knitr::opts_chunk$set(include = FALSE)
#erstmal alles aufraeumen
#rm(list=ls())
#install.packages(ez)
library(ez)
#roundEZ
roundEZ=function(res,digits = 5){
cols=names(res$ANOVA)
cols=cols[!cols=="p<.05" &!cols=="Effect"]
for (icol in cols){
res$ANOVA[[icol]]=round(res$ANOVA[[icol]],digits = digits)
}
cols=names(res$"Mauchly's Test for Sphericity")
cols=cols[!cols=="p<.05" &!cols=="Effect"]
for (icol in cols){
res$"Mauchly's Test for Sphericity"[[icol]]=round(res$"Mauchly's Test for Sphericity"[[icol]],digits = digits)
}
cols=names(res$"Sphericity Corrections")
cols=cols[!cols=="p[GG]<.05" &!cols=="p[HF]<.05" &!cols=="Effect"]
for (icol in cols){
res$"Sphericity Corrections"[[icol]]=round(res$"Sphericity Corrections"[[icol]],digits = digits)
}
return(res)
}require("knitr")
opts_knit$set(root.dir = "/Users/Lea/Documents/EEG/Daten")#Pfad festlegen
opts_knit$set(root.dir = "/Users/Lea/Documents/EEG")
#Die Daten einlesen
daten_rand = read.table("/Users/Lea/Documents/EEG/Lea_MMN_randmeanamps_neu.txt",
header = TRUE)
#Im Long-Format; Format der Namen: Elektrode_Stimulustyp_Bedingung
daten2_rand = reshape(daten_rand, direction = "long",
varying = 1:10, v.names = "Voltage",
timevar = c("Elektrode","Stimulustyp","Bedingung"), sep = "_")
#Keine Automatische Erkennung der Variablenauspruegungen:
#deswegen muss man selbst Hand anlegen
daten2_rand$Stimulustyp = rep(c(rep("STA",max(daten2_rand$id)),
rep("DEV",max(daten2_rand$id))),
ncol(daten_rand)/2)
daten2_rand$Elektrode = c(rep("Fz",2*max(daten2_rand$id)),
rep("Cz",2*max(daten2_rand$id)),
rep("Pz",2*max(daten2_rand$id)),
rep("M1",2*max(daten2_rand$id)),rep("M2",2*max(daten2_rand$id)))
daten2_rand$Bedingung = "rand"
#Bedeutungslose ID-Variable loeschen, damit man sie nicht
#mehr mit der VP-Nummer verwechseln kann
daten2_rand=daten2_rand[,-c(ncol(daten2_rand))]
#Einlesen der Daten der predictable Bedingung
opts_knit$set(root.dir = "/Users/Lea/Documents/EEG")
#Die Daten einlesen
daten3_pred = read.table("/Users/Lea/Documents/EEG/Lea_MMN_predmeanamps_neu.txt",
header = TRUE)
#Im Long-Format; Format der Namen: Elektrode_Stimulustyp_Bedingung
daten4_pred = reshape(daten3_pred, direction = "long",
varying = 1:10, v.names = "Voltage",
timevar = c("Elektrode","Stimulustyp","Bedingung"), sep = "_")
#Keine Automatische Erkennung der Variablenauspraegungen:
#deswegen muss man selbst Hand anlegen
daten4_pred$Stimulustyp = rep(c(rep("STA",max(daten4_pred$id)),
rep("DEV",max(daten4_pred$id))),
ncol(daten3_pred)/2)
daten4_pred$Elektrode = c(rep("Fz",2*max(daten4_pred$id)),
rep("Cz",2*max(daten4_pred$id)),
rep("Pz",2*max(daten4_pred$id)), rep("M1",2*max(daten4_pred$id)),rep("M2",2*max(daten4_pred$id)))
daten4_pred$Bedingung = "pred"
#Bedeutungslose ID-Variable loeschen, damit man sie nicht
#mehr mit der VP-Nummer verwechseln kann
daten4_pred=daten4_pred[,-c(ncol(daten4_pred))]
#Daten in einer Datei
daten_all=rbind(daten2_rand, daten4_pred)#Erzeugung einer neuen Variable
daten_all$VP= rep(1:22,20)
library('ez')
