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## * miceadds 3.11-6 (2021-01-21 11:48:47)
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dat = read.table( file = 'C:/Users/nadja/OneDrive - Ging-Jehli/Dokumente Nadja/00.Research/McLean/EMU/data/EMU_data_cleaned_091522.txt',
header = T,
sep = "")
#out.width="70%"
### Variable: "Conflict2"
dat$conflict2 = dat$sub_reward - dat$sub_averse
#### Forming 4 conflict categories (higher number ==> higher conflict):
quantsConflict = quantile(dat$conflict2, probs=seq(0,1,0.25))
dat$catConfl2 = ifelse(dat$conflict2<=quantsConflict[2],1,
ifelse(dat$conflict2>quantsConflict[2] & dat$conflict2<=quantsConflict[3],2,
ifelse(dat$conflict2>quantsConflict[3] & dat$conflict2<=quantsConflict[4],3,4)))
Quantile-Probability plots
datBySBJByConf2byQuant <- dat %>%
group_by(subj_idx,catConfl2) %>%
summarise(
chAp = sum(response,na.rm=T)/n(),
# RT-Quantiles in terms of 40pixels
RTQ1_Ap_PT40 = quantile(DT40pix[Choice=="approach"],probs=c(0.1)),
RTQ3_Ap_PT40 = quantile(DT40pix[Choice=="approach"],probs=c(0.3)),
RTQ5_Ap_PT40 = quantile(DT40pix[Choice=="approach"],probs=c(0.5)),
RTQ7_Ap_PT40 = quantile(DT40pix[Choice=="approach"],probs=c(0.7)),
RTQ9_Ap_PT40 = quantile(DT40pix[Choice=="approach"],probs=c(0.9)),
RTQ1_Av_PT40 = quantile(DT40pix[Choice=="avoid"],probs=c(0.1)),
RTQ3_Av_PT40 = quantile(DT40pix[Choice=="avoid"],probs=c(0.3)),
RTQ5_Av_PT40 = quantile(DT40pix[Choice=="avoid"],probs=c(0.5)),
RTQ7_Av_PT40 = quantile(DT40pix[Choice=="avoid"],probs=c(0.7)),
RTQ9_Av_PT40 = quantile(DT40pix[Choice=="avoid"],probs=c(0.9))
)
## `summarise()` has grouped output by 'subj_idx'. You can override using the `.groups` argument.
datByConf2byQuant <- datBySBJByConf2byQuant %>%
group_by(catConfl2) %>%
summarise(
CHAp = mean(chAp,na.rm=T),
# RT-Quantiles in terms of 40pixels
mRTQ1_Ap_PT40 = mean(RTQ1_Ap_PT40,na.rm=T)*1000,
mRTQ3_Ap_PT40 = mean(RTQ3_Ap_PT40,na.rm=T)*1000,
mRTQ5_Ap_PT40 = mean(RTQ5_Ap_PT40,na.rm=T)*1000,
mRTQ7_Ap_PT40 = mean(RTQ7_Ap_PT40,na.rm=T)*1000,
mRTQ9_Ap_PT40 = mean(RTQ9_Ap_PT40,na.rm=T)*1000,
mRTQ1_Av_PT40 = mean(RTQ1_Av_PT40,na.rm=T)*1000,
mRTQ3_Av_PT40 = mean(RTQ3_Av_PT40,na.rm=T)*1000,
mRTQ5_Av_PT40 = mean(RTQ5_Av_PT40,na.rm=T)*1000,
mRTQ7_Av_PT40 = mean(RTQ7_Av_PT40,na.rm=T)*1000,
mRTQ9_Av_PT40 = mean(RTQ9_Av_PT40,na.rm=T)*1000
)
datByConf2byQuant$catConfl_Labels = ifelse(datByConf2byQuant$catConfl2==1,'4_reward_Mlower',
ifelse(datByConf2byQuant$catConfl2==2,'3_reward_Llower',
ifelse(datByConf2byQuant$catConfl2==3,'2_reward_Lhigher','1_reward_Mhigher')))
###### Quantile-Probability Plot ###############
accspread=0.05
