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Univariate_analysis.Rmd
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---
title: "R Notebook"
output: rmdformats::readthedown
# output:
# html_document:
# df_print: kable
# keep_md: yes
---
# Pre-processing and visualization
Original code by Kamran Afzali, adapted by Jeanne Racicot
Load packages
```{r echo = T, message=FALSE, warning=FALSE}
library(readr)
library(tidyverse)
library(gridExtra)
library(lmerTest)
library(lme4)
library(glmnet)
library(glmmLasso)
library(MASS)
library(nlme)
library(knitr)
library(kableExtra)
scale_this <- function(x){
(x - mean(x, na.rm=TRUE)) / sd(x, na.rm=TRUE)
}
```
Load data from prefrontal atlas
```{r echo = T, message=FALSE}
data <- read_csv("output_data/dataframe_day2day.csv")
conn_names <- read_csv("output_data/105_connection_names_reversed.csv")
```
Tally
```{r}
data %>% group_by(subject) %>% tally()
by_id <- data %>% group_by(subject)
by_id <- by_id %>%
mutate(daysFromFirstScan_STD=scale_this(daysFromFirstScan))
data$daysFromFirstScan_STD=by_id$daysFromFirstScan_STD
```
```{r}
plots=list()
subs=unique(data$subject)
i=1
for (s in subs) {
x=as.numeric(data$daysFromFirstScan[data$subject==s])
y1=as.numeric(as.character(data$PANAS_PA[data$subject==s]))
PosA=rep("PA",length(y1))
y2=as.numeric(as.character(data$PANAS_NA[data$subject==s]))
NegA=rep("NA",length(y2))
Data=as.data.frame(rbind(cbind(day=x,outcome=PosA,value=as.numeric(as.character(y1))),
cbind(day=x,outcome=NegA,value=as.numeric(as.character(y2)))))
Data$day=as.numeric(as.character(Data$day))
plots[[i]]=ggplot( Data, aes(x = day, y = value, group=outcome, color=outcome)) +
geom_line()+geom_point()+ggtitle(as.character(s))
i=i+1
rm(Data)
}
```
## Outcome plots per subject
```{r fig1, fig.height = 10, fig.width = 14, fig.align = "center"}
do.call(grid.arrange,plots)
```
```{r include=FALSE}
head(data)
```
## Data pre-processiong, creating BP and WP variables
Between person is the mean of every connection for every session and subject
Within person is each connection, centered to 0 (value-mean)
```{r}
dataML <-by_id %>% summarise(
C1BP = mean(connection1),
C2BP = mean(connection2),
C3BP = mean(connection3),
C4BP = mean(connection4),
C5BP = mean(connection5),
C6BP = mean(connection6),
C7BP = mean(connection7),
C8BP = mean(connection8),
C9BP = mean(connection9),
C10BP = mean(connection10),
C11BP = mean(connection11),
C12BP = mean(connection12),
C13BP = mean(connection13),
C14BP = mean(connection14),
C15BP = mean(connection15),
C16BP = mean(connection16),
C17BP = mean(connection17),
C18BP = mean(connection18),
C19BP = mean(connection19),
C20BP = mean(connection20),
C21BP = mean(connection21),
C22BP = mean(connection22),
C23BP = mean(connection23),
C24BP = mean(connection24),
C25BP = mean(connection25),
C26BP = mean(connection26),
C27BP = mean(connection27),
C28BP = mean(connection28),
C29BP = mean(connection29),
C30BP = mean(connection30),
C31BP = mean(connection31),
C32BP = mean(connection32),
C33BP = mean(connection33),
C34BP = mean(connection34),
C35BP = mean(connection35),
C36BP = mean(connection36),
C37BP = mean(connection37),
C38BP = mean(connection38),
C39BP = mean(connection39),
C40BP = mean(connection40),
C41BP = mean(connection41),
C42BP = mean(connection42),
C43BP = mean(connection43),
C44BP = mean(connection44),
C45BP = mean(connection45),
C46BP = mean(connection46),
C47BP = mean(connection47),
C48BP = mean(connection48),
C49BP = mean(connection49),
C50BP = mean(connection50),
C51BP = mean(connection51),
C52BP = mean(connection52),
C53BP = mean(connection53),
C54BP = mean(connection54),
C55BP = mean(connection55),
C56BP = mean(connection56),
C57BP = mean(connection57),
C58BP = mean(connection58),
C59BP = mean(connection59),
C60BP = mean(connection60),
C61BP = mean(connection61),
C62BP = mean(connection62),
C63BP = mean(connection63),
C64BP = mean(connection64),
C65BP = mean(connection65),
C66BP = mean(connection66),
C67BP = mean(connection67),
C68BP = mean(connection68),
C69BP = mean(connection69),
C70BP = mean(connection70),
C71BP = mean(connection71),
C72BP = mean(connection72),
C73BP = mean(connection73),
