5-fms
bernard-liew
2021-11-11
Last updated: 2022-04-13
Checks: 7 0
Knit directory: 2020_cts_bn/
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Load package
rm (list = ls())
# Helper
library (tidyverse)
library (skimr)
library (flextable)
library (officer)
library (arsenal)
library (kableExtra)
library (mice)
library (rstatix)
# Parallel
library (doParallel)
# BN
library (bnlearn)
library (Rgraphviz)
#SEM
library (lavaan)
library (semPlot)
library (semptools)
library (semTools)Load data
df <- rio::import("data/Data SP-FMS-PF-EXP Study for Bernard.sav")
df <- map_df (df, function(x) {attributes(x) <- NULL;x})Explore data
#skim (df)
miss_row <- apply(df, 1, function(x) sum(is.na(x)))
df2 <- df [(miss_row/ncol(df)) < 0.5, ] Descriptives
meanNsd_transform <- function (x) {
m <- round (x[[1]][1], 2)
s <- round (x[[1]][2], 2)
m_s <- paste0(m, "(", s, ")")
return (m_s)
}
tab1 <- tableby ( ~. , data = df2, digits = 2, digits.p = 2) %>%
as.data.frame() %>%
filter (!term %in% c("Nmiss", "range")) %>%
dplyr::select (-c(group.term:term, variable.type, Total))
tab2 <- tab1[-seq (1, 61, 2),]
tab2$label <- c("Age(years)",
"Weight (kg)",
"Height (m)",
"Bmi",
"Numbers of years diagnosed",
"Mean pain",
"Worst pain",
"Pain activity",
"Pain extent ventral",
"Pain extent Dorsal",
"PPT mastoid",
"PPT trapezius",
"PPT rlbow",
"PPT hand",
"PPT posterior iliac",
"PPT trochanter",
"PPT knee",
"PPT tibialis",
"TUG",
"FIQ",
"FHAQ",
"EQ5DL",
"LANSS",
"Pain detect",
"CSI",
"HADS anxiety",
"HADS depression",
"Vigilance",
"Catastotrophizing",
"Fear",
"Sleep")
for(row in 1:nrow(tab2)) {
tab2[row, 2] <- meanNsd_transform (tab2[row, 2])
}
tab2[,2] <- unlist (tab2[,2])
# colnames (tab2) <- c ("Variables",
# "Summary value")
#
# my_path <- paste0("../manuscript_fms/table1",
# ".docx")
#
# ft <- flextable(tab2) %>%
# set_caption(caption = " Table 1.Baseline descriptive characteristics of cohort") %>%
# autofit()
#
# my_doc <- read_docx() %>%
# body_add_flextable(ft)
#
# print (my_doc, target = my_path)Change names
new_names <- c(
"age",
"wt",
"ht",
"bmi",
"yrsDx",
"meanP",
"worstP",
"Pact",
"PextDor",
"PextVent",
"PPTmast",
"PPTtrapz",
"PPTelb",
"PPThand",
"PPTiliac",
"PPTtroc",
"PPTknee",
"PPTtib",
"tug",
"fiq",
"fhaq",
"eq5d",
"lanss",
"painDetect",
"csi",
"anx",
"dep",
"vigil",
"catas",
"fear",
"sleep"
)
names (df2) <- new_namesres <- readRDS("output/res2.RDS")
list2env(res,globalenv())<environment: R_GlobalEnv>Impute
#dat_imp <- mice::mice(df2, m = 20, seed = 155, maxit = 30, meth = "rf")
# saveRDS(dat_imp,
# "output/dat_imp.RDS")
dat_imp <- readRDS ("output/dat_imp.RDS")
plot (dat_imp)
df3 <- mice::complete(dat_imp, "all") %>%
map( ~ .x %>%
mutate_if (is.numeric, scale, center = TRUE, scale = TRUE) %>%
dplyr::select (-c(wt, ht, meanP, eq5d, fhaq, age, bmi)))Correlations
c <- cor(df3[[1]], method = "pearson", use = "complete.obs")
corrplot::corrplot(c, type = "upper", order = "hclust",
