scripts/NB_regression/bike_sharing/BS_NB_plots.R
In rchan26/hierarchicalFusion: Divide-and-Conquer Monte Carlo Fusion
library(DCFusion)
load('BS4_NB.RData')
load('BS8_NB.RData')
load('BS16_NB.RData')
load('BS32_NB.RData')
load('BS64_NB.RData')
load('BS128_NB.RData')
balanced <- list('reg' = c(integrated_abs_distance(full_posterior,
balanced_C4$reg$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C8$reg$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C16$reg$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C32$reg$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C64$reg$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C128$reg$particles$y_samples)),
'adaptive' = c(integrated_abs_distance(full_posterior,
balanced_C4$adaptive$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C8$adaptive$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C16$adaptive$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C32$adaptive$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C64$adaptive$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C128$adaptive$particles$y_samples)))
consensus <- c(integrated_abs_distance(full_posterior,
consensus_mat_4$samples),
integrated_abs_distance(full_posterior,
consensus_mat_8$samples),
integrated_abs_distance(full_posterior,
consensus_mat_16$samples),
integrated_abs_distance(full_posterior,
consensus_mat_32$samples),
integrated_abs_distance(full_posterior,
consensus_mat_64$samples),
integrated_abs_distance(full_posterior,
consensus_mat_128$samples))
neiswanger <- c(integrated_abs_distance(full_posterior,
neiswanger_false_4$samples),
integrated_abs_distance(full_posterior,
neiswanger_false_8$samples),
integrated_abs_distance(full_posterior,
neiswanger_false_16$samples),
integrated_abs_distance(full_posterior,
neiswanger_false_32$samples),
integrated_abs_distance(full_posterior,
neiswanger_false_64$samples),
integrated_abs_distance(full_posterior,
neiswanger_false_128$samples))
weierstrass <- c(integrated_abs_distance(full_posterior,
weierstrass_rejection_4$samples),
integrated_abs_distance(full_posterior,
weierstrass_rejection_8$samples),
integrated_abs_distance(full_posterior,
weierstrass_rejection_16$samples),
integrated_abs_distance(full_posterior,
weierstrass_rejection_32$samples),
integrated_abs_distance(full_posterior,
weierstrass_rejection_64$samples),
integrated_abs_distance(full_posterior,
weierstrass_rejection_128$samples))
plot(x = log(c(4, 8, 16, 32, 64, 128), 2), y = balanced$adaptive,
ylim = c(0, 0.3),
xlab = '',
ylab = '',
xaxt = 'n', yaxt = 'n', lty = 2, lwd = 3, pch = 4, type = 'b')
mtext('log(C, 2)', 1, 2.75, font = 2, cex = 1.5)
mtext('Integrated Absolute Distance', 2, 2.75, font = 2, cex = 1.5)
axis(1, at=c(seq(0, 0.9, 0.1), 0.95), labels=c("0.0", c(seq(0.1, 0.9, 0.1), 0.95)), font = 2, cex = 1.5)
axis(1, at=log(c(4, 8, 16, 32, 64, 128), 2), labels = log(c(4, 8, 16, 32, 64, 128), 2), font = 2, cex = 1.5)
axis(2, at=seq(0, 1, 0.1), labels=c("0.0", seq(0.1, 0.9, 0.1), "1.0"), font = 2, cex = 1.5)
axis(2, at=seq(0, 1, 0.05), labels=rep("", 21), lwd.ticks = 0.5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = balanced$reg,
lty = 3, lwd = 3, type = 'b', pch = 5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = consensus,
lty = 4, lwd = 3, type = 'b', pch = 3, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = neiswanger,
lty = 5, lwd = 3, type = 'b', pch = 2, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = weierstrass,
