3-report
bernard-liew
2020-10-09
Last updated: 2021-04-14
Checks: 6 1
Knit directory: 2020_ODI_network/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 225cdea | Liew | 2021-03-16 | changed graph parameters |
| Rmd | 90ec06e | Liew | 2021-01-07 | updated reporting |
| Rmd | 661e55c | Liew | 2020-12-18 | augmented mgm code to furrr and removed missing first |
| Rmd | dff05ab | bernard-liew | 2020-12-01 | augmented report |
| html | dff05ab | bernard-liew | 2020-12-01 | augmented report |
| Rmd | 035fa50 | bernard-liew | 2020-11-30 | altered report graphics size |
| html | 035fa50 | bernard-liew | 2020-11-30 | altered report graphics size |
| html | 8bd4b9a | bernard-liew | 2020-11-04 | Build site. |
| Rmd | 502ad53 | bernard-liew | 2020-11-04 | build contents for web |
| html | 502ad53 | bernard-liew | 2020-11-04 | build contents for web |
| Rmd | 9e388e3 | Liew | 2020-11-03 | tidied reporting |
| Rmd | 5f1623a | bernard-liew | 2020-10-13 | initial network analysis |
Load package
if (!require("pacman")) install.packages("pacman")Loading required package: pacmanWarning: package 'pacman' was built under R version 4.0.3pacman::p_load(tidyverse,
qgraph,
stats,
bootnet,
igraph,
plotrix,
mgm,
furrr,
cowplot,
officer,
flextable,
arsenal)
# Helper code
meanNsd_transform <- function (x) {
m <- round (x[[1]][1], 2)
s <- round (x[[1]][2], 2)
m_s <- paste0(m, "(", s, ")")
return (m_s)
}Report network without group as variable
ODI labels
# ODI custom figure
nodeLabels <- c("Pain Intensity", "Personal Care", "Lifting", "Walking", "Sitting",
"Standing", "Sleeping", "Social life", "Travelling", "Work/Housework")
nodeLabs <- c("Grp", "Q1.Pain", "Q2.Care", "Q3.Lift", "Q4.Walk", "Q5.Sit",
"Q6.Std", "Q7.Slp", "Q8.Life", "Q9.Trav", "Q10.Work")
ques <- paste0("Q", 1:10)
node_df <- data.frame("Item" = ques,
"Variable" = nodeLabels)Report network with group as variable
Load models
Individualised PT coded as 1.
Advise coded as 0.
# Model with missing data as input
res <- readRDS("output/mgm_raw.RDS")
# Model with complete imputed data as input
#res <- readRDS("output/com.RDS")
nodes <- colnames (res$data[[1]])Descriptive statistics
df <- readRDS("output/dat_odi_nest.RDS")
temp1 <- df$raw %>% select (-data_mgm)
temp2 <- df$com %>% select (-data_mgm) %>% rename (data_com = data)
temp3 <- temp1 %>%
inner_join(temp2, by = "time")
temp4 <- df$raw %>% select (-data) %>% rename (data = data_mgm)
temp5 <- df$com %>% select (-data) %>% rename (data_com = data_mgm)
temp6 <- temp4 %>%
inner_join(temp5, by = "time")
df <- list (odi = temp3,
odi_mgm = temp6)
rm (list = ls(pattern = "temp"))
df2 <- df$odi_mgm %>%
select (time, data) %>%
unnest (data) %>%
mutate_if (is.factor, as.numeric) %>%
mutate (grp = factor (grp),
time = factor (time)) %>%
pivot_longer(cols = starts_with("Q"),
names_to = "Items",
values_to = "val") %>%
mutate (Items = factor (Items, levels = ques, labels = nodeLabels))
df_plot <- df2 %>%
group_by(time, grp,Items) %>%
summarise(Mean = mean (val, na.rm = TRUE),
Sd = sd (val, na.rm = TRUE))
f <- ggplot (df_plot) +
geom_point(aes (x = Items, y = Mean, colour = grp, group = grp),
stat = "identity", position=position_dodge(0.5)) +
geom_errorbar(aes (x = Items, ymin = Mean - Sd,
ymax = Mean + Sd, colour = grp), width = 0, position=position_dodge(0.5)) +
facet_wrap(~ time, scales = "fixed") +
scale_color_manual(values = c("red", "blue"), labels = c("IndPT", "Advice")) +
labs (x = "ODI Items",
y = "Mean (SD) score of items",
colour = "Group") +
theme_cowplot() +
theme(text = element_text(size=16),
axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
f
# pdf(width = 15, height = 8, file = "../manuscript_odi_nw/sm_fig1.pdf")
# f
# dev.off()Plot network
Blue edges - positive correlation
Red edges - negative correlation
The thickness of the edges indicate the magnitude of correlation.