ezANOVA (data=daten_all, dv = Voltage, wid = VP,
within_full = .(Elektrode, Stimulustyp, Bedingung),
within=.(Elektrode, Stimulustyp, Bedingung))## $ANOVA
## Effect DFn DFd F p p<.05
## 2 Elektrode 4 84 10.1352251 9.618648e-07 *
## 3 Stimulustyp 1 21 2.3257183 1.421714e-01
## 4 Bedingung 1 21 0.3204668 5.773273e-01
## 5 Elektrode:Stimulustyp 4 84 0.8845274 4.768910e-01
## 6 Elektrode:Bedingung 4 84 0.2164807 9.285886e-01
## 7 Stimulustyp:Bedingung 1 21 3.1810681 8.895619e-02
## 8 Elektrode:Stimulustyp:Bedingung 4 84 2.6996360 3.600030e-02 *
## ges
## 2 0.0560029143
## 3 0.0204510867
## 4 0.0004317956
## 5 0.0102981231
## 6 0.0005322160
## 7 0.0120015406
## 8 0.0110339608
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 2 Elektrode 0.082088291 2.325113e-07 *
## 5 Elektrode:Stimulustyp 0.001384991 6.729087e-23 *
## 6 Elektrode:Bedingung 0.091026169 5.449620e-07 *
## 8 Elektrode:Stimulustyp:Bedingung 0.052530598 5.559529e-09 *
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05
## 2 Elektrode 0.5764064 0.0001080591 *
## 5 Elektrode:Stimulustyp 0.3109219 0.3781916376
## 6 Elektrode:Bedingung 0.5868177 0.8391389044
## 8 Elektrode:Stimulustyp:Bedingung 0.5254914 0.0757745400
## HFe p[HF] p[HF]<.05
## 2 0.6515832 4.642922e-05 *
## 5 0.3209244 3.811152e-01
## 6 0.6653176 8.632331e-01
## 8 0.5852889 6.892936e-02res=ezANOVA(data = daten_all, dv = Voltage, wid = VP,
type = 3, within = .(Elektrode, Stimulustyp, Bedingung))
roundEZ(res,digits = 5)## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Elektrode 4 84 10.13523 0.00000 * 0.05600
## 3 Stimulustyp 1 21 2.32572 0.14217 0.02045
## 4 Bedingung 1 21 0.32047 0.57733 0.00043
## 5 Elektrode:Stimulustyp 4 84 0.88453 0.47689 0.01030
## 6 Elektrode:Bedingung 4 84 0.21648 0.92859 0.00053
## 7 Stimulustyp:Bedingung 1 21 3.18107 0.08896 0.01200
## 8 Elektrode:Stimulustyp:Bedingung 4 84 2.69964 0.03600 * 0.01103
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 2 Elektrode 0.08209 0 *
## 5 Elektrode:Stimulustyp 0.00138 0 *
## 6 Elektrode:Bedingung 0.09103 0 *
## 8 Elektrode:Stimulustyp:Bedingung 0.05253 0 *
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe
## 2 Elektrode 0.57641 0.00011 * 0.65158
## 5 Elektrode:Stimulustyp 0.31092 0.37819 0.32092
## 6 Elektrode:Bedingung 0.58682 0.83914 0.66532
## 8 Elektrode:Stimulustyp:Bedingung 0.52549 0.07577 0.58529
## p[HF] p[HF]<.05
## 2 0.00005 *
## 5 0.38112
## 6 0.86323
## 8 0.06893res = ezANOVA(data = daten_all[daten_all$Bedingung=='pred',],
dv = Voltage, wid = VP,
type = 3, within = .(Elektrode, Stimulustyp))
roundEZ(res,digits = 5)## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Elektrode 4 84 4.68504 0.00184 * 0.04051
## 3 Stimulustyp 1 21 0.07332 0.78920 0.00096
## 4 Elektrode:Stimulustyp 4 84 0.14962 0.96268 0.00235
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 2 Elektrode 0.04933 0 *
## 4 Elektrode:Stimulustyp 0.00628 0 *
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF]
