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F05_atbglm.R
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F05_atbglm.R
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#' #############################################################
#' Metapopulation ecology links antibiotic resistance, consumption
#' and patient transfers in a network of hospital wards
#'
#' Shapiro et al. 2019
#' (c) Jean-Philippe Rasigade, Julie T Shapiro
#' Université Claude Bernard Lyon 1
#' CIRI Inserm U1111
#'
#' MIT LICENSE
#' #############################################################
#'
#' Computations for generalized linear models using specific
#' classes of antibiotics and accompanying figures.
#'
library(tidyverse) #Tested with version 1.2.1 for R version 3.6.0
library(beeswarm) #Tested with version 0.2.3 for R version 3.6.0
library(data.table)
library(MASS)
load(file = "modeldata.Rdata") # Output from F03_modeldataprep.R
head(transf.dat.factor)
# Identify variant names
taxonName<-unique(transf.dat.factor$BacType)
# Store per-variant model results in a list
ResultList2<-list()
# Loop over variants and populate ResultList2
for(i in 1:length(taxonName)){
selTaxon<-which(transf.dat.factor$BacType == taxonName[i])
# Fit quasi-Poisson GLM
metab = glm(N_patients ~ C_control + S_connectivity + ddd_carba + ddd_c1g_c2g
+ ddd_c3g_classic + ddd_c3g_pyo + ddd_glyco + ddd_oxa
+ ddd_fq + ddd_bsp + ddd_nsp + ddd_amin + ddd_amox,
data = transf.dat.factor,subset=selTaxon, family = quasipoisson)
# Model summary
summary <- summary(metab)
# Access coefficients, standard error, etc
info <- summary$coefficients
# Select beta coefficients for all variables in the model
betas1 <- info[, 'Estimate']
# Calculate the profile confidence intervals
ci95.1 <- confint(metab)
#Convert raw coefficients and confidence intervals to percentages
betas <- (exp(betas1) - 1)*100
ci95 <- (exp(ci95.1) - 1)*100
# Concatenate beta estimates and 95% CIs
betas.cis <- cbind(betas,ci95)
# Gather results
resList<-list(
name = paste(taxonName[i]),
metab = metab,
summary = summary,
betas=betas,
ci95=ci95,
betas.cis=betas.cis
)
# Append results to the model list
ResultList2[[length(ResultList2)+1]]<-resList
}
# Use the [[]] operator to access individual results
ResultList2[[17]]$name
ResultList2[[17]]$betas.cis
##################################################################################################
# Figure 2 of percent change represented by beta coefficients
# and confidence intervals for each antibiotic class in each model
# Extract the betas + CI's table for each element of the list
beta.cisList2 <- lapply(ResultList2, function (x){unlist(x[['betas.cis']])})
# Set names for each list
names.betacisList2 <- unlist(lapply(ResultList2, function(x) x[c('name')]))
# Apply those names to the list
names(beta.cisList2) <- names.betacisList2
# Make a separate list for Abau and Efaec due to much larger confidence intervals
beta.cisList2.a <- beta.cisList2[c(1,2,3,4,5,6,7,8,9,10,11,16,17)]
beta.cisList2.b <- beta.cisList2[c(12,13,14,15)]
# Identify the beta coefficient, lower, and upper confidence intervals for each variable
# and each variant
{
coef_carba.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_carba", rownames(x)),1]))
coef_carba_li.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_carba", rownames(x)),2]))
coef_carba_ui.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_carba", rownames(x)),3]))
coef_c1gc2g.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_c1g_c2g", rownames(x)),1]))
coef_c1gc2g_li.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_c1g_c2g", rownames(x)),2]))
coef_c1gc2g_ui.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_c1g_c2g", rownames(x)),3]))
coef_c3g_classic.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_c3g_classic", rownames(x)),1]))
coef_c3g_classic_li.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_c3g_classic", rownames(x)),2]))
coef_c3g_classic_ui.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_c3g_classic", rownames(x)),3]))
coef_c3g_pyo.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_c3g_pyo", rownames(x)),1]))