matplot(datByConf2byQuant[1,2],datByConf2byQuant[1,3:7], #cond1, Ap-choices
xlim=c(0,1),ylim=c(400,4400),
col='black',
pch=15,
cex=2,
cex.lab=1.5,
cex.axis=1.5,
xlab='Response Frequency (filled: Ap, unfilled: Av)',ylab='RT-quantiles',
axes=F,
main='QP plots')
axis(2,at = seq(400, 4400, by = 200))
axis(1,at = seq(0, 1, by = accspread))
par(new=T)
matplot(1-datByConf2byQuant[1,2],datByConf2byQuant[1,8:12], #cond1, Av-choices
xlim=c(0,1),ylim=c(400,4400),
col='black',
pch=0,
cex=2,
cex.lab=1.5,
cex.axis=1.5,
xlab='',ylab='',
axes=F)
axis(2,at = seq(400, 4400, by = 200))
axis(1,at = seq(0, 1, by = accspread))
par(new=T)
matplot(datByConf2byQuant[2,2],datByConf2byQuant[2,3:7], #cond2, Ap-choices
xlim=c(0,1),ylim=c(400,4400),
col='blue',
pch=15,
cex=2,
cex.lab=1.5,
cex.axis=1.5,
xlab='',ylab='',
axes=F,
main='')
axis(2,at = seq(400, 4400, by = 200))
axis(1,at = seq(0, 1, by = accspread))
par(new=T)
matplot(1-datByConf2byQuant[2,2],datByConf2byQuant[2,8:12], #cond2, Av-choices
xlim=c(0,1),ylim=c(400,4400),
col='blue',
pch=0,
cex=2,
cex.lab=1.5,
cex.axis=1.5,
xlab='',ylab='',
axes=F)
axis(2,at = seq(400, 4400, by = 200))
axis(1,at = seq(0, 1, by = accspread))
par(new=T)
matplot(datByConf2byQuant[3,2],datByConf2byQuant[3,3:7], #cond3, Ap-choices
xlim=c(0,1),ylim=c(400,4400),
col='red',
pch=15,
cex=2,
cex.lab=1.5,
cex.axis=1.5,
xlab='',ylab='',
axes=F,
main='')
axis(2,at = seq(400, 4400, by = 200))
axis(1,at = seq(0, 1, by = accspread))
par(new=T)
matplot(1-datByConf2byQuant[3,2],datByConf2byQuant[3,8:12], #cond3, Av-choices
xlim=c(0,1),ylim=c(400,4400),
col='red',
pch=0,
cex=2,
cex.lab=1.5,
cex.axis=1.5,
xlab='',ylab='',
axes=F)
axis(2,at = seq(400, 4400, by = 200))
axis(1,at = seq(0, 1, by = accspread))
par(new=T)
matplot(datByConf2byQuant[4,2],datByConf2byQuant[4,3:7], #cond4, Ap-choices
xlim=c(0,1),ylim=c(400,4400),
col='green',
pch=15,
cex=2,
cex.lab=1.5,
cex.axis=1.5,
xlab='',ylab='',
axes=F,
main='')
axis(2,at = seq(400, 4400, by = 200))
axis(1,at = seq(0, 1, by = accspread))
par(new=T)
matplot(1-datByConf2byQuant[4,2],datByConf2byQuant[4,8:12], #cond4, Av-choices
xlim=c(0,1),ylim=c(400,4400),
col='green',
pch=0,
cex=2,
cex.lab=1.5,
cex.axis=1.5,
xlab='',ylab='',
axes=F)
axis(2,at = seq(400, 4400, by = 200))
axis(1,at = seq(0, 1, by = accspread))
legend("topright",
legend = c('RewardLowest_Ap','RewardLowest_Av', 'RewardLower_Ap','RewardLower_Av','RewardHigher_Ap','RewardHigher_Av','RewardHighest_Ap','RewardHighest_Av'),
pch = c(15,0,15,0,15,0,15,0),
col = c('black','black','blue','blue','red','red','green','green'),
cex=1,bty="p")
