Mercurial > repos > greg > insect_phenology_model
comparison insect_phenology_model.R @ 0:244c373f2a34 draft
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author | greg |
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date | Tue, 08 Aug 2017 13:14:39 -0400 |
parents | |
children | 24fa0d35a8bf |
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-1:000000000000 | 0:244c373f2a34 |
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1 #!/usr/bin/env Rscript | |
2 | |
3 suppressPackageStartupMessages(library("optparse")) | |
4 | |
5 option_list <- list( | |
6 make_option(c("-a", "--adult_mort"), action="store", dest="adult_mort", type="integer", help="Adjustment rate for adult mortality"), | |
7 make_option(c("-b", "--adult_accum"), action="store", dest="adult_accum", type="integer", help="Adjustment of DD accumulation (old nymph->adult)"), | |
8 make_option(c("-c", "--egg_mort"), action="store", dest="egg_mort", type="integer", help="Adjustment rate for egg mortality"), | |
9 make_option(c("-d", "--latitude"), action="store", dest="latitude", type="double", help="Latitude of selected location"), | |
10 make_option(c("-e", "--location"), action="store", dest="location", help="Selected location"), | |
11 make_option(c("-f", "--min_clutch_size"), action="store", dest="min_clutch_size", type="integer", help="Adjustment of minimum clutch size"), | |
12 make_option(c("-i", "--max_clutch_size"), action="store", dest="max_clutch_size", type="integer", help="Adjustment of maximum clutch size"), | |
13 make_option(c("-j", "--nymph_mort"), action="store", dest="nymph_mort", type="integer", help="Adjustment rate for nymph mortality"), | |
14 make_option(c("-k", "--old_nymph_accum"), action="store", dest="old_nymph_accum", type="integer", help="Adjustment of DD accumulation (young nymph->old nymph)"), | |
15 make_option(c("-o", "--output"), action="store", dest="output", help="Output dataset"), | |
16 make_option(c("-p", "--oviposition"), action="store", dest="oviposition", type="integer", help="Adjustment for oviposition rate"), | |
17 make_option(c("-q", "--photoperiod"), action="store", dest="photoperiod", type="double", help="Critical photoperiod for diapause induction/termination"), | |
18 make_option(c("-s", "--replications"), action="store", dest="replications", type="integer", help="Number of replications"), | |
19 make_option(c("-t", "--se_plot"), action="store", dest="se_plot", help="Plot SE"), | |
20 make_option(c("-u", "--year"), action="store", dest="year", type="integer", help="Starting year"), | |
21 make_option(c("-v", "--temperature_dataset"), action="store", dest="temperature_dataset", help="Temperature data for selected location"), | |
22 make_option(c("-y", "--young_nymph_accum"), action="store", dest="young_nymph_accum", type="integer", help="Adjustment of DD accumulation (egg->young nymph)") | |
23 ) | |
24 | |
25 parser <- OptionParser(usage="%prog [options] file", option_list=option_list) | |
26 args <- parse_args(parser, positional_arguments=TRUE) | |
27 opt <- args$options | |
28 | |
29 data.input=function(loc, year, temperature.dataset) | |
30 { | |
31 expdata <- matrix(rep(0, 365 * 3), nrow=365) | |
