The present Readme file explains all supplementary materials of the paper “How Predictable are Symptoms in Psychopathological Networks? A Reanalysis of 18 Published Datasets”:
Upon request, we are also happy to share the R-code for the preprocessing, analysis, and figures.
We share the data sets that are open access. This is the case for the papers Armour et al. (2016), Goekoop et al. (2014), Koenders et al. (2015), Fried et al. (2015) and McNally et al. (2014). Koenders and colleagues analyse three subgroups, and hence there are three datasets for this paper. The datasets are saved as a standard csv file, and can be opened in R in the following way:
# !!! Set working directory to Supplementary Folder !!!
data_Armour2016 <- read.csv('data/Armour2016.csv')
head(data_Armour2016)## X Q28_01_MONTH Q28_02_MONTH Q28_03_MONTH Q28_04_MONTH Q28_05_MONTH
## 1 1 1 0 1 2 1
## 2 2 2 3 2 2 2
## 3 3 1 0 0 1 0
## 4 4 2 2 0 0 1
## 5 5 2 1 0 2 2
## 6 6 4 3 3 4 3
## Q28_06_MONTH Q28_07_MONTH Q28_08_MONTH Q28_09_MONTH Q28_10_MONTH
## 1 2 2 0 1 1
## 2 3 2 0 2 3
## 3 2 1 1 1 1
## 4 3 4 1 4 0
## 5 1 0 1 1 1
## 6 2 2 0 4 2
## Q28_11_MONTH Q28_12_MONTH Q28_13_MONTH Q28_14_MONTH Q28_15_MONTH
## 1 2 1 2 1 1
## 2 3 3 2 1 1
## 3 1 2 2 1 1
## 4 1 4 4 4 4
## 5 2 1 3 3 2
## 6 4 4 3 3 4
## Q28_16_MONTH Q28_17_MONTH Q28_18_MONTH Q28_19_MONTH Q28_20_MONTH
## 1 1 0 0 2 1
## 2 1 3 2 1 2
## 3 1 1 1 2 2
## 4 0 4 0 0 2
## 5 0 1 0 4 4
## 6 0 4 2 2 4
## Sum_GAD2 Sum_PHQ2 Active_SI_FINAL PCS MCS PPAGE PPGENDER
## 1 2 2 1 49.64593 57.99929 60 1
## 2 3 2 0 25.51176 39.64058 63 1
## 3 4 4 0 48.66976 40.49662 46 2
## 4 0 0 0 25.26158 51.16183 31 1
## 5 2 4 2 53.11736 19.33240 30 2
## 6 5 6 2 28.54240 34.84018 56 1In addition, we saved each data file as an R-list, which contains additional information next to the data itself. Type indicates the type of variable (‘g’ for continuous, ‘c’ for categorical), lev the number of levels (1 for continuous, k for categorical), names the variable names, names_long a longer version of the variable names and info contains some additional information for some data sets.
list_Armour2016 <- readRDS('data/Armour2016.RDS')
