-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathconsequence_white.R
194 lines (156 loc) · 9.87 KB
/
consequence_white.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
#knitr::opts_chunk$set(cache=TRUE, echo=TRUE)
options(mc.cores = parallel::detectCores())
options(scipen=999)
library(dplyr)
#library(foreign)
#library(anytime)
#library(plyr)
#library(psych)
#library(naniar)
#library(nlme)
#library(DataCombine)
library(brms)
#library(ggplot2)
library(cmdstanr)
library(parallel)
#library(cowplot)
#library(brmstools)
data.nssi.thgts <- read.table("/mmfs1/home/jdora/data.nssi.thgts_con.csv", row.names = NULL, header = T, sep = ",", na.strings=c("","NA"))
data.nssi.thgts$PID<-as.numeric(data.nssi.thgts$PID)
data.nssi.thgts$Study <- as.factor(as.character(data.nssi.thgts$Study))
levels(data.nssi.thgts$Study) <- c("Kiekens et al., 2020", "Kuehn et al., in Prep", "Lear et al., 2019", "Bresin et al., 2013", "Selby et al., 2018", "Hochard et al., 2015")
### need to turn this into a factor before fitting model!
data.nssi.thgts$NSSI_thgts <- as.factor(as.character(data.nssi.thgts$NSSI_thgts))
data.nssi.thgts$NSSI_thgts.lag <- as.factor(as.character(data.nssi.thgts$NSSI_thgts.lag))
data.nssi.thgts$NSSI_thgts.lead <- as.factor(as.character(data.nssi.thgts$NSSI_thgts.lead))
data.nssi.thgts$logical <- ifelse(data.nssi.thgts$NSSI_thgts == 1 & lead(data.nssi.thgts$NSSI_thgts) == 0, 1, 0)
data.nssi.thgts$logical <- ifelse(data.nssi.thgts$NSSI_thgts == 0 & lag(data.nssi.thgts$NSSI_thgts) == 1, 1, data.nssi.thgts$logical)
data.nssi.thgts <- data.nssi.thgts[which(data.nssi.thgts$logical == 1),]
data.nssi.thgts <- data.nssi.thgts %>%
group_by(PID) %>%
dplyr::mutate(pair = row_number()) %>%
dplyr::mutate(pair = ifelse(pair %% 2, pair, pair - 1))
data.nssi.thgts$NSSI_thgts <- relevel(data.nssi.thgts$NSSI_thgts, ref = "1")
###
data.nssi.bhx <- read.table("/mmfs1/home/jdora/data.nssi.bhx_con.csv", row.names = NULL, header = T, sep = ",", na.strings=c("","NA"))
data.nssi.bhx$PID<-as.numeric(data.nssi.bhx$PID)
data.nssi.bhx$Study <- as.factor(as.character(data.nssi.bhx$Study))
levels(data.nssi.bhx$Study) <- c("Armey et al., 2011", "Kiekens et al., 2020", "Kuehn et al., in prep", "Lear et al., 2019", "Muehlenkamp et al., 2009", "Santangelo et al., 2017", "Selby et al., 2013", "Bresin et al., 2013", "Selby et al., 2018", "Vansteelandt et al., 2017", "Wolford-Clevenger et al., 2019", "Czyz et al., 2017", "Hochard et al., 2015", "Houben et al., 2017")
