-
Notifications
You must be signed in to change notification settings - Fork 0
/
analysis_questionnaires.R
195 lines (182 loc) · 7.78 KB
/
analysis_questionnaires.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
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
# guess
guess <- Ques %>%
select(ID,Condition,Guess)
guess$Guess <- as.numeric(guess$Guess)
summaryGuess <- guess %>% group_by(Condition) %>%
get_summary_stats(Guess, type = "mean_sd")
summaryGuess$mean100 <- summaryGuess$mean*100
# # A tibble: 3 × 5
# Condition variable n mean sd
# <chr> <chr> <dbl> <dbl> <dbl>
# 1 DLPFC Guess 48 0.812 0.394
# 2 PPC Guess 48 0.646 0.483
# 3 Sham Guess 48 0.521 0.505
guessSham <- guess %>%
subset(Condition == 'Sham')
guessSham %>% identify_outliers(Guess)
guessSham %>% shapiro_test(Guess)
wilcox.test(guessSham$Guess,mu=0.5,alternative = "two.sided",correct = T)
wilcoxonOneSampleR(guessSham$Guess,mu=0.5)
# Wilcoxon signed rank test with continuity correction
#
# data: guessSham$Guess
# V = 612.5, p-value = 0.7773
# alternative hypothesis: true location is not equal to 0.5
# r
# 0.0417
1/ttestBF(guessSham$Guess,mu=0.5)
# [1] Null, mu=0.5 : 6.135253 ±0.06%
#
# Against denominator:
# Alternative, r = 0.707106781186548, mu =/= 0.5
# ---
# Bayes factor type: BFoneSample, JZS
guessPPC <- guess %>%
subset(Condition == 'PPC')
guessPPC %>% identify_outliers(Guess)
guessPPC %>% shapiro_test(Guess)
wilcox.test(guessPPC$Guess,mu=0.5,alternative = "two.sided")
wilcoxonOneSampleR(guessPPC$Guess,mu=0.5)
ttestBF(guessPPC$Guess,mu=0.5)
# data: guessPPC$Guess
# V = 759.5, p-value = 0.04392
# alternative hypothesis: true location is not equal to 0.5
# r
# 0.292
# [1] Alt., r=0.707 : 1.144146 ±0.02%
#
# Against denominator:
# Null, mu = 0.5
guessDLPFC <- guess %>%
subset(Condition == 'DLPFC')
guessDLPFC %>% identify_outliers(Guess)
guessDLPFC %>% shapiro_test(Guess)
wilcox.test(guessDLPFC$Guess,mu=0.5,alternative = "two.sided")
wilcoxonOneSampleR(guessDLPFC$Guess,mu=0.5)
ttestBF(guessDLPFC$Guess,mu=0.5)
# V = 955.5, p-value = 0.00001531
# alternative hypothesis: true location is not equal to 0.5
# r
# 0.625
# [1] Alt., r=0.707 : 10739.71 ±0%
#
# Against denominator:
# Null, mu = 0.5
postRating <- Ques %>%
select(ID,Condition,'Pain level', 'Attention level', 'Fatigue level')
summaryPost <- postRating %>% group_by(Condition) %>%
get_summary_stats(c('Pain level', 'Attention level', 'Fatigue level'), type = "mean_sd")
# 1 DLPFC Attention level 48 4.83 1.43
# 2 DLPFC Fatigue level 48 2.52 1.30
# 3 DLPFC Pain level 48 1.83 1.10
# 4 PPC Attention level 48 4.65 1.85
# 5 PPC Fatigue level 48 2.46 1.43
# 6 PPC Pain level 48 1.48 0.743
# 7 Sham Attention level 48 5.23 1.39
# 8 Sham Fatigue level 48 2.40 1.40
# 9 Sham Pain level 48 1.33 0.724
pain <- postRating %>% select(ID,Condition,score = 'Pain level')
outlier<-pain %>%
group_by(Condition) %>%
identify_outliers(score)
data.frame(outlier)
normality<-pain %>%
group_by(Condition) %>%
shapiro_test(score)
data.frame(normality)
res<-anova_test(data=pain,dv=score,wid=ID,within=Condition)
get_anova_table(res)
pair<-pain %>%
pairwise_t_test(score~Condition,paired=TRUE, p.adjust.method = "bonferroni" )
data.frame(pair)
cohensD(pain[pain$Condition == 'DLPFC',]$score,
pain[pain$Condition == 'Sham',]$score,
method = "paired" )
ttestBF(pain[pain$Condition == 'DLPFC',]$score,
pain[pain$Condition == 'Sham',]$score,
paired=TRUE)
# .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
# 1 score DLPFC PPC 48 48 2.311132 47 0.025 0.076 ns
# 2 score DLPFC Sham 48 48 3.065942 47 0.004 0.011 *
# 3 score PPC Sham 48 48 1.069078 47 0.290 0.870 ns
# 0.4425306
attention <- postRating %>% select(ID,Condition,score = 'Attention level')
attention %>%
group_by(Condition) %>%