par (mfrow = c(3,2))
p1 <- plot (res$nw[[1]], title = "Baseline", label.cex = 2, vsize = 15, curve = 0.4, curveAll = TRUE, labels = nodeLabs, title.cex = 4)
plot (res$nw[[2]], title = "Week 5", layout = p1$layout, label.cex = 2, vsize = 15, curve = 0.4, curveAll = TRUE, labels = nodeLabs, title.cex = 4)
plot (res$nw[[3]], title = "Week 10", layout = p1$layout, label.cex = 2, vsize = 15, curve = 0.4, curveAll = TRUE, labels = nodeLabs, title.cex = 4)
plot (res$nw[[4]], title = "Week 26", layout = p1$layout, label.cex = 2, vsize = 15, curve = 0.4, curveAll = TRUE, labels = nodeLabs, title.cex = 4)
plot (res$nw[[5]], title = "Week 52", layout = p1$layout, label.cex = 2, vsize = 15, curve = 0.4, curveAll = TRUE, labels = nodeLabs, title.cex = 4)
# plot.new()
# addtable2plot(0,0,node_df,
# xpad=1, ypad=1,
# bty='o',
# display.rownames = FALSE,
# hlines = TRUE,
# vlines = TRUE)
Plot edge weights stability
Confidence interval
w_fig <- map (res$edgewts, plot, order = "sample", CIstyle = "quantiles")
w_fig <- map (w_fig, ~.x +
theme(text = element_text(size = 16)),
include = "all", print = FALSE, scale = "relative")
cowplot::plot_grid(plotlist = w_fig, labels = c("Wk0","Wk 5", "Wk 10", "Wk 26", "wk52" ),
vjust = 1, hjust = -1, ncol = 2)
Plot 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.
# Plot centrality
c_fig <- map (res$nw, centralityPlot, include = c("Closeness", "Strength", "Betweenness"),
print = FALSE, scale = "relative", labels = nodeLabs) %>%
map (~.x +
scale_x_continuous(breaks= c(0, 0.5, 1), lim = c(0, 1)) +
theme(text = element_text(size = 20),
axis.text.x = element_text(angle = 45, hjust = 1)),
include = "all", print = FALSE, scale = "relative")
#tiff(width = 15, height = 15, units = "in", res = 100, file = "output/odi_strength.tiff")
cowplot::plot_grid(plotlist = c_fig, labels = c("Wk0","Wk 5", "Wk 10", "Wk 26", "wk52" ), vjust = 1, hjust = 0, ncol = 2)
#dev.off()Plot centrality stability
Is the centrality order stable?
# Plot centrality stability
s_fig <- map (res$centr_stb, plot, statistics = c("closeness", "strength", "betweenness"))
s_fig <- map (s_fig, ~.x +
ylab ("Ave Corr") +
theme(text = element_text(size = 20),
axis.text.x = element_text(angle = 90, hjust = 1)),
include = "all", print = FALSE, scale = "relative")
#tiff(width = 15, height = 15, units = "in", res = 100, file = "output/odi_stability.tiff")
cowplot::plot_grid(plotlist = s_fig, labels = c("Wk0","Wk 5", "Wk 10", "Wk 26", "wk52" ), vjust = 1, hjust = -1, ncol = 2)
#dev.off()Get CS coefficient
The stability of centrality estimation, and results in a centrality-stability coefficient (CS-coefficient) that should not be lower than 0.25 and preferably above 0.5
cs_coef <- res %>%
select (time, cor_stb) %>%
unnest () %>%
ungroup() %>%
mutate (measure = rep (c("betweenness", "closeness", "edge", "expectedInfluence", "strength"), 5)) %>%
mutate (CS = round (cor_stb, 2)) %>%
filter (measure %in% c("betweenness", "closeness","strength")) %>%
dplyr::select (time, measure, CS)
cs_coef %>%
knitr::kable (caption = "Stability of centrality indices")| time | measure | CS |
|---|---|---|
| 0 | betweenness | 0.00 |
