Outline : From User's Perspective
Data
head(dta[,1:6],5)
Subject Channel Condition Time1 Time2 Time3 1 Sub1 Channel_1 L 9.41547e-07 5.66481e-07 -2.69333e-07 2 Sub1 Channel_2 L 4.76202e-07 1.33424e-07 -4.35254e-07 3 Sub1 Channel_3 L 6.23525e-07 4.88530e-07 1.41329e-07 4 Sub1 Channel_4 L 8.10356e-07 5.98575e-07 2.28668e-07 5 Sub1 Channel_5 L 7.50515e-07 5.26700e-07 2.60588e-07
tail(dta[,1:6],5)
Subject Channel Condition Time1 Time2 Time3 396 Sub20 Channel_6 H -1.04115e-06 -1.05311e-06 -8.54260e-07 397 Sub20 Channel_7 H -1.39371e-07 -6.96321e-07 -9.61779e-07 398 Sub20 Channel_8 H -1.60774e-07 -1.26477e-07 6.32308e-08 399 Sub20 Channel_9 H -6.81029e-08 1.82143e-07 3.34033e-07 400 Sub20 Channel_10 H 2.39154e-07 3.43515e-07 3.86194e-07
Data
str(dta[,1:9],digits.d=1)
'data.frame': 400 obs. of 9 variables: $ Subject : Factor w/ 20 levels "Sub1","Sub2",..: 1 1 1 1 1 1 1 1 1 1 ... $ Channel : Factor w/ 10 levels "Channel_1","Channel_2",..: 1 2 3 4 5 6 7 8 9 10 ... $ Condition: Factor w/ 2 levels "L","H": 1 1 1 1 1 1 1 1 1 1 ... $ Time1 : num 9e-07 5e-07 6e-07 8e-07 8e-07 ... $ Time2 : num 6e-07 1e-07 5e-07 6e-07 5e-07 ... $ Time3 : num -3e-07 -4e-07 1e-07 2e-07 3e-07 ... $ Time4 : num -1e-06 -8e-07 -1e-07 5e-09 9e-08 ... $ Time5 : num -1e-06 -6e-07 -3e-08 4e-09 7e-08 ... $ Time6 : num -7e-07 -6e-08 2e-07 -3e-08 9e-08 ...
Look at Data : Overall
timepoint <- seq(-2,14.96,by=0.16)
Fig0 <- edaplot(data = dta,
frames = timepoint ,
datacol=4:110,
subjcol=1,
chancol=2,
othvarcol=3)+
geom_line(aes(col=Condition))+
facet_grid(Channel~Subject)
Look at Data : Overall
Look at Data : Detail
timepoint <- seq(-2,14.96,by=0.16)
Fig1 <- edaplot(data = dta,
frames = timepoint ,
datacol=4:110,
subjcol=1,
chancol=2,
othvarcol=3,
select_subj = c("Sub7","Sub10","Sub12"))+
geom_line(aes(col=Condition))+
facet_grid(Subject~Channel)+
theme_bw()
Look at Data : Detail
Look at Data : Detail
timepoint <- seq(-2,14.96,by=0.16)
Fig2 <- edaplot(data = dta,frames = timepoint ,
datacol=4:110,subjcol=1,chancol=2,othvarcol=3,
outlinesub = "Sub7",outcolor = "red")+
facet_grid(.~Channel)+theme_bw()
Fig3 <- edaplot(data = dta,frames = timepoint,
datacol=4:110,subjcol=1,chancol=2,othvarcol=3,
outlinesub = "Sub10",outcolor = "green")+
facet_grid(.~Channel)+theme_bw()
Fig4 <- edaplot(data = dta,frames = timepoint ,
datacol=4:110,subjcol=1,chancol=2,othvarcol=3,
outlinesub = "Sub12",outcolor = "blue")+
facet_grid(.~Channel)+theme_bw()
Look at Data : Detail
Data Selection : Delete Subject 7,10,12
dta2 <- data_select(data = dta,frames = timepoint ,
datacol=4:110,subjcol=1,
chancol=2,othvarcol=3,
select_subj = c(paste("Sub",
c(1:6,8:9,11,13:20),sep="")))
head(dta2[,1:6])