32 namedat <- paste(loc, year, ".Rdat", sep="") | |
33 temp.data <- read.csv(file=temperature.dataset, header=T) | |
34 | |
35 expdata[,1] <- c(1:365) | |
36 # Minimum | |
37 expdata[,2] <- temp.data[c(1:365), 3] | |
38 # Maximum | |
39 expdata[,3] <- temp.data[c(1:365), 2] | |
40 save(expdata, file=namedat) | |
41 namedat | |
42 } | |
43 | |
44 daylength=function(latitude) | |
45 { | |
46 # from Forsythe 1995 | |
47 p=0.8333 | |
48 dl <- NULL | |
49 for (i in 1:365) { | |
50 theta <- 0.2163108 + 2 * atan(0.9671396 * tan(0.00860 * (i - 186))) | |
51 phi <- asin(0.39795 * cos(theta)) | |
52 dl[i] <- 24 - 24 / pi * acos((sin(p * pi / 180) + sin(latitude * pi / 180) * sin(phi)) / (cos(latitude * pi / 180) * cos(phi))) | |
53 } | |
54 dl # return a vector of daylength in 365 days | |
55 } | |
56 | |
57 hourtemp=function(latitude, date, temperature_file_path) | |
58 { | |
59 load(temperature_file_path) | |
60 threshold <- 14.17 # base development threshold for BMSB | |
61 dnp <- expdata[date, 2] # daily minimum | |
62 dxp <- expdata[date, 3] # daily maximum | |
63 dmean <- 0.5 * (dnp + dxp) | |
64 dd <- 0 # initialize degree day accumulation | |
65 | |
66 if (dxp<threshold) { | |
67 dd <- 0 | |
68 } | |
69 else { | |
70 dlprofile <- daylength(latitude) # extract daylength data for entire year | |
71 T <- NULL # initialize hourly temperature | |
72 dh <- NULL #initialize degree hour vector | |
73 # date <- 200 | |
74 y <- dlprofile[date] # calculate daylength in given date | |
75 z <- 24 - y # night length | |
76 a <- 1.86 # lag coefficient | |
77 b <- 2.20 # night coefficient | |
78 #tempdata <- read.csv("tempdata.csv") #import raw data set | |
79 # Should be outside function otherwise its redundant | |
80 risetime <- 12 - y / 2 # sunrise time | |
81 settime <- 12 + y / 2 # sunset time | |
82 ts <- (dxp - dnp) * sin(pi * (settime - 5) / (y + 2 * a)) + dnp | |
83 for (i in 1:24) { | |
84 if (i > risetime && i<settime) { | |
85 m <- i - 5 # number of hours after Tmin until sunset | |
86 T[i]=(dxp - dnp) * sin(pi * m / (y + 2 * a)) + dnp | |
87 if (T[i]<8.4) { | |
88 dh[i] <- 0 | |
89 } | |
90 else { | |
91 dh[i] <- T[i] - 8.4 | |
92 } | |
93 } | |
94 else if (i > settime) { | |
95 n <- i - settime | |
96 T[i]=dnp + (ts - dnp) * exp( - b * n / z) | |
97 if (T[i]<8.4) { | |
98 dh[i] <- 0 | |
99 } | |
100 else { | |
101 dh[i] <- T[i] - 8.4 | |
102 } | |
103 } | |
104 else { | |
105 n <- i + 24 - settime | |
106 T[i]=dnp + (ts - dnp) * exp( - b * n / z) | |
107 if (T[i]<8.4) { | |
108 dh[i] <- 0 | |
109 } | |
110 else { | |
111 dh[i] <- T[i] - 8.4 | |
112 } | |
113 } | |
114 } | |
115 dd <- sum(dh) / 24 | |
116 } | |
117 return=c(dmean, dd) | |
118 return | |
119 } | |
120 | |
121 dev.egg = function(temperature) | |
122 { | |
123 dev.rate= -0.9843 * temperature + 33.438 | |
124 return = dev.rate | |
125 return | |
126 } | |
127 | |
128 dev.young = function(temperature) | |
129 { | |
130 n12 <- -0.3728 * temperature + 14.68 | |
131 n23 <- -0.6119 * temperature + 25.249 | |
132 dev.rate = mean(n12 + n23) | |
133 return = dev.rate | |
134 return | |
135 } | |
136 | |
137 dev.old = function(temperature) | |
138 { | |
139 n34 <- -0.6119 * temperature + 17.602 | |
