3-network
bernard-liew
2020-09-24
Last updated: 2020-11-27
Checks: 6 1
Knit directory: 2020_cts_bn/
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Load package
if (!require("pacman")) install.packages("pacman")Loading required package: pacmanWarning: package 'pacman' was built under R version 3.6.1pacman::p_load(tidyverse,
qgraph,
stats,
bootnet,
igraph,
mgm,
cowplot,
huge,
EGAnet)Clean data
Import data
if (file.exists("output/network_res.RDS")) {
dat <- readRDS("output/network_res.RDS")
list2env(dat ,.GlobalEnv)
skip_eval <- TRUE
} else {
dat <- readRDS ("output/dat.RDS")
skip_eval <- FALSE
}Collapse variables
if (skip_eval == FALSE) {
dat <- dat %>%
mutate (ppt_medn = (ppt_medn_aff_base + ppt_medn_naff_base)/2,
ppt_uln = (ppt_uln_aff_base + ppt_uln_naff_base)/2,
ppt_radn = (ppt_radn_aff_base + ppt_radn_naff_base)/2,
ppt_neck = (ppt_neck_aff_base + ppt_neck_naff_base)/2,
ppt_cts = (ppt_cts_aff_base + ppt_cts_naff_base)/2,
ppt_ta = (ppt_ta_aff_base + ppt_ta_naff_base)/2) %>%
select (-c(ppt_medn_aff_base:ppt_ta_naff_base))
}Get baseline variables
if (skip_eval == FALSE) {
dat_base <- dat[, !grepl ("naff.*base", names (dat))]
dat_base <- dat_base[, grepl ("aff|base", names (dat_base))]
dat_base <- dat_base %>%
select (-worst_pain_base, - aff_side) %>%
rename (func = cts_func_base,
severity = cts_severe_base)
names (dat_base) <- str_remove_all (names(dat_base), "_base|mean_|_aff")
node_labs <- names(dat_base)
names(dat_base) <- paste0("V", 1:ncol(dat_base))
dat_base %>%
pivot_longer(cols = everything (),
names_to = "var",
values_to = "val") %>%
ggplot () +
geom_histogram (aes (val)) +
facet_wrap (~ var, ncol = 5, scales = "free_x")
}Network analysis
On baseline data
if (skip_eval == FALSE) {
df <- dat_base
df <- huge.npn (df)
set.seed (1)
nw <- estimateNetwork(df,
default="EBICglasso",
corMethod = "cor",
tuning = 0.5,
lambda.min.ratio = 0.001,
corArgs =
list(method = "pearson",
use = "pairwise.complete.obs"))
}Plot network
Arcs in blue means a positive correlation between connecting variables.
Arcs in red means a netative correlation between connecting variables.
Thickness of arcs gives you a qualitative indication of correlation magnitude.
nw_plot <- plot (nw, nodeNames = node_labs, layout = "spring", legend = TRUE)
wts_df <- nw_plot$Edgelist %>%
bind_cols() %>%
select (from, to, weight) %>%
mutate_all (round, 2)Centrality
Reference for definition: Robinaugh, D. J., Millner, A. J., & McNally, R. J. (2016). Identifying highly influential nodes in the complicated grief network. Journal of Abnormal Psychology, 125(6), 747–757. doi:10.1037/abn0000181
High centrality nodes have strong connections to many other nodes, and act as hubs that connect otherwise disparate nodes to one another.
Low centrality nodes exist on the periphery of the network, with fewer and weaker connections to other nodes of the network.
Strength is the sum of the absolute value of its connections with other nodes in the network.
Degree can be straightforwardly generalized to weighted networks by considering the sum of the weights of the connections (in absolute value), instead of their number. This generalization is called strength.
Closeness centrality is defined as the inverse of the sum of the distances of the focal node from all the other nodes in the network. Closeness is the average shortest path between a given node and the remaining nodes in the network. Nodes with higher closeness are more proximally connected to the rest of the network.
Betweenness is the number of times in which a given node lies on the shortest path between two other nodes.
Expected influence is the sum of a node’s connections and represents the relative importance of a node in a network (Robinaugh et al., 2016) –relative because even in weakly connected networks (with overall low edge weights), there will always be a node with a high expected influence in case of standardized results.
The greater the value of centrality indices to one, the more important the variable.
centr <- centralityPlot(nw,
include = "all",
scale = "relative",
labels = node_labs)Note: relative centrality indices are shown on x-axis rather than raw centrality indices.