| 0 | closeness | 0.00 |
| 0 | strength | 0.59 |
| 5 | betweenness | 0.36 |
| 5 | closeness | 0.28 |
| 5 | strength | 0.36 |
| 10 | betweenness | 0.05 |
| 10 | closeness | 0.13 |
| 10 | strength | 0.28 |
| 26 | betweenness | 0.13 |
| 26 | closeness | 0.28 |
| 26 | strength | 0.36 |
| 52 | betweenness | 0.00 |
| 52 | closeness | 0.00 |
| 52 | strength | 0.44 |
sessionInfo()R version 4.0.2 (2020-06-22)
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] arsenal_3.5.0 flextable_0.6.1 officer_0.3.16 cowplot_1.1.1
[5] furrr_0.2.1 future_1.21.0 mgm_1.2-10 plotrix_3.7-8
[9] igraph_1.2.6 bootnet_1.4.6 qgraph_1.6.5 forcats_0.5.0
[13] stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
[17] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.3 tidyverse_1.3.0
[21] pacman_0.5.1
loaded via a namespace (and not attached):
[1] R.utils_2.10.1 tidyselect_1.1.0 htmlwidgets_1.5.3
[4] grid_4.0.2 munsell_0.5.0 codetools_0.2-18
[7] withr_2.3.0 colorspace_2.0-0 NetworkToolbox_1.4.1
[10] highr_0.8 uuid_0.1-4 knitr_1.30
[13] rstudioapi_0.13 stats4_4.0.2 listenv_0.8.0
[16] labeling_0.4.2 huge_1.3.4.1 git2r_0.27.1
[19] mnormt_2.0.2 farver_2.0.3 rprojroot_2.0.2
[22] parallelly_1.24.0 vctrs_0.3.6 generics_0.1.0
[25] xfun_0.20 R6_2.5.0 doParallel_1.0.16
[28] smacof_2.1-1 reshape_0.8.8 assertthat_0.2.1
[31] promises_1.1.1 scales_1.1.1 nnet_7.3-14
[34] gtable_0.3.0 globals_0.14.0 weights_1.0.1
[37] workflowr_1.6.2 rlang_0.4.10 systemfonts_0.3.2
[40] splines_4.0.2 wordcloud_2.6 broom_0.7.4.9000
[43] checkmate_2.0.0 yaml_2.2.1 reshape2_1.4.4
[46] abind_1.4-5 modelr_0.1.8 d3Network_0.5.2.1
[49] backports_1.2.1 httpuv_1.5.4 Hmisc_4.4-2
[52] tools_4.0.2 psych_2.0.12 lavaan_0.6-7
[55] ellipsis_0.3.1 RColorBrewer_1.1-2 polynom_1.4-0
[58] Rcpp_1.0.6 plyr_1.8.6 base64enc_0.1-3
[61] rpart_4.1-15 pbapply_1.4-3 haven_2.3.1
[64] cluster_2.1.0 fs_1.5.0 survey_4.0
[67] magrittr_2.0.1 data.table_1.14.0 openxlsx_4.2.3
[70] reprex_0.3.0 tmvnsim_1.0-2 mvtnorm_1.1-1
[73] matrixcalc_1.0-3 whisker_0.4 hms_0.5.3
[76] evaluate_0.14 rio_0.5.16 jpeg_0.1-8.1
[79] readxl_1.3.1 gridExtra_2.3 shape_1.4.5
[82] compiler_4.0.2 ellipse_0.4.2 mice_3.12.0
[85] GGMncv_2.0.0 crayon_1.3.4 R.oo_1.24.0
[88] htmltools_0.5.0 corpcor_1.6.9 later_1.1.0.1
[91] Formula_1.2-4 snow_0.4-3 lubridate_1.7.9.2
[94] DBI_1.1.0 relaimpo_2.2-3 dbplyr_2.0.0
[97] MASS_7.3-53 boot_1.3-25 IsingSampler_0.2.1
[100] Matrix_1.2-18 IsingFit_0.3.1 car_3.0-10
[103] cli_2.2.0 heplots_1.3-7 mitools_2.4
[106] R.methodsS3_1.8.1 gdata_2.18.0 parallel_4.0.2
[109] BDgraph_2.63 pkgconfig_2.0.3 numDeriv_2016.8-1.1
[112] foreign_0.8-81 xml2_1.3.2 foreach_1.5.1
[115] pbivnorm_0.6.0 rvest_0.3.6 digest_0.6.27
[118] rmarkdown_2.6 cellranger_1.1.0 htmlTable_2.1.0
[121] glassoFast_1.0 gdtools_0.2.3 curl_4.3
[124] gtools_3.8.2 rjson_0.2.20 lifecycle_0.2.0
[127] nlme_3.1-151 glasso_1.11 jsonlite_1.7.2
[130] carData_3.0-4 fansi_0.4.1 pillar_1.4.7
[133] lattice_0.20-41 httr_1.4.2 survival_3.2-7
[136] glue_1.4.2 networktools_1.2.3 zip_2.1.1
[139] fdrtool_1.2.16 png_0.1-7 iterators_1.0.13
[142] candisc_0.8-3 glmnet_4.0-2 class_7.3-17
[145] stringi_1.5.3 nnls_1.4 latticeExtra_0.6-29
[148] eigenmodel_1.11 e1071_1.7-4