140 n45 <- -0.4408 * temperature + 19.036 | |
141 dev.rate = mean(n34 + n45) | |
142 return = dev.rate | |
143 return | |
144 } | |
145 | |
146 dev.emerg = function(temperature) | |
147 { | |
148 emerg.rate <- -0.5332 * temperature + 24.147 | |
149 return = emerg.rate | |
150 return | |
151 } | |
152 | |
153 mortality.egg = function(temperature) | |
154 { | |
155 if (temperature < 12.7) { | |
156 mort.prob = 0.8 | |
157 } | |
158 else { | |
159 mort.prob = 0.8 - temperature / 40.0 | |
160 if (mort.prob < 0) { | |
161 mort.prob = 0.01 | |
162 } | |
163 } | |
164 return = mort.prob | |
165 return | |
166 } | |
167 | |
168 mortality.nymph = function(temperature) | |
169 { | |
170 if (temperature < 12.7) { | |
171 mort.prob = 0.03 | |
172 } | |
173 else { | |
174 mort.prob = temperature * 0.0008 + 0.03 | |
175 } | |
176 return = mort.prob | |
177 return | |
178 } | |
179 | |
180 mortality.adult = function(temperature) | |
181 { | |
182 if (temperature < 12.7) { | |
183 mort.prob = 0.002 | |
184 } | |
185 else { | |
186 mort.prob = temperature * 0.0005 + 0.02 | |
187 } | |
188 return = mort.prob | |
189 return | |
190 } | |
191 | |
192 cat("Replications: ", opt$replications, "\n") | |
193 cat("Photoperiod: ", opt$photoperiod, "\n") | |
194 cat("Oviposition rate: ", opt$oviposition, "\n") | |
195 cat("Egg mortality rate: ", opt$egg_mort, "\n") | |
196 cat("Nymph mortality rate: ", opt$nymph_mort, "\n") | |
197 cat("Adult mortality rate: ", opt$adult_mort, "\n") | |
198 cat("Min clutch size: ", opt$min_clutch_size, "\n") | |
199 cat("Max clutch size: ", opt$max_clutch_size, "\n") | |
200 cat("(egg->young nymph): ", opt$young_nymph_accum, "\n") | |
201 cat("(young nymph->old nymph): ", opt$old_nymph_accum, "\n") | |
202 cat("(old nymph->adult): ", opt$adult_accum, "\n") | |
203 | |
204 # Read in the input temperature datafile | |
205 temperature_file_path <- data.input(opt$location, opt$year, opt$temperature_dataset) | |
206 | |
207 # Initialize matrix for results from all replications | |
208 S0.rep <- S1.rep <- S2.rep <- S3.rep <- S4.rep <- S5.rep <- matrix(rep(0, 365 * opt$replications), ncol = opt$replications) | |
209 newborn.rep <- death.rep <- adult.rep <- pop.rep <- g0.rep <- g1.rep <- g2.rep <- g0a.rep <- g1a.rep <- g2a.rep <- matrix(rep(0, 365 * opt$replications), ncol=opt$replications) | |
210 | |
211 # loop through replications | |
212 for (N.rep in 1:opt$replications) { | |
213 # during each replication | |
214 # start with 1000 individuals -- user definable as well? | |
215 n <- 1000 | |
216 # Generation, Stage, DD, T, Diapause | |
217 vec.ini <- c(0, 3, 0, 0, 0) | |
218 # overwintering, previttelogenic, DD=0, T=0, no-diapause | |
219 vec.mat <- rep(vec.ini, n) | |
220 # complete matrix for the population | |
221 vec.mat <- t(matrix(vec.mat, nrow=5)) | |
222 # complete photoperiod profile in a year, requires daylength function | |
223 ph.p <- daylength(opt$latitude) | |
224 | |
225 # time series of population size | |
226 tot.pop <- NULL | |
227 # gen.0 pop size | |
228 gen0.pop <- rep(0, 365) | |
229 gen1.pop <- rep(0, 365) | |
230 gen2.pop <- rep(0, 365) | |
231 S0 <- S1 <- S2 <- S3 <- S4 <- S5 <- rep(0, 365) | |