Accuracy
Edge weights stability
if (skip_eval == FALSE) {
edge_wts <- bootnet(nw,
nBoots = 2000,
nCores = 6,
statistics = "edge")
}
plot (edge_wts, satistics = "edge", order = "sample")
plot (edge_wts, plot = "difference", satistics = "edge", order = "sample", onlyNonZero = TRUE)Expected significance level given number of bootstrap samples is approximately: 0.05
Centrality stability
if (skip_eval == FALSE) {
stats2boot <- c("edge", "strength", "expectedInfluence", "closeness", "betweenness")
centr_stb <- bootnet(nw,
nBoots = 2000,
nCores = 6,
statistics = stats2boot,
type = "case")
cor_stb <- corStability (centr_stb)
}
plot (centr_stb, statistics = stats2boot[-1])
Save data
dat2save <- list ("df" = df,
"nw" = nw,
"nw_plot" = nw_plot,
"centr" = centr,
"centr_stb" = centr_stb,
"edge_wts" = edge_wts,
"cor_stb" = cor_stb,
"node_labs" = node_labs,
"stats2boot" = stats2boot
)
saveRDS(dat2save,
"output/network_res.RDS")
sessionInfo()R version 3.6.0 (2019-04-26)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=English_United Kingdom.1252
[2] LC_CTYPE=English_United Kingdom.1252
[3] LC_MONETARY=English_United Kingdom.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United Kingdom.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] EGAnet_0.9.6 huge_1.3.4.1 cowplot_1.0.0 mgm_1.2-10
[5] igraph_1.2.4.2 bootnet_1.4.3 qgraph_1.6.5 forcats_0.4.0
[9] stringr_1.4.0 dplyr_1.0.2 purrr_0.3.3 readr_1.3.1
[13] tidyr_1.0.2 tibble_3.0.3 ggplot2_3.3.2 tidyverse_1.3.0
[17] pacman_0.5.1
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.1.4 Hmisc_4.2-0
[4] BDgraph_2.63 workflowr_1.6.2 plyr_1.8.4
[7] splines_3.6.0 candisc_0.8-3 digest_0.6.19
[10] foreach_1.4.4 htmltools_0.4.0 matrixcalc_1.0-3
[13] gdata_2.18.0 fansi_0.4.0 magrittr_1.5
[16] checkmate_1.9.3 doParallel_1.0.15 cluster_2.0.9
[19] openxlsx_4.1.4 modelr_0.1.5 wordcloud_2.6
[22] R.utils_2.8.0 jpeg_0.1-8 colorspace_1.4-1
[25] blob_1.2.1 rvest_0.3.5 mitools_2.4
[28] haven_2.2.0 pan_1.6 xfun_0.7
[31] crayon_1.3.4 jsonlite_1.7.1 networktools_1.2.3
[34] lme4_1.1-21 survival_2.44-1.1 iterators_1.0.10
[37] glue_1.4.2 relaimpo_2.2-3 gtable_0.3.0
[40] NetworkToolbox_1.4.0 nnls_1.4 car_3.0-2
[43] weights_1.0.1 shape_1.4.4 jomo_2.6-7
[46] abind_1.4-5 scales_1.1.1 mvtnorm_1.0-10
[49] DBI_1.0.0 Rcpp_1.0.2 plotrix_3.7-5
[52] htmlTable_1.13.1 foreign_0.8-71 Formula_1.2-3
[55] survey_3.36 heplots_1.3-5 stats4_3.6.0
[58] glmnet_3.0-1 htmlwidgets_1.5.1 httr_1.4.1
[61] RColorBrewer_1.1-2 lavaan_0.6-5 acepack_1.4.1
[64] ellipsis_0.3.0 IsingFit_0.3.1 mice_3.7.0
[67] farver_2.0.3 pkgconfig_2.0.2 R.methodsS3_1.7.1
[70] nnet_7.3-12 dbplyr_1.4.4 labeling_0.3
[73] tidyselect_1.1.0 rlang_0.4.7 reshape2_1.4.3
[76] later_0.8.0 polynom_1.4-0 munsell_0.5.0
[79] cellranger_1.1.0 tools_3.6.0 cli_2.0.1
[82] generics_0.0.2 IsingSampler_0.2.1 broom_0.5.4
[85] fdrtool_1.2.15 evaluate_0.14 yaml_2.2.0
[88] knitr_1.27 fs_1.3.0 zip_2.0.4
[91] mitml_0.3-7 glasso_1.11 pbapply_1.4-0
[94] nlme_3.1-139 whisker_0.3-2 R.oo_1.22.0
[97] smacof_2.1-1 xml2_1.2.2 compiler_3.6.0
[100] rstudioapi_0.11 curl_4.3 png_0.1-7
[103] e1071_1.7-1 reprex_0.3.0 pbivnorm_0.6.0
[106] stringi_1.4.3 eigenmodel_1.11 lattice_0.20-38
[109] Matrix_1.2-17 psych_1.8.12 nloptr_1.2.1
[112] vctrs_0.3.4 pillar_1.4.3 lifecycle_0.2.0
[115] data.table_1.12.8 corpcor_1.6.9 httpuv_1.5.2
[118] R6_2.4.0 latticeExtra_0.6-28 promises_1.0.1
[121] rio_0.5.16 gridExtra_2.3 codetools_0.2-16
[124] boot_1.3-22 MASS_7.3-51.4 gtools_3.8.1
[127] assertthat_0.2.1 rprojroot_1.3-2 rjson_0.2.20
[130] withr_2.1.2 mnormt_1.5-5 parallel_3.6.0
[133] hms_0.5.3 grid_3.6.0 rpart_4.1-15
[136] class_7.3-15 minqa_1.2.4 rmarkdown_2.3
[139] carData_3.0-2 git2r_0.27.1 d3Network_0.5.2.1
[142] lubridate_1.7.4 base64enc_0.1-3 ellipse_0.4.1