3-mediation_late
bernard-liew
2020-06-10
Last updated: 2020-07-03
Checks: 6 1
Knit directory: 2020_LBPcausal/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | e41cc86 | bernard-liew | 2020-07-03 | Build site. |
| Rmd | ed632b0 | bernard-liew | 2020-07-03 | Initial bayesian network analysis |
| Rmd | f5b55d7 | bernard-liew | 2020-07-02 | removed function variable |
| Rmd | 807f52c | bernard-liew | 2020-07-02 | added all possible change values |
| Rmd | 127e9d3 | bernard-liew | 2020-06-11 | Original analysis |
Load library
# Helper packages
library (tidyverse)
library (tidyselect)
library (arsenal)
library (janitor)
library (magrittr)
library (Rgraphviz)
library (corrr)
# Import
library(readxl)
library (xlsx)
# Missing data
library (mice)
library (VIM)
# Modelling
library (bnlearn)
library (caret)
# Parallel
library (doParallel)Introduction
This is a bayesian network analysis where the variables are the change scores between week 52 and baseline.
Import data
rm (list = ls())
df_list <- readRDS("output/df_change.RDS")Subset data
df <- df_list[["wk52_base"]]
names(df)[1:8] <- paste0(str_remove(names(df)[1:8] , "wk52_"), "_late")Exploratory analysis
Between group comparisons
tableby (subgrp ~., data = df, digits = 2, digits.p = 2) %>%
summary ()
| | dhr (N=54) | nrdp (N=8) | rdp (N=63) | mfp (N=91) | mtg (N=74) | Total (N=290) | p value|
|:--------------------------------------|:-----------------:|:-----------------:|:-----------------:|:-----------------:|:-----------------:|:-----------------:|-------:|
|**osw_late** | | | | | | | 0.10|
| N-Miss | 1 | 0 | 6 | 4 | 2 | 13 | |
| Mean (SD) | -18.81 (16.70) | -14.50 (7.15) | -11.42 (11.50) | -13.85 (12.69) | -14.21 (15.68) | -14.41 (14.13) | |
| Range | -48.00 - 24.00 | -22.00 - 0.00 | -40.00 - 20.00 | -35.00 - 27.56 | -58.00 - 30.00 | -58.00 - 30.00 | |
|**lbp_late** | | | | | | | 0.06|
| N-Miss | 1 | 0 | 6 | 4 | 1 | 12 | |
| Mean (SD) | -2.07 (2.78) | -2.44 (3.62) | -2.40 (2.24) | -2.72 (2.44) | -3.33 (2.21) | -2.68 (2.47) | |
| Range | -6.50 - 4.00 | -9.00 - 2.00 | -7.00 - 2.00 | -7.00 - 4.00 | -8.00 - 2.00 | -9.00 - 4.00 | |
|**lp_late** | | | | | | | 0.24|
| N-Miss | 1 | 1 | 19 | 18 | 10 | 49 | |
| Mean (SD) | -3.41 (2.99) | -2.86 (2.12) | -2.14 (2.67) | -2.71 (2.60) | -2.48 (3.05) | -2.70 (2.82) | |
| Range | -8.00 - 1.50 | -5.00 - 0.00 | -7.00 - 2.50 | -9.00 - 6.00 | -8.00 - 7.00 | -9.00 - 7.00 | |
|**pain_cope_success_late** | | | | | | | 0.63|
| N-Miss | 2 | 1 | 11 | 18 | 12 | 44 | |
| Mean (SD) | -1.30 (3.09) | -1.27 (3.36) | -2.13 (2.88) | -1.36 (3.38) | -1.82 (3.55) | -1.62 (3.26) | |
| Range | -6.00 - 7.00 | -5.00 - 5.00 | -9.00 - 4.00 | -9.00 - 7.00 | -8.00 - 7.00 | -9.00 - 7.00 | |
|**anx_late** | | | | | | | 0.05|
| N-Miss | 2 | 1 | 11 | 17 | 12 | 43 | |