### need to turn this into a factor before fitting model!
data.nssi.bhx$NSSI_bhx <- as.factor(as.character(data.nssi.bhx$NSSI_bhx))
data.nssi.bhx$NSSI_bhx.lag <- as.factor(as.character(data.nssi.bhx$NSSI_bhx.lag))
data.nssi.bhx$NSSI_bhx.lead <- as.factor(as.character(data.nssi.bhx$NSSI_bhx.lead))
data.nssi.bhx$logical <- ifelse(data.nssi.bhx$NSSI_bhx == 1 & lead(data.nssi.bhx$NSSI_bhx) == 0, 1, 0)
data.nssi.bhx$logical <- ifelse(data.nssi.bhx$NSSI_bhx == 0 & lag(data.nssi.bhx$NSSI_bhx) == 1, 1, data.nssi.bhx$logical)
data.nssi.bhx <- data.nssi.bhx[which(data.nssi.bhx$logical == 1),]
data.nssi.bhx <- data.nssi.bhx %>%
group_by(PID) %>%
dplyr::mutate(pair = row_number()) %>%
dplyr::mutate(pair = ifelse(pair %% 2, pair, pair - 1))
data.nssi.bhx$NSSI_bhx <- relevel(data.nssi.bhx$NSSI_bhx, ref = "1")
###
data.sui.thgts <- read.table("/mmfs1/home/jdora/data.sui.thgts_con.csv", row.names = NULL, header = T, sep = ",", na.strings=c("","NA"))
data.sui.thgts$PID<-as.numeric(data.sui.thgts$PID)
data.sui.thgts$Study <- as.factor(as.character(data.sui.thgts$Study))
levels(data.sui.thgts$Study) <- c("Kiekens et al., 2020", "Kleiman et al., 2017", "Kleiman et al., 2018", "Kuehn et al., in prep", "Peters et al., 2020", "Salim et al., 2019", "Bresin et al., 2013", "Wolford-Clevenger et al., 2019", "Czyz et al., 2017", "Forkmann et al., 2018", "Husky et al., 2017", "Kaurin et al., under review", "Kaurin et al., 2020")
### need to turn this into a factor before fitting model!
data.sui.thgts$suicidal_thgts <- as.factor(as.character(data.sui.thgts$suicidal_thgts))
data.sui.thgts$suicidal_thgts.lag <- as.factor(as.character(data.sui.thgts$suicidal_thgts.lag))
data.sui.thgts$suicidal_thgts.lead <- as.factor(as.character(data.sui.thgts$suicidal_thgts.lead))
data.sui.thgts$logical <- ifelse(data.sui.thgts$suicidal_thgts == 1 & lead(data.sui.thgts$suicidal_thgts) == 0, 1, 0)
data.sui.thgts$logical <- ifelse(data.sui.thgts$suicidal_thgts == 0 & lag(data.sui.thgts$suicidal_thgts) == 1, 1, data.sui.thgts$logical)
data.sui.thgts <- data.sui.thgts[which(data.sui.thgts$logical == 1),]
data.sui.thgts <- data.sui.thgts %>%
group_by(PID) %>%
dplyr::mutate(pair = row_number()) %>%
dplyr::mutate(pair = ifelse(pair %% 2, pair, pair - 1))
data.sui.thgts$suicidal_thgts <- relevel(data.sui.thgts$suicidal_thgts, ref = "1")
###
data.nssi.thgts$white <- ifelse(data.nssi.thgts$Study == "Kiekens et al., 2020", NA, ifelse(
data.nssi.thgts$Study == "Kuehn et al., in Prep", 0.53, ifelse(
data.nssi.thgts$Study == "Lear et al., 2019", 0.81, ifelse(
data.nssi.thgts$Study == "Bresin et al., 2013", NA, ifelse(
data.nssi.thgts$Study == "Selby et al., 2018", 0.38, NA
)
)
)
))
data.nssi.thgts$white <- scale(data.nssi.thgts$white, center = T, scale = F)
prior <- c(set_prior("normal(0, 1)", class = "b", coef = "Intercept"),
set_prior("normal(0, 0.5)", class = "b", coef = "NSSI_thgts0"),
set_prior("normal(0, 0.5)", class = "b", coef = "white"),
set_prior("normal(0, 0.5)", class = "b", coef = "NSSI_thgts0:white"),
set_prior("student_t(3,0,2.5)", class="sd"),
set_prior("student_t(3,0,2.5", class="sigma"))
model_nssi_thgts_consequence_white <-brm(NA.standard.CWP ~ 0 + Intercept + NSSI_thgts*white + ( 0 + Intercept + NSSI_thgts | Study/PID/pair), prior=prior, data=data.nssi.thgts, control = list(adapt_delta = 0.99, max_treedepth=15),
sample_prior = TRUE, iter = 6000, chains = 4, backend = "cmdstanr", threads = threading(9))