C74BP = mean(connection74),
C75BP = mean(connection75),
C76BP = mean(connection76),
C77BP = mean(connection77),
C78BP = mean(connection78),
C79BP = mean(connection79),
C80BP = mean(connection80),
C81BP = mean(connection81),
C82BP = mean(connection82),
C83BP = mean(connection83),
C84BP = mean(connection84),
C85BP = mean(connection85),
C86BP = mean(connection86),
C87BP = mean(connection87),
C88BP = mean(connection88),
C89BP = mean(connection89),
C90BP = mean(connection90),
C91BP = mean(connection91),
C92BP = mean(connection92),
C93BP = mean(connection93),
C94BP = mean(connection94),
C95BP = mean(connection95),
C96BP = mean(connection96),
C97BP = mean(connection97),
C98BP = mean(connection98),
C99BP = mean(connection99),
C100BP = mean(connection100),
C101BP = mean(connection101),
C102BP = mean(connection102),
C103BP = mean(connection103),
C104BP = mean(connection104),
C105BP = mean(connection105)
) %>%
inner_join(data, by = "subject") %>%
mutate(C1WP=connection1 -C1BP,
C2WP=connection2 -C2BP,
C3WP=connection3 -C3BP,
C4WP=connection4 -C4BP,
C5WP=connection5 -C5BP,
C6WP=connection6 -C6BP,
C7WP=connection7 -C7BP,
C8WP=connection8 -C8BP,
C9WP=connection9 -C9BP,
C10WP=connection10-C10BP,
C11WP=connection11-C11BP,
C12WP=connection12-C12BP,
C13WP=connection13-C13BP,
C14WP=connection14-C14BP,
C15WP=connection15-C15BP,
C16WP=connection16-C16BP,
C17WP=connection17-C17BP,
C18WP=connection18-C18BP,
C19WP=connection19-C19BP,
C20WP=connection20-C20BP,
C21WP=connection21-C21BP,
C22WP=connection22-C22BP,
C23WP=connection23-C23BP,
C24WP=connection24-C24BP,
C25WP=connection25-C25BP,
C26WP=connection26-C26BP,
C27WP=connection27-C27BP,
C28WP=connection28-C28BP,
C29WP=connection29-C29BP,
C30WP=connection30-C30BP,
C31WP=connection31 -C31BP,
C32WP=connection32 -C32BP,
C33WP=connection33 -C33BP,
C34WP=connection34 -C34BP,
C35WP=connection35 -C35BP,
C36WP=connection36 -C36BP,
C37WP=connection37 -C37BP,
C38WP=connection38 -C38BP,
C39WP=connection39 -C39BP,
C40WP=connection40 -C40BP,
C41WP=connection41 -C41BP,
C42WP=connection42 -C42BP,
C43WP=connection43 -C43BP,
C44WP=connection44 -C44BP,
C45WP=connection45 -C45BP,
C46WP=connection46 -C46BP,
C47WP=connection47 -C47BP,
C48WP=connection48 -C48BP,
C49WP=connection49 -C49BP,
C50WP=connection50 -C50BP,
C51WP=connection51 -C51BP,
C52WP=connection52 -C52BP,
C53WP=connection53 -C53BP,
C54WP=connection54 -C54BP,
C55WP=connection55 -C55BP,
C56WP=connection56 -C56BP,
C57WP=connection57 -C57BP,
C58WP=connection58 -C58BP,
C59WP=connection59 -C59BP,
C60WP=connection60 -C60BP,
C61WP=connection61 -C61BP,
C62WP=connection62 -C62BP,
C63WP=connection63 -C63BP,
C64WP=connection64 -C64BP,
C65WP=connection65 -C65BP,
C66WP=connection66 -C66BP,
C67WP=connection67 -C67BP,
C68WP=connection68 -C68BP,
C69WP=connection69 -C69BP,
C70WP=connection70 -C70BP,
C71WP=connection71 -C71BP,
C72WP=connection72 -C72BP,
C73WP=connection73 -C73BP,
C74WP=connection74 -C74BP,
C75WP=connection75 -C75BP,
C76WP=connection76 -C76BP,
C77WP=connection77 -C77BP,
C78WP=connection78 -C78BP,
C79WP=connection79 -C79BP,
C80WP=connection80 -C80BP,
C81WP=connection81 -C81BP,
C82WP=connection82 -C82BP,
C83WP=connection83 -C83BP,
C84WP=connection84 -C84BP,
C85WP=connection85 -C85BP,
C86WP=connection86 -C86BP,
C87WP=connection87 -C87BP,
C88WP=connection88 -C88BP,
C89WP=connection89 -C89BP,
C90WP=connection90 -C90BP,
C91WP=connection91 -C91BP,
C92WP=connection92 -C92BP,
C93WP=connection93 -C93BP,
C94WP=connection94 -C94BP,
C95WP=connection95 -C95BP,
C96WP=connection96 -C96BP,
C97WP=connection97 -C97BP,
C98WP=connection98 -C98BP,
C99WP=connection99 -C99BP,
C100WP=connection100 -C100BP,
C101WP=connection101 -C101BP,
C102WP=connection102 -C102BP,
C103WP=connection103 -C103BP,
C104WP=connection104 -C104BP,
C105WP=connection105 -C105BP
)
```
# Linear Models
## A note on the massive univariate models
PANAS_PA = fixed intercept (group level mean of PA) + C1WP (effect of connection1 within person) + C1BP (effect of connection1 between person) + daysFromFirstScan_STD (linear effect of time) + 1|subject (random intercept or deviation of the person level mean with respect to the group level mean PA).