coef_c3g_pyo_li.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_c3g_pyo", rownames(x)),2]))
coef_c3g_pyo_ui.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_c3g_pyo", rownames(x)),3]))
coef_glyco.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_glyco", rownames(x)),1]))
coef_glyco_li.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_glyco", rownames(x)),2]))
coef_glyco_ui.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_glyco", rownames(x)),3]))
coef_oxa.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_oxa", rownames(x)),1]))
coef_oxa_li.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_oxa", rownames(x)),2]))
coef_oxa_ui.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_oxa", rownames(x)),3]))
coef_fq.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_fq", rownames(x)),1]))
coef_fq_li.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_fq", rownames(x)),2]))
coef_fq_ui.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_fq", rownames(x)),3]))
coef_bsp.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_bsp", rownames(x)),1]))
coef_bsp_li.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_bsp", rownames(x)),2]))
coef_bsp_ui.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_bsp", rownames(x)),3]))
coef_nsp.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_nsp", rownames(x)),1]))
coef_nsp_li.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_nsp", rownames(x)),2]))
coef_nsp_ui.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_nsp", rownames(x)),3]))
coef_amin.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_amin", rownames(x)),1]))
coef_amin_li.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_amin", rownames(x)),2]))
coef_amin_ui.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_amin", rownames(x)),3]))
coef_amox.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_amox", rownames(x)),1]))
coef_amox_li.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_amox", rownames(x)),2]))
coef_amox_ui.a <- unlist(lapply(beta.cisList2.a, function(x) x[grep("ddd_amox", rownames(x)),3]))
}
# Repeat for Abau and Efae
{
coef_carba.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_carba", rownames(x)),1]))
coef_carba_li.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_carba", rownames(x)),2]))
coef_carba_ui.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_carba", rownames(x)),3]))
coef_c1gc2g.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_c1g_c2g", rownames(x)),1]))
coef_c1gc2g_li.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_c1g_c2g", rownames(x)),2]))
coef_c1gc2g_ui.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_c1g_c2g", rownames(x)),3]))
coef_c3g_classic.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_c3g_classic", rownames(x)),1]))
coef_c3g_classic_li.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_c3g_classic", rownames(x)),2]))
coef_c3g_classic_ui.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_c3g_classic", rownames(x)),3]))
coef_c3g_pyo.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_c3g_pyo", rownames(x)),1]))
coef_c3g_pyo_li.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_c3g_pyo", rownames(x)),2]))
coef_c3g_pyo_ui.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_c3g_pyo", rownames(x)),3]))
coef_glyco.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_glyco", rownames(x)),1]))
coef_glyco_li.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_glyco", rownames(x)),2]))
coef_glyco_ui.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_glyco", rownames(x)),3]))
coef_oxa.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_oxa", rownames(x)),1]))
coef_oxa_li.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_oxa", rownames(x)),2]))
coef_oxa_ui.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_oxa", rownames(x)),3]))
coef_fq.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_fq", rownames(x)),1]))
coef_fq_li.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_fq", rownames(x)),2]))