232 g0.adult <- g1.adult <- g2.adult <- rep(0, 365) | |
233 N.newborn <- N.death <- N.adult <- rep(0, 365) | |
234 dd.day <- rep(0, 365) | |
235 | |
236 # start tick | |
237 ptm <- proc.time() | |
238 | |
239 # all the days | |
240 for (day in 1:365) { | |
241 # photoperiod in the day | |
242 photoperiod <- ph.p[day] | |
243 temp.profile <- hourtemp(opt$latitude, day, temperature_file_path) | |
244 mean.temp <- temp.profile[1] | |
245 dd.temp <- temp.profile[2] | |
246 dd.day[day] <- dd.temp | |
247 # trash bin for death | |
248 death.vec <- NULL | |
249 # new born | |
250 birth.vec <- NULL | |
251 | |
252 # all individuals | |
253 for (i in 1:n) { | |
254 # find individual record | |
255 vec.ind <- vec.mat[i,] | |
256 # first of all, still alive? | |
257 # adjustment for late season mortality rate | |
258 if (opt$latitude < 40.0) { | |
259 post.mort <- 1 | |
260 day.kill <- 300 | |
261 } | |
262 else { | |
263 post.mort <- 2 | |
264 day.kill <- 250 | |
265 } | |
266 if (vec.ind[2] == 0) { | |
267 # egg | |
268 death.prob = opt$egg_mort * mortality.egg(mean.temp) | |
269 } | |
270 else if (vec.ind[2] == 1 | vec.ind[2] == 2) { | |
271 death.prob = opt$nymph_mort * mortality.nymph(mean.temp) | |
272 } | |
273 else if (vec.ind[2] == 3 | vec.ind[2] == 4 | vec.ind[2] == 5) { | |
274 # for adult | |
275 if (day < day.kill) { | |
276 death.prob = opt$adult_mort * mortality.adult(mean.temp) | |
277 } | |
278 else { | |
279 # increase adult mortality after fall equinox | |
280 death.prob = opt$adult_mort * post.mort * mortality.adult(mean.temp) | |
281 } | |
282 } | |
283 # (or dependent on temperature and life stage?) | |
284 u.d <- runif(1) | |
285 if (u.d < death.prob) { | |
286 death.vec <- c(death.vec, i) | |
287 } | |
288 else { | |
289 # aggregrate index of dead bug | |
290 # event 1 end of diapause | |
291 if (vec.ind[1] == 0 && vec.ind[2] == 3) { | |
292 # overwintering adult (previttelogenic) | |
293 if (photoperiod > opt$photoperiod && vec.ind[3] > 68 && day < 180) { | |
294 # add 68C to become fully reproductively matured | |
295 # transfer to vittelogenic | |
296 vec.ind <- c(0, 4, 0, 0, 0) | |
297 vec.mat[i,] <- vec.ind | |
298 } | |
299 else { | |
300 # add to DD | |
301 vec.ind[3] <- vec.ind[3] + dd.temp | |
302 # add 1 day in current stage | |
303 vec.ind[4] <- vec.ind[4] + 1 | |
304 vec.mat[i,] <- vec.ind | |
305 } | |
306 } | |
307 if (vec.ind[1] != 0 && vec.ind[2] == 3) { | |
308 # NOT overwintering adult (previttelogenic) | |
309 current.gen <- vec.ind[1] | |
310 if (vec.ind[3] > 68) { | |
311 # add 68C to become fully reproductively matured | |
312 # transfer to vittelogenic | |
313 vec.ind <- c(current.gen, 4, 0, 0, 0) | |
314 vec.mat[i,] <- vec.ind | |
315 } | |
316 else { | |
317 # add to DD | |
318 vec.ind[3] <- vec.ind[3] + dd.temp | |
319 # add 1 day in current stage | |
320 vec.ind[4] <- vec.ind[4] + 1 | |
321 vec.mat[i,] <- vec.ind | |
322 } | |
323 } | |
324 | |
325 # event 2 oviposition -- where population dynamics comes from | |
326 if (vec.ind[2] == 4 && vec.ind[1] == 0 && mean.temp > 10) { | |
327 # vittelogenic stage, overwintering generation | |
328 if (vec.ind[4] == 0) { | |
