2-bn_analysis
Bernard
2020-09-08
Last updated: 2021-06-29
Checks: 6 1
Knit directory: 2020_cts_bn/
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| html | 7f3e45a | bernard-liew | 2021-06-28 | Build site. |
| Rmd | 9fc2349 | bernard-liew | 2021-06-28 | Publish BN results |
| Rmd | f439ba8 | bernard-liew | 2021-06-25 | changed graph and added emg. |
| Rmd | 53feb1c | bernard-liew | 2021-06-23 | added bayesian networks analysis |
| Rmd | c7e5ff7 | bernard-liew | 2021-04-29 | added reporting of analysis |
| Rmd | 278a36b | bernard-liew | 2021-04-14 | added bn viewer |
| html | 278a36b | bernard-liew | 2021-04-14 | added bn viewer |
| Rmd | 5ff7ce6 | bernard-liew | 2021-04-14 | Added bnviewer |
| Rmd | 880adc7 | bernard-liew | 2021-04-13 | updated bn 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 | fe17d91 | bernard-liew | 2020-10-26 | initial analysis |
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)
library (cowplot)
library (ggpubr)
library (flextable)
library (officer)
# BN
library (bnlearn)
# Model
library (caret)
# Feature parallel
library (doParallel)
# Plot
library (Rgraphviz)
library (bnviewer)
# Helper
return_marginal <- function (x) {
x <- x[-1, , drop = FALSE]
x <- apply (x, 1, mean)
return (x)
}Import data
dat <- readRDS ("output/dat.RDS")
load ("output/bn_data2.RData")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 ("mean", names (dat), value = TRUE),
grep ("ppt", 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()
names (df.bn)[grepl ("years", names (df.bn))] <- "duration"
names (df.bn)[grepl ("extent", names (df.bn))] <- "area"
names (df.bn)[grepl ("cts_base", names (df.bn))] <- "ct_base"
names (df.bn)[grepl ("ppt", names (df.bn))] <- str_remove (names (df.bn)[grepl ("ppt", names (df.bn))], "ppt_")
names (df.bn)[grepl ("cts", names (df.bn))] <- str_remove (names (df.bn)[grepl ("cts", names (df.bn))], "cts_")
names (df.bn)[grepl ("worst", names (df.bn))] <- str_remove (names (df.bn)[grepl ("worst", names (df.bn))], "worst_")
df.bn$grp <- factor (df.bn$grp)
rx.var <- "grp"
demo.var = grep("age|duration|emg|area", colnames (df.bn), value = TRUE)
base.var = grep("_base", colnames (df.bn), value = TRUE)
mth1.var = grep("_1m", colnames (df.bn), value = TRUE)
mth3.var = grep("_3m", colnames (df.bn), value = TRUE)
mth6.var = grep("_6m", colnames (df.bn), value = TRUE)
outcome.var = grep("_12m", 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))) %>%
filter (!(grepl (paste0(rx.var, collapse = "|"), from) &
grepl (paste0(c(demo.var, base.var), collapse = "|"),to)))
bl <- anti_join(pair_var, tiers_keep, by = "rowname") %>%
filter (from != to) %>%
filter (!(grepl ("_12m", from) &
grepl ("_12m", to))) %>%
select (from, to)Explore
df.bn.num <- df.bn %>%
select_if(is.numeric)
M <- cor(df.bn.num)
corrplot::corrplot(M, method = "circle")
Descriptive
ord <- names (df.bn) [!grepl ("emg", names (df.bn))]
df_plot <- df.bn %>%
select (-emg) %>%
pivot_longer(cols = -grp,
names_to = "var",
values_to = "val") %>%
mutate (var = factor (var, levels = ord )) %>%
group_by(grp, var) %>%
summarize (Mean = mean (val),
Sd = sd (val)) %>%
ggplot () +
geom_point (aes (x = grp, y = Mean), stat = "identity") +
geom_errorbar(aes (x = grp, ymin = Mean - Sd, ymax = Mean + Sd), width = 0) +
scale_x_discrete(labels=c("1" = "MT",
"2" = "Surgery")) +
xlab ("Group") +
ylab ("Scores") +
facet_wrap(~ var, ncol = 4, scales = "free") +
theme_cowplot()`summarise()` regrouping output by 'grp' (override with `.groups` argument)df_plot
Build the final model using model averaging
set.seed (123)
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", render = FALSE)
graph::nodeRenderInfo(g) = list(fontsize=14)
# Make color
arc_col <- data.frame(arcs = names (edgeRenderInfo(g)$col)) %>%
separate(arcs, c("parent", "child"), sep = "~")
coef_fit <- coef(fit) %>%
map_if (is.vector, ~.x[!grepl("Intercept", names (.x))]) %>%
map_if (is.matrix, return_marginal) %>%
unlist ()
coef_fit <- data.frame(arcs = names (coef_fit), coefs = coef_fit) %>%
separate(arcs, c ("child", "parent"), sep = "[.]")