PANAS_NA = fixed intercept (group level mean of NA) + C1WP (effect of connection1 within person) + C1BP (effect of connection1 between person) + daysFromFirstScan_STD (linear effect of time) + 1|subject (random intercept or deviation of the person level mean with respect to the group level mean NA).
Alors:\
intercept = group level mean of the dependent variable\
daysFromFirstScan_STD = linear effect of time\
C_X_WP= effect of connection "_X_" within person\
C_X_BP= effect of connection "_X_" between person
## Model for positive affectivity with BP effects
```{r, results = FALSE, message = FALSE}
results_PA=c()
for (i in 1:105) {
vars = paste("C",i,"WP + C",i,"BP",sep= "")
otherVars <- c("daysFromFirstScan_STD","(1|subject)")
formList=lapply(vars,function(x) {reformulate(c(otherVars,x),response="PANAS_PA")})
print(formList)
res=summary(lapply(formList,lmer,data=dataML)[[1]])$coefficients
results_PA=rbind(res,results_PA)
}
results_PA=as.data.frame(results_PA)
results_PA$sig=results_PA$`Pr(>|t|)`<0.05
```
FDR correction BP
```{r}
clean_results_PA = results_PA %>%
filter(str_detect(row.names(results_PA), 'BP'))%>%
mutate(pval.adj = p.adjust (`Pr(>|t|)`,"fdr"))%>%
mutate(conn_names, .before=1)%>%as.data.frame()
clean_results_PA%>% kbl(booktabs = T, linesep = "") %>%
kable_paper(full_width = F) %>%column_spec(1:9, color = "white", background = case_when(clean_results_PA$pval.adj<0.10~"green",
clean_results_PA$'Pr(>|t|)'<0.05~"gold",
TRUE~"lightgray"))
```
FDR correction WP
```{r}
clean_results_PA = results_PA %>%
filter(str_detect(row.names(results_PA), 'WP'))%>%
mutate(pval.adj = p.adjust (`Pr(>|t|)`,"fdr"))%>%
mutate(conn_names, .before=1)%>%as.data.frame()
clean_results_PA%>% kbl(booktabs = T, linesep = "") %>%
kable_paper(full_width = F) %>%column_spec(1:9, color = "white", background = case_when(clean_results_PA$pval.adj<0.10~"green",
clean_results_PA$'Pr(>|t|)'<0.05~"gold",
TRUE~"lightgray"))
```
## Model for negative affectivity with BP effects
```{r, results = FALSE, message = FALSE}
results_NA=c()
for (i in 1:105) {
vars = paste("C",i,"WP + C",i,"BP",sep= "")
otherVars <- c("daysFromFirstScan_STD","(1|subject)")
formList=lapply(vars,function(x) {reformulate(c(otherVars,x),response="PANAS_NA")})
print(formList)
res=summary(lapply(formList,lmer,data=dataML)[[1]])$coefficients
results_NA=rbind(res,results_NA)
}
results_NA=as.data.frame(results_NA)
results_NA$sig=results_NA$`Pr(>|t|)`<0.05
```
FDR correction BP
```{r}
clean_results_NA = results_NA %>%
filter(str_detect(row.names(results_NA), 'BP'))%>%
mutate(pval.adj = p.adjust (`Pr(>|t|)`,"fdr"))%>%
mutate(conn_names, .before=1)%>%as.data.frame()
clean_results_NA%>% kbl(booktabs = T, linesep = "") %>%
kable_paper(full_width = F) %>%column_spec(1:9, color = "white", background = case_when(clean_results_NA$pval.adj<0.10~"green",
clean_results_NA$'Pr(>|t|)'<0.05~"gold",
TRUE~"lightgray"))
```
FDR correction WP
```{r}
clean_results_NA = results_NA %>%
filter(str_detect(row.names(results_NA), 'WP'))%>%
mutate(pval.adj = p.adjust (`Pr(>|t|)`,"fdr"))%>%
mutate(conn_names, .before=1)%>%as.data.frame()
clean_results_NA%>% kbl(booktabs = T, linesep = "") %>%
kable_paper(full_width = F) %>%column_spec(1:9, color = "white", background = case_when(clean_results_NA$pval.adj<0.10~"green",
clean_results_NA$'Pr(>|t|)'<0.05~"gold",
TRUE~"lightgray"))
```