lty = 6, lwd = 3, type = 'b', pch = 1, col = 'red')
legend(x = 2, y = 0.3,
legend = c('D&C-GBF (regular mesh)',
'D&C-GBF (adaptive mesh)',
'CMC',
'KDEMC',
'WRS'),
lwd = rep(3, 6),
lty = c(3,2,4,5,6),
pch = c(5,4,3,2,1),
col = c(rep('black', 2), rep('red', 3)),
cex = 1.25,
text.font = 2,
bty = 'n')
######################### TIME
balanced_time <- list('reg' = c(sum(unlist(balanced_C4$reg$time)),
sum(unlist(balanced_C8$reg$time)),
sum(unlist(balanced_C16$reg$time)),
sum(unlist(balanced_C32$reg$time)),
sum(unlist(balanced_C64$reg$time)),
sum(unlist(balanced_C128$reg$time))),
'adaptive' = c(sum(unlist(balanced_C4$adaptive$time)),
sum(unlist(balanced_C8$adaptive$time)),
sum(unlist(balanced_C16$adaptive$time)),
sum(unlist(balanced_C32$adaptive$time)),
sum(unlist(balanced_C64$adaptive$time)),
sum(unlist(balanced_C128$adaptive$time))))
consensus_time <- c(consensus_mat_4$time,
consensus_mat_8$time,
consensus_mat_16$time,
consensus_mat_32$time,
consensus_mat_64$time,
consensus_mat_128$time)
neiswanger_time <- c(neiswanger_false_4$time,
neiswanger_false_8$time,
neiswanger_false_16$time,
neiswanger_false_32$time,
neiswanger_false_64$time,
neiswanger_false_128$time)
weierstrass_time <- c(weierstrass_rejection_4$time,
weierstrass_rejection_8$time,
weierstrass_rejection_16$time,
weierstrass_rejection_32$time,
weierstrass_rejection_64$time,
weierstrass_rejection_128$time)
plot(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(balanced_time$adaptive, 2),
ylim = c(-2, 18),
xlab = '',
ylab = '',
yaxt = 'n',
xaxt = 'n', lty = 2, lwd = 3, pch = 4, type = 'b')
mtext('log(C, 2)', 1, 2.75, font = 2, cex = 1.5)
mtext('log(Time elapsed in seconds, 2)', 2, 2.75, font = 2, cex = 1.5)
axis(1, at=c(seq(0, 0.9, 0.1), 0.95), labels=c("0.0", c(seq(0.1, 0.9, 0.1), 0.95)), font = 2, cex = 1.5)
axis(1, at=log(c(4, 8, 16, 32, 64, 128), 2), labels = log(c(4, 8, 16, 32, 64, 128), 2), font = 2, cex = 1.5)
axis(2, at=seq(-4, 20, 2), labels = seq(-4, 20, 2), font = 2, cex = 1.5)
axis(2, at=seq(-4, 20, 1), labels=rep("", 25), lwd.ticks = 0.5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(balanced_time$reg, 2),
lty = 3, lwd = 3, type = 'b', pch = 5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(consensus_time, 2),
lty = 4, lwd = 3, type = 'b', pch = 3, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(neiswanger_time, 2),
lty = 5, lwd = 3, type = 'b', pch = 2, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(weierstrass_time, 2),
lty = 6, lwd = 3, type = 'b', pch = 1, col = 'red')
legend(x = 2, y = 18,
legend = c('D&C-GBF (regular mesh)',
'D&C-GBF (adaptive mesh)',
'CMC',
'KDEMC',
'WRS'),
lwd = rep(3, 6),
lty = c(3,2,4,5,6),
pch = c(5,4,3,2,1),
col = c(rep('black', 2), rep('red', 3)),
cex = 1.25,
text.font = 2,
bty = 'n')
##### ISBA poster #####
plot(x = log(c(4, 8, 16, 32, 64, 128), 2), y = balanced$adaptive,
ylim = c(0, 0.4),
xlab = '',
ylab = '',
xaxt = 'n', lty = 1, lwd = 3, pch = 1, type = 'b')
mtext('log(C, 2)', 1, 2.75, font = 2, cex = 1.5)
mtext('Integrated Absolute Distance', 2, 2.75, font = 2, cex = 1.5)
axis(1, at=c(seq(0, 0.9, 0.1), 0.95), labels=c("0.0", c(seq(0.1, 0.9, 0.1), 0.95)), font = 2, cex = 1.5)
axis(1, at=log(c(4, 8, 16, 32, 64, 128), 2), labels = log(c(4, 8, 16, 32, 64, 128), 2), font = 2, cex = 1.5)
axis(2, at=seq(0, 1, 0.1), labels=c("0.0", seq(0.1, 0.9, 0.1), "1.0"), font = 2, cex = 1.5)
axis(2, at=seq(0, 1, 0.1), labels=rep("", 11), lwd.ticks = 0.5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = consensus,
