
data = EXP1, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Reaction Time", xlab = "Experiment 1",#titles for x and y axes
)
##STUDIO 2
EXP2 = read.xlsx("dati_tms_EXP2.xlsx", sheetIndex = 1)Specifico i fattori
EXP2$sito = as.factor(EXP2$sito)
EXP2$bw = as.factor(EXP2$bw)
EXP2$Grasp= as.factor(EXP2$Grasp)
EXP2$Movement = as.factor(EXP2$Movement)
EXP2$Subject = as.factor (EXP2$Subject)#Grasping Asynchrony
ASY_EXP2 = lmer (Asynchrony ~ bw * sito * Movement +
(1|Subject), control = lmerControl(optimizer = "bobyqa"),
data = EXP2)
isSingular(ASY_EXP2)## [1] FALSEsummary(ASY_EXP2)## Linear mixed model fit by REML ['lmerMod']
## Formula: Asynchrony ~ bw * sito * Movement + (1 | Subject)
## Data: EXP2
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 39010.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8506 -0.7425 -0.1798 0.5811 7.1245
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 821.8 28.67
## Residual 13518.8 116.27
## Number of obs: 3160, groups: Subject, 15
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 171.829 9.426 18.230
## bwWhite -17.747 8.295 -2.139
## sitovpm 1.320 8.238 0.160
## MovementImitative -28.946 8.270 -3.500
## bwWhite:sitovpm 12.457 11.721 1.063
## bwWhite:MovementImitative 15.445 11.710 1.319
## sitovpm:MovementImitative 10.838 11.673 0.928
## bwWhite:sitovpm:MovementImitative -16.648 16.549 -1.006
##
## Correlation of Fixed Effects:
## (Intr) bwWhit sitvpm MvmntI bwWht: bwW:MI stv:MI
## bwWhite -0.436
## sitovpm -0.439 0.498
## MovmntImttv -0.437 0.496 0.500
## bwWht:stvpm 0.308 -0.708 -0.703 -0.351
## bwWht:MvmnI 0.308 -0.708 -0.353 -0.706 0.501
## stvpm:MvmnI 0.310 -0.352 -0.706 -0.708 0.496 0.500
## bwWht:st:MI -0.218 0.501 0.498 0.500 -0.708 -0.708 -0.705Anova(ASY_EXP2)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Asynchrony
## Chisq Df Pr(>Chisq)
## bw 3.6852 1 0.05490 .
## sito 4.4940 1 0.03401 *
## Movement 23.3007 1 0.000001385 ***
## bw:sito 0.2462 1 0.61975
## bw:Movement 0.7383 1 0.39020
## sito:Movement 0.0954 1 0.75740
## bw:sito:Movement 1.0120 1 0.31441
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1non ci sono interazioni significative, quindi plotto i dati
pirateplot(formula = Asynchrony ~ bw + sito + Movement, #dependent variable ~ independent
data = EXP2, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Grasping Asynchrony", xlab = "Experiment 2",#titles for x and y axes
)
Guardo main effect sito
pirateplot(formula = Asynchrony ~ sito, #dependent variable ~ independent
data = EXP2, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Grasping Asynchrony", xlab = "Experiment 2",#titles for x and y axes
)
Guardo main effect Movement
pirateplot(formula = Asynchrony ~ Movement, #dependent variable ~ independent
data = EXP2, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Grasping Asynchrony", xlab = "Experiment 2",#titles for x and y axes
)
#Movement Time
MT_EXP2 = lmer(MovTime ~ bw * sito * Movement +
(1+sito*bw|Subject), control = lmerControl(optimizer = "bobyqa"),
data = EXP2)
summary(MT_EXP2)## Linear mixed model fit by REML ['lmerMod']
## Formula: MovTime ~ bw * sito * Movement + (1 + sito * bw | Subject)
## Data: EXP2
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 41817.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6970 -0.6989 -0.0768 0.6399 4.5265
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Subject (Intercept) 29141 170.71
## sitovpm 13913 117.95 -0.68
## bwWhite 1661 40.75 -0.67 0.56
## sitovpm:bwWhite 6872 82.90 0.67 -0.94 -0.44
## Residual 32035 178.98
## Number of obs: 3160, groups: Subject, 15
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1304.871 44.992 29.002
## bwWhite 9.786 16.567 0.591
## sitovpm 5.373 33.007 0.163
## MovementImitative 7.162 12.766 0.561
## bwWhite:sitovpm -28.003 28.021 -0.999
## bwWhite:MovementImitative -10.326 18.042 -0.572
## sitovpm:MovementImitative -12.379 17.986 -0.688
## bwWhite:sitovpm:MovementImitative 23.157 25.484 0.909
##
## Correlation of Fixed Effects:
## (Intr) bwWhit sitvpm MvmntI bwWht: bwW:MI stv:MI
## bwWhite -0.523
## sitovpm -0.666 0.479
## MovmntImttv -0.142 0.386 0.194
## bwWht:stvpm 0.568 -0.565 -0.837 -0.228
## bwWht:MvmnI 0.100 -0.548 -0.137 -0.707 0.324
## stvpm:MvmnI 0.101 -0.274 -0.273 -0.710 0.321 0.502
## bwWht:st:MI -0.071 0.388 0.192 0.501 -0.458 -0.708 -0.706isSingular(MT_EXP2)## [1] FALSEAnova(MT_EXP2)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: MovTime
## Chisq Df Pr(>Chisq)
## bw 0.0001 1 0.9903
## sito 1.5579 1 0.2120
## Movement 0.0604 1 0.8059
## bw:sito 0.4304 1 0.5118
## bw:Movement 0.0101 1 0.9200
## sito:Movement 0.0044 1 0.9472
## bw:sito:Movement 0.8257 1 0.3635Svolgo i posthoc
#emmeans/posthoc