str(list_Armour2016)## List of 6
## $ data :'data.frame': 221 obs. of 27 variables:
## ..$ : num [1:221] 1 2 1 2 2 4 2 1 1 1 ...
## ..$ : num [1:221] 0 3 0 2 1 3 1 2 0 2 ...
## ..$ : num [1:221] 1 2 0 0 0 3 1 2 0 0 ...
## ..$ : num [1:221] 2 2 1 0 2 4 1 2 3 2 ...
## ..$ : num [1:221] 1 2 0 1 2 3 2 1 1 1 ...
## ..$ : num [1:221] 2 3 2 3 1 2 2 2 1 2 ...
## ..$ : num [1:221] 2 2 1 4 0 2 1 2 1 1 ...
## ..$ : num [1:221] 0 0 1 1 1 0 1 0 1 1 ...
## ..$ : num [1:221] 1 2 1 4 1 4 0 2 2 1 ...
## ..$ : num [1:221] 1 3 1 0 1 2 0 1 1 4 ...
## ..$ : num [1:221] 2 3 1 1 2 4 1 1 1 4 ...
## ..$ : num [1:221] 1 3 2 4 1 4 0 3 1 1 ...
## ..$ : num [1:221] 2 2 2 4 3 3 0 0 1 0 ...
## ..$ : num [1:221] 1 1 1 4 3 3 0 0 0 1 ...
## ..$ : num [1:221] 1 1 1 4 2 4 0 0 1 1 ...
## ..$ : num [1:221] 1 1 1 0 0 0 0 0 0 4 ...
## ..$ : num [1:221] 0 3 1 4 1 4 1 3 3 4 ...
## ..$ : num [1:221] 0 2 1 0 0 2 1 2 2 1 ...
## ..$ : num [1:221] 2 1 2 0 4 2 1 1 1 1 ...
## ..$ : num [1:221] 1 2 2 2 4 4 1 0 1 1 ...
## ..$ : num [1:221] 2 3 4 0 2 5 0 1 1 6 ...
## ..$ : num [1:221] 2 2 4 0 4 6 1 0 0 6 ...
## ..$ : num [1:221] 1 0 0 0 2 2 0 0 0 3 ...
## ..$ : num [1:221] 49.6 25.5 48.7 25.3 53.1 ...
## ..$ : num [1:221] 58 39.6 40.5 51.2 19.3 ...
## ..$ : num [1:221] 60 63 46 31 30 56 74 65 27 25 ...
## ..$ : num [1:221] 1 1 2 1 2 1 1 1 2 2 ...
## $ type : chr [1:27] "g" "g" "g" "g" ...
## $ lev : num [1:27] 1 1 1 1 1 1 1 1 1 1 ...
## $ names : chr [1:27] "Q28_01_MONTH" "Q28_02_MONTH" "Q28_03_MONTH" "Q28_04_MONTH" ...
## $ names_long: NULL
## $ info : NULLhead(list_Armour2016$data)##
## 1 1 0 1 2 1 2 2 0 1 1 2 1 2 1 1 1 0 0 2 1 2 2 1 49.64593 57.99929 60 1
## 2 2 3 2 2 2 3 2 0 2 3 3 3 2 1 1 1 3 2 1 2 3 2 0 25.51176 39.64058 63 1
## 3 1 0 0 1 0 2 1 1 1 1 1 2 2 1 1 1 1 1 2 2 4 4 0 48.66976 40.49662 46 2
## 4 2 2 0 0 1 3 4 1 4 0 1 4 4 4 4 0 4 0 0 2 0 0 0 25.26158 51.16183 31 1
## 5 2 1 0 2 2 1 0 1 1 1 2 1 3 3 2 0 1 0 4 4 2 4 2 53.11736 19.33240 30 2
## 6 4 3 3 4 3 2 2 0 4 2 4 4 3 3 4 0 4 2 2 4 5 6 2 28.54240 34.84018 56 1The estimated weighted adjacency matrices are saved as csv files separately for each dataset, and can be loaded as follows:
wadj_Armour2016 <- read.csv('adjmatrices/wadj_Armour2016.csv', header=T)
round(wadj_Armour2016, 2)## Q28_01_MONTH Q28_02_MONTH Q28_03_MONTH Q28_04_MONTH Q28_05_MONTH
