/
NonIdent-Fig1.R
342 lines (321 loc) · 17.9 KB
/
NonIdent-Fig1.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
library(plyr); library(data.table); library(abind); library(coda); library(plotrix); library(sp); library(emdbook); library(multicore);
rm(list=ls(all=T)); gc()
source('RakFunctions.R')
####################################################################################################
## Show that duration & infectivity of acute phase are not identifiable
## Create Figure 1.
####################################################################################################
## Load our MCMC refit of Holl Model to the real data (with exclusion criteria error)
load(file = file.path('HollModRefit','HollModRefit.Rdata'))
outtab <- outtab[,,'XbErr']
print(outtab) ## Holl Mod refit with MCMC
fexp <- (fout$exposts) ## posteriors
fexp$bp.ac <- fexp$bp * fexp$acute.sc ## acute hazard = chronic hazard * RH[ac]
quantile(fexp$bp.ac, c(0.025, .5, .975))*12 ## acute hazad per peson-year
fmcmc <- as.mcmc(fout$posts)
meds <- apply(fout$exposts[,c('acute.sc','dur.ac')], 2, median) ## medians of these parameters
apply(fout$exposts[,c('acute.sc','dur.ac')], 2, function(x) quantile(x, c(.025,.5,.975)))
outdir <- file.path('FiguresAndTables','HollingsworthAn')
if(!file.exists(outdir)) dir.create(outdir)
## Get 95% posterior contour using volume of points in that region. USING 90% because HPD tends to
## overshoot a bit, true values for EHMacute CI's given in text
HollCont <- HPDregionplot(fmcmc, vars = c('acute.sc','dur.ac'), prob = c(.95), lims = c(-1.5,10,-2,11), #n = 28, h = c(1,.08),
bty = 'n', xlab = expression(RH['acute']), ylab = 'acute phase duration (months)')
graphics.off()
HollXY <- with(HollCont[[1]], cbind(x=x,y=y))
HollXY <- rbind(HollXY, tail(HollXY,1))
mean(point.in.polygon(fmcmc[,'acute.sc'],fmcmc[,'dur.ac'],HollXY[,'x'], HollXY[,'y']))
####################################################################################################
## ABC-SMC results
####################################################################################################
abcdir <- 'abcSummary'
finalbatch <- 5
ABCoutdir <- 'FiguresAndTables/abcFig'
load(file=file.path(abcdir, paste0('IntermedDistr',finalbatch,'.Rdata'))) ## Load last distribution (already filtered)
## Get 95% posterior contour using volume of points in that region, .89 chosen based on point.in.polygon below and to visually match the true EHMacute quantile (which has 95% upper bound of 63, so contour can't go over the 70 color boundary
abcCont <- HPDregionplot(pmatChosen, vars = c('logacute.sc','logdur.ac'), prob = c(.87), lims = c(-1.5,10,-2,11), n = 28, h =c(.8,1),# h = c(.3,.1),
bty = 'n', xlab = expression(RH['acute']), ylab = 'acute phase duration (months)')
abcCIs <- log(quants(pmatChosen[,c('acute.sc','dur.ac')]))
abcEHM <- quants(pmatChosen[,'EHMacute',drop=F])
graphics.off()
abcXY <- with(abcCont[[1]], cbind(x=x,y=y))
abcXY <- rbind(abcXY, tail(abcXY,1))