emmeans_interaction <- emmeans(MT_EXP2, ~ bw * sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3160' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3160)' 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 = 3160' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3160)' 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 interactionsemmeans_interaction1 <- emmeans(MT_EXP2, ~ bw * Movement)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3160' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3160)' 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 = 3160' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3160)' 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 interactionsemmeans_interaction2 <- emmeans(MT_EXP2, ~ Movement * sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3160' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3160)' 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 = 3160' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3160)' 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 interactionsposthoc_interaction <- pairs(emmeans_interaction, adjust = "bonferroni")
posthoc_interaction1 <- pairs(emmeans_interaction1, adjust = "bonferroni")
posthoc_interaction2 <- pairs(emmeans_interaction2, adjust = "bonferroni")
summary(posthoc_interaction)## contrast estimate SE df z.ratio p.value
## Black v - White v -4.623 13.9 Inf -0.334 1.0000
## Black v - Black vpm 0.816 31.8 Inf 0.026 1.0000
## Black v - White vpm 12.618 22.7 Inf 0.555 1.0000
## White v - Black vpm 5.439 27.5 Inf 0.197 1.0000
## White v - White vpm 17.241 15.6 Inf 1.107 1.0000
## Black vpm - White vpm 11.802 21.3 Inf 0.555 1.0000
##
## Results are averaged over the levels of: Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_interaction1)## contrast estimate SE df z.ratio p.value
## Black Complementary - White Complementary 4.216 14.44 Inf 0.292 1.0000
## Black Complementary - Black Imitative -0.973 8.99 Inf -0.108 1.0000
## Black Complementary - White Imitative 1.991 14.41 Inf 0.138 1.0000
## White Complementary - Black Imitative -5.188 14.44 Inf -0.359 1.0000
## White Complementary - White Imitative -2.225 9.03 Inf -0.246 1.0000
## Black Imitative - White Imitative 2.964 14.40 Inf 0.206 1.0000
##
## Results are averaged over the levels of: sito
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_interaction2)## contrast estimate SE df z.ratio p.value
## Complementary v - Imitative v -2.00 9.02 Inf -0.222 1.0000
## Complementary v - Complementary vpm 8.63 22.62 Inf 0.382 1.0000
## Complementary v - Imitative vpm 7.43 22.60 Inf 0.329 1.0000
## Imitative v - Complementary vpm 10.63 22.60 Inf 0.470 1.0000
## Imitative v - Imitative vpm 9.43 22.59 Inf 0.417 1.0000
## Complementary vpm - Imitative vpm -1.20 9.00 Inf -0.133 1.0000
##
## Results are averaged over the levels of: bw
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testscontrollo la distribuzione dei residui del modello
res = residuals(MT_EXP2)
plot(density(res))
plot(MT_EXP2)
qqnorm(residuals(MT_EXP2))
qqline(residuals(MT_EXP2))
plotto i dati
pirateplot(formula = MovTime ~ bw + sito + Movement, #dependent variable ~ independent
data = EXP2, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Movement Time", xlab = "Experiment 2",#titles for x and y axes
)
#Start - RT
RT_EXP2 = lmer(Start ~ bw * sito * Movement +
(1+sito*bw|Subject), control = lmerControl(optimizer = "bobyqa"),
data = EXP2)
summary(RT_EXP2)## Linear mixed model fit by REML ['lmerMod']
## Formula: Start ~ bw * sito * Movement + (1 + sito * bw | Subject)
## Data: EXP2
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 37649.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8586 -0.6389 -0.0617 0.5418 4.9565
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Subject (Intercept) 12071.8 109.87