## 1 0.00 0.15 0.00 0.28 0.07
## 2 0.15 0.00 0.42 0.00 0.00
## 3 0.00 0.42 0.00 0.20 0.11
## 4 0.28 0.00 0.20 0.00 0.25
## 5 0.07 0.00 0.11 0.25 0.00
## 6 0.00 0.00 0.00 0.06 0.14
## 7 0.00 0.00 0.00 0.13 0.10
## 8 0.00 0.00 0.00 0.00 0.00
## 9 0.00 0.00 0.00 0.12 0.00
## 10 0.00 0.00 0.00 0.07 0.00
## 11 0.15 0.00 0.00 0.00 0.00
## 12 0.00 0.00 0.07 0.00 0.00
## 13 0.00 0.00 0.00 0.00 0.00
## 14 0.00 0.00 0.00 0.00 0.00
## 15 0.00 0.00 0.00 0.00 0.13
## 16 0.00 0.08 0.00 0.00 0.00
## 17 0.00 0.00 0.00 0.00 0.00
## 18 0.00 0.00 0.00 0.00 0.11
## 19 0.00 0.00 0.00 0.00 0.00
## 20 0.00 0.00 0.00 0.00 0.00
## 21 0.00 0.00 0.00 0.00 0.10
## 22 0.00 0.00 0.00 0.00 0.00
## 23 0.00 0.00 0.00 0.00 0.00
## 24 0.00 0.00 0.00 0.00 0.00
## 25 -0.12 0.00 0.00 0.00 0.00
## 26 0.00 0.00 0.00 0.00 0.00
## 27 0.00 0.00 0.00 0.00 0.00
## Q28_06_MONTH Q28_07_MONTH Q28_08_MONTH Q28_09_MONTH Q28_10_MONTH
## 1 0.00 0.00 0.00 0.00 0.00
## 2 0.00 0.00 0.00 0.00 0.00
## 3 0.00 0.00 0.00 0.00 0.00
## 4 0.06 0.13 0.00 0.12 0.07
## 5 0.14 0.10 0.00 0.00 0.00
## 6 0.00 0.18 0.04 0.00 0.00
## 7 0.18 0.00 0.00 0.00 0.00
## 8 0.04 0.00 0.00 0.00 0.00
## 9 0.00 0.00 0.00 0.00 0.00
## 10 0.00 0.00 0.00 0.00 0.00
## 11 0.00 0.00 0.00 0.21 0.45
## 12 0.00 0.12 0.00 0.15 0.00
## 13 0.00 0.00 0.00 0.00 0.00
## 14 0.00 0.00 0.00 0.00 0.00
## 15 0.00 0.10 0.00 0.07 0.00
## 16 0.00 0.00 0.00 0.00 0.00
## 17 0.00 0.00 0.00 0.07 0.00
## 18 0.00 0.00 0.00 0.00 0.00
## 19 0.00 -0.09 0.07 0.00 0.00
## 20 0.00 0.00 0.00 0.00 0.00
## 21 0.00 0.00 0.00 0.00 0.00
## 22 0.00 0.00 0.00 0.00 0.00
## 23 0.00 0.00 0.00 0.00 0.00
## 24 0.00 0.00 -0.15 0.00 0.00
## 25 0.00 0.00 0.00 -0.09 0.00
## 26 0.00 0.00 0.00 0.00 0.00
## 27 0.00 0.00 0.00 0.00 0.00
## Q28_11_MONTH Q28_12_MONTH Q28_13_MONTH Q28_14_MONTH Q28_15_MONTH
## 1 0.15 0.00 0.00 0.00 0.00
## 2 0.00 0.00 0.00 0.00 0.00
## 3 0.00 0.07 0.00 0.00 0.00
## 4 0.00 0.00 0.00 0.00 0.00
## 5 0.00 0.00 0.00 0.00 0.13
## 6 0.00 0.00 0.00 0.00 0.00
## 7 0.00 0.12 0.00 0.00 0.10
## 8 0.00 0.00 0.00 0.00 0.00
## 9 0.21 0.15 0.00 0.00 0.07
## 10 0.45 0.00 0.00 0.00 0.00
## 11 0.00 0.00 0.00 0.00 0.13
## 12 0.00 0.00 0.12 0.00 0.05
## 13 0.00 0.12 0.00 0.43 0.00
## 14 0.00 0.00 0.43 0.00 0.00
## 15 0.13 0.05 0.00 0.00 0.00
## 16 0.00 0.00 0.00 0.00 0.18
## 17 0.00 0.00 0.00 0.00 0.00
## 18 0.00 0.00 0.10 0.00 0.00
## 19 0.00 0.24 0.08 0.00 0.00
## 20 0.00 0.00 0.11 0.00 0.00
## 21 0.00 0.00 0.00 0.00 0.00
## 22 0.00 0.12 0.00 0.14 0.00
## 23 0.00 -0.08 0.00 0.00 0.00
## 24 0.00 -0.10 0.00 0.00 0.00
## 25 0.00 0.00 0.00 0.00 -0.10
## 26 0.00 0.00 0.00 0.00 0.00
## 27 0.00 0.00 0.00 0.00 0.00
## Q28_16_MONTH Q28_17_MONTH Q28_18_MONTH Q28_19_MONTH Q28_20_MONTH
## 1 0.00 0.00 0.00 0.00 0.00
## 2 0.08 0.00 0.00 0.00 0.00
## 3 0.00 0.00 0.00 0.00 0.00
## 4 0.00 0.00 0.00 0.00 0.00