new_col <- arc_col %>%
left_join(coef_fit, by = c("parent", "child")) %>%
mutate (coefs = replace_na(coefs,88)) %>%
mutate (col = ifelse (coefs < 0, "red",
ifelse (coefs == 88, "black", "blue")))
new_arc_col <- new_col$col
names (new_arc_col) <- names (edgeRenderInfo(g)$col)
nodeRenderInfo(g)$fill[base.var] = "cyan"
nodeRenderInfo(g)$fill[demo.var] = "cornsilk"
nodeRenderInfo(g)$fill[mth1.var] = "tan1"
nodeRenderInfo(g)$fill[mth3.var] = "gold"
nodeRenderInfo(g)$fill[mth6.var] = "yellow"
nodeRenderInfo(g)$fill[outcome.var] = "tomato"
edgeRenderInfo(g)$col <- new_arc_col
graph::nodeRenderInfo(g) = list(fontsize=14)
renderGraph(g)
viewer(avg,
bayesianNetwork.width = "100%",
bayesianNetwork.height = "80vh",
bayesianNetwork.layout = "layout_with_sugiyama",
bayesianNetwork.title="Bayesian Network of CTS recovery",
node.font = list(color = "black", face="Arial", size = 16),
bayesianNetwork.footer = "Fig. 1 - Layout with Sugiyama"
)Performance evaluation using nested cross validation.
Inner is bootstrap resampling for model averaging. Outer is bootstrap resampling k = 25 for performance evaluation.
set.seed (1245)
flds <- createFolds(1:nrow(df.bn),
k = 10, returnTrain = TRUE)
n_boot = 200
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()
############
boot2 <- boot.strength(train,
R = 200,
algorithm = "hc",
algorithm.args = list (blacklist = bl))
#############
avg2 <- averaged.network(boot2, threshold = 0.5)
fit2 <- bn.fit (avg2, train, method = "mle")
num.var <- test %>%
select_if (is.numeric) %>%
names ()
corr.df = structure(numeric(length (num.var)), names = num.var)
for (n in num.var) {
corr.df[n] = cor(predict(fit2,
data = test,
node = n,
method = "bayes-lw"),
test[n])
}
corr.df.list[[k]] <- corr.df
}
corr.df <- bind_cols (corr.df.list) %>%
apply (1, mean)
names (corr.df) <- num.varcorr.df area age duration pain_base func_base severe_base -0.235157623 -0.113856083 -0.004330285 0.283785575 0.475795256 0.466466429 dep_base pain_1m func_1m severe_1m pain_3m func_3m 0.472637900 0.652991599 0.782958596 0.657706145 0.794676527 0.790054248 severe_3m pain_6m func_6m severe_6m pain_12m func_12m 0.770363752 0.742966202 0.873145448 0.744787953 0.783177399 0.850130968 severe_12m 0.852193578
Correlation performance table
Save data
Report
Influence on group on function at 12 month
set.seed (123)
sim = cpdist(fit, nodes = c("grp", "func_12m"), n = 10^4,
evidence = (TRUE))
mod <- lm(func_12m ~ grp, data = sim)
summary (mod)
Call:
lm(formula = func_12m ~ grp, data = sim)
Residuals:
Min 1Q Median 3Q Max
-2.0900 -0.3526 -0.0100 0.3507 2.0499
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.439107 0.007442 193.389 <2e-16 ***
grp2 0.089763 0.010634 8.441 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5316 on 9998 degrees of freedom