summary(model_nssi_thgts_consequence_white)
data.nssi.bhx$white <- ifelse(data.nssi.bhx$Study == "Armey et al., 2011", NA, ifelse(
data.nssi.bhx$Study == "Kiekens et al., 2020", NA, ifelse(
data.nssi.bhx$Study == "Kuehn et al., in Prep", 0.53, ifelse(
data.nssi.bhx$Study == "Lear et al., 2019", 0.81, ifelse(
data.nssi.bhx$Study == "Muehlenkamp et al., 2009", 0.97, ifelse(
data.nssi.bhx$Study == "Santangelo et al., 2017", NA, ifelse(
data.nssi.bhx$Study == "Selby et al., 2013", 0.64, ifelse(
data.nssi.bhx$Study == "Bresin et al., 2013", NA, ifelse(
data.nssi.bhx$Study == "Selby et al., 2018", 0.38, ifelse(
data.nssi.bhx$Study == "Vansteelandt et al., 2017", NA, ifelse(
data.nssi.bhx$Study == "Wolford-Clevenger et al., 2019", 0.82, ifelse(
data.nssi.bhx$Study == "Czyz et al., 2017", 0.76, ifelse(
data.nssi.bhx$Study == "Hochard et al., 2015", NA, NA)
)
)
)
)
)
)
)
)
)
)
)
)
data.nssi.bhx$white <- scale(data.nssi.bhx$white, center = T, scale = F)
prior <- c(set_prior("normal(0, 1)", class = "b", coef = "Intercept"),
set_prior("normal(0, 0.5)", class = "b", coef = "NSSI_bhx0"),
set_prior("normal(0, 0.5)", class = "b", coef = "white"),
set_prior("normal(0, 0.5)", class = "b", coef = "NSSI_bhx0:white"),
set_prior("student_t(3,0,2.5)", class="sd"),
set_prior("student_t(3,0,2.5", class="sigma"))
model_NSSI_bhx_consequence_white <-brm(NA.standard.CWP ~ 0 + Intercept + NSSI_bhx*white + ( 0 + Intercept + NSSI_bhx | Study/PID/pair), prior=prior, data=data.nssi.bhx, control = list(adapt_delta = 0.99, max_treedepth=15),
sample_prior = TRUE, iter = 6000, chains = 4, backend = "cmdstanr", threads = threading(9))
summary(model_NSSI_bhx_consequence_white)
data.sui.thgts$white <- ifelse(data.sui.thgts$Study == "Kiekens et al., 2020", NA, ifelse(
data.sui.thgts$Study == "Kleiman et al., 2017", 0.75, ifelse(
data.sui.thgts$Study == "Kleiman et al., 2018", 0.73, ifelse(
data.sui.thgts$Study == "Kuehn et al., in Prep", 0.53, ifelse(
data.sui.thgts$Study == "Peters et al., 2020", NA, ifelse(
data.sui.thgts$Study == "Salim et al., 2019", 0.62, ifelse(
data.sui.thgts$Study == "Bresin et al., 2013", NA, ifelse(
data.sui.thgts$Study == "Wolford-Clevenger et al., 2019", 0.82, ifelse(
data.sui.thgts$Study == "Czyz et al., 2017", 0.76, ifelse(
data.sui.thgts$Study == "Forkmann et al., 2018", NA, ifelse(
data.sui.thgts$Study == "Husky et al., 2017", 1, ifelse(
data.sui.thgts$Study == "Kaurin et al., under review", 0.74, 0.74)
)
)
)
)
)
)
)
)
)
)
)
data.sui.thgts$white <- scale(data.sui.thgts$white, center = T, scale = F)
prior <- c(set_prior("normal(0, 1)", class = "b", coef = "Intercept"),
set_prior("normal(0, 0.5)", class = "b", coef = "suicidal_thgts0"),
set_prior("normal(0, 0.5)", class = "b", coef = "white"),
set_prior("normal(0, 0.5)", class = "b", coef = "suicidal_thgts0:white"),
set_prior("student_t(3,0,2.5)", class="sd"),
set_prior("student_t(3,0,2.5", class="sigma"))
model_suicidal_thgts_consequence_white <-brm(NA.standard.CWP ~ 0 + Intercept + suicidal_thgts*white + ( 0 + Intercept + suicidal_thgts | Study/PID/pair), prior=prior, data=data.sui.thgts, control = list(adapt_delta = 0.99, max_treedepth=15),
sample_prior = TRUE, iter = 6000, chains = 4, backend = "cmdstanr", threads = threading(9))
summary(model_suicidal_thgts_consequence_white)
save.image("consequence_white.RData", compress = "xz")