## sitovpm 9977.2 99.89 -0.69
## bwWhite 763.7 27.64 -0.23 0.55
## sitovpm:bwWhite 6082.0 77.99 0.29 -0.71 -0.83
## Residual 10081.8 100.41
## Number of obs: 3114, groups: Subject, 15
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 274.408 28.821 9.521
## bwWhite -7.326 10.163 -0.721
## sitovpm 21.584 26.771 0.806
## MovementImitative -18.340 7.197 -2.548
## bwWhite:sitovpm 16.362 22.583 0.725
## bwWhite:MovementImitative 10.445 10.200 1.024
## sitovpm:MovementImitative 5.852 10.153 0.576
## bwWhite:sitovpm:MovementImitative -27.739 14.406 -1.926
##
## Correlation of Fixed Effects:
## (Intr) bwWhit sitvpm MvmntI bwWht: bwW:MI stv:MI
## bwWhite -0.244
## sitovpm -0.687 0.467
## MovmntImttv -0.125 0.354 0.134
## bwWht:stvpm 0.293 -0.749 -0.695 -0.159
## bwWht:MvmnI 0.088 -0.505 -0.095 -0.706 0.227
## stvpm:MvmnI 0.088 -0.251 -0.190 -0.709 0.225 0.500
## bwWht:st:MI -0.062 0.358 0.134 0.500 -0.321 -0.708 -0.705isSingular(RT_EXP2)## [1] FALSEAnova(RT_EXP2)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Start
## Chisq Df Pr(>Chisq)
## bw 0.0558 1 0.81327
## sito 2.0054 1 0.15674
## Movement 22.5716 1 0.000002025 ***
## bw:sito 0.0125 1 0.91104
## bw:Movement 0.2308 1 0.63092
## sito:Movement 1.2110 1 0.27114
## bw:sito:Movement 3.7078 1 0.05416 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1controllo la distribuzione dei residui del modello
res = residuals(RT_EXP2)
plot(density(res))
plot(RT_EXP2)
qqnorm(residuals(RT_EXP2))
qqline(residuals(RT_EXP2))
Svolgo i posthoc
#emmeans/posthoc
emmeans_interaction <- emmeans(RT_EXP2, ~ bw * sito * Movement)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3114' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3114)' 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 = 3114' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3114)' or larger];
## but be warned that this may result in large computation time and memory use.posthoc_interaction <- pairs(emmeans_interaction, adjust = "bonferroni")
summary(posthoc_interaction)## contrast estimate SE df z.ratio
## Black v Complementary - White v Complementary 7.326 10.16 Inf 0.721
## Black v Complementary - Black vpm Complementary -21.584 26.77 Inf -0.806
## Black v Complementary - White vpm Complementary -30.620 19.99 Inf -1.532
## Black v Complementary - Black v Imitative 18.340 7.20 Inf 2.548
## Black v Complementary - White v Imitative 15.220 10.13 Inf 1.503
## Black v Complementary - Black vpm Imitative -9.096 26.77 Inf -0.340
## Black v Complementary - White vpm Imitative -0.839 19.97 Inf -0.042
## White v Complementary - Black vpm Complementary -28.909 23.79 Inf -1.215
## White v Complementary - White vpm Complementary -37.946 19.66 Inf -1.931
## White v Complementary - Black v Imitative 11.014 10.16 Inf 1.084
## White v Complementary - White v Imitative 7.895 7.23 Inf 1.092
## White v Complementary - Black vpm Imitative -16.422 23.79 Inf -0.690
## White v Complementary - White vpm Imitative -8.164 19.64 Inf -0.416
## Black vpm Complementary - White vpm Complementary -9.037 16.41 Inf -0.551
## Black vpm Complementary - Black v Imitative 39.924 26.77 Inf 1.491
## Black vpm Complementary - White v Imitative 36.804 23.77 Inf 1.548
## Black vpm Complementary - Black vpm Imitative 12.488 7.16 Inf 1.744
## Black vpm Complementary - White vpm Imitative 20.745 16.39 Inf 1.266
## White vpm Complementary - Black v Imitative 48.960 19.99 Inf 2.449
## White vpm Complementary - White v Imitative 45.841 19.64 Inf 2.335
## White vpm Complementary - Black vpm Imitative 21.524 16.41 Inf 1.312
## White vpm Complementary - White vpm Imitative 29.782 7.22 Inf 4.122
## Black v Imitative - White v Imitative -3.120 10.13 Inf -0.308
## Black v Imitative - Black vpm Imitative -27.436 26.77 Inf -1.025
## Black v Imitative - White vpm Imitative -19.179 19.97 Inf -0.960
## White v Imitative - Black vpm Imitative -24.316 23.77 Inf -1.023
## White v Imitative - White vpm Imitative -16.059 19.62 Inf -0.819
## Black vpm Imitative - White vpm Imitative 8.257 16.39 Inf 0.504
## p.value
## 1.0000
## 1.0000
## 1.0000
## 0.3031
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 0.4011
## 0.5479
## 1.0000
## 0.0011
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
## 1.0000
##
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 28 testsCalcolo medie e ds