329 # just turned in vittelogenic stage | |
330 n.birth=round(runif(1, 2 + opt$min_clutch_size, 8 + opt$max_clutch_size)) | |
331 } | |
332 else { | |
333 # daily probability of birth | |
334 p.birth = opt$oviposition * 0.01 | |
335 u1 <- runif(1) | |
336 if (u1 < p.birth) { | |
337 n.birth=round(runif(1, 2, 8)) | |
338 } | |
339 } | |
340 # add to DD | |
341 vec.ind[3] <- vec.ind[3] + dd.temp | |
342 # add 1 day in current stage | |
343 vec.ind[4] <- vec.ind[4] + 1 | |
344 vec.mat[i,] <- vec.ind | |
345 if (n.birth > 0) { | |
346 # add new birth -- might be in different generations | |
347 # generation + 1 | |
348 new.gen <- vec.ind[1] + 1 | |
349 # egg profile | |
350 new.ind <- c(new.gen, 0, 0, 0, 0) | |
351 new.vec <- rep(new.ind, n.birth) | |
352 # update batch of egg profile | |
353 new.vec <- t(matrix(new.vec, nrow=5)) | |
354 # group with total eggs laid in that day | |
355 birth.vec <- rbind(birth.vec, new.vec) | |
356 } | |
357 } | |
358 | |
359 # event 2 oviposition -- for gen 1. | |
360 if (vec.ind[2] == 4 && vec.ind[1] == 1 && mean.temp > 12.5 && day < 222) { | |
361 # vittelogenic stage, 1st generation | |
362 if (vec.ind[4] == 0) { | |
363 # just turned in vittelogenic stage | |
364 n.birth=round(runif(1, 2 + opt$min_clutch_size, 8 + opt$max_clutch_size)) | |
365 } | |
366 else { | |
367 # daily probability of birth | |
368 p.birth = opt$oviposition * 0.01 | |
369 u1 <- runif(1) | |
370 if (u1 < p.birth) { | |
371 n.birth = round(runif(1, 2, 8)) | |
372 } | |
373 } | |
374 # add to DD | |
375 vec.ind[3] <- vec.ind[3] + dd.temp | |
376 # add 1 day in current stage | |
377 vec.ind[4] <- vec.ind[4] + 1 | |
378 vec.mat[i,] <- vec.ind | |
379 if (n.birth > 0) { | |
380 # add new birth -- might be in different generations | |
381 # generation + 1 | |
382 new.gen <- vec.ind[1] + 1 | |
383 # egg profile | |
384 new.ind <- c(new.gen, 0, 0, 0, 0) | |
385 new.vec <- rep(new.ind, n.birth) | |
386 # update batch of egg profile | |
387 new.vec <- t(matrix(new.vec, nrow=5)) | |
388 # group with total eggs laid in that day | |
389 birth.vec <- rbind(birth.vec, new.vec) | |
390 } | |
391 } | |
392 | |
393 # event 3 development (with diapause determination) | |
394 # event 3.1 egg development to young nymph (vec.ind[2]=0 -> egg) | |
395 if (vec.ind[2] == 0) { | |
396 # egg stage | |
397 # add to DD | |
398 vec.ind[3] <- vec.ind[3] + dd.temp | |
399 if (vec.ind[3] >= (68 + opt$young_nymph_accum)) { | |
400 # from egg to young nymph, DD requirement met | |
401 current.gen <- vec.ind[1] | |
402 # transfer to young nym stage | |
403 vec.ind <- c(current.gen, 1, 0, 0, 0) | |
404 } | |
405 else { | |
406 # add 1 day in current stage | |
407 vec.ind[4] <- vec.ind[4] + 1 | |
408 } | |
409 vec.mat[i,] <- vec.ind | |
410 } | |
411 | |
412 # event 3.2 young nymph to old nymph (vec.ind[2]=1 -> young nymph: determines diapause) | |
413 if (vec.ind[2] == 1) { | |
414 # young nymph stage | |
415 # add to DD | |
416 vec.ind[3] <- vec.ind[3] + dd.temp | |
417 if (vec.ind[3] >= (250 + opt$old_nymph_accum)) { | |
418 # from young to old nymph, DD requirement met | |
419 current.gen <- vec.ind[1] | |
420 # transfer to old nym stage | |
421 vec.ind <- c(current.gen, 2, 0, 0, 0) | |
