6 6, 2016
Outline : From User's Perspective
Data
head(dta[,1:6],5)
Subject Channel Condition value.1 value.2 value.3 1 subj1 Fp1 nonword -1.545226 -1.889412 -1.463414 2 subj1 Fp2 nonword -1.373787 -1.853133 -1.751572 3 subj1 F3 nonword -2.856959 -2.690639 -0.926768 4 subj1 F4 nonword -2.302168 -2.675416 -1.816674 5 subj1 C3 nonword -4.778307 -4.948525 -2.399150
tail(dta[,1:6],5)
Subject Channel Condition value.1 value.2 value.3 1356 subj9 FT7 word 1.809377 2.3673330 1.6798440 1357 subj9 FT8 word 1.007716 1.2813160 1.0245420 1358 subj9 A2 word -1.071328 -0.8085886 0.2553779 1359 subj9 VEOG1 word 17.208140 24.4108600 24.2048600 1360 subj9 HEOG1 word 2.666729 4.6142750 4.8465790
Data
str(dta[,1:9],digits.d=1)
'data.frame': 1360 obs. of 9 variables: $ Subject : Factor w/ 20 levels "subj1","subj2",..: 1 1 1 1 1 1 1 1 1 1 ... $ Channel : Factor w/ 34 levels "Fp1","Fp2","F3",..: 1 2 3 4 5 6 7 8 9 10 ... $ Condition: Factor w/ 2 levels "nonword","word": 1 1 1 1 1 1 1 1 1 1 ... $ value.1 : num -2 -1 -3 -2 -5 ... $ value.2 : num -2 -2 -3 -3 -5 ... $ value.3 : num -1.5 -1.8 -0.9 -1.8 -2.4 ... $ value.4 : num -1.3 -1.9 -0.2 -1.5 -1.3 ... $ value.5 : num -1 -1.6 0.8 -0.8 0.5 ... $ value.6 : num -1.7 -1.6 0.3 -0.8 1.1 ...
Look at Data : Overall
Look at Data : Detail,
Data Selection : Delete Subject 1
dta2 <- data_select(data = dta,frames = timepoint ,
datacol=4:110,subjcol=1,
chancol=2,othvarcol=3,
select_subj = c(paste("subj",c(2:20),sep="")),
select_chan = c(levels(dta$Channel)[c(1:28,30:32)]))
dta2$Channel <- as.factor(as.character(dta2$Channel))
dta2$Subject <- as.factor(as.character(dta2$Subject))
head(dta2[,1:6])
Subject Channel Condition value.1 value.2 value.3 1 subj2 Fp1 nonword -1.50746400 -2.75760800 -3.1641520 2 subj2 Fp1 word 0.30547570 -0.18280880 -1.0255810 3 subj2 Fp2 nonword 2.41785600 3.23360400 2.7211370 4 subj2 Fp2 word 3.80168600 5.28677300 4.5428270 5 subj2 F3 nonword -10.06585000 -14.17141000 -12.4640200 6 subj2 F3 word 0.02098846 -0.05941113 -0.2208613
Data Exploration : Focus on "Condition"
Data Summarize : by "Condition"
Average Data Exploration : Put on 10/10 system
scalp_plot(chan_data = dtasum, frames = timepoint, datacol =3:428, chancol =1 ,cpvarcol =2 , ylim=c(-10,10),color = c("blue","red"))
[1] "The following electrodes will not be plotted :" [1] "A2" "T3" "T4" "T5" "T6"
TableGrob (9 x 11) "arrange": 27 grobs
z cells name grob
C3 1 ( 5- 5, 4- 4) arrange gtable[layout]
C4 2 ( 5- 5, 8- 8) arrange gtable[layout]
CP3 3 ( 6- 6, 4- 4) arrange gtable[layout]
CP4 4 ( 6- 6, 8- 8) arrange gtable[layout]
CPZ 5 ( 6- 6, 6- 6) arrange gtable[layout]
CZ 6 ( 5- 5, 6- 6) arrange gtable[layout]
F3 7 ( 3- 3, 4- 4) arrange gtable[layout]
F4 8 ( 3- 3, 8- 8) arrange gtable[layout]
F7 9 ( 3- 3, 2- 2) arrange gtable[layout]
F8 10 ( 3- 3,10-10) arrange gtable[layout]
FC3 11 ( 4- 4, 4- 4) arrange gtable[layout]
FC4 12 ( 4- 4, 8- 8) arrange gtable[layout]
FCZ 13 ( 4- 4, 6- 6) arrange gtable[layout]
Fp1 14 ( 1- 1, 5- 5) arrange gtable[layout]
Fp2 15 ( 1- 1, 7- 7) arrange gtable[layout]
FPZ 16 ( 1- 1, 6- 6) arrange gtable[layout]
FT7 17 ( 4- 4, 2- 2) arrange gtable[layout]
FT8 18 ( 4- 4,10-10) arrange gtable[layout]
FZ 19 ( 3- 3, 6- 6) arrange gtable[layout]
O1 20 ( 9- 9, 5- 5) arrange gtable[layout]
O2 21 ( 9- 9, 7- 7) arrange gtable[layout]
P3 22 ( 7- 7, 4- 4) arrange gtable[layout]
P4 23 ( 7- 7, 8- 8) arrange gtable[layout]
PZ 24 ( 7- 7, 6- 6) arrange gtable[layout]
TP7 25 ( 6- 6, 2- 2) arrange gtable[layout]
TP8 26 ( 6- 6,10-10) arrange gtable[layout]
27 ( 1- 1,11-11) arrange gtable[guide-box]
Data Exploration : Bootstrap Interval
Fig <- ciplot(dta2,frames = timepoint,
datacol=4:429,
subjcol=1,
chancol=2,
othvarcol=3,
cpvarcol = 3,
fun=samplemean <- function(x, d){return(mean(x[d]))},
bootnum=500,
bootintval=c(.025,.975),
bootalpha=0.5)+
ylim(-5,5)
Data Exploration : Bootstrap Interval
Multiple Comparison : Chan_test
dta2_test <- chan_test(dta2,4:429,chancol=2,testtype="erpfatest",
design_model=(~Subject+Condition),
design0_model=(~Subject))
