3-network
bernard-liew
2020-09-24
Last updated: 2020-12-17
Checks: 6 1
Knit directory: 2020_cts_bn/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | e196111 | bernard-liew | 2020-12-16 | augmented reporting |
| Rmd | 8a9ca68 | bernard-liew | 2020-11-27 | fine tuned network analysis |
| html | 8a9ca68 | bernard-liew | 2020-11-27 | fine tuned network analysis |
| html | c142098 | bernard-liew | 2020-11-23 | Build site. |
| Rmd | 2266d36 | bernard-liew | 2020-11-23 | added explanation to graphs |
| html | a6948ce | bernard-liew | 2020-10-26 | initial analysis |
| Rmd | 89af6df | bernard-liew | 2020-10-26 | initial commit |
| Rmd | fe17d91 | bernard-liew | 2020-10-26 | initial analysis |
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,
arsenal,
flextable,
officer,
cowplot,
huge)Import data
if (file.exists("output/network_res.RDS")) {
dat2 <- readRDS("output/network_res.RDS")
list2env(dat2 ,.GlobalEnv)
skip_eval <- TRUE
} else {
dat <- readRDS ("output/dat.RDS")
skip_eval <- FALSE
}
# Helper for nice descriptive table
meanNsd_transform <- function (x) {
m <- round (x[[1]][1], 2)
s <- round (x[[1]][2], 2)
m_s <- paste0(m, "(", s, ")")
return (m_s)
}Clean data
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")
}Descriptive summary
dat_sub <- dat %>%
dplyr::select (age,
pain_years,
mean_pain_base,
ppt_medn_aff_base,
ppt_uln_aff_base,
ppt_radn_aff_base,
ppt_neck_aff_base,
ppt_cts_aff_base,
ppt_ta_aff_base,
cts_func_base,
cts_severe_base,
dep_base)
names (dat_sub) <- str_remove_all (names(dat_sub), "_base|mean_|_aff")
tab1 <- tableby ( ~. , data = dat_sub, digits = 2, digits.p = 2)
summary (tab1)| Overall (N=105) | |
|---|---|
| age | |
| Mean (SD) | 46.82 (9.17) |
| Range | 21.00 - 64.00 |
| pain_years | |
| Mean (SD) | 3.24 (2.91) |
| Range | 0.50 - 18.00 |
| pain | |
| Mean (SD) | 5.02 (1.75) |
| Range | 0.00 - 8.00 |
| ppt_medn | |
| Mean (SD) | 194.32 (54.39) |
| Range | 82.00 - 333.00 |
| ppt_uln | |
| Mean (SD) | 299.30 (80.82) |
| Range | 93.00 - 498.00 |
| ppt_radn | |
| Mean (SD) | 226.43 (64.02) |
| Range | 89.00 - 458.00 |
| ppt_neck | |
| Mean (SD) | 172.20 (48.47) |
| Range | 63.00 - 307.00 |
| ppt_cts | |
| Mean (SD) | 352.77 (113.98) |
| Range | 127.00 - 652.00 |
| ppt_ta | |
| Mean (SD) | 325.20 (88.15) |
| Range | 101.00 - 544.00 |
| cts_func | |
| Mean (SD) | 2.35 (0.57) |
| Range | 1.12 - 4.25 |
| cts_severe | |
| Mean (SD) | 2.63 (0.66) |
| Range | 1.27 - 5.00 |
| dep | |
| Mean (SD) | 3.84 (2.37) |
| Range | 0.00 - 9.00 |
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)
wts_df %>%
mutate (weight_abs = abs (weight)) %>%
slice_max (weight_abs, n = 10)# A tibble: 10 x 4
from to weight weight_abs
<dbl> <dbl> <dbl> <dbl>
1 2 4 0.34 0.34
2 8 10 0.3 0.3
3 6 7 0.290 0.290
4 8 9 0.25 0.25
5 3 7 0.24 0.24
6 5 7 0.23 0.23
7 2 6 0.2 0.2
8 1 9 0.2 0.2
9 4 5 0.19 0.19
10 2 3 0.18 0.18 Centrality
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.
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.
