-
Notifications
You must be signed in to change notification settings - Fork 0
/
discrim_script.R
154 lines (131 loc) · 6.26 KB
/
discrim_script.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
data <- read.csv("discrimdata.csv")
#exclude bilinguals
discrimdata<- subset(data, subject!="jw5c" & subject!="fnoe")
#subset by contrast
iI<-subset(discrimdata, tributesto=="i/I"| tributesto2=="i/I")
uy<-subset(discrimdata, tributesto=="u/y"| tributesto2=="u/y")
ui<-subset(discrimdata, tributesto=="u/i"| tributesto2=="u/i")
yi<-subset(discrimdata, tributesto=="y/i"| tributesto2=="y/i")
#split by id
iInsid<-split(iI, as.character(iI$subject))
uynsid<-split(uy, as.character(uy$subject))
uinsid<-split(ui, as.character(ui$subject))
yinsid<-split(yi, as.character(yi$subject))
#contingency tables: spanish (I hope I can find a more elegant way of doing this)
for (i in 1:length(iInsid)) {
iInsid[[i]]<-table(as.character(iInsid[[i]]$vowelcontrast), as.character(iInsid[[i]]$RESP))
}
for (i in 1:length(uynsid)) {
uynsid[[i]]<-table(as.character(uynsid[[i]]$vowelcontrast), as.character(uynsid[[i]]$RESP))
}
for (i in 1:length(uinsid)) {
uinsid[[i]]<-table(as.character(uinsid[[i]]$vowelcontrast), as.character(uinsid[[i]]$RESP))
}
for (i in 1:length(yinsid)) {
yinsid[[i]]<-table(as.character(yinsid[[i]]$vowelcontrast), as.character(yinsid[[i]]$RESP))
}
#this loop collapses signals with noises and adds row and column names
# spanish
for (i in 1:length(iInsid)) {
iInsid[[i]]<-rbind(iInsid[[i]][1,] + iInsid[[i]][3,], iInsid[[i]][2,])+0.5
dimnames(iInsid[[i]]) = list(c("noiseiI", "signaliI"), c("rsame", "rdiff"))
}
for (i in 1:length(uynsid)) {
uynsid[[i]]<-rbind(uynsid[[i]][1,] + uynsid[[i]][3,], uynsid[[i]][2,])+0.5
dimnames(uynsid[[i]]) = list(c("noiseuy", "signaluy"), c("rsame", "rdiff"))
}
for (i in 1:length(uinsid)) {
uinsid[[i]]<-rbind(uinsid[[i]][1,] + uinsid[[i]][3,], uinsid[[i]][2,])+0.5
dimnames(uinsid[[i]]) = list(c("noiseui", "signalui"), c("rsame", "rdiff"))
}
for (i in 1:length(yinsid)) {
yinsid[[i]]<-rbind(yinsid[[i]][1,] + yinsid[[i]][3,], yinsid[[i]][2,])+0.5
dimnames(yinsid[[i]]) = list(c("noiseyi", "signalyi"), c("rsame", "rdiff"))
}
#we calculate dprime
library(mysensR)
nsiddiI<-vector("list", length(iInsid))
for (i in 1:length(iInsid)) {
nsiddiI[[i]]<-as.matrix(summary(mysensR::samediff(iInsid[[i]][1,1], iInsid[[i]][1,2], iInsid[[i]][2,1], iInsid[[i]][2,2]))[[10]])
}
nsidduy<-vector("list", length(uynsid))
for (i in 1:length(uynsid)) {
nsidduy[[i]]<-as.matrix(summary(mysensR::samediff(uynsid[[i]][1,1], uynsid[[i]][1,2],uynsid[[i]][2,1], uynsid[[i]][2,2]))[[10]])
}
nsiddui<-vector("list", length(uinsid))
for (i in 1:length(uinsid)) {
nsiddui[[i]]<-as.matrix(summary(mysensR::samediff(uinsid[[i]][1,1], uinsid[[i]][1,2],uinsid[[i]][2,1], uinsid[[i]][2,2]))[[10]])
}
nsiddyi<-vector("list", length(yinsid))
for (i in 1:length(yinsid)) {
nsiddyi[[i]]<-as.matrix(summary(mysensR::samediff(yinsid[[i]][1,1], yinsid[[i]][1,2],yinsid[[i]][2,1], yinsid[[i]][2,2]))[[10]])
}
#and this loop creates a vector of dprime values
#spanish