mean(point.in.polygon(pmatChosen$logacute.sc,pmatChosen$logdur.ac,abcXY[,'x'], abcXY[,'y']))
drawHazProf <- function(yt, acute.sc = 101, dur.ac = .75, examnum = 1, lab=T) {
polygon(c(0,dur.ac,dur.ac,0), yt + c(0,0,acute.sc,acute.sc), col = col.ac, border=NA) ## acute
polygon(c(100,110,110,100), yt + c(0,0,7,7),col = col.lt, border=NA) ## late
polygon(c(0,110,110,0), yt + c(0,0,1,1), col = col.ch, border=NA) ## chronic
segments(110,yt,120,yt, lty = 3, col = col.aids)
points(-8,yt+5, pch = 21, cex = 2.5)
points(-8,yt+5, pch = as.character(examnum))
if(lab) {
text(1.5, yt + 12, bquote(RH[acute]==paste(.(acute.sc), ' for ', .(dur.ac), ' months')),pos = 4, col = col.ac)
}}
hazLabs <- function(yt=30) { text(5,yt, 'acute', col = col.ac, pos=1)
text(60,yt, 'chronic', col = col.ch, pos=1)
text(103,yt, 'late', col = col.lt, pos=1)
text(118,yt, 'AIDS', col = col.aids, pos=1)}
## Contour/image plot settings
xs <- seq(-1,7.2, by = .05) ##log(seq(0.1,8, by = 1))
ys <- seq(-2.4,2.7, by = .05) ##log(seq(0.1, 11, by = .5))
zs <- outer(exp(xs)-1,exp(ys))
range(zs)
which(zs==min(zs),T)
zs[1,103]
ys[103]
levels <- c(-10,-2,0,2,10,20,50,100,200,500,1000,10000,10^5)
trfxn <- function(x) log(x+10)
utrfxn <- function(x) exp(x)-10
levels <- utrfxn(seq(trfxn(min(zs)),trfxn(max(zs)), l = 20))
levels[which.min(abs(levels))] <- 0
levels <- c(-10, levels[levels>=0])
levels <- round(levels, -1)
levels <- c(-10,0,10,20,40,70,130,250,500,1000,2000,4000,7000,13000,20000)
cols <- colorRampPalette(c('skyblue2','purple','orange','red'))(length(levels)-1)
## blevels <- c(-25,-10,-2,0)
## bcols <- colorRampPalette(c('dark green','purple'))(length(blevels)-1)
## cols <- c(bcols[-length(bcols)], cols)
## levels <- c(blevels[-length(blevels)],levels)
mar1 <- c(4,2,2.5,1)
mar2 <- c(4,5,2.5,0)
options("scipen"=999)
col.ac <- 'brown'
col.ch <- 'black'
col.lt <- gray(.3)
col.aids <- gray(.6)
xlab1 <- expression(RH[acute])
ylab1 <- expression(d[acute])
xlab2 <- 'relative infectivity of acute phase vs. chronic phase'
ylab2 <- 'acute phase duration in months'
####################################################################################################
## Figure 1. Show EHM diagram and RH[acute]/d[acute] collinearity plot with MCMC posteriors from refit of Holl Mod.
showpts <- FALSE
for(ff in 1:3) {
if(ff==1) pdf(file.path(ABCoutdir,'Figure 1 - EHM diagram and RH_ac d_ac collinearity.pdf'), w = 6.83, h = 5)
if(ff==2) png(file.path(ABCoutdir,'Figure 1 - EHM diagram and RH_ac d_ac collinearity.png'), w = 6.83, h = 5, units='in',res=200)
if(ff==3) tiff(file.path(ABCoutdir,'Figure 1 - EHM diagram and RH_ac d_ac collinearity.tiff'), w = 6.83, h = 5, units='in',res=300)
par('ps'=12)
layout(t(matrix(c(1:2,4,1,3,4),3,2)), w = c(.8,1,.27))
## ################################################
## conceptual diagram
par(mar=mar1)
plot(0,0, type = 'n', xlim = c(-10,125), ylim = c(0, 600), bty = 'n', axes=F, xlab='', ylab='')
title(main='A', adj = 0)
