2-bn_analysis
Bernard
2020-09-08
Last updated: 2020-10-26
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Knit directory: 2020_cts_bn/
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Introduction
This is an example of a Bayesian network, with directed arcs. I only perform the analysis on a subset of provided variables.
Import libraries
rm (list = ls())
# Helper
library (tidyverse)
# BN
library (bnlearn)
# Model
library (caret)
# Feature parallel
library (doParallel)
# Plot
library (Rgraphviz)Import data
dat <- readRDS ("output/dat.RDS")Collapse variables
dat <- dat %>%
mutate (ppt_medn_base = (ppt_medn_aff_base + ppt_medn_naff_base)/2,
ppt_uln_base = (ppt_uln_aff_base + ppt_uln_naff_base)/2,
ppt_radn_base = (ppt_radn_aff_base + ppt_radn_naff_base)/2,
ppt_neck_base = (ppt_neck_aff_base + ppt_neck_naff_base)/2,
ppt_cts_base = (ppt_cts_aff_base + ppt_cts_naff_base)/2,
ppt_ta_base = (ppt_ta_aff_base + ppt_ta_naff_base)/2) %>%
select (-c(ppt_medn_aff_base:ppt_ta_naff_base))BN analysis
Create blacklist
var.excl <- c(grep ("groc", names (dat), value = TRUE) ,
grep ("worst", names (dat), value = TRUE) ,
grep ("base", names (dat), value = TRUE) ,
"age", "pain_years", "pain_extent", "aff_side", "emg")
df.bn = as.data.frame (dat)[, !names (dat) %in% var.excl] %>%
na.omit()
df.bn$grp <- factor (df.bn$grp)
rx.var <- "grp"
demo.var = grep("age|sex|years|emg", colnames (df.bn), value = TRUE)
base.var = grep("_base", colnames (df.bn), value = TRUE)
mth1.var = grep("_1", colnames (df.bn), value = TRUE)
mth3.var = grep("_3", colnames (df.bn), value = TRUE)
mth6.var = grep("_6", colnames (df.bn), value = TRUE)
outcome.var = grep("_12", colnames (df.bn), value = TRUE)
pair_var <- expand.grid(from = names (df.bn),
to = names (df.bn)) %>%
rownames_to_column()
tiers_keep <- pair_var %>%
filter (!(grepl (paste0(outcome.var, collapse = "|"),from))) %>%
filter (!(grepl (paste0(rx.var, collapse = "|"),to))) %>%
filter (!(grepl (paste0(mth6.var, collapse = "|"), from) &
grepl (paste0(c(demo.var, base.var, mth1.var, mth3.var), collapse = "|"),to))) %>%
filter (!(grepl (paste0(mth3.var, collapse = "|"), from) &
grepl (paste0(c(demo.var, base.var, mth1.var), collapse = "|"),to))) #%>%
# filter (!(grepl (paste0(mth1.var, collapse = "|"), from) &
# grepl (paste0(c(demo.var, base.var), collapse = "|"),to))) %>%
# filter (!(grepl (paste0(base.var, collapse = "|"), from) &
# grepl (paste0(c(demo.var), collapse = "|"),to)))
bl <- anti_join(pair_var, tiers_keep, by = "rowname") %>%
filter (from != to) %>%
select (from, to)Build the final model using model averaging
boot <- boot.strength(df.bn,
R = 200,
algorithm = "hc",
algorithm.args = list (blacklist = bl))Get averaged model
avg <- averaged.network(boot, threshold = 0.5)
fit <- bn.fit (avg, df.bn, method = "mle")
g <- strength.plot(avg, boot, shape = "ellipse")
graph::nodeRenderInfo(g) = list(fontsize=18)
Rgraphviz::renderGraph(g)
Performance evaluation using nested cross validation.
Not run yet.
Inner is bootstrap resampling for model averaging. Outer is bootstrap resampling k = 25 for performance evaluation.
set.seed (2564)
flds <- createFolds(1:nrow(df.bn),
k = 10, 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()
doParallel::registerDoParallel(7)
############
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 = 5,
maximize.args = list(blacklist = bl,
k = log(nrow(boot.sample))))
}
#############
stopImplicitCluster()
bootstr <- custom.strength(boot, nodes = names(train))
avg <- averaged.network(bootstr, threshold = 0.7)
fit <- bn.fit (avg, train, method = "mle")
imp.list = impute (fit, data = test, method = "bayes-lw")
inames = names (imp.list) [-c(1:2)]
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.dfSave data
save(avg,
bl,
demo.var,
mth3.var,
mth6.var,
outcome.var,
boot,
bootstr,
corr.df,
corr.df.list,
df.bn,
fit,
file = "output/bn_data.RData")
sessionInfo()R version 3.6.0 (2019-04-26)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)
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] Rgraphviz_2.22.0 graph_1.56.0 BiocGenerics_0.24.0
[4] doParallel_1.0.15 iterators_1.0.10 foreach_1.4.4
[7] caret_6.0-84 lattice_0.20-38 bnlearn_4.5
[10] forcats_0.4.0 stringr_1.4.0 dplyr_1.0.2
[13] purrr_0.3.3 readr_1.3.1 tidyr_1.0.2
[16] tibble_3.0.3 ggplot2_3.2.1 tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] nlme_3.1-139 fs_1.3.0 lubridate_1.7.4 DiceDesign_1.8-1
[5] httr_1.4.1 rprojroot_1.3-2 tools_3.6.0 backports_1.1.4
[9] R6_2.4.0 rpart_4.1-15 DBI_1.0.0 lazyeval_0.2.2
[13] colorspace_1.4-1 nnet_7.3-12 withr_2.1.2 tidyselect_1.1.0
[17] compiler_3.6.0 git2r_0.27.1 cli_2.0.1 rvest_0.3.5
[21] xml2_1.2.2 scales_1.1.1 digest_0.6.19 rmarkdown_2.3
[25] pkgconfig_2.0.2 htmltools_0.4.0 lhs_1.0.1 dbplyr_1.4.4
[29] rlang_0.4.7 readxl_1.3.1 rstudioapi_0.11 generics_0.0.2
[33] jsonlite_1.6 ModelMetrics_1.2.2 magrittr_1.5 Matrix_1.2-17
[37] Rcpp_1.0.2 munsell_0.5.0 fansi_0.4.0 GPfit_1.0-8
[41] lifecycle_0.2.0 stringi_1.4.3 whisker_0.3-2 yaml_2.2.0
[45] MASS_7.3-51.4 plyr_1.8.4 recipes_0.1.9 blob_1.2.1
[49] promises_1.0.1 crayon_1.3.4 haven_2.2.0 splines_3.6.0
[53] hms_0.5.3 knitr_1.27 pillar_1.4.3 dials_0.0.4
[57] stats4_3.6.0 reshape2_1.4.3 codetools_0.2-16 parsnip_0.0.5
[61] reprex_0.3.0 glue_1.4.2 evaluate_0.14 data.table_1.12.8
[65] modelr_0.1.5 vctrs_0.3.4 httpuv_1.5.2 cellranger_1.1.0
[69] gtable_0.3.0 assertthat_0.2.1 xfun_0.7 gower_0.2.0
[73] prodlim_2018.04.18 broom_0.5.4 later_0.8.0 class_7.3-15
[77] survival_2.44-1.1 timeDate_3043.102 workflowr_1.6.2 lava_1.6.5
[81] workflows_0.1.0 ellipsis_0.3.0 ipred_0.9-9