coef_fq_ui.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_fq", rownames(x)),3]))
coef_bsp.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_bsp", rownames(x)),1]))
coef_bsp_li.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_bsp", rownames(x)),2]))
coef_bsp_ui.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_bsp", rownames(x)),3]))
coef_nsp.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_nsp", rownames(x)),1]))
coef_nsp_li.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_nsp", rownames(x)),2]))
coef_nsp_ui.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_nsp", rownames(x)),3]))
coef_amin.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_amin", rownames(x)),1]))
coef_amin_li.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_amin", rownames(x)),2]))
coef_amin_ui.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_amin", rownames(x)),3]))
coef_amox.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_amox", rownames(x)),1]))
coef_amox_li.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_amox", rownames(x)),2]))
coef_amox_ui.b <- unlist(lapply(beta.cisList2.b, function(x) x[grep("ddd_amox", rownames(x)),3]))
}
###########################################################################################
# Prepare the features of the figure
# Set variant labels
buglabels.a <- c("EC", "3GCREC", "CREC", "KP", "3GCRKP", "CRKP", "EB", "3GCREB",
"CREB", "PA", "CRPA", "SA", "MRSA")
# Labels for Abau and Efae
buglabels.b <-c("AB", "CRAB", "EF", "VREF")
# Set colors for the beta coefficient points for each taxon
bugcolors.a <- c("violetred1", "violetred3", "violetred4",
"aquamarine", "aquamarine3", "aquamarine4",
"goldenrod1", "goldenrod3", "goldenrod4",
"turquoise2", "turquoise4"
,"lightpink", "lightpink3")
# Set colors for the beta coefficient points for Abau and Efae
bugcolors.b <- c("red2", "red4",
"olivedrab3", "olivedrab4")
ord <- T
errorbar_width <- 0.04
###################################################################
# Generate Figure 2
# Panel 1 (Figure 2a) - all taxa except Abau and Efae
svg(file = "atbmod_pane1.svg", 4, 10)
{
showpanes <- function(yl) {
rect(0.75, yl[1], 3.25, yl[2], col = rgb(0.9,0.9,0.9,0.3), border = NA)
rect(3.75, yl[1], 6.25, yl[2], col = rgb(0.9,0.9,0.9,0.3), border = NA)
rect(6.75, yl[1], 9.25, yl[2], col = rgb(0.9,0.9,0.9,0.3), border = NA)
rect(9.75, yl[1], 11.25, yl[2], col = rgb(0.9,0.9,0.9,0.3), border = NA)
rect(11.75, yl[1], 13.25, yl[2], col = rgb(0.9,0.9,0.9,0.3), border = NA)
}
par(mfrow = c(12,1))
par(mar = c(0.4,4,0,4))
p <- length(coef_carba.a)
marker.cex <- 1.2
yl <- c(-0.5, 0.5)
yl <- c(-5, 5)
plot(coef_amin.a[ord], ylim = yl, xaxt = "n", xlab = "", ylab = "AMIN", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_amin_li.a[ord], 1:p, coef_amin_ui.a[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_amin.a[ord], pch = 19, col = bugcolors.a, cex = marker.cex)
yl <- c(-30, 45)
plot(coef_fq.a[ord], ylim = yl, xaxt = "n", xlab = "", ylab = "FQ", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_fq_li.a[ord], 1:p, coef_fq_ui.a[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_fq.a[ord], pch = 19, col = bugcolors.a, cex = marker.cex)
yl <- c(-30, 35)
plot(coef_glyco.a[ord], ylim = yl, xaxt = "n", xlab = "", ylab = "VAN/TEC", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_glyco_li.a[ord], 1:p, coef_glyco_ui.a[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_glyco.a[ord], pch = 19, col = bugcolors.a, cex = marker.cex)
yl <- c(-20, 15)
plot(coef_oxa.a[ord], ylim = yl, xaxt = "n", xlab = "", ylab = "OXA", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_oxa_li.a[ord], 1:p, coef_oxa_ui.a[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_oxa.a[ord], pch = 19, col = bugcolors.a, cex = marker.cex)
yl <- c(-25, 90)
plot(coef_carba.a[ord], ylim = yl, xaxt = "n", xlab = "", ylab = "IPM/MEM", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_carba_li.a[ord], 1:p, coef_carba_ui.a[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_carba.a[ord], pch = 19, col = bugcolors.a, cex = marker.cex)
yl <- c(-10, 110)