tl.col = "black", tl.srt = 45)
Measurement model using CFA
Model
Plot
cfa0_dummy <- cfa (cfa_form0,
data = df3[[1]],
std.lv = FALSE,
auto.fix.first = TRUE,
estimator = "MLR",
se = "robust.huber.white")
fig <- semPlotModel(cfa0_dummy)
x = data.frame (summary (cfa0))
fig@Pars$est <- x$est[1: length (fig@Pars$est)]
fig2 <- semPaths (fig,
what = "path",
whatLabels= "est",
nCharNodes = 0,
freeStyle = c( "black","1"),
edge.label.cex = 0.5,
residuals = FALSE)
indicator_order <- c("PPTmast", "PPTtrapz","PPTelb", "PPThand",
"PPTiliac" , "PPTtroc" , "PPTknee" , "PPTtib",
"PextDor" , "PextVent", "Pact" , "worstP",
"lanss", "painDetect" , "csi",
"anx" , "dep" , "vigil" , "catas" , "fear")
indicator_factor <- c("Sensitivity", "Sensitivity","Sensitivity", "Sensitivity",
"Sensitivity", "Sensitivity","Sensitivity", "Sensitivity",
"Severity" , "Severity", "Severity" , "Severity",
"Sensitization", "Sensitization" , "Sensitization",
"Distress" , "Distress" , "Distress" , "Distress" , "Distress")
factor_layout <- matrix(c(NA, "Sensitivity", NA, NA, NA, "Severity",
NA,NA, NA, NA, NA, NA,
NA,NA, NA, NA, NA, NA,
NA,"Sensitization", NA, NA, NA, "Distress"),
byrow = TRUE, 4, 6)
factor_point_to <- matrix(c(NA,"up", NA, NA, NA, "right",
NA,NA, NA, NA, NA, NA,
NA,NA, NA, NA, NA, NA,
NA,"down", NA, NA, NA, "down"),
byrow = TRUE, 4, 6)
indicator_spread <- c(Sensitivity = 5,
Severity = 4,
Sensitization = 4,
Distress = 4)
indicator_push <- c(Sensitivity =2,
Severity = 2,
Sensitization = 2,
Distress = 2)
fig_mod <- set_sem_layout(fig2,
indicator_order = indicator_order,
indicator_factor = indicator_factor,
factor_layout = factor_layout,
factor_point_to = factor_point_to,
indicator_push = indicator_push,
indicator_spread = indicator_spread)
plot(fig_mod)
# png ("../manuscript_fms/fig1.png", height = 10, width = 15, units = "in", res = 100)
# plot(fig_mod)
# dev.off()Bootstrap
l2 = c(LETTERS, sort(do.call("paste0", expand.grid(LETTERS, LETTERS))))Mediation
Montecarlo bootstrap coefficients
myParams <- c("a1", "b1", "a2", "b2", "a3", "b3", "c")
(coefs <- coef(m0)[myParams])
(AsyCovMat <- vcov(m0)[myParams, myParams])
expres <- c( a1 = "a1",
a2 = "a2",
a3 = "a3",
b1 = "b1",
b2 = "b2",
b3 = "b3",
c = "c",
ind1 = "a1*b1",
ind2 = "a2*b2",
ind3 = "a3*b3",
total = "c + (a1*b1) + (a2*b2) + (a3*b3)",
contrast1 = "ind1-ind2",
contrast2 = "ind1-ind3",
contrast3 = "ind2-ind3")
m0_coef <- monteCarloCI(expr = expres,
ACM = AsyCovMat,
coefs = coefs)
m0_coef <- m0_coef %>%
rownames_to_column("label")Plot
m0_dummy <- sem (sem_form0,
data = df3[[1]],
std.lv = FALSE,
auto.fix.first = TRUE,
estimator = "MLR",
se = "robust.huber.white")
fig <- semPlotModel(m0_dummy )
x = data.frame (summary (m0))
fig@Pars$est <- x$est[1: length (fig@Pars$est)]
manifest_var <- c("PPTmast", "PPTtrapz","PPTelb", "PPThand", "PPTiliac" , "PPTtroc" , "PPTknee" , "PPTtib", "PextDor" , "PextVent", "Pact" , "yrsDx" , "worstP", "lanss", "painDetect" , "csi", "anx" , "dep" , "vigil" , "catas" , "fear")