title(xlab='years since infection', mgp = c(2.5,0,0))
title(ylab='hazard profile', mgp=c(.5,1,0))
axis(1, at = seq(0,120, by = 12), 0:10)
## hazard profiles
drawHazProf(600, 16, 5,1)
drawHazProf(510, 26, 3,2)
drawHazProf(370, 101, .75,3)
hazLabs(360)
# drawHazProf(400, 42, 1.5,3)
rhmn <- signif(exp(abcCIs['50%','acute.sc']),2)
dmn <- signif(exp(abcCIs['50%','dur.ac']),2)
drawHazProf(100, rhmn, dmn,4)
hazLabs(90)
## ################################################
## contour plot of EHM from Hollingsworth et al. variable hazard survival model refit with MCMC
par(mar=mar2)
image(xs, ys, zs, breaks = levels, xlim = c(-1,6.2), ylim = c(-2.4,2.6), mgp = c(3,0,0),
col = cols, axes = F, xlab='',ylab=ylab2)
if(showpts) with(fout$posts, points(acute.sc, dur.ac, pch=16, cex = .4, col = gray(.6)))
title(main='B', adj = 0)
title(xlab=xlab2, mgp=c(2,0,0))
xts <- c(1:9, seq(10, 90, by = 10), seq(100, 500, by = 100))
xsh <- c(1,10,100)
xls <- xts
xls[!xls %in% xsh] <- NA
axis(1, at = log(xts), lab = xls)
yts <- c(seq(.1,.9, by = .1),1:10)
ysh <- c(.1,1,10)
yls <- yts
yls[!yls %in% ysh] <- NA
axis(2, at = log(yts), lab = yls, las = 2)
lines(HollCont[[1]]$x, HollCont[[1]]$y)
text(log(38), log(5.8), '95% credible contour', pos = 4, srt=-45)
points(log(c(101,26,16)),log(c(.75,3,5)), pch=as.character(3:1))
points(log(c(101,26,16)),log(c(.75,3,5)), pch = 21, cex = 2.5)
text(log(outtab['50%','acute.sc']), log(.14), '95% CrI', pos = 3)
text(log(4), log(outtab['50%','dur.ac']), '95% CrI', pos = 2, srt=90)
with(data.frame(outtab), arrows(log(acute.sc[c(1)]), log(.15), log(acute.sc[c(3)]), log(.15), len = .05, angle = 90, code = 3))
with(data.frame(outtab), arrows(log(4), log(dur.ac[c(1)]), log(4), log(dur.ac[c(3)]), len = .05, angle = 90, code = 3))
## contour plot of EHM from Bellan et al. couple transmission model fit with ABC SMC
par(mar=mar2)
image(xs, ys, zs, breaks = levels, xlim = c(-1,6.2), ylim = c(-2.4,2.6), mgp = c(3,0,0),
col = cols, axes = F, xlab='', ylab=ylab2)
if(showpts) with(pmatChosen, points(logacute.sc, logdur.ac, pch=16, cex = .4, col = gray(.6)))
title(main='C', adj = 0)
title(xlab=xlab2, mgp=c(2,0,0))
points(abcCIs['50%','acute.sc'], abcCIs['50%','dur.ac'], pch = as.character(4))
points(abcCIs['50%','acute.sc'], abcCIs['50%','dur.ac'], pch = 21, cex = 2.5)
xts <- c(1:9, seq(10, 90, by = 10), seq(100, 500, by = 100))
xsh <- c(1,10,100)
xls <- xts
xls[!xls %in% xsh] <- NA
axis(1, at = log(xts), lab = xls)
yts <- c(seq(.1,.9, by = .1),1:10)
ysh <- c(.1,1,10)
yls <- yts
yls[!yls %in% ysh] <- NA
axis(2, at = log(yts), lab = yls, las = 2)
lines(abcCont[[1]]$x, abcCont[[1]]$y) #
arrows(abcCIs['2.5%','acute.sc'], log(.2), abcCIs['97.5%','acute.sc'], log(.2), len = .05, angle = 90, code = 3)
arrows(log(.45), abcCIs['2.5%','dur.ac'], log(.45), abcCIs['97.5%','dur.ac'], len = .05, angle = 90, code = 3)
## Palette legend
par(mar=rep(0,4))
plot(0,0,type="n",axes=F, xlim = c(-.1,.2), ylim = c(-.1,.9), xlab = '', ylab = '')