## 5 0.00 0.00 0.11 0.00 0.00
## 6 0.00 0.00 0.00 0.00 0.00
## 7 0.00 0.00 0.00 -0.09 0.00
## 8 0.00 0.00 0.00 0.07 0.00
## 9 0.00 0.07 0.00 0.00 0.00
## 10 0.00 0.00 0.00 0.00 0.00
## 11 0.00 0.00 0.00 0.00 0.00
## 12 0.00 0.00 0.00 0.24 0.00
## 13 0.00 0.00 0.10 0.08 0.11
## 14 0.00 0.00 0.00 0.00 0.00
## 15 0.18 0.00 0.00 0.00 0.00
## 16 0.00 0.00 0.09 0.00 0.00
## 17 0.00 0.00 0.30 0.00 0.00
## 18 0.09 0.30 0.00 0.10 0.07
## 19 0.00 0.00 0.10 0.00 0.16
## 20 0.00 0.00 0.07 0.16 0.00
## 21 0.00 0.00 0.09 0.00 0.00
## 22 0.00 0.00 0.00 0.11 0.00
## 23 0.27 0.00 0.00 0.00 0.00
## 24 0.00 -0.08 0.00 0.00 0.00
## 25 0.00 0.00 0.00 -0.15 0.00
## 26 0.00 0.00 0.00 -0.05 0.00
## 27 0.00 0.00 0.00 0.00 0.08
## Sum_GAD2 Sum_PHQ2 Active_SI_FINAL PCS MCS PPAGE PPGENDER
## 1 0.00 0.00 0.00 0.00 -0.12 0.00 0.00
## 2 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 3 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 4 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 5 0.10 0.00 0.00 0.00 0.00 0.00 0.00
## 6 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 7 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 8 0.00 0.00 0.00 -0.15 0.00 0.00 0.00
## 9 0.00 0.00 0.00 0.00 -0.09 0.00 0.00
## 10 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 12 0.00 0.12 -0.08 -0.10 0.00 0.00 0.00
## 13 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 14 0.00 0.14 0.00 0.00 0.00 0.00 0.00
## 15 0.00 0.00 0.00 0.00 -0.10 0.00 0.00
## 16 0.00 0.00 0.27 0.00 0.00 0.00 0.00
## 17 0.00 0.00 0.00 -0.08 0.00 0.00 0.00
## 18 0.09 0.00 0.00 0.00 0.00 0.00 0.00
## 19 0.00 0.11 0.00 0.00 -0.15 -0.05 0.00
## 20 0.00 0.00 0.00 0.00 0.00 0.00 0.08
## 21 0.00 0.33 0.00 0.00 -0.27 0.00 0.00
## 22 0.33 0.00 0.29 0.00 -0.27 0.00 0.00
## 23 0.00 0.29 0.00 0.00 -0.09 0.00 0.00
## 24 0.00 0.00 0.00 0.00 0.00 -0.10 0.12
## 25 -0.27 -0.27 -0.09 0.00 0.00 0.00 -0.10
## 26 0.00 0.00 0.00 -0.10 0.00 0.00 -0.18
## 27 0.00 0.00 0.00 0.12 -0.10 -0.18 0.00The estimated predictability measures are saved as csv files separately for each dataset. The error indicates the predictability measure, the error type indicates the type of computed predictability (See Haslbeck & Waldorp, 2016b), and the last column contains the variable names. The predictability table can be loaded in R as follows:
pred_Armour2016 <- read.csv('predictability/pred_Armour2016.csv', header=T)
head(pred_Armour2016)## Variable Error
## 1 Q28_01_MONTH 0.504
## 2 Q28_02_MONTH 0.539
## 3 Q28_03_MONTH 0.590
## 4 Q28_04_MONTH 0.579
## 5 Q28_05_MONTH 0.548
## 6 Q28_06_MONTH 0.215