dprimensiI<-vector("numeric", length(nsiddiI))
for (i in 1:length(nsiddiI)) {
dprimensiI[i]<-nsiddiI[[i]][2]
}
dprimensuy<-vector("numeric", length(nsidduy))
for (i in 1:length(nsidduy)) {
dprimensuy[i]<-nsidduy[[i]][2]
}
dprimensui<-vector("numeric", length(nsiddui))
for (i in 1:length(nsiddui)) {
dprimensui[i]<-nsiddui[[i]][2]
}
dprimensyi<-vector("numeric", length(nsiddyi))
for (i in 1:length(nsiddyi)) {
dprimensyi[i]<-nsiddyi[[i]][2]
}
#this creates a dataframe with dprimes
ids<-unique(as.character(discrimdata$subject))
df<-data.frame(id=ids, longshort=dprimensiI, frontback=dprimensui, rounded=dprimensuy, frontround=dprimensyi)
colnames(df)<-c("id", "/i-\u026A/", "/u-i/", "/u-y/", "/i-y/")
test<-data.frame(subj=rep(df$id, 4), contrast=stack(df, select= c(-id)))
colnames(test)<-c("subj", "dprime", "contrast")
#Stats, dprime
library(rstatix)
#effect
yesorno<- test %>% friedman_test(dprime ~ contrast |subj)
#effect size
test %>% friedman_effsize(dprime ~ contrast |subj)
#post-hoc comparison
pwc<- test %>% wilcox_test(dprime ~ contrast, paired=TRUE, p.adjust.method = "bonferroni", ref.group="/u-i/", detailed = T) #significant to alpha .05 with outlier, to 0.01 without
#Stats, RTs
library(car)
library(lme4)
discrimdata$rt<-as.numeric(discrimdata$rt)
discnoNA<-discrimdata[!is.na(discrimdata$rt),]
discnoNA$cont<-ifelse(discnoNA$vowelcontrast=="i_short/i_long", "i/\u026A",
ifelse(discnoNA$vowelcontrast=="i_long/i_long","i/i",
ifelse(discnoNA$vowelcontrast=="i_short/i_short", "\u026A/\u026A",
ifelse(discnoNA$vowelcontrast=="u/y", "u/y",
ifelse(discnoNA$vowelcontrast=="y/y", "y/y",
ifelse(discnoNA$vowelcontrast=="u/u", "u/u",
ifelse(discnoNA$vowelcontrast=="u/i_long", "u/i", "i/y")))))))
discnoNA$trialType<-ifelse(discnoNA$sameordiffstims==0, "same", "different")
discnoNA$cont<-as.factor(discnoNA$cont)
discnoNA$subject<-as.factor(discnoNA$subject)
discnoNA$stimulus<-as.factor(discnoNA$stimulus)
discnoNA$cont<-relevel(discnoNA$cont, ref="u/i")
#model 1: all together
amodel2<-lmer(rt ~ cont + (1|subject) + (1|stimulus), data=discnoNA)
library(emmeans)
m2comp<-emmeans(amodel2, list(pairwise ~ cont))
#model 2: separated, only different trials
discnoNAdiff<-subset(discnoNA, trialType=="different")
amodel3<-lmer(rt ~ cont + (1|subject) + (1|stimulus), data=discnoNAdiff)
em3<-emmeans(amodel3, list(pairwise ~ cont))
#item analysis
discnoNA$RESP<-as.factor(discnoNA$RESP)
discnoNA$sameordiffstims<-as.factor(discnoNA$sameordiffstims)
discnoNA$correct<- ifelse(discnoNA$sameordiffstims == discnoNA$RESP, "correct", "incorrect")
itemreg<-glmer(RESP ~ stimulus + (1|subject), family=binomial, data=discnoNA)
#plots
library(ggplot2)
library(ggpubr)
p<-ggboxplot(test, x="contrast", y="dprime", add="jitter", color="black")
ggpar(p, xlab = "Contrast", ylab = "Sensitivity (d')")
rtplotdisc<-ggplot(discnoNA, aes(x=cont, y=rt, color=trialType)) + geom_jitter(aes(x=cont, y=rt), colour="grey", width=0.2) + geom_boxplot(outlier.shape = NA, alpha = 0.1) + theme_classic(base_size=12)
ggpar(rtplotdisc, xlab = "Stimuli pair", ylab = "Reaction time (ms)")