summary_statsRT_EXP2 <- EXP2 %>%
group_by(Movement) %>%
summarise(
mean_MovTime = mean(Start, na.rm = TRUE), # Calcola la media
sd_MovTime = sd(Start, na.rm = TRUE) # Calcola la deviazione standard
)plotto i dati
pirateplot(formula = Start ~ bw + sito + Movement, #dependent variable ~ independent
data = EXP2, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Reaction Time", xlab = "Experiment 2",#titles for x and y axes
)
plotto il main effect
pirateplot(formula = Start ~ Movement, #dependent variable ~ independent
data = EXP2, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Reaction Time", xlab = "Experiment 2",#titles for x and y axes
)
##STUDIO 3
EXP3 = read.xlsx("dati_tms_EXP3.xlsx", sheetIndex = 1)Specifico i fattori
EXP3$sito = as.factor(EXP3$sito)
EXP3$bw = as.factor(EXP3$bw)
EXP3$Grasp= as.factor(EXP3$Grasp)
EXP3$Movement = as.factor(EXP3$Movement)
EXP3$Subject = as.factor (EXP3$Subject)#Grasping Asynchrony
ASY_EXP3 = lmer (Asynchrony ~ bw * sito * Movement +
(1+bw * sito|Subject), control = lmerControl(optimizer = "bobyqa"),
data = EXP3)
isSingular(ASY_EXP3)## [1] FALSEsummary(ASY_EXP3)## Linear mixed model fit by REML ['lmerMod']
## Formula: Asynchrony ~ bw * sito * Movement + (1 + bw * sito | Subject)
## Data: EXP3
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 61041.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9298 -0.7579 -0.1685 0.5869 7.4976
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Subject (Intercept) 1088.2 32.99
## bwWhite 322.7 17.97 -0.41
## sitovertex 559.8 23.66 -0.18 0.58
## bwWhite:sitovertex 1131.8 33.64 0.51 -0.37 -0.43
## Residual 11209.6 105.88
## Number of obs: 5012, groups: Subject, 24
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 149.519 7.948 18.812
## bwWhite -2.986 7.019 -0.425
## sitovertex -2.733 7.786 -0.351
## MovementImitative -12.137 5.981 -2.029
## bwWhite:sitovertex 13.503 10.961 1.232
## bwWhite:MovementImitative 3.288 8.445 0.389
## sitovertex:MovementImitative 5.284 8.551 0.618
## bwWhite:sitovertex:MovementImitative -2.876 11.974 -0.240
##
## Correlation of Fixed Effects:
## (Intr) bwWhit stvrtx MvmntI bwWht: bwW:MI stv:MI
## bwWhite -0.500
## sitovertex -0.383 0.514
## MovmntImttv -0.374 0.424 0.382
## bwWht:stvrt 0.473 -0.588 -0.604 -0.272
## bwWht:MvmnI 0.265 -0.604 -0.271 -0.708 0.386
## stvrtx:MvmI 0.262 -0.297 -0.549 -0.700 0.390 0.495
## bwWht:st:MI -0.187 0.426 0.392 0.500 -0.548 -0.705 -0.714Svolgo i posthoc
#emmeans/posthoc
emmeans_interaction <- emmeans(ASY_EXP3, ~ bw * sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5012' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5012)' 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 = 5012' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5012)' 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 interactionsemmeans_interaction1 <- emmeans(ASY_EXP3, ~ bw * Movement)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5012' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5012)' 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 = 5012' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5012)' 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 interactionsemmeans_interaction2 <- emmeans(ASY_EXP3, ~ Movement * sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5012' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5012)' 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 = 5012' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5012)' 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 interactionsemmeans_sito <- emmeans(ASY_EXP3, ~ sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5012' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5012)' 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 = 5012' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5012)' 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 interactionsemmeans_bw <- emmeans(ASY_EXP3, ~ bw)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5012' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5012)' 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 = 5012' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5012)' 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 interactionsemmeans_Movement <- emmeans(ASY_EXP3, ~ Movement)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5012' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5012)' 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 = 5012' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5012)' 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 interactionsposthoc_interaction <- pairs(emmeans_interaction, adjust = "bonferroni")