Multiple Comparison : Chan_test
List of 10 $ pval : Named num [1:426] 0.92 0.947 0.951 0.664 0.283 ... ..- attr(*, "names")= chr [1:426] "value.1" "value.2" "value.3" "value.4" ... $ correctedpval: Named num [1:426] 0.989 0.989 0.989 0.988 0.69 ... ..- attr(*, "names")= chr [1:426] "value.1" "value.2" "value.3" "value.4" ... $ significant : Named int [1:50] 148 149 150 151 152 153 154 161 162 181 ... ..- attr(*, "names")= chr [1:50] "value.148" "value.149" "value.150" "value.151" ... $ pi0 : num 1 $ test : Named num [1:426] 0.01051 0.00465 0.00389 0.1965 1.23797 ... ..- attr(*, "names")= chr [1:426] "value.1" "value.2" "value.3" "value.4" ... $ df1 : int 15 $ df0 : int 16 $ nbf : int 3 $ signal : num [1, 1:426] -0.0643 -0.0549 0.0408 0.1621 0.335 ... $ r2 : Named num [1:426] 0.0007 0.00031 0.000259 0.01293 0.076239 ... ..- attr(*, "names")= chr [1:426] "value.1" "value.2" "value.3" "value.4" ...
Multiple Comparison : Test results
Fig <- mcplot(tests_rst = dta2_test,
type = "test",
multi = T,
cor = FALSE,
data = dta2 ,
frames = seq(0,1700,by=4) ,
datacol = 4:429, subjcol = 1 , chancol = 2 , othvarcol = 3,
cpvarcol = 429 ,
significant_col = "pink" , significant_alpha = 0.2)
Multiple Comparison : Test results
NULL
Multiple Comparison : Test results about Signal
Fig <- mcplot(tests_rst = dta2_test,
type = "signal",
multi = T,cor = FALSE,
data = dta2,frames = seq(0,1700,by=4),
datacol = 4:429, subjcol = 1 , chancol = 2 , othvarcol =3,
cpvarcol = 3,
significant_col = "pink" , significant_alpha = 0.2,
ci.type = "boot",
level = 0.95,
fun = samplemean <- function(x, d){return(mean(x[d]))},
bootnum = 10,
bootalpha=0.3)
Multiple Comparison : Test results about Signal
Multiple Comparison : Test results about Signal
Fig <- Fig+
scale_color_manual(values = c("Blue","Red"),name = "Cond")+
scale_fill_manual(values = c("Blue","Red"),name = "Cond")+
labs(list(title = "Channels Test Results",x="Time Points"))+
theme_bw()+
theme(legend.position=c(0.7,0.075),
legend.direction = "horizontal")
Multiple Comparison : Test results about Signal
Multiple Comparison : Test results about Signal
Fig <- mcplot(tests_rst = dta2_test,
type = "signal",
multi = T,cor = FALSE,
data = dta2,frames = seq(0,1700,by=4),
datacol = 4:429, subjcol = 1 , chancol = 2 , othvarcol =3,
cpvarcol = 3,
significant_col = "pink" , significant_alpha = 0.2,
ci.type = "scb",
level = 0.95,
ci.alpha = 0.3,
cv.degree=2,
cv.interval = NULL,
scbtype = "normal")+
theme(legend.position=c(0.7,0.075),
legend.direction = "horizontal")
Multiple Comparison : Test results about Signal
Results : Single Electrode "TP7"
dtaTP7 <- data_select(data = dta2,frames = timepoint,
datacol=4:110,subjcol=1,
chancol=2,othvarcol=3,
select_chan = "TP7")
Fig <- mcplot(tests_rst = dta2_test$TP7,
type = "signal",
multi = F,cor = FALSE,
data = dtaTP7,frames = timepoint,
datacol = 4:429, subjcol = 1 , chancol = 2 , othvarcol =3,
cpvarcol = 3, significant_col = "grey50" , significant_alpha = 0.3,
ci.type = "boot")+
theme_bw()+theme(legend.position = "none")+
scale_x_continuous(breaks=c(seq(0,1700,by=64)))
Results : Single Electrode "TP7"
Results : Single Electrode "TP7"
for (i in c(100,320,960)){
nam <- paste("T",i,sep="")
assign(nam,coord_plot(tests_rst =dta2_test,frames=timepoint,
elect_coord=readRDS("Elect_Location.RData"),
type = "correctedpval",logscale = T,
show = i,
point_size = 10,
show_na_ele = F,
text = T,text_size = 3,text_col = "black",
circle = T,nose = T,cir_nose_col="black")+
scale_fill_gradient2(low="blue",high = "red",
midpoint = -log(0.05),
na.value = "grey80",
limit=c(0,25),
guide_colorbar(title = "-Log \nCorrected \np-value"))+
theme_nothing(legend = T)+
labs(list(title = paste("Frames = ",i))))
}
[1] "The following electrodes will not be plot :" [1] "A2" "T3" "T4" "T5" "T6" [1] "The following electrodes will not be plot :" [1] "A2" "T3" "T4" "T5" "T6" [1] "The following electrodes will not be plot :" [1] "A2" "T3" "T4" "T5" "T6"
Results : Single Electrode "TP7"
grid.arrange(T100,T320,T960,ncol=3)