lty = 4, lwd = 3, type = 'b', pch = 4, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = neiswanger,
lty = 5, lwd = 3, type = 'b', pch = 5, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = weierstrass,
lty = 6, lwd = 3, type = 'b', pch = 6, col = 'red')
legend(x = 2, y = 0.4,
legend = c('D&C-GBF',
'CMC',
'KDEMC',
'WRS'),
lwd = rep(3, 5),
lty = c(1,4,5,6),
pch = c(1,4,5,6),
col = c(rep('black', 1), rep('red', 3)),
cex = 1.25,
text.font = 2,
bty = 'n')
plot(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(balanced_time$adaptive, 2),
ylim = c(-4, 22),
xlab = '',
ylab = '',
yaxt = 'n',
xaxt = 'n', lty = 1, lwd = 3, pch = 1, type = 'b')
mtext('log(C, 2)', 1, 2.75, font = 2, cex = 1.5)
mtext('log(Time elapsed in seconds, 2)', 2, 2.75, font = 2, cex = 1.5)
axis(1, at=c(seq(0, 0.9, 0.1), 0.95), labels=c("0.0", c(seq(0.1, 0.9, 0.1), 0.95)), font = 2, cex = 1.5)
axis(1, at=log(c(4, 8, 16, 32, 64, 128), 2), labels = log(c(4, 8, 16, 32, 64, 128), 2), font = 2, cex = 1.5)
axis(2, at=seq(-4, 22, 4), labels = seq(-4, 22, 4), font = 2, cex = 1.5)
axis(2, at=seq(-4, 22, 1), labels=rep("", 27), lwd.ticks = 0.5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(consensus_time, 2),
lty = 4, lwd = 3, type = 'b', pch = 4, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(neiswanger_time, 2),
lty = 5, lwd = 3, type = 'b', pch = 5, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(weierstrass_time, 2),
lty = 6, lwd = 3, type = 'b', pch = 6, col = 'red')
legend(x = 2, y = 22,
legend = c('D&C-GBF',
'CMC',
'KDEMC',
'WRS'),
lwd = rep(3, 5),
lty = c(1,4,5,6),
pch = c(1,4,5,6),
col = c(rep('black', 1), rep('red', 3)),
cex = 1.25,
text.font = 2,
bty = 'n')
rchan26/hierarchicalFusion documentation built on Sept. 11, 2022, 10:30 p.m.
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library(DCFusion)
load('BS4_NB.RData')
load('BS8_NB.RData')
load('BS16_NB.RData')
load('BS32_NB.RData')
load('BS64_NB.RData')
load('BS128_NB.RData')
balanced <- list('reg' = c(integrated_abs_distance(full_posterior,
balanced_C4$reg$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C8$reg$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C16$reg$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C32$reg$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C64$reg$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C128$reg$particles$y_samples)),
'adaptive' = c(integrated_abs_distance(full_posterior,
balanced_C4$adaptive$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C8$adaptive$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C16$adaptive$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C32$adaptive$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C64$adaptive$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C128$adaptive$particles$y_samples)))
consensus <- c(integrated_abs_distance(full_posterior,
consensus_mat_4$samples),
integrated_abs_distance(full_posterior,
consensus_mat_8$samples),
integrated_abs_distance(full_posterior,
consensus_mat_16$samples),
integrated_abs_distance(full_posterior,
consensus_mat_32$samples),
integrated_abs_distance(full_posterior,
consensus_mat_64$samples),
integrated_abs_distance(full_posterior,
consensus_mat_128$samples))
neiswanger <- c(integrated_abs_distance(full_posterior,
neiswanger_false_4$samples),
integrated_abs_distance(full_posterior,
neiswanger_false_8$samples),
integrated_abs_distance(full_posterior,
neiswanger_false_16$samples),