posthoc_interaction1 <- pairs(emmeans_interaction1, adjust = "bonferroni")
posthoc_interaction2 <- pairs(emmeans_interaction2, adjust = "bonferroni")
posthoc_sito <- contrast(emmeans_sito, adjust = "bonferroni")
posthoc_bw <- contrast(emmeans_bw, adjust = "bonferroni")
posthoc_Movement <- contrast(emmeans_Movement, adjust = "bonferroni")
summary(posthoc_interaction)## contrast estimate SE df z.ratio p.value
## Black mpfc - White mpfc 1.3414 5.60 Inf 0.240 1.0000
## Black mpfc - Black vertex 0.0913 6.51 Inf 0.014 1.0000
## Black mpfc - White vertex -10.6319 8.66 Inf -1.227 1.0000
## White mpfc - Black vertex -1.2501 5.93 Inf -0.211 1.0000
## White mpfc - White vertex -11.9733 7.73 Inf -1.549 0.7286
## Black vertex - White vertex -10.7232 7.80 Inf -1.375 1.0000
##
## Results are averaged over the levels of: Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_interaction1)## contrast estimate SE df z.ratio p.value
## Black Complementary - White Complementary -3.77 5.84 Inf -0.645 1.0000
## Black Complementary - Black Imitative 9.49 4.28 Inf 2.221 0.1582
## Black Complementary - White Imitative 3.88 5.83 Inf 0.665 1.0000
## White Complementary - Black Imitative 13.26 5.84 Inf 2.271 0.1388
## White Complementary - White Imitative 7.64 4.19 Inf 1.824 0.4090
## Black Imitative - White Imitative -5.62 5.83 Inf -0.964 1.0000
##
## Results are averaged over the levels of: sito
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_interaction2)## contrast estimate SE df z.ratio p.value
## Complementary mpfc - Imitative mpfc 10.49 4.22 Inf 2.485 0.0777
## Complementary mpfc - Complementary vertex -4.02 6.25 Inf -0.643 1.0000
## Complementary mpfc - Imitative vertex 2.63 6.24 Inf 0.421 1.0000
## Imitative mpfc - Complementary vertex -14.51 6.25 Inf -2.323 0.1211
## Imitative mpfc - Imitative vertex -7.86 6.24 Inf -1.260 1.0000
## Complementary vertex - Imitative vertex 6.65 4.24 Inf 1.566 0.7041
##
## Results are averaged over the levels of: bw
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_sito)## contrast estimate SE df z.ratio p.value
## mpfc effect -2.97 2.74 Inf -1.084 0.5569
## vertex effect 2.97 2.74 Inf 1.084 0.5569
##
## Results are averaged over the levels of: bw, Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 2 testssummary(posthoc_bw)## contrast estimate SE df z.ratio p.value
## Black effect -2.35 2.5 Inf -0.937 0.6976
## White effect 2.35 2.5 Inf 0.937 0.6976
##
## Results are averaged over the levels of: sito, Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 2 testssummary(posthoc_Movement)## contrast estimate SE df z.ratio p.value
## Complementary effect 4.28 1.5 Inf 2.863 0.0084
## Imitative effect -4.28 1.5 Inf -2.863 0.0084
##
## Results are averaged over the levels of: bw, sito
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 2 testsplotto i dati
pirateplot(formula = Asynchrony ~ bw + sito + Movement, #dependent variable ~ independent
data = EXP3, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Grasping Asynchrony", xlab = "Experiment 3",#titles for x and y axes
)
#Movement Time
MT_EXP3 = lmer(MovTime ~ bw * sito * Movement +
(1+sito*bw|Subject), control = lmerControl(optimizer = "bobyqa"),
data = EXP3)
summary(MT_EXP3)## Linear mixed model fit by REML ['lmerMod']
## Formula: MovTime ~ bw * sito * Movement + (1 + sito * bw | Subject)
## Data: EXP3
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 66980.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0898 -0.7053 -0.1120 0.6362 5.8623
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Subject (Intercept) 15368 123.97
## sitovertex 8719 93.37 -0.37
## bwWhite 3480 58.99 -0.03 0.72
## sitovertex:bwWhite 6423 80.14 0.36 -0.72 -0.71
## Residual 34252 185.07
## Number of obs: 5033, groups: Subject, 24
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1211.803 26.355 45.980