Multiple R-squared: 0.007076, Adjusted R-squared: 0.006977
F-statistic: 71.25 on 1 and 9998 DF, p-value: < 2.2e-16df_plot <- sim %>%
group_by(grp) %>%
summarize (Mean = mean (func_12m),
Sd = sd (func_12m)) %>%
ggplot () +
geom_bar (aes (x = grp, y = Mean), stat = "identity") +
scale_x_discrete(labels=c("1" = "MT",
"2" = "Surgery")) +
geom_errorbar(aes (x = grp, ymin = Mean - Sd, ymax = Mean + Sd), width = 0) +
xlab ("Group") +
ylab ("Function 12m") +
theme_cowplot()`summarise()` ungrouping output (override with `.groups` argument)df_plot
What happens when func_1m was unchanged
set.seed (123)
avg.mutilated = mutilated(avg, evidence = list(func_1m = 0))
#strength.plot(avg.mutilated, boot)
fitted.mutilated = bn.fit (avg.mutilated , df.bn, method = "mle")
fitted.mutilated$func_1m = list(coef = c("(Intercept)" = 0), sd = 0)
sim = cpdist(fitted.mutilated , nodes = c("grp", "func_12m"), n = 10^4,
evidence = (TRUE))
mod <- lm(func_12m ~ grp, data = sim)
summary (mod)
Call:
lm(formula = func_12m ~ grp, data = sim)
Residuals:
Min 1Q Median 3Q Max
-2.10634 -0.34455 -0.00336 0.34088 1.93805
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.073754 0.007228 148.557 < 2e-16 ***
grp2 0.034930 0.010329 3.382 0.000723 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5163 on 9998 degrees of freedom
Multiple R-squared: 0.001143, Adjusted R-squared: 0.001043
F-statistic: 11.44 on 1 and 9998 DF, p-value: 0.0007228Influence on baseline pain on function at 12 month
set.seed (123)
sim = cpdist(fit, nodes = c("pain_base", "func_12m"), n = 10^4,
evidence = (TRUE))
mod <- lm(func_12m ~ pain_base, data = sim)
summary (mod)
Call:
lm(formula = func_12m ~ pain_base, data = sim)
Residuals:
Min 1Q Median 3Q Max
-2.12508 -0.35562 -0.00966 0.34814 2.10990
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.428436 0.023457 60.896 <2e-16 ***
pain_base 0.007811 0.003266 2.392 0.0168 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5333 on 9998 degrees of freedom
Multiple R-squared: 0.0005717, Adjusted R-squared: 0.0004718
F-statistic: 5.719 on 1 and 9998 DF, p-value: 0.0168sim %>%
ggscatter(x = "pain_base", y = "func_12m", add = "reg.line") +
stat_regline_equation(label.x = 3, label.y = 4)`geom_smooth()` using formula 'y ~ x'
What happens when dep_base was unchanged
set.seed (123)
avg.mutilated = mutilated(avg, evidence = list(dep_base = 0))
#strength.plot(avg.mutilated, boot)
fitted.mutilated = bn.fit (avg.mutilated , df.bn, method = "mle")
fitted.mutilated$dep_base = list(coef = c("(Intercept)" = 0), sd = 0)
sim = cpdist(fitted.mutilated, nodes = c("pain_base", "func_12m"), n = 10^4,
evidence = (TRUE))
mod <- lm(func_12m ~ pain_base, data = sim)
summary (mod)
Call:
lm(formula = func_12m ~ pain_base, data = sim)