| Mean (SD) | -2.15 (2.87) | -4.71 (3.35) | -1.45 (2.47) | -1.69 (2.96) | -2.22 (2.88) | -1.96 (2.87) | |
| Range | -9.00 - 3.00 | -10.00 - -2.00 | -8.00 - 3.00 | -9.00 - 6.00 | -7.00 - 6.00 | -10.00 - 6.00 | |
|**depress_late** | | | | | | | 0.02|
| N-Miss | 2 | 1 | 13 | 17 | 13 | 46 | |
| Mean (SD) | -1.75 (3.03) | -4.43 (3.60) | -0.99 (2.80) | -0.76 (3.13) | -1.40 (2.70) | -1.28 (3.00) | |
| Range | -9.00 - 7.00 | -10.00 - -1.00 | -7.00 - 7.00 | -8.00 - 8.00 | -7.00 - 6.00 | -10.00 - 8.00 | |
|**pain_persist_late** | | | | | | | 0.03|
| N-Miss | 2 | 1 | 12 | 18 | 12 | 45 | |
| Mean (SD) | -2.25 (3.35) | -6.29 (2.69) | -1.66 (3.50) | -2.40 (3.60) | -2.19 (3.64) | -2.27 (3.56) | |
| Range | -9.00 - 8.00 | -9.00 - -1.00 | -9.00 - 8.00 | -10.00 - 6.00 | -8.00 - 8.00 | -10.00 - 8.00 | |
|**fear_late** | | | | | | | 0.90|
| N-Miss | 5 | 2 | 12 | 18 | 13 | 50 | |
| Mean (SD) | -4.57 (6.65) | -2.33 (10.41) | -3.63 (6.34) | -4.35 (7.00) | -4.50 (7.05) | -4.23 (6.86) | |
| Range | -20.00 - 10.00 | -19.00 - 8.00 | -25.00 - 10.00 | -21.00 - 18.00 | -24.00 - 9.00 | -25.00 - 18.00 | |
|**grp** | | | | | | | 0.99|
| advice | 26 (48.1%) | 3 (37.5%) | 30 (47.6%) | 43 (47.3%) | 35 (47.3%) | 137 (47.2%) | |
| individualisedphysio | 28 (51.9%) | 5 (62.5%) | 33 (52.4%) | 48 (52.7%) | 39 (52.7%) | 153 (52.8%) | |
|**id** | | | | | | | < 0.01|
| Mean (SD) | 1526.96 (209.06) | 4571.12 (52.77) | 5529.14 (211.09) | 2514.09 (228.03) | 3515.42 (234.58) | 3297.53 (1403.37) | |
| Range | 1101.00 - 1806.00 | 4501.00 - 4652.00 | 5101.00 - 5903.00 | 2101.00 - 2951.00 | 3101.00 - 3905.00 | 1101.00 - 5903.00 | |Correlation plots
df %>%
select_if (is.numeric) %>%
select(-id) %>%
correlate() %>%
rearrange() %>%
network_plot(colors = c("red", "green"))
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'Registered S3 method overwritten by 'seriation':
method from
reorder.hclust gclus
BN analysis
Early change
Create blacklist
df.bn = as.data.frame (df)
df.bn$id <- NULL
df.bn$subgrp <- NULL # since the earlier descriptives show no difference using ANOVA
tiers_bl = list (colnames (df.bn)[colnames(df.bn) %in% grep ("grp",colnames(df.bn), value = TRUE)],
colnames (df.bn)[colnames(df.bn) %in% grep ("late",colnames(df.bn), value = TRUE)])
bl1 = tiers2blacklist(tiers_bl)
bl = rbind(bl1)Create whitelist
wl = matrix(c("grp", "lbp_late"), nrow = 1, ncol = 2, byrow = TRUE,
dimnames = list(NULL,c("from", "to")))
wl = rbind(wl,
c("grp", "lp_late"),
c("grp", "osw_late")) Build BN model
Just with blacklist
doParallel::registerDoParallel(7)
n_boot = 200
############
boot_bl <- foreach (B = 1: n_boot) %dopar%{
boot.sample = df.bn[sample(nrow(df.bn),
nrow(df.bn), replace = TRUE), ]
bnlearn::structural.em(boot.sample, impute = "bayes-lw", max.iter = 3,
maximize.args = list(blacklist = bl))