## bwWhite 8.849 15.930 0.556
## sitovertex -3.914 21.878 -0.179
## MovementImitative -15.514 10.435 -1.487
## bwWhite:sitovertex -6.802 22.169 -0.307
## bwWhite:MovementImitative 8.317 14.719 0.565
## sitovertex:MovementImitative 8.291 14.945 0.555
## bwWhite:sitovertex:MovementImitative -9.453 20.890 -0.453
##
## Correlation of Fixed Effects:
## (Intr) bwWhit stvrtx MvmntI bwWht: bwW:MI stv:MI
## bwWhite -0.149
## sitovertex -0.403 0.629
## MovmntImttv -0.197 0.326 0.237
## bwWht:stvrt 0.351 -0.705 -0.701 -0.234
## bwWht:MvmnI 0.139 -0.463 -0.168 -0.709 0.333
## stvrtx:MvmI 0.137 -0.227 -0.341 -0.698 0.337 0.495
## bwWht:st:MI -0.098 0.326 0.244 0.500 -0.473 -0.705 -0.715isSingular(MT_EXP3)## [1] FALSESvolgo i posthoc
#emmeans/posthoc
emmeans_interaction <- emmeans(MT_EXP3, ~ bw * sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5033' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5033)' 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 = 5033' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5033)' 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 interactionsemmeans_interaction1 <- emmeans(MT_EXP3, ~ bw * Movement)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5033' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5033)' 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 = 5033' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5033)' 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 interactionsemmeans_interaction2 <- emmeans(MT_EXP3, ~ Movement * sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5033' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5033)' 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 = 5033' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5033)' 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 interactionsemmeans_sito <- emmeans(MT_EXP3, ~ sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5033' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5033)' 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 = 5033' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5033)' 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 interactionsemmeans_bw <- emmeans(MT_EXP3, ~ bw)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5033' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5033)' 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 = 5033' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5033)' 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 interactionsemmeans_Movement <- emmeans(MT_EXP3, ~ Movement)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 5033' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 5033)' 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 = 5033' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 5033)' 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 interactionsposthoc_interaction <- pairs(emmeans_interaction, adjust = "bonferroni")
posthoc_interaction1 <- pairs(emmeans_interaction1, adjust = "bonferroni")
posthoc_interaction2 <- pairs(emmeans_interaction2, adjust = "bonferroni")
posthoc_sito <- contrast(emmeans_sito, adjust = "bonferroni")
posthoc_bw <- contrast(emmeans_bw, adjust = "bonferroni")
posthoc_Movement <- contrast(emmeans_Movement, adjust = "bonferroni")
summary(posthoc_interaction)## contrast estimate SE df z.ratio p.value
## Black mpfc - White mpfc -13.007 14.1 Inf -0.921 1.0000
## Black mpfc - Black vertex -0.232 20.6 Inf -0.011 1.0000
## Black mpfc - White vertex -1.711 20.7 Inf -0.082 1.0000
## White mpfc - Black vertex 12.776 15.4 Inf 0.828 1.0000
## White mpfc - White vertex 11.297 15.4 Inf 0.736 1.0000
## Black vertex - White vertex -1.479 13.9 Inf -0.107 1.0000
##
## Results are averaged over the levels of: Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_interaction1)## contrast estimate SE df z.ratio p.value
## Black Complementary - White Complementary -5.45 11.30 Inf -0.482 1.0000
## Black Complementary - Black Imitative 11.37 7.47 Inf 1.521 0.7690
## Black Complementary - White Imitative 2.33 11.28 Inf 0.206 1.0000
## White Complementary - Black Imitative 16.82 11.30 Inf 1.488 0.8204
## White Complementary - White Imitative 7.78 7.30 Inf 1.066 1.0000
## Black Imitative - White Imitative -9.04 11.29 Inf -0.801 1.0000
##
## Results are averaged over the levels of: sito
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_interaction2)## contrast estimate SE df z.ratio p.value