integrated_abs_distance(full_posterior,
neiswanger_false_32$samples),
integrated_abs_distance(full_posterior,
neiswanger_false_64$samples),
integrated_abs_distance(full_posterior,
neiswanger_false_128$samples))
weierstrass <- c(integrated_abs_distance(full_posterior,
weierstrass_rejection_4$samples),
integrated_abs_distance(full_posterior,
weierstrass_rejection_8$samples),
integrated_abs_distance(full_posterior,
weierstrass_rejection_16$samples),
integrated_abs_distance(full_posterior,
weierstrass_rejection_32$samples),
integrated_abs_distance(full_posterior,
weierstrass_rejection_64$samples),
integrated_abs_distance(full_posterior,
weierstrass_rejection_128$samples))
plot(x = log(c(4, 8, 16, 32, 64, 128), 2), y = balanced$adaptive,
ylim = c(0, 0.3),
xlab = '',
ylab = '',
xaxt = 'n', yaxt = 'n', lty = 2, lwd = 3, pch = 4, type = 'b')
mtext('log(C, 2)', 1, 2.75, font = 2, cex = 1.5)
mtext('Integrated Absolute Distance', 2, 2.75, font = 2, cex = 1.5)
axis(1, at=c(seq(0, 0.9, 0.1), 0.95), labels=c("0.0", c(seq(0.1, 0.9, 0.1), 0.95)), font = 2, cex = 1.5)
axis(1, at=log(c(4, 8, 16, 32, 64, 128), 2), labels = log(c(4, 8, 16, 32, 64, 128), 2), font = 2, cex = 1.5)
axis(2, at=seq(0, 1, 0.1), labels=c("0.0", seq(0.1, 0.9, 0.1), "1.0"), font = 2, cex = 1.5)
axis(2, at=seq(0, 1, 0.05), labels=rep("", 21), lwd.ticks = 0.5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = balanced$reg,
lty = 3, lwd = 3, type = 'b', pch = 5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = consensus,
lty = 4, lwd = 3, type = 'b', pch = 3, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = neiswanger,
lty = 5, lwd = 3, type = 'b', pch = 2, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = weierstrass,
lty = 6, lwd = 3, type = 'b', pch = 1, col = 'red')
legend(x = 2, y = 0.3,
legend = c('D&C-GBF (regular mesh)',
'D&C-GBF (adaptive mesh)',
'CMC',
'KDEMC',
'WRS'),
lwd = rep(3, 6),
lty = c(3,2,4,5,6),
pch = c(5,4,3,2,1),
col = c(rep('black', 2), rep('red', 3)),
cex = 1.25,
text.font = 2,
bty = 'n')
######################### TIME
balanced_time <- list('reg' = c(sum(unlist(balanced_C4$reg$time)),
sum(unlist(balanced_C8$reg$time)),
sum(unlist(balanced_C16$reg$time)),
sum(unlist(balanced_C32$reg$time)),
sum(unlist(balanced_C64$reg$time)),
sum(unlist(balanced_C128$reg$time))),
'adaptive' = c(sum(unlist(balanced_C4$adaptive$time)),
sum(unlist(balanced_C8$adaptive$time)),
sum(unlist(balanced_C16$adaptive$time)),
sum(unlist(balanced_C32$adaptive$time)),
sum(unlist(balanced_C64$adaptive$time)),
sum(unlist(balanced_C128$adaptive$time))))
consensus_time <- c(consensus_mat_4$time,
consensus_mat_8$time,
consensus_mat_16$time,
consensus_mat_32$time,
consensus_mat_64$time,
consensus_mat_128$time)
neiswanger_time <- c(neiswanger_false_4$time,
neiswanger_false_8$time,
neiswanger_false_16$time,
neiswanger_false_32$time,
neiswanger_false_64$time,
neiswanger_false_128$time)
weierstrass_time <- c(weierstrass_rejection_4$time,
weierstrass_rejection_8$time,
weierstrass_rejection_16$time,
weierstrass_rejection_32$time,
weierstrass_rejection_64$time,
weierstrass_rejection_128$time)
plot(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(balanced_time$adaptive, 2),
ylim = c(-2, 18),
xlab = '',
ylab = '',
yaxt = 'n',
xaxt = 'n', lty = 2, lwd = 3, pch = 4, type = 'b')
mtext('log(C, 2)', 1, 2.75, font = 2, cex = 1.5)
mtext('log(Time elapsed in seconds, 2)', 2, 2.75, font = 2, cex = 1.5)
axis(1, at=c(seq(0, 0.9, 0.1), 0.95), labels=c("0.0", c(seq(0.1, 0.9, 0.1), 0.95)), font = 2, cex = 1.5)