422 if (photoperiod < opt$photoperiod && day > 180) { | |
423 vec.ind[5] <- 1 | |
424 } # prepare for diapausing | |
425 } | |
426 else { | |
427 # add 1 day in current stage | |
428 vec.ind[4] <- vec.ind[4] + 1 | |
429 } | |
430 vec.mat[i,] <- vec.ind | |
431 } | |
432 | |
433 # event 3.3 old nymph to adult: previttelogenic or diapausing? | |
434 if (vec.ind[2] == 2) { | |
435 # old nymph stage | |
436 # add to DD | |
437 vec.ind[3] <- vec.ind[3] + dd.temp | |
438 if (vec.ind[3] >= (200 + opt$adult_accum)) { | |
439 # from old to adult, DD requirement met | |
440 current.gen <- vec.ind[1] | |
441 if (vec.ind[5] == 0) { | |
442 # non-diapausing adult -- previttelogenic | |
443 vec.ind <- c(current.gen, 3, 0, 0, 0) | |
444 } | |
445 else { | |
446 # diapausing | |
447 vec.ind <- c(current.gen, 5, 0, 0, 1) | |
448 } | |
449 } | |
450 else { | |
451 # add 1 day in current stage | |
452 vec.ind[4] <- vec.ind[4] + 1 | |
453 } | |
454 vec.mat[i,] <- vec.ind | |
455 } | |
456 | |
457 # event 4 growing of diapausing adult (unimportant, but still necessary)## | |
458 if (vec.ind[2] == 5) { | |
459 vec.ind[3] <- vec.ind[3] + dd.temp | |
460 vec.ind[4] <- vec.ind[4] + 1 | |
461 vec.mat[i,] <- vec.ind | |
462 } | |
463 } # else if it is still alive | |
464 } # end of the individual bug loop | |
465 | |
466 # find how many died | |
467 n.death <- length(death.vec) | |
468 if (n.death > 0) { | |
469 vec.mat <- vec.mat[-death.vec, ] | |
470 } | |
471 # remove record of dead | |
472 # find how many new born | |
473 n.newborn <- length(birth.vec[,1]) | |
474 vec.mat <- rbind(vec.mat, birth.vec) | |
475 # update population size for the next day | |
476 n <- n - n.death + n.newborn | |
477 | |
478 # aggregate results by day | |
479 tot.pop <- c(tot.pop, n) | |
480 # egg | |
481 s0 <- sum(vec.mat[,2] == 0) | |
482 # young nymph | |
483 s1 <- sum(vec.mat[,2] == 1) | |
484 # old nymph | |
485 s2 <- sum(vec.mat[,2] == 2) | |
486 # previtellogenic | |
487 s3 <- sum(vec.mat[,2] == 3) | |
488 # vitellogenic | |
489 s4 <- sum(vec.mat[,2] == 4) | |
490 # diapausing | |
491 s5 <- sum(vec.mat[,2] == 5) | |
492 # overwintering adult | |
493 gen0 <- sum(vec.mat[,1] == 0) | |
494 # first generation | |
495 gen1 <- sum(vec.mat[,1] == 1) | |
496 # second generation | |
497 gen2 <- sum(vec.mat[,1] == 2) | |
498 # sum of all adults | |
499 n.adult <- sum(vec.mat[,2] == 3) + sum(vec.mat[,2] == 4) + sum(vec.mat[,2] == 5) | |
500 # gen.0 pop size | |
501 gen0.pop[day] <- gen0 | |
502 gen1.pop[day] <- gen1 | |
503 gen2.pop[day] <- gen2 | |
504 S0[day] <- s0 | |
505 S1[day] <- s1 | |
506 S2[day] <- s2 | |
507 S3[day] <- s3 | |
508 S4[day] <- s4 | |
509 S5[day] <- s5 | |
510 g0.adult[day] <- sum(vec.mat[,1] == 0) | |
511 g1.adult[day] <- sum((vec.mat[,1] == 1 & vec.mat[,2] == 3) | (vec.mat[,1] == 1 & vec.mat[,2] == 4) | (vec.mat[,1] == 1 & vec.mat[,2] == 5)) | |
512 g2.adult[day] <- sum((vec.mat[,1]== 2 & vec.mat[,2] == 3) | (vec.mat[,1] == 2 & vec.mat[,2] == 4) | (vec.mat[,1] == 2 & vec.mat[,2] == 5)) | |
513 | |
514 N.newborn[day] <- n.newborn | |
515 N.death[day] <- n.death | |
516 N.adult[day] <- n.adult | |
517 #print(c(N.rep, day, n, n.adult)) | |