plot(coef_bsp.a[ord], ylim = yl, xaxt = "n", xlab = "", ylab = "TZP", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_bsp_li.a[ord], 1:p, coef_bsp_ui.a[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_bsp.a[ord], pch = 19, col = bugcolors.a, cex = marker.cex)
yl <- c(-40, 50)
plot(coef_c3g_pyo.a[ord], ylim = yl, xaxt = "n", xlab = "", ylab = "CTZ/FEP", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_c3g_pyo_li.a[ord], 1:p, coef_c3g_pyo_ui.a[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_c3g_pyo.a[ord], pch = 19, col = bugcolors.a, cex = marker.cex)
yl <- c(-50, 90)
plot(coef_c3g_classic.a[ord], ylim = yl, xaxt = "n", xlab = "", ylab = "CTX/CRO", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_c3g_classic_li.a[ord], 1:p, coef_c3g_classic_ui.a[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_c3g_classic.a[ord], pch = 19, col = bugcolors.a, cex = marker.cex)
yl <- c(-20, 25)
plot(coef_c1gc2g.a[ord], ylim = yl, xaxt = "n", xlab = "", ylab = "1GC/2GC", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_c1gc2g_li.a[ord], 1:p, coef_c1gc2g_ui.a[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_c1gc2g.a[ord], pch = 19, col = bugcolors.a, cex = marker.cex)
yl <- c(-50, 30)
plot(coef_nsp.a[ord], ylim = yl, xaxt = "n", xlab = "", ylab = "AMC", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_nsp_li.a[ord], 1:p, coef_nsp_ui.a[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_nsp.a[ord], pch = 19, col = bugcolors.a, cex = marker.cex)
yl <- c(-1.5, 1.5)
plot(coef_amox.a[ord], ylim = yl, xaxt = "n", xlab = "", ylab = "AMX", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_amox_li.a[ord], 1:p, coef_amox_ui.a[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_amox.a[ord], pch = 19, col = bugcolors.a, cex = marker.cex)
axis(1, at = 1:p, labels = buglabels.a[ord], las = 2)
}
dev.off()
# Panel 2 (Figure 2b) - Abau and Efae
svg(file = "atbmod_pane2.svg", 1.9, 10)
{
showpanes <- function(yl) {
rect(0.75, yl[1], 2.25, yl[2], col = rgb(0.9,0.9,0.9,0.3), border = NA)
rect(2.75, yl[1], 4.25, yl[2], col = rgb(0.9,0.9,0.9,0.3), border = NA)
}
par(mfrow = c(12,1))
par(mar = c(0.4,4,0,4))
p <- length(coef_carba.b)
marker.cex <- 1.2
yl <- c(-1, 1) * 1.25
xl <- c(0.75, 4.25)
yl <- c(-30, 10)
plot(coef_amin.b[ord], ylim = yl, xlim = xl, xaxt = "n", xlab = "", ylab = "AMIN", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_amin_li.b[ord], 1:p, coef_amin_ui.b[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_amin.b[ord], pch = 19, col = bugcolors.b, cex = marker.cex)
yl <- c(-65, 25)
plot(coef_fq.b[ord], ylim = yl, xlim = xl, xaxt = "n", xlab = "", ylab = "FQ", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_fq_li.b[ord], 1:p, coef_fq_ui.b[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_fq.b[ord], pch = 19, col = bugcolors.b, cex = marker.cex)
yl <- c(-55, 150)
plot(coef_glyco.b[ord], ylim = yl, xlim = xl, xaxt = "n", xlab = "", ylab = "VAN/TEC", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_glyco_li.b[ord], 1:p, coef_glyco_ui.b[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_glyco.b[ord], pch = 19, col = bugcolors.b, cex = marker.cex)
yl <- c(-25, 70)
plot(coef_oxa.b[ord], ylim = yl, xlim = xl, xaxt = "n", xlab = "", ylab = "OXA", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_oxa_li.b[ord], 1:p, coef_oxa_ui.b[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_oxa.b[ord], pch = 19, col = bugcolors.b, cex = marker.cex)
yl <- c(-20, 150)
plot(coef_carba.b[ord], ylim = yl, xlim = xl, xaxt = "n", xlab = "", ylab = "IPM/MEM", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_carba_li.b[ord], 1:p, coef_carba_ui.b[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_carba.b[ord], pch = 19, col = bugcolors.b, cex = marker.cex)
yl <- c(-40, 200)
plot(coef_bsp.b[ord], ylim = yl, xlim = xl, xaxt = "n", xlab = "", ylab = "TZP", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_bsp_li.b[ord], 1:p, coef_bsp_ui.b[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_bsp.b[ord], pch = 19, col = bugcolors.b, cex = marker.cex)
yl <- c(-30, 150)