fig2 <- drop_nodes(fig, manifest_var)
fig2 <- semPaths (fig2,
what = "path",
whatLabels= "est",
fixedStyle = c( "white","0"),
freeStyle = c( "black","1"),
residuals = FALSE,
nCharNodes = 0,
curveAdjacent = c("cov", "reg"),
curvature = 2,
intercept = TRUE,
edge.label.cex = 0.8,
nodeLabels = c("Sleep", "Sens", "Severity", "Senstz", "Distress"),
exoCov = FALSE,
color = list (lat = "grey", man = "white"),
edge.label.position = 0.5)
indicator_order <- c("Sleep", "Sens", "Severity", "Senstz", "Distress")
indicator_factor <- c("Sleep", "Sens", "Severity", "Senstz", "Distress")
factor_layout <- matrix(c(NA,NA, NA, NA, "Sens", NA, NA,
NA, NA, NA,NA, NA, NA,NA,
NA,NA, NA, "Senstz", NA, NA, NA,
NA,NA, NA, NA, NA, NA, NA,
"Distress",NA, NA, NA, NA, NA, "Severity",
NA,NA, NA, NA, NA, NA, NA,
NA,NA, NA, NA, NA, NA, NA,
NA,NA, NA, NA, "Sleep", NA, NA),
byrow = TRUE, 8, 7)
factor_point_to <- matrix(c(NA,NA, NA, NA, "left", NA, NA,
NA, NA, NA,NA, NA, NA,NA,
NA,NA, NA, "left", NA, NA, NA,
NA,NA, NA, NA, NA, NA, NA,
"left",NA, NA, NA, NA, NA, "left",
NA,NA, NA, NA, NA, NA, NA,
NA,NA, NA, NA, NA, NA, NA,
NA,NA, NA, NA, "left", NA, NA),
byrow = TRUE, 8, 7)
fig_mod <- set_sem_layout(fig2,
indicator_order = indicator_order,
indicator_factor = indicator_factor,
factor_layout = factor_layout,
factor_point_to = factor_point_to)
plot(fig_mod)
# png ("../manuscript_fms/fig2.png", height = 10, width = 15, units = "in", res = 100)
# plot(fig_mod)
# dev.off()Report
# Export lavaan table
param_ex<- summary (m0, standardized = TRUE) %>%
filter (op != "~~") %>%
rename (DV = lhs,
IV = rhs,
Coef = est,
SE = se,
Pval = pvalue) %>%
data.frame () %>%
left_join(m0_coef, by = c("label")) %>%
rename (`LB` = ci.lower,
`UB` = ci.upper) %>%
mutate_if (is.numeric, round, 3) %>%
mutate (Sig = ifelse (Pval < 0.05, "s", "ns")) %>%
mutate (Type = case_when(
op == "=~" ~ "LV",
op == "~" ~ "Reg",
TRUE ~ "Med"
)) %>%
dplyr::select ( DV, IV, Coef, SE, `LB`, `UB`,Pval, Sig, Type) %>%
mutate (Coef = as.numeric (Coef))
param_ex$DV[28:34] <- c("Indirect 1", "Indirect 2", "Indirect 3",
"Total", "Indirect Contrast 1", "Indirect Contrast 2", "Indirect Contrast 3")
param_ex$IV[28:34] <- c("Sensitization", "Sensitivity", "Sleep",
" ",
"Indirect 1 - Indirect 2", "Indirect 1 - Indirect 3", "Indirect 2 - Indirect 3")
param_ex <- param_ex%>%
mutate (Type = factor (Type, levels = c("Med", "Reg", "LV"))) %>%
arrange (Type, DV)
# Export lavaan table
# my_path <- paste0("../manuscript_fms/table2",
# ".docx")
#
# ft <- flextable(param_ex) %>%
# set_caption(caption = " Table 2.Parameter estimates for model") %>%
# autofit()
#
# my_doc <- read_docx() %>%
# body_add_flextable(ft)
#
# print (my_doc, target = my_path)Sensitivity analysis
Save data
res <- list (cfa0 = cfa0,
cfa0_coef = cfa0_coef,
