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examples-analysis.R
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## Code by Neil MacLaren 5/31/2022
## R1 updates 12/14/2022
library(igraph)
library(doublewells)
### This file produces the various examples figures
## options
save_plots <- TRUE # FALSE
palette("R4")
load("./data/examples-lower-r1.rda")
load("./data/examples-upper-r1.rda")
# For the runs with the assessment samples
# use Fig 2 only
## load("./data/examples-lower-r1alt.rda")
## load("./data/examples-upper-r1alt.rda")
### Figure 1. use examples_lower
### x = D, y1 = prop lower state, y2 = avg ac
nodesets <- c("all", "lower", "sentinel")
signals <- c("avgac")
columns <- paste(signals, nodesets, sep = "_")
d_imgfile <- "./img/examples-multistage-transition.pdf"
wd <- 9
ht <- 8
colors <- 1:length(nodesets)
ylims <- list(
c(0, 110),
c(0, 70)
)
if(save_plots) {
cairo_pdf(d_imgfile, width = wd, height = ht, family = "Bitsream Vera Sans")
} else dev.new(width = wd, height = ht)
par(mfrow = c(2, 1), mar = c(3.5, 4, 1, 4)+.4, xpd = TRUE)
for(i in 1:length(examples_lower)) {
df <- examples_lower[[i]]$df
df$p_lowerstate <- round((df$n_lowerstate/max(df$n_lowerstate))*100)
df$p_upperstate <- round( (1 - (df$n_lowerstate/max(df$n_lowerstate)))*100 )
plot(df$Ds, df$p_upperstate,#df$p_lowerstate,
type = "l", lwd = 3, lty = 1,
col = palette()[length(palette())],
xlim = range(df$Ds), ylim = c(0, 100),
xlab = expression(italic(D)), ylab = "% Nodes in Upper State", yaxt = "n",
cex.lab = 1.75, cex.axis = 1.75)
axis(side = 2, at = c(0, 50, 100), labels = c(0, 50, 100), cex.axis = 1.75)
cvs <- rev(find_critical_values(examples_lower[[i]]))
arrows(
x0 = cvs - .01, x1 = cvs,
y0 = df[df$Ds %in% cvs, "p_upperstate"] + 10, y1 = df[df$Ds %in% cvs, "p_upperstate"] + 1,
length = .1, angle = 20
)
if(i == 1) {
legend("topright", inset = c(0, -.15),
ncol = 4, bty = "n",
lty = 1, lwd = 4,
col = c(length(palette()), colors),
cex = 1.25,
legend = c("State", "All", "Lower", "High Input")
)
}
mtext(LETTERS[i], adj = 0.01, line = -2.5, cex = 3, font = 2)
par(new = TRUE)
plotsamples <- 1:nrow(df)
matplot(df$Ds[plotsamples], df[plotsamples, columns],
type = "l", lwd = 2, lty = 1,
col = colors,
xlim = range(df$Ds), ylim = c(0, 1), axes = FALSE,
xlab = "", ylab = "")
axis(4, at = pretty(c(0, 1)), cex.axis = 1.75)
mtext("Average Autocorrelation",side = 4, cex = 1.75, font = 1, line = 3)
}
if(save_plots) dev.off()
## Figure 2. use examples_lower
nodesets <- c("all", "lower", "sentinel", "upper", "lowrank", "random", "largecorr", "largesd")
signals <- c("maxeig", "maxsd", "avgsd", "maxac", "avgac")
tau_imgfile <- "./img/examples-taus.pdf"
## tau_imgfile <- "./img/examples-taus-alt.pdf"
pch <- colors <- 1:length(nodesets)
pt.lwd <- 3
pt.cex <- 3
adjust <- c(0.25, -0.25)
ht <- 7
wd <- 14
if(save_plots) {
cairo_pdf(tau_imgfile, height = ht, width = wd, family = "Bitsream Vera Sans")
} else dev.new(height = ht, width = wd)
par(mar = c(4, 7, 0, 6.1) + 0.5, xpd = TRUE)
yticklabels <- c("Dom. Eig.", "Max. SD", "Avg. SD", "Max. AC", "Avg. AC")
ylim <- c(0.5, length(signals) + 0.5)
xlim <- c(-1, 1)
plot(NULL, xlab = "", ylab = "", yaxt = "n", ylim = ylim, xlim = xlim,
bty = "n", cex.axis = 2.5)
title(xlab = "τ", cex.lab = 2.5, cex.axis = 2.5, font.lab = 3)
axis(side = 2, at = 5:1, labels = yticklabels, tick = FALSE, las = 1, cex.lab = 1.75, cex.axis = 1.75)
text(x = -1, y = c(5.25, 4.75), labels = c("Power-law network", "Dolphin network"),
adj = c(0, .5), col = "black", cex = 1.5)
legend("bottomright", cex = 1.25, inset = c(-.1, 0),
legend = c("All", "Lower State", "High Input",