## 2 Elektrode 0.56852 0.01091 * 0.64122 0.00805
## 4 Elektrode:Stimulustyp 0.33014 0.77090 0.34367 0.78035
## p[HF]<.05
## 2 *
## 4res = ezANOVA(data = daten_all[daten_all$Bedingung=='rand',],
dv = Voltage, wid = VP,
type = 3, detailed = TRUE, within = .(Elektrode, Stimulustyp))
roundEZ(res, digits = 5)## $ANOVA
## Effect DFn DFd SSn SSd F p p<.05
## 1 (Intercept) 1 21 0.00695 18.29704 0.00798 0.92967
## 2 Elektrode 4 84 6.46263 11.11217 12.21321 0.00000 *
## 3 Stimulustyp 1 21 6.17963 19.13661 6.78136 0.01657 *
## 4 Elektrode:Stimulustyp 4 84 3.83232 24.93524 3.22751 0.01627 *
## ges
## 1 0.00009
## 2 0.08084
## 3 0.07757
## 4 0.04957
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 2 Elektrode 0.14809 3e-05 *
## 4 Elektrode:Stimulustyp 0.00878 0e+00 *
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF]
## 2 Elektrode 0.55392 0.00003 * 0.62213 0.00001
## 4 Elektrode:Stimulustyp 0.38004 0.06518 0.40355 0.06181
## p[HF]<.05
## 2 *
## 4#Fz
res = ezANOVA (data=daten_all[daten_all$Bedingung == 'rand' & daten_all$Elektrode == 'Fz',],
dv = Voltage, wid = VP, within = Stimulustyp)
roundEZ(res, digits = 4)## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Stimulustyp 1 21 6.0962 0.0222 * 0.1591#Cz
res = ezANOVA (data=daten_all[daten_all$Bedingung == 'rand' & daten_all$Elektrode == 'Cz',],
dv = Voltage, wid = VP, within = Stimulustyp)
roundEZ(res, digits = 4)## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Stimulustyp 1 21 5.2901 0.0318 * 0.1266#Pz
res = ezANOVA (data=daten_all[daten_all$Bedingung == 'rand' & daten_all$Elektrode == 'Pz',],
dv = Voltage, wid = VP, within = Stimulustyp)
roundEZ(res, digits = 4)## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Stimulustyp 1 21 4.6055 0.0437 * 0.1217#M1
res = ezANOVA (data=daten_all[daten_all$Bedingung == 'rand' & daten_all$Elektrode == 'M1',],
dv = Voltage, wid = VP, within = Stimulustyp)
roundEZ(res, digits = 4)## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Stimulustyp 1 21 0.3567 0.5567 0.0072#M2
res = ezANOVA (data=daten_all[daten_all$Bedingung == 'rand' & daten_all$Elektrode == 'M2',],
dv = Voltage, wid = VP, within = Stimulustyp)
roundEZ(res, digits = 4)## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Stimulustyp 1 21 0.0962 0.7595 0.0026ezStats(data=daten_all, dv = Voltage, wid = VP,
within=.(Elektrode))## Elektrode N Mean SD FLSD
## 1 Cz 22 -0.20076896 0.4855416 0.1582892
## 2 Fz 22 -0.20045253 0.3573031 0.1582892
## 3 M1 22 0.08186593 0.2350155 0.1582892
## 4 M2 22 0.17573022 0.1788832 0.1582892
## 5 Pz 22 0.10335384 0.5190624 0.1582892ezStats(data=daten_all, dv = Voltage, wid = VP,
within=.(Elektrode, Stimulustyp, Bedingung))## Elektrode Stimulustyp Bedingung N Mean SD FLSD