legseq <- levels[levels<=1000] # c(0,10,20,40,70,130,240,1000)
show <- levels<=1000 #& levels > -5
sbcolor.legend(.09,.1,.15,.8, legend=levels[show], legseq=legseq, rect.col = cols[show], gradient = "y", cex = .8, browse=F)
text(.025, .85, expression(paste(EHM['acute'])), pos = 3, cex = 1.2)
dev.off()
}
####################################################################################################
####################################################################################################
## Same for late phase
####################################################################################################
fout$exposts <- ddply(fout$exposts, .(), transform, atr.month.ltaids = (late.sc-1)*(dur.lt) -dur.aids)
head(fout$exposts)
ltp <- c('late.sc','dur.lt')
cont.lt <- HPDregionplot(fmcmc, vars = ltp, prob = c(.95), lims = c(-1.5,10,-2,11), n = 80, h = c(.5,.1),
bty = 'n', xlab = expression(RH['late']), ylab = 'late phase duration (months)')
meds.lt <- apply(fout$exposts[,ltp], 2, median)
apply(fout$exposts[,ltp], 2, function(x) quantile(x, c(.025,.975)))
adp <- c('late.sc','dur.aids')
cont.ad <- HPDregionplot(fmcmc, vars = adp, prob = c(.95), lims = c(-1.5,10,-2,11), n = 80, h = c(.5,.1),
bty = 'n', xlab = expression(RH['late']), ylab = 'aids phase duration (months)')
meds.ad <- apply(fout$exposts[,adp], 2, median)
apply(fout$exposts[,adp], 2, function(x) quantile(x, c(.025,.975)))
## ehm.late plot
pdf(file.path(outdir,'late contour (LOG).pdf'), w = 6.83, h = 3)
ct <- .7
layout(matrix(c(1:3),1,3), w = c(1,.3,1))
## RHlate vs dur.lt
par(cex.lab = ct, cex.axis = ct, cex.main = ct)
par(mar=c(3,3,.3,0))
xs <- seq(-1,7.2, by = .05) ##log(seq(0.1,8, by = 1))
ys <- seq(-2.4,6, by = .05) ##log(seq(0.1, 11, by = .5))
levels <- c(0,2,5,10,25,50,70,100,200,500,1000,10000,10^5)
cols <- colorRampPalette(c('purple','orange','red'))(length(levels)-1)
image(xs, ys, outer(exp(xs)-1,exp(ys)), breaks = levels, xlim = c(-1,5.7), ylim = c(-2.4,5), mgp = c(2,0,0),
col = cols, axes = F, xlab = expression(paste(RH['late'])), ylab = 'late phase duration (months)')
xts <- c(1:9, seq(10, 90, by = 10), seq(100, 300, by = 100))
xsh <- c(1,10,100,1000)
xls <- xts
xls[!xls %in% xsh] <- NA
axis(1, at = log(xts), lab = xls)
yts <- c(seq(.1,.9, by = .1),1:9, seq(10, 100, by = 10))
ysh <- c(.1,1,10,100)
yls <- yts
yls[!yls %in% ysh] <- NA
axis(2, at = log(yts), lab = yls, las = 2)
lines(cont.lt[[1]]$x, cont.lt[[1]]$y)
## points(fout$posts$late.sc, fout$posts$dur.lt, cex = .3)
points(log(outtab[4,'late.sc']), log(outtab[4,'dur.lt']), pch = 15)#, col = 'points')
## text(log(outtab[4,'late.sc']), log(outtab[4,'dur.lt']), 'Hollingsworth', pos=3, cex=.5)
points(log(meds.lt['late.sc']), log(meds.lt['dur.lt']), pch = 19)#, col = 'points')
## text(log(meds.lt['late.sc']), log(meds.lt['dur.lt']), 'median', pos=1, cex=.5)
with(data.frame(outtab), axis(1, at = log(late.sc[c(1,3)]), lab = NA, line = -1.5))