"Upper State", "Lower Half", "Random", "Large Corr.", "Large SD"),
pch = pch, col = colors, pt.cex = pt.cex*.75, pt.lwd = pt.lwd*.75,
bty = "n")
segments(y0 = 1:4 + .5, x0 = -1, x1 = 1, lty = 1, lwd = .5, col = "gray")
segments(x0 = c(-.5, 0, .5), y0 = .5, y1 = length(signals) + .5, lty = 2, lwd = .5, col = "gray")
for(i in 1:length(examples_lower)) {
df <- examples_lower[[i]]$df
rdf <- df[, -c(grep("lowinput", colnames(df)))]
dat <- as.data.frame(Kendall_correlations(rdf)$means)
dat$fullname <- rownames(dat)
dat <- cbind(dat, do.call(rbind, strsplit(dat$fullname, "_")))
dat <- dat[, -grep("fullname", colnames(dat))]
colnames(dat) <- c("tau", "signal", "set")
rownames(dat) <- NULL
dat$signal <- factor(dat$signal, levels = rev(signals))
dat$set <- factor(dat$set, levels = nodesets)
dat$signal_ <- as.numeric(dat$signal) + adjust[i]
dat$set_ <- as.numeric(dat$set)
points(signal_ ~ tau, data = dat, col = dat$set_, pch = dat$set_, lwd = pt.lwd, cex = pt.cex)
print(summary(dat$tau[dat$set %in% c("all", "lower", "sentinel")]))
}
if(save_plots) dev.off()
## Figure 3. use examples_upper
nodesets <- c("all", "lower", "sentinel", "upper", "lowinput_sentinel", "random")
signals <- c("maxeig", "maxsd", "avgsd", "maxac", "avgac")
tau_imgfile_u <- "./img/examples-upper-taus.pdf"
pch <- colors <- 1:length(nodesets)
pt.lwd <- 3
pt.cex <- 3
adjust <- c(0.25, -0.25)
ht <- 7
wd <- 14
if(save_plots) {
cairo_pdf(tau_imgfile_u, height = ht, width = wd, family = "Bitsream Vera Sans")
} else dev.new(height = ht, width = wd)
par(mar = c(4, 7, 0, 6.1) + 0.5, xpd = TRUE)
yticklabels <- c("Dom. Eig.", "Max. SD", "Avg. SD", "Max. AC", "Avg. AC")
ylim <- c(0.5, length(signals) + 0.5)
xlim <- c(-1, 1)
plot(NULL, xlab = "", ylab = "", yaxt = "n", ylim = ylim, xlim = xlim,
bty = "n", cex.lab = 2.5, cex.axis = 2.5)
title(xlab = "τ", cex.lab = 2.5, cex.lab = 2.5, font.lab = 3)
axis(side = 2, at = 5:1, labels = yticklabels, tick = FALSE, las = 1, cex.lab = 1.75, cex.axis = 1.75)
text(x = .85, y = c(5.25, 4.75), labels = c("Power-law network", "Dolphin network"),
adj = c(0, .5), col = "black", cex = 1.5)
legend("bottomright", cex = 1.25, inset = c(-.12, 0),
legend = c("All", "Lower State", "High Input", "Upper State", "Low Input", "Random"),
pch = pch, col = colors, pt.cex = pt.cex*.75, pt.lwd = pt.lwd*.75,
bty = "n")
segments(y0 = 1:4 + .5, x0 = -1, x1 = 1, lty = 1, lwd = .5, col = "gray")
segments(x0 = c(-.5, 0, .5), y0 = .5, y1 = length(signals) + .5, lty = 2, lwd = .5, col = "gray")
for(i in 1:length(examples_upper)) {
df <- examples_upper[[i]]$df
rdf <- df[, -c(grep("lowrank", colnames(df)))]
dat <- as.data.frame(Kendall_correlations(rdf)$means)
dat$fullname <- rownames(dat)
dat$fullname <- gsub("lowinput_sentinel", "lowinputSentinel", dat$fullname)
dat <- cbind(dat, do.call(rbind, strsplit(dat$fullname, "_")))
dat <- dat[, -grep("fullname", colnames(dat))]
colnames(dat) <- c("tau", "signal", "set")
dat$set <- gsub("lowinputSentinel", "lowinput_sentinel", dat$set)
rownames(dat) <- NULL
dat$signal <- factor(dat$signal, levels = rev(signals))
dat$set <- factor(dat$set, levels = nodesets)
dat$signal_ <- as.numeric(dat$signal) + adjust[i]
dat$set_ <- as.numeric(dat$set)
points(signal_ ~ tau, data = dat, col = dat$set_, pch = dat$set_, lwd = pt.lwd, cex = pt.cex)
}
if(save_plots) dev.off()
### Figure 4(SI). use examples_upper
nodesets <- c("all", "upper", "lowinput_sentinel")
signals <- c("avgac")
columns <- paste(signals, nodesets, sep = "_")
u_imgfile <- "./img/examples-multistage-transition-upper.pdf"
wd <- 9
ht <- 8
colors <- c(1, 4, 5)
ylims <- list(