identify_outliers(score)
data.frame(outlier)
attention %>%
group_by(Condition) %>%
shapiro_test(score)
res<-anova_test(data=attention,dv=score,wid=ID,within=Condition)
get_anova_table(res)
pair<-attention %>%
pairwise_t_test(score~Condition,paired=TRUE, p.adjust.method = "bonferroni" )
data.frame(pair)
# 1 score DLPFC PPC 48 48 0.8366271 47 0.407 1.000 ns
# 2 score DLPFC Sham 48 48 -1.6152720 47 0.113 0.339 ns
# 3 score PPC Sham 48 48 -2.3614130 47 0.022 0.067 ns
Fatigue <- postRating %>% select(ID,Condition,score = 'Fatigue level')
outlier<-Fatigue %>%
group_by(Condition) %>%
identify_outliers(score)
data.frame(outlier)
normality<-Fatigue %>%
group_by(Condition) %>%
shapiro_test(score)
data.frame(normality)
res<-anova_test(data=Fatigue,dv=score,wid=ID,within=Condition)
get_anova_table(res)
pair<-Fatigue %>%
pairwise_t_test(score~Condition,paired=TRUE, p.adjust.method = "bonferroni" )
data.frame(pair)
# 1 score DLPFC PPC 48 48 0.2820039 47 0.779 1 ns
# 2 score DLPFC Sham 48 48 0.6215315 47 0.537 1 ns
# 3 score PPC Sham 48 48 0.3745644 47 0.710 1 ns
# adverse effects
adverse <- Ques[10:29]
general_related<- function (df){
DF <- as.data.frame(df[["prop.tbl"]])
DF$x <- as.numeric(DF$x)
DF$y <- as.numeric(DF$y)
# general report 2 mild - 4 severe
general <- DF %>% subset(x != 1)
general_percent <- sum(general$Freq)
# tDCS-related 3 possible- 5 definite
relate <- general %>% subset(y >= 3)
relate_percent <- sum(relate$Freq)
output <- c(general_percent*100, relate_percent*100)
return(output)
}
Headache <- CrossTable(adverse$Headache,adverse$`tDCS related headache`, prop.t=TRUE, prop.r=TRUE, prop.c=TRUE)
Neck <- CrossTable(adverse$`Neck pain`,adverse$`tDCS related neck pain`, prop.t=TRUE, prop.r=TRUE, prop.c=TRUE)
Scalp <- CrossTable(adverse$`Scalp pain`,adverse$`tDCS related scalp pain`, prop.t=TRUE, prop.r=TRUE, prop.c=TRUE)
Tingling <- CrossTable(adverse$Tingling,adverse$`tDCS related tingling`, prop.t=TRUE, prop.r=TRUE, prop.c=TRUE)
Itching <- CrossTable(adverse$Itching ,adverse$`tDCS related Itching`, prop.t=TRUE, prop.r=TRUE, prop.c=TRUE)
Buring <- CrossTable(adverse$`Buring sensation` ,adverse$`tDCS related bruning sensation`, prop.t=TRUE, prop.r=TRUE, prop.c=TRUE)
Skin <- CrossTable(adverse$`Skin redness` ,adverse$`tDCS related skin redness`, prop.t=TRUE, prop.r=TRUE, prop.c=TRUE)
Sleepiness <- CrossTable(adverse$Sleepiness ,adverse$`tDCS related sleepiness`, prop.t=TRUE, prop.r=TRUE, prop.c=TRUE)
Trouble <- CrossTable(adverse$`Trouble concentrating` ,adverse$`tDCS related trouble concentrating`, prop.t=TRUE, prop.r=TRUE, prop.c=TRUE)
Acute <- CrossTable(adverse$`Acute mood change` ,adverse$`tDCS related acute mood change`, prop.t=TRUE, prop.r=TRUE, prop.c=TRUE)
adverseT <- rbind(general_related(Headache),
general_related(Neck),
general_related(Scalp),
general_related(Tingling),
general_related(Itching),
general_related(Buring),
general_related(Skin),
general_related(Sleepiness),
general_related(Trouble),
general_related(Acute)
)
adverseT <- as.data.frame(adverseT)
adverseT <- cbind(
c("Headache","Neck pain","Scalp pain", "Tingling","Itching", "Burning sensation", "Skin redness", "Sleepiness", "Trouble concentrating", "Acute mood change"),
adverseT
)
colnames(adverseT) <- c("Symptom","General (%)","TDCS-Related (%)")
nice_adverseT <- nice_table(adverseT,
title = c("Table X", "Self-Reported Adverse Effects After Stimulation"),
footnote = c("A total of 144 sessions (three sessions for each participant). General: percentage of reported mild to severe symptom; TDCS-Related: percentage of reported symptom that is at least possibly related to stimulation."),
separate.header = TRUE)
save_as_docx(nice_adverseT, path = paste(outputFolder,'Adverse Effect Table.docx',sep = '/'))