Residuals:
Min 1Q Median 3Q Max
-2.16542 -0.35020 -0.00913 0.34734 2.07557
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.390981 0.023364 59.535 <2e-16 ***
pain_base 0.004212 0.003253 1.295 0.195
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5312 on 9998 degrees of freedom
Multiple R-squared: 0.0001676, Adjusted R-squared: 6.764e-05
F-statistic: 1.676 on 1 and 9998 DF, p-value: 0.1954sim %>%
ggscatter(x = "pain_base", y = "func_12m", add = "reg.line") +
stat_regline_equation(label.x = 3, label.y = 4)`geom_smooth()` using formula 'y ~ x'
Influence on severity at 1month on function at 12 month
set.seed (123)
sim = cpdist(fit, nodes = c("severe_1m", "func_12m"), n = 10^4,
evidence = (TRUE))
mod <- lm(func_12m ~ severe_1m, data = sim)
summary (mod)
Call:
lm(formula = func_12m ~ severe_1m, data = sim)
Residuals:
Min 1Q Median 3Q Max
-2.09772 -0.35035 -0.01204 0.34508 2.01013
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.23048 0.01812 67.92 <2e-16 ***
severe_1m 0.14791 0.01015 14.57 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5279 on 9998 degrees of freedom
Multiple R-squared: 0.02081, Adjusted R-squared: 0.02071
F-statistic: 212.4 on 1 and 9998 DF, p-value: < 2.2e-16sim %>%
ggscatter(x = "severe_1m", y = "func_12m", add = "reg.line") +
stat_regline_equation(label.x = 3, label.y = 4)`geom_smooth()` using formula 'y ~ x'
What happens when 6 month function was unchanged
set.seed (123)
avg.mutilated = mutilated(avg, evidence = list(func_6m = 0))
#strength.plot(avg.mutilated, boot)
fitted.mutilated = bn.fit (avg.mutilated , df.bn, method = "mle")
fitted.mutilated$func_6m = list(coef = c("(Intercept)" = 0), sd = 0)
sim = cpdist(fitted.mutilated, nodes = c("severe_1m", "func_12m"), n = 10^4,
evidence = (TRUE))
mod <- lm(func_12m ~ severe_1m, data = sim)
summary (mod)
Call:
lm(formula = func_12m ~ severe_1m, data = sim)
Residuals:
Min 1Q Median 3Q Max
-1.00982 -0.28169 -0.08793 0.17133 2.07530
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.221260 0.014436 15.327 <2e-16 ***
severe_1m -0.006393 0.008087 -0.791 0.429
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4207 on 9998 degrees of freedom
Multiple R-squared: 6.252e-05, Adjusted R-squared: -3.75e-05
F-statistic: 0.6251 on 1 and 9998 DF, p-value: 0.4292sim %>%
ggscatter(x = "severe_1m", y = "func_12m", add = "reg.line") +
stat_regline_equation(label.x = 3, label.y = 4)`geom_smooth()` using formula 'y ~ x'
Influence of EMG on function at 12 month
set.seed (123)
sim = cpdist(fit, nodes = c("emg", "func_12m"), n = 10^4,
evidence = (TRUE))
mod <- lm(func_12m ~ emg, data = sim)
summary (aov(mod)) Df Sum Sq Mean Sq F value Pr(>F)
emg 2 39.8 19.887 70.86 <2e-16 ***
Residuals 9997 2805.6 0.281