with(data.frame(outtab), axis(2, at = log(dur.lt[c(1,3)]), lab = NA, line = -1))
## Palette legend
par(mar=rep(0,4), cex.lab = ct, cex.axis = ct, cex.main = ct)
plot(0,0,type="n",axes=F, xlim = c(-.1,.2), ylim = c(-.1,.9), xlab = '', ylab = '')
color.legend(.09,.1,.15,.8, levels, rect.col = cols, gradient = "y", cex = ct)
text(.025, .8, expression(paste(EHM['late'])), pos = 3, cex = ct)
####################################################################################################
## RHlate vs dur.aids
par(cex.lab = ct, cex.axis = ct, cex.main = ct)
par(mar=c(3,4,.3,1))
xs <- seq(-1,7.2, by = .05) ##log(seq(0.1,8, by = 1))
ys <- seq(-2.4,6, by = .05) ##log(seq(0.1, 11, by = .5))
levels <- c(0,2,5,10,25,50,70,100,200,500,1000,10000,10^5)
cols <- rep(NA, length(levels)-1) #colorRampPalette(c('purple','orange','red'))(length(levels)-1)
## filled.contour(xs, ys, outer(exp(xs),exp(ys)), level = levels, xlim = c(.5,7.2), ylim = c(-2,3),
## col = cols, plot.axes=F)
image(xs, ys, outer(exp(xs),exp(ys)), breaks = levels, xlim = c(-1,5.7), ylim = c(-2.4,5), mgp = c(2,0,0),
col = cols, axes = F, xlab = expression(paste(RH['late'])), ylab = 'AIDS phase duration (months)')
xts <- c(1:9, seq(10, 90, by = 10), seq(100, 200, by = 100))
xsh <- c(1,10,100,1000)
xls <- xts
xls[!xls %in% xsh] <- NA
axis(1, at = log(xts), lab = xls)
yts <- c(seq(.1,.9, by = .1),1:9, seq(10, 100, by = 10))
ysh <- c(.1,1,10,100)
yls <- yts
yls[!yls %in% ysh] <- NA
axis(2, at = log(yts), lab = yls, las = 2)
lines(cont.ad[[1]]$x, cont.ad[[1]]$y)
## points(fout$posts$late.sc, fout$posts$dur.ad, cex = .3)
points(log(outtab[4,'late.sc']), log(outtab[4,'dur.aids']), pch = 15)#, col = 'points')
## text(log(outtab[4,'late.sc']), log(outtab[4,'dur.aids']), 'Hollingsworth', pos=3, cex=.5)
points(log(meds.ad['late.sc']), log(meds.ad['dur.aids']), pch = 19)#, col = 'points')
## text(log(meds.ad['late.sc']), log(meds.ad['dur.aids']), 'median', pos=1, cex=.5)
with(data.frame(outtab), axis(1, at = log(late.sc[c(1,3)]), lab = NA, line = -1.5))
with(data.frame(outtab), axis(2, at = log(dur.aids[c(1,3)]), lab = NA, line = -1))
dev.off()
####################################################################################################
####################################################################################################
## Same for late+AIDS phase
####################################################################################################
fout$exposts <- ddply(fout$exposts, .(), transform, atr.month.ltaids = (late.sc-1)*(dur.lt) -dur.aids, dur.ltaids = dur.lt + dur.aids,
ltaids.sc = (late.sc*dur.lt + 0*dur.aids)/(dur.lt+dur.aids))
outtab <- ddply(outtab, .(), transform, atr.month.ltaids = (late.sc-1)*(dur.lt) -dur.aids, dur.ltaids = dur.lt + dur.aids,
ltaids.sc = (late.sc*dur.lt + 0*dur.aids)/(dur.lt+dur.aids))
head(fout$exposts)
posts <- fout$exposts
posts[,c('ltaids.sc','dur.ltaids','atr.month.ltaids')] <- log(posts[,c('ltaids.sc','dur.ltaids','atr.month.ltaids')])