518 } # end of 365 days | |
519 | |
520 dd.cum <- cumsum(dd.day) | |
521 # collect all the outputs | |
522 S0.rep[,N.rep] <- S0 | |
523 S1.rep[,N.rep] <- S1 | |
524 S2.rep[,N.rep] <- S2 | |
525 S3.rep[,N.rep] <- S3 | |
526 S4.rep[,N.rep] <- S4 | |
527 S5.rep[,N.rep] <- S5 | |
528 newborn.rep[,N.rep] <- N.newborn | |
529 death.rep[,N.rep] <- N.death | |
530 adult.rep[,N.rep] <- N.adult | |
531 pop.rep[,N.rep] <- tot.pop | |
532 g0.rep[,N.rep] <- gen0.pop | |
533 g1.rep[,N.rep] <- gen1.pop | |
534 g2.rep[,N.rep] <- gen2.pop | |
535 g0a.rep[,N.rep] <- g0.adult | |
536 g1a.rep[,N.rep] <- g1.adult | |
537 g2a.rep[,N.rep] <- g2.adult | |
538 } | |
539 | |
540 # save(dd.day, dd.cum, S0.rep, S1.rep, S2.rep, S3.rep, S4.rep, S5.rep, newborn.rep, death.rep, adult.rep, pop.rep, g0.rep, g1.rep, g2.rep, g0a.rep, g1a.rep, g2a.rep, file=opt$output) | |
541 # maybe do not need to export this bit, but for now just leave it as-is | |
542 # do we need to export this Rdat file? | |
543 | |
544 # Data analysis and visualization | |
545 # default: plot 1 year of result | |
546 # but can be expanded to accommodate multiple years | |
547 n.yr <- 1 | |
548 day.all <- c(1:365 * n.yr) | |
549 | |
550 # mean value for adults | |
551 sa <- apply((S3.rep + S4.rep + S5.rep), 1, mean) | |
552 # mean value for nymphs | |
553 sn <- apply((S1.rep + S2.rep), 1,mean) | |
554 # mean value for eggs | |
555 se <- apply(S0.rep, 1, mean) | |
556 # mean value for P | |
557 g0 <- apply(g0.rep, 1, mean) | |
558 # mean value for F1 | |
559 g1 <- apply(g1.rep, 1, mean) | |
560 # mean value for F2 | |
561 g2 <- apply(g2.rep, 1, mean) | |
562 # mean value for P adult | |
563 g0a <- apply(g0a.rep, 1, mean) | |
564 # mean value for F1 adult | |
565 g1a <- apply(g1a.rep, 1, mean) | |
566 # mean value for F2 adult | |
567 g2a <- apply(g2a.rep, 1, mean) | |
568 | |
569 # SE for adults | |
570 sa.se <- apply((S3.rep + S4.rep + S5.rep), 1, sd) / sqrt(opt$replications) | |
571 # SE for nymphs | |
572 sn.se <- apply((S1.rep + S2.rep) / sqrt(opt$replications), 1, sd) | |
573 # SE for eggs | |
574 se.se <- apply(S0.rep, 1, sd) / sqrt(opt$replications) | |
575 # SE value for P | |
576 g0.se <- apply(g0.rep, 1, sd) / sqrt(opt$replications) | |
577 # SE for F1 | |
578 g1.se <- apply(g1.rep, 1, sd) / sqrt(opt$replications) | |
579 # SE for F2 | |
580 g2.se <- apply(g2.rep, 1, sd) / sqrt(opt$replications) | |
581 # SE for P adult | |
582 g0a.se <- apply(g0a.rep, 1, sd) / sqrt(opt$replications) | |
583 # SE for F1 adult | |
584 g1a.se <- apply(g1a.rep, 1, sd) / sqrt(opt$replications) | |
585 # SE for F2 adult | |
586 g2a.se <- apply(g2a.rep, 1, sd) / sqrt(opt$replications) | |
587 | |
588 dev.new(width=20, height=20) | |
589 | |
590 # Start PDF device driver to save charts to output. | |
591 pdf(file=opt$output, height=20, width=20, bg="white") | |
592 | |
593 par(mar = c(5, 6, 4, 4), mfrow=c(3, 1)) | |
594 | |
595 # Subfigure 2: population size by life stage | |
596 plot(day.all, sa, main = "BSMB Total Population Size by Life Stage", type = "l", ylim = c(0, max(se + se.se, sn + sn.se, sa + sa.se)), axes = F, lwd = 2, xlab = "", ylab = "Number", cex = 2, cex.lab = 2, cex.axis = 2, cex.main = 2) | |