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov(mod)) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = mod)
$emg
diff lwr upr p adj
2-1 0.13702213 0.10517451 0.16886975 0.00000
3-1 0.01896211 -0.01501423 0.05293846 0.39058
3-2 -0.11806001 -0.14676984 -0.08935018 0.00000df_plot <- sim %>%
group_by(emg) %>%
summarize (Mean = mean (func_12m),
Sd = sd (func_12m)) %>%
ggplot () +
geom_bar (aes (x = emg, y = Mean), stat = "identity") +
scale_x_discrete(labels=c("1" = "Mild",
"2" = "Moderate",
"3" = "Severe")) +
geom_errorbar(aes (x = emg, ymin = Mean - Sd, ymax = Mean + Sd), width = 0) +
xlab ("EMG classification") +
ylab ("Function 12m") +
theme_cowplot()`summarise()` ungrouping output (override with `.groups` argument)df_plot
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19041)
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] bnviewer_0.1.6 Rgraphviz_2.34.0 graph_1.68.0
[4] BiocGenerics_0.36.0 doParallel_1.0.16 iterators_1.0.13
[7] foreach_1.5.1 caret_6.0-86 lattice_0.20-41
[10] bnlearn_4.6.1 officer_0.3.16 flextable_0.6.1
[13] ggpubr_0.4.0 cowplot_1.1.1 forcats_0.5.0
[16] stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4
[19] readr_1.4.0 tidyr_1.1.2 tibble_3.0.4
[22] ggplot2_3.3.3 tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] colorspace_2.0-0 ggsignif_0.6.0 ellipsis_0.3.1
[4] class_7.3-17 rio_0.5.16 rprojroot_2.0.2
[7] base64enc_0.1-3 fs_1.5.0 rstudioapi_0.13
[10] farver_2.0.3 prodlim_2019.11.13 fansi_0.4.1
[13] lubridate_1.7.9.2 xml2_1.3.2 codetools_0.2-18
[16] splines_4.0.2 knitr_1.30 polynom_1.4-0
[19] jsonlite_1.7.2 workflowr_1.6.2 pROC_1.16.2
[22] broom_0.7.4.9000 dbplyr_2.0.0 compiler_4.0.2
[25] httr_1.4.2 backports_1.2.1 assertthat_0.2.1
[28] Matrix_1.2-18 cli_2.2.0 later_1.1.0.1
[31] visNetwork_2.0.9 htmltools_0.5.0 tools_4.0.2
[34] igraph_1.2.6 gtable_0.3.0 glue_1.4.2
[37] reshape2_1.4.4 Rcpp_1.0.6 carData_3.0-4
[40] cellranger_1.1.0 vctrs_0.3.6 nlme_3.1-151
[43] timeDate_3043.102 xfun_0.20 gower_0.2.2
[46] openxlsx_4.2.3 rvest_0.3.6 lifecycle_0.2.0
[49] rstatix_0.6.0 MASS_7.3-53 scales_1.1.1
[52] ipred_0.9-9 hms_0.5.3 promises_1.1.1
[55] yaml_2.2.1 curl_4.3 gdtools_0.2.3
[58] rpart_4.1-15 stringi_1.5.3 corrplot_0.84
[61] zip_2.1.1 lava_1.6.8.1 rlang_0.4.10
[64] pkgconfig_2.0.3 systemfonts_0.3.2 evaluate_0.14
[67] htmlwidgets_1.5.3 labeling_0.4.2 recipes_0.1.15
[70] tidyselect_1.1.0 plyr_1.8.6 magrittr_2.0.1
[73] R6_2.5.0 generics_0.1.0 DBI_1.1.0
[76] mgcv_1.8-33 pillar_1.4.7 haven_2.3.1
[79] whisker_0.4 foreign_0.8-81 withr_2.3.0
[82] survival_3.2-7 abind_1.4-5 nnet_7.3-14
[85] modelr_0.1.8 crayon_1.3.4 car_3.0-10
[88] uuid_0.1-4 rmarkdown_2.6 readxl_1.3.1
[91] data.table_1.14.0 git2r_0.27.1 ModelMetrics_1.2.2.2
[94] reprex_0.3.0 digest_0.6.27 httpuv_1.5.4
[97] stats4_4.0.2 munsell_0.5.0