}
#############See results
bootstr = custom.strength(boot_bl, nodes = names(df.bn))
avg = averaged.network(bootstr, threshold = 0.5)Warning in averaged.network.backend(strength = strength, nodes = nodes, : arc
anx_late -> depress_late would introduce cycles in the graph, ignoring.fit = bn.fit (avg, df.bn, method = "mle")
g = strength.plot(avg,
bootstr,
shape = "rectangle",
main = "Figure")
graph::nodeRenderInfo(g) = list(fontsize=18)With blacklist and whitelist
doParallel::registerDoParallel(7)
n_boot = 200
############
boot_wl = foreach (B = 1: n_boot) %dopar%{
boot.sample = df.bn[sample(nrow(df.bn),
nrow(df.bn), replace = TRUE), ]
bnlearn::structural.em(boot.sample, impute = "bayes-lw", max.iter = 3,
maximize.args = list(blacklist = bl))
}
#############See results
bootstr = custom.strength(boot_bl, nodes = names(df.bn))
avg = averaged.network(bootstr, threshold = 0.5)Warning in averaged.network.backend(strength = strength, nodes = nodes, : arc
anx_late -> depress_late would introduce cycles in the graph, ignoring.fit = bn.fit (avg, df.bn, method = "mle")
g = strength.plot(avg,
bootstr,
shape = "rectangle",
highlight = list (arcs = wl),
main = "Figure")
graph::nodeRenderInfo(g) = list(fontsize=18)Performance evaluation
On model with just blacklist
Nested Cross validation. Inner is bootstrap resampling for model averaging. Outer is 10 fold CV for performance evaluation.
flds <- flds <- createFolds(1:nrow(df.bn),
k = 10, list = TRUE, returnTrain = TRUE)
n_boot = 200
doParallel::registerDoParallel(7)
corr.df.list <- list()
for (k in seq_along(flds)) {
train <- df.bn [flds[[k]], ] %>% as.data.frame()
test <- df.bn [-flds[[k]], ] %>% as.data.frame()
############
boot = foreach (B = 1: n_boot) %dopar%{
boot.sample = train[sample(nrow(train),
nrow(train), replace = TRUE), ]
bnlearn::structural.em(boot.sample, impute = "bayes-lw", max.iter = 3,
maximize.args = list(blacklist = bl,
#whitelist = wl.list[[n]],
k = log(nrow(boot.sample))))
}
#############
bootstr <- custom.strength(boot, nodes = names(train))
avg <- averaged.network(bootstr, threshold = 0.5)
fit <- bn.fit (avg, train, method = "mle")
imp.list = impute (fit, data = test, method = "bayes-lw")
inames = names (imp.list) [!names (imp.list) %in% c("grp", "subgrp")]
corr.df = structure(numeric(length (inames)), names = inames)
for (var in inames) {
corr.df[var] = cor(predict(fit, data = imp.list, var, method = "bayes-lw"),
imp.list[, var])
}
corr.df.list[[k]] <- corr.df
}
corr.df <- bind_cols (corr.df.list) %>%
apply (1, mean)
names (corr.df) <- inames
corr.df
cat ("The mean correlation is:", mean (corr.df))On model with blacklist and whitelist
Nested Cross validation. Inner is bootstrap resampling for model averaging. Outer is 10 fold CV for performance evaluation.