597 # Young and old nymphs | |
598 lines(day.all, sn, lwd = 2, lty = 1, col = 2) | |
599 # Eggs | |
600 lines(day.all, se, lwd = 2, lty = 1, col = 4) | |
601 axis(1, at = c(1:12) * 30 - 15, cex.axis = 2, labels = c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")) | |
602 axis(2, cex.axis = 2) | |
603 leg.text <- c("Egg", "Nymph", "Adult") | |
604 legend("topleft", leg.text, lty = c(1, 1, 1), col = c(4, 2, 1), cex = 2) | |
605 if (opt$se_plot == 1) { | |
606 # add SE lines to plot | |
607 # SE for adults | |
608 lines (day.all, sa + sa.se, lty = 2) | |
609 lines (day.all, sa - sa.se, lty = 2) | |
610 # SE for nymphs | |
611 lines (day.all, sn + sn.se, col = 2, lty = 2) | |
612 lines (day.all, sn - sn.se, col = 2, lty = 2) | |
613 # SE for eggs | |
614 lines (day.all, se + se.se, col = 4, lty = 2) | |
615 lines (day.all, se - se.se, col = 4, lty = 2) | |
616 } | |
617 | |
618 # Subfigure 3: population size by generation | |
619 plot(day.all, g0, main = "BSMB Total Population Size by Generation", type = "l", ylim = c(0, max(g2)), axes = F, lwd = 2, xlab = "", ylab = "Number", cex = 2, cex.lab = 2, cex.axis = 2, cex.main = 2) | |
620 lines(day.all, g1, lwd = 2, lty = 1, col = 2) | |
621 lines(day.all, g2, lwd = 2, lty = 1, col = 4) | |
622 axis(1, at = c(1:12) * 30 - 15, cex.axis = 2, labels = c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")) | |
623 axis(2, cex.axis = 2) | |
624 leg.text <- c("P", "F1", "F2") | |
625 legend("topleft", leg.text, lty = c(1, 1, 1), col =c(1, 2, 4), cex = 2) | |
626 if (opt$se_plot == 1) { | |
627 # add SE lines to plot | |
628 # SE for adults | |
629 lines (day.all, g0 + g0.se, lty = 2) | |
630 lines (day.all, g0 - g0.se, lty = 2) | |
631 # SE for nymphs | |
632 lines (day.all, g1 + g1.se, col = 2, lty = 2) | |
633 lines (day.all, g1 - g1.se, col = 2, lty = 2) | |
634 # SE for eggs | |
635 lines (day.all, g2 + g2.se, col = 4, lty = 2) | |
636 lines (day.all, g2 - g2.se, col = 4, lty = 2) | |
637 } | |
638 | |
639 # Subfigure 4: adult population size by generation | |
640 plot(day.all, g0a, ylim = c(0, max(g2a) + 100), main = "BSMB Adult Population Size by Generation", type = "l", axes = F, lwd = 2, xlab = "Year", ylab = "Number", cex = 2, cex.lab = 2, cex.axis = 2, cex.main = 2) | |
641 lines(day.all, g1a, lwd = 2, lty = 1, col = 2) | |
642 lines(day.all, g2a, lwd = 2, lty = 1, col = 4) | |
643 axis(1, at = c(1:12) * 30 - 15, cex.axis = 2, labels = c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")) | |
644 axis(2, cex.axis = 2) | |
645 leg.text <- c("P", "F1", "F2") | |
646 legend("topleft", leg.text, lty = c(1, 1, 1), col = c(1, 2, 4), cex = 2) | |
647 if (opt$se_plot == 1) { | |
648 # add SE lines to plot | |
649 # SE for adults | |
650 lines (day.all, g0a + g0a.se, lty = 2) | |
651 lines (day.all, g0a - g0a.se, lty = 2) | |
652 # SE for nymphs | |
653 lines (day.all, g1a + g1a.se, col = 2, lty = 2) | |
654 lines (day.all, g1a - g1a.se, col = 2, lty = 2) | |
655 # SE for eggs | |
656 lines (day.all, g2a + g2a.se, col = 4, lty = 2) | |
657 lines (day.all, g2a - g2a.se, col = 4, lty = 2) | |
658 } | |
659 | |
660 # Turn off device driver to flush output. | |
661 dev.off() |