## Complementary mpfc - Imitative mpfc 11.36 7.36 Inf 1.543 0.7370
## Complementary mpfc - Complementary vertex 7.31 16.17 Inf 0.452 1.0000
## Complementary mpfc - Imitative vertex 15.11 16.16 Inf 0.935 1.0000
## Imitative mpfc - Complementary vertex -4.04 16.16 Inf -0.250 1.0000
## Imitative mpfc - Imitative vertex 3.75 16.15 Inf 0.232 1.0000
## Complementary vertex - Imitative vertex 7.79 7.41 Inf 1.051 1.0000
##
## Results are averaged over the levels of: bw
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_sito)## contrast estimate SE df z.ratio p.value
## mpfc effect 2.77 7.65 Inf 0.362 1.0000
## vertex effect -2.77 7.65 Inf -0.362 1.0000
##
## Results are averaged over the levels of: bw, Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 2 testssummary(posthoc_bw)## contrast estimate SE df z.ratio p.value
## Black effect -3.62 5.01 Inf -0.723 0.9389
## White effect 3.62 5.01 Inf 0.723 0.9389
##
## Results are averaged over the levels of: sito, Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 2 testssummary(posthoc_Movement)## contrast estimate SE df z.ratio p.value
## Complementary effect 4.79 2.61 Inf 1.833 0.1336
## Imitative effect -4.79 2.61 Inf -1.833 0.1336
##
## Results are averaged over the levels of: bw, sito
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 2 testsplotto i dati
pirateplot(formula = MovTime ~ bw + sito + Movement, #dependent variable ~ independent
data = EXP3, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Movement Time", xlab = "Experiment 3",#titles for x and y axes
)
#Start - RT
RT_EXP3 = lmer(Start ~ bw * sito * Movement +
(1+sito*bw|Subject), control = lmerControl(optimizer = "bobyqa"),
data = EXP3)
summary(RT_EXP3)## Linear mixed model fit by REML ['lmerMod']
## Formula: Start ~ bw * sito * Movement + (1 + sito * bw | Subject)
## Data: EXP3
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 61284.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9067 -0.6195 -0.0898 0.5406 4.8416
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Subject (Intercept) 11442 106.97
## sitovertex 6791 82.41 -0.21
## bwWhite 2726 52.21 -0.13 0.52
## sitovertex:bwWhite 3861 62.13 -0.33 -0.27 -0.42
## Residual 12450 111.58
## Number of obs: 4977, groups: Subject, 24
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 389.944 22.287 17.496
## bwWhite -24.178 12.396 -1.950
## sitovertex -9.828 18.107 -0.543
## MovementImitative 4.427 6.324 0.700
## bwWhite:sitovertex 20.760 15.675 1.324
## bwWhite:MovementImitative -3.962 8.922 -0.444
## sitovertex:MovementImitative 5.207 9.059 0.575
## bwWhite:sitovertex:MovementImitative -4.162 12.667 -0.329
##
## Correlation of Fixed Effects:
## (Intr) bwWhit stvrtx MvmntI bwWht: bwW:MI stv:MI
## bwWhite -0.181
## sitovertex -0.245 0.502
## MovmntImttv -0.141 0.254 0.174
## bwWht:stvrt -0.201 -0.499 -0.362 -0.201
## bwWht:MvmnI 0.100 -0.362 -0.123 -0.709 0.286
## stvrtx:MvmI 0.099 -0.177 -0.251 -0.698 0.290 0.495
## bwWht:st:MI -0.071 0.255 0.179 0.499 -0.406 -0.704 -0.715isSingular(RT_EXP3)## [1] FALSESvolgo i posthoc
#emmeans/posthoc
emmeans_interaction <- emmeans(RT_EXP3, ~ bw * sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 4977' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 4977)' 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 = 4977' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 4977)' 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 interactionsemmeans_interaction1 <- emmeans(RT_EXP3, ~ bw * Movement)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 4977' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 4977)' 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 = 4977' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 4977)' 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 interactionsemmeans_interaction2 <- emmeans(RT_EXP3, ~ Movement * sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 4977' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 4977)' 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 = 4977' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 4977)' 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 