c(0, 110),
c(0, 70)
)
if(save_plots) {
cairo_pdf(
u_imgfile, width = wd, height = ht, family = "Bitsream Vera Sans"
)
} else dev.new(width = wd, height = ht)
par(mfrow = c(2, 1), mar = c(3.5, 4, 1, 4)+.4, xpd = TRUE)
for(i in 1:length(examples_upper)) {
df <- examples_upper[[i]]$df
df$p_lowerstate <- round((df$n_lowerstate/max(df$n_lowerstate))*100)
df$p_upperstate <- round( (1 - (df$n_lowerstate/max(df$n_lowerstate)))*100 )
plot(df$Ds, df$p_upperstate,#df$p_lowerstate,
type = "l", lwd = 3, lty = 1,
col = palette()[length(palette())],
xlim = rev(range(df$Ds)), ylim = c(0, 100),
xlab = expression(italic(D)), ylab = "% Nodes in Upper State", yaxt = "n",
cex.lab = 1.75, cex.axis = 1.75)
axis(side = 2, at = c(0, 50, 100), labels = c(0, 50, 100), cex.axis = 1.75)
cvs <- find_critical_values(examples_upper[[i]])
cvs <- sapply(as.integer(names(cvs)), function(x) min(df$Ds[which(df$n_lowerstate == x)]))
arrows(
x0 = cvs - .01, x1 = cvs,
y0 = df[df$Ds %in% cvs, "p_upperstate"] + 10, y1 = df[df$Ds %in% cvs, "p_upperstate"] + 1,
length = .1, angle = 20
)
if(i == 1) {
legend("topright", inset = c(0, -.15),
ncol = 4, bty = "n",
lty = 1, lwd = 4,
col = c(length(palette()), colors),
cex = 1.25,
legend = c("State", "All", "Upper", "Low Input")
)
}
mtext(LETTERS[i], adj = 0.01, line = -2.75, cex = 3, font = 2)
par(new = TRUE)
plotsamples <- 1:nrow(df)
matplot(df$Ds[plotsamples], df[plotsamples, columns],
type = "l", lwd = 2, lty = 1,
col = colors,
xlim = rev(range(df$Ds)), ylim = c(0, 1), axes = FALSE,
xlab = "", ylab = "")
axis(4, at = pretty(c(0, 1)), cex.axis = 1.75)
mtext("Average Autocorrelation",side = 4, cex = 1.75, font = 1, line = 3)
}
if(save_plots) dev.off()
## New code below here, Wednesday, December 14, 2022
# How does the τ compare for the different transitions (from
# stable ranges)?
### Figure ? (SI?)
plotseries <- function(colname, pch) {
points(kdat$Ds, kdat[, colname], pch = pch, lwd = 2, cex = 2, col = pch) # kdat$n_steps/15
lines(kdat$Ds, kdat[, colname], lty = 1, lwd = 2, col = pch)
}
# This line requires a run of simulations that saves a copy of
# the X matrix produced by each simulation. Such .rda files are
# time consuming to save and load.
load("./data/examples-lower-r1-withX.rda")
ht <- 7; wd <- 14
if(save_plots) {
cairo_pdf(
"./img/performance-stable-ranges.pdf", height = ht, width = wd, family = "Bitsream Vera Sans"
)
} else dev.new(height = ht, width = wd)
par(mfrow = c(1, 2), mar = c(4, 4, 1, 1) + .4)
for(i in 1:2) {
df <- examples_lower[[i]]$df
rdf <- df[, -c(grep("lowinput", colnames(df)))]
kdat <- Kendall_correlations(rdf)$kendalls[
, c("avgac_all", "avgac_lower", "avgac_sentinel",
"n_steps", "n_lowerstate")
]
maxDs <- tapply(rdf$Ds, as.factor(rdf$n_lowerstate), max)
maxDs <- data.frame(
n_lowerstate = as.numeric(names(maxDs)),
Ds = maxDs
)
kdat$Ds <- maxDs$Ds[match(kdat$n_lowerstate, maxDs$n_lowerstate)]
actual <- sentinel_performance(examples_lower[[i]], tr_nodes = TRUE)
plot(NULL, xlab = "", ylab = "", xlim = range(kdat$Ds), ylim = c(0.5, 1.0))
title(xlab = "D", cex.lab = 1.75, cex.axis = 1.75, font.lab = 3)
title(ylab = "τ", cex.lab = 1.75, cex.axis = 1.75, font.lab = 3)
mtext(LETTERS[i], adj = 0.02, line = -2.75, cex = 3, font = 2)
plotseries("avgac_all", 1)
plotseries("avgac_lower", 2)
plotseries("avgac_sentinel", 3)
points(kdat$Ds, actual, pch = 4, lwd = 2, cex = 2, col = 4)
lines(kdat$Ds, actual, lty = 1, lwd = 2, cex = 2, col = 4)
if(i == 1) {
legend("bottomleft", legend = c("All", "Lower State", "High Input", "Actual"), bty = "n",
col = 1:4, pch = 1:4, pt.lwd = 2, cex = 1.25)
}
}
if(save_plots) dev.off()