Subject Channel Condition Time1 Time2 Time3 1 Sub1 Channel_1 L 9.41547e-07 5.66481e-07 -2.69333e-07 2 Sub1 Channel_2 L 4.76202e-07 1.33424e-07 -4.35254e-07 3 Sub1 Channel_3 L 6.23525e-07 4.88530e-07 1.41329e-07 4 Sub1 Channel_4 L 8.10356e-07 5.98575e-07 2.28668e-07 5 Sub1 Channel_5 L 7.50515e-07 5.26700e-07 2.60588e-07 6 Sub1 Channel_6 L 5.26136e-07 8.06925e-08 -2.61174e-07
dim(dta2)
[1] 340 110
Downsample : smoothing data
dta3 <- downsample(data = dta2,
datacol = 4:110,
binwidth = 10,
movinginterval=5)
timepoint2 <- seq(-2,14.96,by = 0.6525)
Fig4 <- edaplot(data = dta3,
frames = timepoint2 ,
datacol= 4:29,
subjcol=1,
chancol=2,
othvarcol=3)+
geom_line(aes(col=Subject))+
facet_wrap(~Channel)
Downsample:smoothing data
Data Exploration : Focus on "Condition"
Fig5 <- edaplot(data = dta3,
frames = timepoint2 ,
datacol= 4:29,
subjcol=1,
chancol=2,
othvarcol=3)+
geom_line(aes(col=Condition))+
facet_wrap(~Channel)+
stat_summary(aes(group=Condition,shape=Condition),
fun.y = "mean",col = "purple",
size = 1, geom = "point")+
theme_bw()+
theme(legend.position="bottom")+
labs(list(x="Signal",y="Time Points"))
Data Exploration: Focus on "Condition"
Data Summarize : by "Condition"
dtasum <- data_summarize(data=dta3,
frames = timepoint2,
datacol = 4:29,
subjcol = 1,
chancol = 2,
othvarcol = 3,
summarycol= c(2,3),
fun=mean)
head(dtasum[,c(1:5)])
Channel Condition value.1 value.2 value.3 1 Channel_1 L -1.898317e-07 1.902874e-07 7.609002e-07 2 Channel_2 L -4.462120e-08 -3.553222e-09 3.033979e-07 3 Channel_3 L -9.023315e-09 -5.554516e-08 1.806674e-07 4 Channel_4 L -7.345304e-08 -1.542970e-08 3.596421e-07 5 Channel_5 L -3.494380e-08 -4.848521e-09 2.369591e-07 6 Channel_6 L 3.810523e-08 -7.817846e-08 1.162215e-07
Average Data Exploration : Focus on "aveRT", "Condition"
Fig7 <- edaplot(data = dtasum,
frames = timepoint2 ,
datacol= 3:28,
#subjcol=NULL,
chancol=1,
othvarcol=2)+
geom_line(aes(col=Condition))+
facet_wrap(~Channel)+
theme_bw()+
#scale_size_discrete(name="Average RT",range=c(1,2))+
theme(legend.position="bottom")+
labs(list(title="Average HRF curve",
x="Time Points",y="Signal"))
Average Data Exploration
Average Data Exploration
Fig8 <- edaplot(data = dtasum,
frames = timepoint2 ,
datacol= 3:108,
subjcol=NULL,
chancol=1,
othvarcol=2)+
geom_line(aes(col=Condition))+
scale_color_manual(values= c("blue","red"))+
facet_wrap(~Channel)+
theme_bw()+
theme(legend.position="bottom")+
labs(list(title="Average HRF curve",
x="Time Points",y="Signal"))
Average Data Exploration
Data Exploration : Bootstrap Confidence Interval
Fig9 <- ciplot(dta3,frames=timepoint2,
datacol=4:29,subjcol=1,
chancol=2,othvarcol=3,
cpvarcol = 3 ,
fun=samplemean <- function(x, d){return(mean(x[d]))}, # boot package
bootnum=500, # bootsraping number
bootintval=c(.025,.975), # bootstrap confidence interval
bootalpha=0.5)
Data Exploration : Bootstrap Confidence Interval
