• R: https://www.r-project.org
• RStudio: https://www.rstudio.com

Download Code (optional):

http://jmbh.github.io/data/CS_WS_Utrecht_March15.Rmd

# For Estimation
# install.packages('mgm')
library(mgm)

# For Visualization
# install.packages('qgraph') 
library(qgraph)

Subset of 16 continuous variables of larger ESM dataset:

• One individual diagnosed with Major Depression
• Up to 10 measurements a day
• Over 238 consecutive days
• In total 1476 Measurement points

Openly available: https://osf.io/j4fg8/

We select 16 mood related variables: Feeling relaxed, feeling down, feeling irritated, feeling lonely, feeling anxious, feeling satisfied, etc.

data <- read.csv('http://jmbh.github.io/data/mood_data.csv', 
                 header = TRUE)

data[1:3, 1:8]

##       date beepno beeptime Relaxed Down Irritated Satisfied Lonely
## 1 13/08/12      1    08:58       5   -1         1         5     -1
## 2 14/08/12      5    14:32       4    0         3         3      0
## 3 14/08/12      6    16:17       4    0         2         3      0

data_mood <- data[, -c(1:3)] # delete id variables
dim(data_mood)

## [1] 1476   16

p <- ncol(data_mood)

mgm_obj <- mgmfit(data = data_mood, 
               type = rep('g', p), 
               lev = rep(1, p),
               d = 1,
               lambda.sel = 'CV', 
               pbar = FALSE)

## 
##  Note that signs of edge weights (if defined) are stored in fitobject$signs. See ?mgmfit for more info.

# Subset of weighted adjacency matrix
round(mgm_obj$wadj, 2)[1:4, 1:4]

##      [,1] [,2] [,3] [,4]
## [1,] 0.00 0.10 0.25 0.13
## [2,] 0.10 0.00 0.00 0.11
## [3,] 0.25 0.00 0.00 0.06
## [4,] 0.13 0.11 0.06 0.00

# Add signs to weighted adjacency matrix
wadj <- mgm_obj$wadj * mgm_obj$signs
wadj[is.na(wadj)] <- 0

round(wadj, 2)[1:4, 1:4]

##       [,1]  [,2]  [,3]  [,4]
## [1,]  0.00  0.10 -0.25  0.13
## [2,]  0.10  0.00  0.00 -0.11
## [3,] -0.25  0.00  0.00 -0.06
## [4,]  0.13 -0.11 -0.06  0.00

Q <- qgraph(wadj, layout = 'spring', labels = colnames(data_mood))

pred_obj <- predict(mgm_obj, data_mood, error.continuous = 'VarExpl')

pred_obj$error[1:10,]

##        Variable Error ErrorType
## 1       Relaxed 0.605   VarExpl
## 2          Down 0.761   VarExpl
## 3     Irritated 0.712   VarExpl
## 4     Satisfied 0.731   VarExpl
## 5        Lonely 0.730   VarExpl
## 6       Anxious 0.617   VarExpl
## 7  Enthusiastic 0.674   VarExpl
## 8    Suspicious 0.485   VarExpl
## 9      Cheerful 0.712   VarExpl
## 10       Guilty 0.581   VarExpl

qgraph(wadj, layout = 'spring', labels = colnames(data_mood), 
       pie = pred_obj$error$Error, pieColor = 'darkgrey')