flds <- flds <- createFolds(1:nrow(df.bn),
k = 10, list = TRUE, returnTrain = TRUE)
n_boot = 200
doParallel::registerDoParallel(7)
corr.df.list <- list()
for (k in seq_along(flds)) {
train <- df.bn [flds[[k]], ] %>% as.data.frame()
test <- df.bn [-flds[[k]], ] %>% as.data.frame()
############
boot = foreach (B = 1: n_boot) %dopar%{
boot.sample = train[sample(nrow(train),
nrow(train), replace = TRUE), ]
bnlearn::structural.em(boot.sample, impute = "bayes-lw", max.iter = 3,
maximize.args = list(blacklist = bl,
whitelist = wl,
k = log(nrow(boot.sample))))
}
#############
bootstr <- custom.strength(boot, nodes = names(train))
avg <- averaged.network(bootstr, threshold = 0.5)
fit <- bn.fit (avg, train, method = "mle")
imp.list = impute (fit, data = test, method = "bayes-lw")
inames = names (imp.list) [!names (imp.list) %in% c("grp", "subgrp")]
corr.df = structure(numeric(length (inames)), names = inames)
for (var in inames) {
corr.df[var] = cor(predict(fit, data = imp.list, var, method = "bayes-lw"),
imp.list[, var])
}
corr.df.list[[k]] <- corr.df
}
corr.df <- bind_cols (corr.df.list) %>%
apply (1, mean)
names (corr.df) <- inames
corr.df
cat ("The mean correlation is:", mean (corr.df))
sessionInfo()R version 3.6.2 (2019-12-12)
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] grid parallel stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] doParallel_1.0.15 iterators_1.0.12 foreach_1.5.0
[4] caret_6.0-86 lattice_0.20-38 bnlearn_4.5
[7] VIM_5.1.1 data.table_1.12.8 colorspace_1.4-1
[10] mice_3.9.0 xlsx_0.6.3 readxl_1.3.1
[13] corrr_0.4.2 Rgraphviz_2.30.0 graph_1.64.0
[16] BiocGenerics_0.32.0 magrittr_1.5 janitor_2.0.1
[19] arsenal_3.4.0 tidyselect_1.1.0 forcats_0.5.0
[22] stringr_1.4.0 dplyr_0.8.4 purrr_0.3.3
[25] readr_1.3.1 tidyr_1.0.0 tibble_3.0.1
[28] ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] backports_1.1.5 plyr_1.8.6 sp_1.4-2
[4] splines_3.6.2 digest_0.6.23 htmltools_0.5.0
[7] viridis_0.5.1 gdata_2.18.0 fansi_0.4.0
[10] cluster_2.1.0 gclus_1.3.2 openxlsx_4.1.5
[13] recipes_0.1.13 modelr_0.1.8 gower_0.2.2
[16] ggrepel_0.8.2 blob_1.2.1 rvest_0.3.5
[19] haven_2.2.0 xfun_0.15 crayon_1.3.4
[22] jsonlite_1.6 survival_3.2-3 zoo_1.8-8
[25] glue_1.3.1 registry_0.5-1 gtable_0.3.0
[28] ipred_0.9-9 car_3.0-8 DEoptimR_1.0-8
[31] abind_1.4-5 scales_1.1.1 DBI_1.1.0
[34] Rcpp_1.0.3 viridisLite_0.3.0 laeken_0.5.1
[37] foreign_0.8-72 stats4_3.6.2 lava_1.6.7
[40] prodlim_2019.11.13 vcd_1.4-7 httr_1.4.1
[43] gplots_3.0.3 ellipsis_0.3.1 farver_2.0.3
[46] pkgconfig_2.0.3 rJava_0.9-12 nnet_7.3-14
[49] dbplyr_1.4.4 labeling_0.3 rlang_0.4.6
[52] reshape2_1.4.4 later_1.0.0 munsell_0.5.0
[55] cellranger_1.1.0 tools_3.6.2 cli_2.0.2
[58] generics_0.0.2 ranger_0.12.1 broom_0.5.6
[61] evaluate_0.14 yaml_2.2.1 ModelMetrics_1.2.2.2
[64] knitr_1.29 fs_1.3.1 zip_2.0.4
[67] robustbase_0.93-6 caTools_1.18.0 dendextend_1.13.4
[70] nlme_3.1-142 whisker_0.4 xml2_1.2.2
[73] compiler_3.6.2 rstudioapi_0.11 curl_4.3
[76] e1071_1.7-3 reprex_0.3.0 stringi_1.4.3
[79] highr_0.8 Matrix_1.2-18 vctrs_0.3.1
[82] pillar_1.4.4 lifecycle_0.2.0 lmtest_0.9-37
[85] bitops_1.0-6 seriation_1.2-8 httpuv_1.5.2
[88] R6_2.4.1 promises_1.1.0 TSP_1.1-10
[91] gridExtra_2.3 KernSmooth_2.23-17 rio_0.5.16
[94] codetools_0.2-16 boot_1.3-25 MASS_7.3-51.6
[97] gtools_3.8.2 assertthat_0.2.1 xlsxjars_0.6.1
[100] rprojroot_1.3-2 withr_2.2.0 hms_0.5.3
[103] rpart_4.1-15 timeDate_3043.102 class_7.3-17
[106] rmarkdown_2.3 snakecase_0.11.0 carData_3.0-4
[109] git2r_0.27.1 pROC_1.16.2 lubridate_1.7.4