The greater the value of centrality indices to one, the more important the variable.
centr <- centralityPlot(nw,
include = c("Strength", "Closeness", "Betweenness"),
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", plot = "area", order = "sample", CIstyle = "SE")
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 = c("strength", "closeness", "betweenness"))
Save data
dat2save <- list ("dat" = dat,
"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,
"Sim" = Sim)
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] huge_1.3.4.1 cowplot_1.1.0 officer_0.3.15 flextable_0.5.11
[5] arsenal_3.5.0 igraph_1.2.6 bootnet_1.4.3 qgraph_1.6.5
[9] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4
[13] readr_1.4.0 tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2
[17] tidyverse_1.3.0 pacman_0.5.1
loaded via a namespace (and not attached):
[1] utf8_1.1.4 R.utils_2.10.1 tidyselect_1.1.0
[4] htmlwidgets_1.5.2 grid_3.6.0 munsell_0.5.0
[7] codetools_0.2-18 withr_2.3.0 colorspace_2.0-0
[10] NetworkToolbox_1.4.0 highr_0.8 knitr_1.30
[13] uuid_0.1-4 rstudioapi_0.13 stats4_3.6.0
[16] labeling_0.4.2 git2r_0.27.1 mnormt_2.0.2
[19] farver_2.0.3 rprojroot_2.0.2 vctrs_0.3.5
[22] generics_0.1.0 xfun_0.19 R6_2.5.0
[25] doParallel_1.0.16 smacof_2.1-1 assertthat_0.2.1
[28] promises_1.1.1 scales_1.1.1 nnet_7.3-14
[31] gtable_0.3.0 weights_1.0.1 workflowr_1.6.2
[34] rlang_0.4.8 systemfonts_0.3.2 splines_3.6.0
[37] wordcloud_2.6 broom_0.7.2 checkmate_2.0.0
[40] yaml_2.2.1 reshape2_1.4.4 abind_1.4-5
[43] modelr_0.1.8 d3Network_0.5.2.1 backports_1.2.0
[46] httpuv_1.5.4 Hmisc_4.4-1 tools_3.6.0
[49] psych_2.0.9 lavaan_0.6-7 ellipsis_0.3.1
[52] RColorBrewer_1.1-2 polynom_1.4-0 Rcpp_1.0.5
[55] plyr_1.8.6 base64enc_0.1-3 rpart_4.1-15
[58] pbapply_1.4-3 haven_2.3.1 cluster_2.1.0
[61] fs_1.5.0 survey_4.0 magrittr_2.0.1
[64] data.table_1.13.2 openxlsx_4.2.3 reprex_0.3.0
[67] tmvnsim_1.0-2 mvtnorm_1.1-1 matrixcalc_1.0-3
[70] whisker_0.4 hms_0.5.3 evaluate_0.14
[73] rio_0.5.16 jpeg_0.1-8.1 readxl_1.3.1
[76] gridExtra_2.3 shape_1.4.5 compiler_3.6.0
[79] ellipse_0.4.2 mice_3.12.0 crayon_1.3.4
[82] R.oo_1.24.0 htmltools_0.5.0 corpcor_1.6.9
[85] later_1.1.0.1 Formula_1.2-4 lubridate_1.7.9.2
[88] DBI_1.1.0 relaimpo_2.2-3 mgm_1.2-10
[91] dbplyr_2.0.0 MASS_7.3-53 boot_1.3-25
[94] IsingSampler_0.2.1 Matrix_1.2-18 IsingFit_0.3.1
[97] car_3.0-10 cli_2.2.0 heplots_1.3-7
[100] mitools_2.4 R.methodsS3_1.8.1 gdata_2.18.0
[103] parallel_3.6.0 BDgraph_2.63 pkgconfig_2.0.3
[106] foreign_0.8-71 xml2_1.3.2 foreach_1.5.1
[109] pbivnorm_0.6.0 rvest_0.3.6 digest_0.6.27
[112] rmarkdown_2.5 cellranger_1.1.0 htmlTable_2.1.0
[115] gdtools_0.2.2 curl_4.3 gtools_3.8.2
[118] rjson_0.2.20 lifecycle_0.2.0 nlme_3.1-150
[121] glasso_1.11 jsonlite_1.7.1 carData_3.0-4
[124] fansi_0.4.1 pillar_1.4.7 lattice_0.20-41
[127] httr_1.4.2 plotrix_3.7-8 survival_3.2-7
[130] glue_1.4.2 networktools_1.2.3 zip_2.1.1
[133] fdrtool_1.2.15 png_0.1-7 iterators_1.0.13
[136] candisc_0.8-3 glmnet_4.0-2 class_7.3-17
[139] stringi_1.4.6 nnls_1.4 latticeExtra_0.6-29
[142] eigenmodel_1.11 e1071_1.7-4