axis(1, at=log(c(4, 8, 16, 32, 64, 128), 2), labels = log(c(4, 8, 16, 32, 64, 128), 2), font = 2, cex = 1.5)
axis(2, at=seq(-4, 20, 2), labels = seq(-4, 20, 2), font = 2, cex = 1.5)
axis(2, at=seq(-4, 20, 1), labels=rep("", 25), lwd.ticks = 0.5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(balanced_time$reg, 2),
lty = 3, lwd = 3, type = 'b', pch = 5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(consensus_time, 2),
lty = 4, lwd = 3, type = 'b', pch = 3, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(neiswanger_time, 2),
lty = 5, lwd = 3, type = 'b', pch = 2, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(weierstrass_time, 2),
lty = 6, lwd = 3, type = 'b', pch = 1, col = 'red')
legend(x = 2, y = 18,
legend = c('D&C-GBF (regular mesh)',
'D&C-GBF (adaptive mesh)',
'CMC',
'KDEMC',
'WRS'),
lwd = rep(3, 6),
lty = c(3,2,4,5,6),
pch = c(5,4,3,2,1),
col = c(rep('black', 2), rep('red', 3)),
cex = 1.25,
text.font = 2,
bty = 'n')
##### ISBA poster #####
plot(x = log(c(4, 8, 16, 32, 64, 128), 2), y = balanced$adaptive,
ylim = c(0, 0.4),
xlab = '',
ylab = '',
xaxt = 'n', lty = 1, lwd = 3, pch = 1, type = 'b')
mtext('log(C, 2)', 1, 2.75, font = 2, cex = 1.5)
mtext('Integrated Absolute Distance', 2, 2.75, font = 2, cex = 1.5)
axis(1, at=c(seq(0, 0.9, 0.1), 0.95), labels=c("0.0", c(seq(0.1, 0.9, 0.1), 0.95)), font = 2, cex = 1.5)
axis(1, at=log(c(4, 8, 16, 32, 64, 128), 2), labels = log(c(4, 8, 16, 32, 64, 128), 2), font = 2, cex = 1.5)
axis(2, at=seq(0, 1, 0.1), labels=c("0.0", seq(0.1, 0.9, 0.1), "1.0"), font = 2, cex = 1.5)
axis(2, at=seq(0, 1, 0.1), labels=rep("", 11), lwd.ticks = 0.5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = consensus,
lty = 4, lwd = 3, type = 'b', pch = 4, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = neiswanger,
lty = 5, lwd = 3, type = 'b', pch = 5, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = weierstrass,
lty = 6, lwd = 3, type = 'b', pch = 6, col = 'red')
legend(x = 2, y = 0.4,
legend = c('D&C-GBF',
'CMC',
'KDEMC',
'WRS'),
lwd = rep(3, 5),
lty = c(1,4,5,6),
pch = c(1,4,5,6),
col = c(rep('black', 1), rep('red', 3)),
cex = 1.25,
text.font = 2,
bty = 'n')
plot(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(balanced_time$adaptive, 2),
ylim = c(-4, 22),
xlab = '',
ylab = '',
yaxt = 'n',
xaxt = 'n', lty = 1, lwd = 3, pch = 1, type = 'b')
mtext('log(C, 2)', 1, 2.75, font = 2, cex = 1.5)
mtext('log(Time elapsed in seconds, 2)', 2, 2.75, font = 2, cex = 1.5)
axis(1, at=c(seq(0, 0.9, 0.1), 0.95), labels=c("0.0", c(seq(0.1, 0.9, 0.1), 0.95)), font = 2, cex = 1.5)
axis(1, at=log(c(4, 8, 16, 32, 64, 128), 2), labels = log(c(4, 8, 16, 32, 64, 128), 2), font = 2, cex = 1.5)
axis(2, at=seq(-4, 22, 4), labels = seq(-4, 22, 4), font = 2, cex = 1.5)
axis(2, at=seq(-4, 22, 1), labels=rep("", 27), lwd.ticks = 0.5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(consensus_time, 2),
lty = 4, lwd = 3, type = 'b', pch = 4, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(neiswanger_time, 2),
lty = 5, lwd = 3, type = 'b', pch = 5, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(weierstrass_time, 2),
lty = 6, lwd = 3, type = 'b', pch = 6, col = 'red')
legend(x = 2, y = 22,
legend = c('D&C-GBF',
'CMC',
'KDEMC',
'WRS'),
lwd = rep(3, 5),
lty = c(1,4,5,6),
pch = c(1,4,5,6),
col = c(rep('black', 1), rep('red', 3)),
cex = 1.25,
text.font = 2,
bty = 'n')
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