ltp <- c('ltaids.sc','dur.ltaids')
cont.ltaids <- HPDregionplot(posts, vars = ltp, prob = c(.95), lims = c(-1.5,10,-2,11), n = 80, h = c(.5,.1))
meds.ltaids <- apply(posts[,ltp], 2, median)
cis <- apply(exp(posts[,ltp]), 2, function(x) quantile(x, c(.025,.975)))
apply(exp(posts[,ltp]), 2, range)
quantile(fout$exposts$atr.month.ltaids, c(.025, .5, .975))
## EHM.ltaids plot
pdf(file.path(outdir,'EHM lateaids (LOG).pdf'), w = 3.27, h = 3)
ct <- .6
layout(matrix(c(1:2),1,2), w = c(1,.25))
## (RHlate*dur.lt + RHaids*dur.aids)/(dur.lt + dur.aids) vs (dur.lt+dur.aids)
par(cex.lab = ct, cex.axis = ct, cex.main = ct)
par(mar=c(3,3,.3,0))
xs <- seq(-1,3, by = .05) ##log(seq(0.1,8, by = 1))
ys <- seq(0,4, by = .05) ##log(seq(0.1, 11, by = .5))
pal <- colorRamp(c('purple','orange','red'))
levels <- c(-25,-10,-5,-2,0,2,5,10,25,50,70,100,200,500,1000)
rg <- range(levels)
cols <- colorRampPalette(c('purple','orange','red'))(length(levels)-1)
## filled.contour(xs, ys, outer(exp(xs),exp(ys)), level = levels, xlim = c(.5,7.2), ylim = c(-2,3),
## col = cols, plot.axes=F)
zs <- outer(exp(xs)-1,exp(ys))
image(xs, ys, zs, breaks = levels, xlim = c(-.4,2.5), ylim = c(0,4), mgp = c(2,0,0),
col = cols, axes = F, xlab = expression(paste((RH['late']*d['late']+RH['AIDS']*d['AIDS'])/(d['late']+d['AIDS']))),
ylab = expression(paste((d['late']+d['AIDS']))))
xts <- c(1:10)
xsh <- c(1,10,100,1000)
xls <- xts
xls[!xls %in% xsh] <- NA
axis(1, at = log(xts), lab = xls)
yts <- c(1:9, seq(10, 50, by = 10))
ysh <- c(.1,1,10,100)
yls <- yts
yls[!yls %in% ysh] <- NA
axis(2, at = log(yts), lab = yls, las = 2)
lines(cont.ltaids[[1]]$x, cont.ltaids[[1]]$y)
#with(posts, points(ltaids.sc, dur.ltaids, cex = .08, pch = 19))
points(log(outtab[4,'ltaids.sc']), log(outtab[4,'dur.ltaids']), pch = 15, cex = .8, col = gray(.2))#, col = 'points')
## text(log(outtab[4,'late.sc']), log(outtab[4,'dur.lt']), 'Hollingsworth', pos=3, cex=.5)
points(meds.ltaids['ltaids.sc'], meds.ltaids['dur.ltaids'], pch = 19, cex = .8, col=gray(.3))#, col = 'points')
## text(log(meds.lt['late.sc']), log(meds.lt['dur.lt']), 'median', pos=1, cex=.5)
with(data.frame(cis), axis(1, at = log(ltaids.sc[c(1,2)]), lab = NA, line = -1.5))
with(data.frame(cis), axis(2, at = log(dur.ltaids[c(1,2)]), lab = NA, line = -1))
## Palette legend
par(mar=rep(0,4), cex.lab = ct, cex.axis = ct, cex.main = ct)
plot(0,0,type="n",axes=F, xlim = c(-.1,.2), ylim = c(-.1,.9), xlab = '', ylab = '')
ticks <- levels#[-length(levels)]#ticks[-length(ticks)]
#cols <- apply(pal(trf(ticks)), 1, function(x) rgb(x[1],x[2],x[3], maxColorValue=255))
color.legend(.09,.1,.15,.8, ticks, rect.col = cols, gradient = "y", cex = ct)
text(.025, .8, expression(paste(EHM['late'+'AIDS'])), pos = 3, cex = ct)
graphics.off()
## pdf(file.path(outdir,'Figure SX - fitted RHacute dacute.pdf'), w = 4, h = 4)
## with(pmatChosen, plot(acute.sc, dur.ac, cex = .3, log='xy', xlim = c(.5,200), ylim = c(.5,10), las = 3))
## graphics.off()