m0 = m0,
m0_coef = m0_coef,
m1 = m1,
m1_coef = m1_coef,
df = df,
dat_imp = dat_imp,
df3 = df3,
my.sa = my.sa,
boot = boot)
saveRDS (res,
"output/res2.RDS")
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)
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] semTools_0.5-5 semptools_0.2.9.3 semPlot_1.1.2
[4] lavaan_0.6-9 Rgraphviz_2.38.0 graph_1.72.0
[7] BiocGenerics_0.40.0 bnlearn_4.7 doParallel_1.0.16
[10] iterators_1.0.13 foreach_1.5.1 rstatix_0.7.0
[13] mice_3.13.0 kableExtra_1.3.4 arsenal_3.6.3
[16] officer_0.4.0 flextable_0.6.9 skimr_2.1.3
[19] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[22] purrr_0.3.4 readr_2.0.2 tidyr_1.1.4
[25] tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1
loaded via a namespace (and not attached):
[1] utf8_1.2.2 tidyselect_1.1.1 lme4_1.1-27.1
[4] htmlwidgets_1.5.4 munsell_0.5.0 codetools_0.2-18
[7] withr_2.5.0 colorspace_2.0-2 highr_0.9
[10] OpenMx_2.19.8 knitr_1.37 uuid_1.0-2
[13] rstudioapi_0.13 stats4_4.1.2 mi_1.0
[16] emmeans_1.7.0 git2r_0.28.0 repr_1.1.3
[19] mnormt_2.0.2 rprojroot_2.0.2 TH.data_1.1-0
[22] coda_0.19-4 vctrs_0.3.8 generics_0.1.1
[25] xfun_0.30 R6_2.5.1 arm_1.12-2
[28] assertthat_0.2.1 promises_1.2.0.1 scales_1.1.1
[31] multcomp_1.4-17 nnet_7.3-16 gtable_0.3.0
[34] sandwich_3.0-1 workflowr_1.6.2 rlang_1.0.2
[37] systemfonts_1.0.3 splines_4.1.2 broom_0.7.12
[40] checkmate_2.0.0 yaml_2.2.1 reshape2_1.4.4
[43] abind_1.4-5 modelr_0.1.8 backports_1.4.1
[46] httpuv_1.6.3 Hmisc_4.6-0 tools_4.1.2
[49] psych_2.1.9 ellipsis_0.3.2 jquerylib_0.1.4
[52] RColorBrewer_1.1-2 Rsolnp_1.16 Rcpp_1.0.8
[55] plyr_1.8.6 base64enc_0.1-3 rockchalk_1.8.144
[58] rpart_4.1-15 pbapply_1.5-0 zoo_1.8-9
[61] qgraph_1.9 haven_2.4.3 cluster_2.1.2
[64] fs_1.5.0 magrittr_2.0.1 data.table_1.14.2
[67] openxlsx_4.2.4 reprex_2.0.1 truncnorm_1.0-8
[70] tmvnsim_1.0-2 mvtnorm_1.1-3 whisker_0.4
[73] hms_1.1.1 evaluate_0.14 xtable_1.8-4
[76] XML_3.99-0.8 rio_0.5.27 jpeg_0.1-9
[79] readxl_1.3.1 gridExtra_2.3 compiler_4.1.2
[82] crayon_1.4.2 minqa_1.2.4 htmltools_0.5.2
[85] corpcor_1.6.10 later_1.3.0 tzdb_0.2.0
[88] Formula_1.2-4 RcppParallel_5.1.4 lubridate_1.8.0
[91] DBI_1.1.1 corrplot_0.91 kutils_1.70
[94] dbplyr_2.1.1 MASS_7.3-54 boot_1.3-28
[97] Matrix_1.3-4 car_3.0-12 cli_3.2.0
[100] igraph_1.2.7 pkgconfig_2.0.3 sem_3.1-13
[103] foreign_0.8-81 xml2_1.3.2 pbivnorm_0.6.0
[106] svglite_2.0.0 bslib_0.3.1 webshot_0.5.2
[109] estimability_1.3 rvest_1.0.2 digest_0.6.28
[112] rmarkdown_2.11 cellranger_1.1.0 htmlTable_2.3.0
[115] lisrelToR_0.1.4 gdtools_0.2.3 curl_4.3.2
[118] gtools_3.9.2 nloptr_1.2.2.2 lifecycle_1.0.1
[121] nlme_3.1-153 glasso_1.11 jsonlite_1.8.0
[124] carData_3.0-4 viridisLite_0.4.0 fansi_0.5.0
[127] pillar_1.6.4 lattice_0.20-45 fastmap_1.1.0
[130] httr_1.4.2 survival_3.2-13 glue_1.6.1
[133] zip_2.2.0 fdrtool_1.2.17 png_0.1-7
[136] stringi_1.7.5 sass_0.4.0 regsem_1.8.0
[139] latticeExtra_0.6-29