interactionsemmeans_sito <- emmeans(RT_EXP3, ~ sito)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 4977' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 4977)' 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 = 4977' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 4977)' 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 interactionsemmeans_bw <- emmeans(RT_EXP3, ~ bw)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 4977' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 4977)' 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 = 4977' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 4977)' 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 interactionsemmeans_Movement <- emmeans(RT_EXP3, ~ Movement)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 4977' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 4977)' 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 = 4977' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 4977)' 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 interactionsposthoc_interaction <- pairs(emmeans_interaction, adjust = "bonferroni")
posthoc_interaction1 <- pairs(emmeans_interaction1, adjust = "bonferroni")
posthoc_interaction2 <- pairs(emmeans_interaction2, adjust = "bonferroni")
posthoc_sito <- contrast(emmeans_sito, adjust = "bonferroni")
posthoc_bw <- contrast(emmeans_bw, adjust = "bonferroni")
posthoc_Movement <- contrast(emmeans_Movement, adjust = "bonferroni")
summary(posthoc_interaction)## contrast estimate SE df z.ratio p.value
## Black mpfc - White mpfc 26.16 11.6 Inf 2.263 0.1417
## Black mpfc - Black vertex 7.22 17.5 Inf 0.412 1.0000
## Black mpfc - White vertex 14.70 23.1 Inf 0.636 1.0000
## White mpfc - Black vertex -18.93 15.3 Inf -1.234 1.0000
## White mpfc - White vertex -11.45 18.7 Inf -0.614 1.0000
## Black vertex - White vertex 7.48 13.6 Inf 0.550 1.0000
##
## Results are averaged over the levels of: Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_interaction1)## contrast estimate SE df z.ratio p.value
## Black Complementary - White Complementary 13.798 10.87 Inf 1.269 1.0000
## Black Complementary - Black Imitative -7.031 4.53 Inf -1.552 0.7235
## Black Complementary - White Imitative 12.810 10.87 Inf 1.179 1.0000
## White Complementary - Black Imitative -20.829 10.87 Inf -1.916 0.3319
## White Complementary - White Imitative -0.988 4.43 Inf -0.223 1.0000
## Black Imitative - White Imitative 19.841 10.86 Inf 1.826 0.4069
##
## Results are averaged over the levels of: sito
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_interaction2)## contrast estimate SE df z.ratio p.value
## Complementary mpfc - Imitative mpfc -2.446 4.46 Inf -0.548 1.0000
## Complementary mpfc - Complementary vertex -0.552 16.93 Inf -0.033 1.0000
## Complementary mpfc - Imitative vertex -6.124 16.93 Inf -0.362 1.0000
## Imitative mpfc - Complementary vertex 1.895 16.93 Inf 0.112 1.0000
## Imitative mpfc - Imitative vertex -3.678 16.93 Inf -0.217 1.0000
## Complementary vertex - Imitative vertex -5.573 4.50 Inf -1.240 1.0000
##
## Results are averaged over the levels of: bw
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 6 testssummary(posthoc_sito)## contrast estimate SE df z.ratio p.value
## mpfc effect -1.06 8.31 Inf -0.127 1.0000
## vertex effect 1.06 8.31 Inf 0.127 1.0000
##
## Results are averaged over the levels of: bw, Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 2 testssummary(posthoc_bw)## contrast estimate SE df z.ratio p.value
## Black effect 8.41 5.2 Inf 1.618 0.2114
## White effect -8.41 5.2 Inf -1.618 0.2114
##
## Results are averaged over the levels of: sito, Movement
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 2 testssummary(posthoc_Movement)## contrast estimate SE df z.ratio p.value
## Complementary effect -2 1.58 Inf -1.266 0.4109
## Imitative effect 2 1.58 Inf 1.266 0.4109
##
## Results are averaged over the levels of: bw, sito
## Degrees-of-freedom method: asymptotic
## P value adjustment: bonferroni method for 2 testsplotto i dati
pirateplot(formula = Start ~ bw + sito + Movement, #dependent variable ~ independent
data = EXP3, #data
theme = 3, #set theme
gl.col = "white",
ylab = "Reaction Time", xlab = "Experiment 3",#titles for x and y axes
)