plot(coef_c3g_pyo.b[ord], ylim = yl, xlim = xl, xaxt = "n", xlab = "", ylab = "CTZ/FEP", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_c3g_pyo_li.b[ord], 1:p, coef_c3g_pyo_ui.b[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_c3g_pyo.b[ord], pch = 19, col = bugcolors.b, cex = marker.cex)
yl <- c(-70, 160)
plot(coef_c3g_classic.b[ord], ylim = yl, xlim = xl, xaxt = "n", xlab = "", ylab = "CTX/CRO", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_c3g_classic_li.b[ord], 1:p, coef_c3g_classic_ui.b[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_c3g_classic.b[ord], pch = 19, col = bugcolors.b, cex = marker.cex)
yl <- c(-30, 90)
plot(coef_c1gc2g.b[ord], ylim = yl, xlim = xl, xaxt = "n", xlab = "", ylab = "1GC/2GC", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_c1gc2g_li.b[ord], 1:p, coef_c1gc2g_ui.b[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_c1gc2g.b[ord], pch = 19, col = bugcolors.b, cex = marker.cex)
yl <- c(-40, 90)
plot(coef_nsp.b[ord], ylim = yl, xlim = xl, xaxt = "n", xlab = "", ylab = "AMC", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_nsp_li.b[ord], 1:p, coef_nsp_ui.b[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_nsp.b[ord], pch = 19, col = bugcolors.b, cex = marker.cex)
yl <- c(-6, 2)
plot(coef_amox.b[ord], ylim = yl, xlim = xl, xaxt = "n", xlab = "", ylab = "AMX", bty = "n", type = "n")
showpanes(yl)
abline(0,0, lty = 2, col = "lightgrey")
arrows(1:p, coef_amox_li.b[ord], 1:p, coef_amox_ui.b[ord], length = errorbar_width, angle = 90, code = 3, col = "darkgrey")
points(coef_amox.b[ord], pch = 19, col = bugcolors.b, cex = marker.cex)
axis(1, at = 1:p, labels = buglabels.b[ord], las = 2)
}
dev.off()
#' #############################################################
#'
# Extract GLM results
models <- lapply(ResultList2, function(x) x$betas.cis[-(1:3),])
names(models) <- unlist(lapply(ResultList2, function(x) x$name))
print(models)
stop()
# Define possibly selective associations between infection incidence
# and classes of antibiotics
possibly_selective <- list(
ESCCOL_C3G_R = "ddd_c3g_classic",
ESCCOL_CARBA_R = "ddd_carba",
KLEPNE_S = "ddd_amox",
KLEPNE_C3G_R = "ddd_c3g_classic",
KLEPNE_CARBA_R = "ddd_carba",
ENTCLO_S = "ddd_nsp",
ENTCLO_C3G_R = c("ddd_c3g_classic", "ddd_bsp"),
ENTCLO_CARBA_R = "ddd_carba",
PSEAER_S = "ddd_c3g_classic",
PSEAER_CARBA_R = "ddd_carba",
ACIBAU_CARBA_S = "ddd_c3g_classic",
ACIBAU_CARBA_R = "ddd_carba",
ENCFAC_VANCO_S = c("ddd_c3g_classic"),
ENCFAC_VANCO_R = "ddd_glyco",
STAAUR_OXA_S = "ddd_amox",
STAAUR_OXA_R = c("ddd_c1g_c2g", "ddd_oxa", "ddd_nsp")
)
# Categorize antibiotic/variant associations as possibly selective or
# not, with its beta coefficient and lower and upper confidence intervals from
# the antibiotic models in ResultsList2.
pcmodel <- sapply(names(models), function(bug) {
mat <- models[[bug]]
dt <- data.table(mat)[
, drug := rownames(mat)
][
, bug := bug
][
, pc := FALSE
]
pcfilt <- which(dt$drug %in% possibly_selective[[bug]])
dt[ pcfilt, pc := TRUE]
return(dt)
}, simplify = F) %>% rbindlist
#Set up display labels for the boxplot (below)
#Possibly selective are labelled as "Poss. selective", all others labelled "Other"
pcmodel[, pc_display := c("Other", "Poss. selective")[pc + 1]][
, pc_display := factor(pc_display, levels = c("Poss. selective", "Other"))
]
# Boxplot showing the difference in the magnitude of the beta coefficients
# for possibly selective associations vs. Other
{
svg("possiblyselective_boxplot_2.svg", 4, 6)
boxplot(betas ~ pc_display, pcmodel, ylim = c(-20, 40), outline = F, xlab = "", ylab = "Percent change in incidence")
beeswarm(betas ~ pc_display, pcmodel, method = "hex", add = T, pch = 19, col = c(rgb(0.8,0,0,.2), rgb(0,0,.8,.2)))
dev.off()
}
# Means and difference of the means of coefficients in possibly selective vs other
# associations
t.test(betas ~ pc_display, pcmodel)
# Wilcoxon test for boxplot figure significance
wilcox.test(betas ~ pc_display, pcmodel)
# Find resulting figures in files atbmod_pane1.svg, atbmod_pane2.svg and possiblyselective_boxplot_2.svg
#####################################################################################################