## 1 Cz DEV pred 22 -0.24245781 1.0075720 0.2660116
## 2 Cz DEV rand 22 -0.48155621 0.9001619 0.2660116
## 3 Cz STA pred 22 -0.17904177 0.8854458 0.2660116
## 4 Cz STA rand 22 0.09997994 0.6418857 0.2660116
## 5 Fz DEV pred 22 -0.18826831 0.9562445 0.2660116
## 6 Fz DEV rand 22 -0.46707550 0.6954176 0.2660116
## 7 Fz STA pred 22 -0.19112121 0.8468214 0.2660116
## 8 Fz STA rand 22 0.04465491 0.4912674 0.2660116
## 9 M1 DEV pred 22 -0.01783650 0.4859942 0.2660116
## 10 M1 DEV rand 22 0.06965490 0.4230190 0.2660116
## 11 M1 STA pred 22 0.14843876 0.4117468 0.2660116
## 12 M1 STA rand 22 0.12720654 0.2428641 0.2660116
## 13 M2 DEV pred 22 0.18115793 0.4454431 0.2660116
## 14 M2 DEV rand 22 0.20566808 0.3487959 0.2660116
## 15 M2 STA pred 22 0.13845748 0.3937429 0.2660116
## 16 M2 STA rand 22 0.17763738 0.1889058 0.2660116
## 17 Pz DEV pred 22 0.04597905 0.8334133 0.2660116
## 18 Pz DEV rand 22 -0.13657477 0.8610132 0.2660116
## 19 Pz STA pred 22 0.08738863 0.8295528 0.2660116
## 20 Pz STA rand 22 0.41662247 0.6443794 0.2660116res = ezStats (data=daten_all[daten_all$Bedingung == 'rand',],
dv = Voltage, wid = VP,
within=.(Elektrode, Stimulustyp))
roundEZ(res, digits = 4)## Elektrode Stimulustyp N Mean SD FLSD
## 1 Cz DEV 22 -0.48155621 0.9001619 0.3266783
## 2 Cz STA 22 0.09997994 0.6418857 0.3266783
## 3 Fz DEV 22 -0.46707550 0.6954176 0.3266783
## 4 Fz STA 22 0.04465491 0.4912674 0.3266783
## 5 M1 DEV 22 0.06965490 0.4230190 0.3266783
## 6 M1 STA 22 0.12720654 0.2428641 0.3266783
## 7 M2 DEV 22 0.20566808 0.3487959 0.3266783
## 8 M2 STA 22 0.17763738 0.1889058 0.3266783
## 9 Pz DEV 22 -0.13657477 0.8610132 0.3266783
## 10 Pz STA 22 0.41662247 0.6443794 0.3266783res = ezStats (data=daten_all[daten_all$Bedingung == 'pred',],
dv = Voltage, wid = VP,
within=.(Elektrode, Stimulustyp))
roundEZ(res, digits = 4)## Elektrode Stimulustyp N Mean SD FLSD
## 1 Cz DEV 22 -0.24245781 1.0075720 0.4067697
## 2 Cz STA 22 -0.17904177 0.8854458 0.4067697
## 3 Fz DEV 22 -0.18826831 0.9562445 0.4067697
## 4 Fz STA 22 -0.19112121 0.8468214 0.4067697
## 5 M1 DEV 22 -0.01783650 0.4859942 0.4067697
## 6 M1 STA 22 0.14843876 0.4117468 0.4067697
## 7 M2 DEV 22 0.18115793 0.4454431 0.4067697
## 8 M2 STA 22 0.13845748 0.3937429 0.4067697
## 9 Pz DEV 22 0.04597905 0.8334133 0.4067697
## 10 Pz STA 22 0.08738863 0.8295528 0.4067697res = ezStats (data=daten_all[daten_all$Bedingung == 'rand',],
dv = Voltage, wid = VP,
within=.(Elektrode))
roundEZ(res, digits = 4)## Elektrode N Mean SD FLSD
## 1 Cz 22 -0.19078813 0.5094646 0.1542048
## 2 Fz 22 -0.21121030 0.3552727 0.1542048
## 3 M1 22 0.09843072 0.2605632 0.1542048
## 4 M2 22 0.19165273 0.1836745 0.1542048
## 5 Pz 22 0.14002385 0.4613210 0.1542048res = ezStats (data=daten_all[daten_all$Bedingung == 'rand',],
dv = Voltage, wid = VP,
within=.(Stimulustyp))
roundEZ(res, digits = 4)## Stimulustyp N Mean SD FLSD
## 1 DEV 22 -0.1619767 0.4909869 0.2676851
## 2 STA 22 0.1732202 0.3397689 0.2676851res = ezStats (data=daten_all[daten_all$Bedingung == 'pred',],
dv = Voltage, wid = VP,
within=.(Elektrode))
roundEZ(res, digits = 4)## Elektrode N Mean SD FLSD
## 1 Cz 22 -0.21074979 0.5737546 0.2176931
## 2 Fz 22 -0.18969476 0.4373629 0.2176931
## 3 M1 22 0.06530113 0.2475777 0.2176931
## 4 M2 22 0.15980770 0.2100556 0.2176931
## 5 Pz 22 0.06668384 0.6904578 0.2176931