/archive/source.archive/bm.only/trait.rjmcmc.11172010.R

http://github.com/eastman/auteur · R · 742 lines · 623 code · 83 blank · 36 comment · 113 complexity · 8559d1d1884098c4610b656a2f5f2cf4 MD5 · raw file 

    

  1. ## Written by LJ HARMON (2008), AL HIPP (2008), and JM EASTMAN (2010)

    2. 

  2. 

    3. 

  3. rjMCMC.trait<-function (phy, data, ngen=1000, sampleFreq=100, probMergeSplit=0.05, probRoot=0.01, lambdaK=log(2), constrainK=FALSE, jumpsize=2, fileBase="result", simplestart=FALSE) 

    4. 

  4. {

    5. 

  5. 	model="BM"

    6. 

  6. 	heat=1

    7. 

  7. #	require(ouch)

    8. 

  8. 	require(geiger)

    9. 

  9. 		

    10. 

  10. ### prepare data for rjMCMC

    11. 

  11. 	cur.model		<- model								

    12. 

  12. 

    13. 

  13. 	dataList		<- prepare.data(phy, data)			

    14. 

  14. 	ape.tre			<- cur.tre <- dataList$ape.tre			

    15. 

  15. 	orig.dat		<- dataList$orig.dat						

    16. 

  16. 	nn				<- length(ape.tre$edge.length)	

    17. 

  17. 	node.des		<- sapply(unique(c(ape.tre$edge[1,1],ape.tre$edge[,2])), function(x) get.descendants.of.node(x, ape.tre))

    18. 

  18. 	names(node.des) <- c(ape.tre$edge[1,1], unique(ape.tre$edge[,2]))

    19. 

  19. 	subtrees		<- subtrees(phy)

    20. 

  20. 	subtrees.marker <- sapply(subtrees, function(x) return(min(x$node.label)))

    21. 

  21. 

    22. 

  22. # initialize parameters

    23. 

  23. 	if(is.numeric(constrainK) & (constrainK > length(ape.tre$edge) | constrainK < 1)) stop("Constraint on rate shifts is nonsensical. Ensure that constrainK is at least 1 and less than the number of available nodes in the tree.")

    24. 

  24. 

    25. 

  25. 	if(simplestart | is.numeric(constrainK) ) {

    26. 

  26. 		if(is.numeric(constrainK)) {

    27. 

  27. 			init.rate	<- generate.starting.point(data, ape.tre, node.des, theta=FALSE, K=constrainK, jumpsize=jumpsize)

    28. 

  28. 		} else {

    29. 

  29. 			init.rate	<- list(values=rep(fitContinuous(phy,data)$Trait1$beta,length(phy$edge.length)),delta=rep(0,length(phy$edge.length)))

    30. 

  30. 		}

    31. 

  31. 	} else {

    32. 

  32. 		init.rate	<- generate.starting.point(data, ape.tre, node.des, theta=FALSE, K=constrainK, jumpsize=jumpsize )

    33. 

  33. 	}

    34. 

  34. 	

    35. 

  35.     cur.rates		<- init.rate$values			

    36. 

  36. 	cur.delta.rates	<- init.rate$delta

    37. 

  37. 	cur.root		<- adjust.value(mean(orig.dat), jumpsize)

    38. 

  38. 	cur.tre			<- apply.BMM.to.apetree(ape.tre, cur.rates)

    39. 

  39. 	

    40. 

  40. 	mod.cur = new.bm.lik.fn(cur.rates, cur.root, cur.tre, orig.dat)

    41. 

  41. 	

    42. 

  42. # proposal counts

    43. 

  43. 	nRateProp =		0	

    44. 

  44. 	nRateSwapProp = 0									

    45. 

  45. 	nRcatProp =		0										

    46. 

  46. 	nRootProp =		0

    47. 

  47. 	nRateOK =		0											

    48. 

  48. 	nRateSwapOK =	0

    49. 

  49. 	nRcatOK =		0											

    50. 

  50. 	nRootOK =		0

    51. 

  51. 

    52. 

  52. 	cOK=c(												

    53. 

  53. 		nRateOK,

    54. 

  54. 		nRateSwapOK,

    55. 

  55. 		nRcatOK, 

    56. 

  56. 		nRootOK 

    57. 

  57. 	)

    58. 

  58. 		

    59. 

  59. 	tickerFreq=ceiling(ngen/30)

    60. 

  60. 	

    61. 

  61. # file handling

    62. 

  62. 	parmBase=paste(model, fileBase, "parameters/",sep=".")

    63. 

  63. 	if(!file.exists(parmBase)) dir.create(parmBase)

    64. 

  64. 	parlogger(model=model, init=TRUE, node.des, parmBase=parmBase)

    65. 

  65. 	errorLog=file(paste(parmBase,paste(cur.model, fileBase, "rjTraitErrors.log",sep="."),sep="/"),open='w+')

    66. 

  66. 	generate.error.message(i=NULL, mod.cur=NULL, mod.new=NULL, lnR=NULL, errorLog=errorLog, init=TRUE)

    67. 

  67. 	runLog=file(paste(parmBase,paste(cur.model, fileBase, "rjTrait.log",sep="."),sep="/"),open='w+')

    68. 

  68. 	generate.log(bundled.parms=NULL, cur.model, file=runLog, init=TRUE)

    69. 

  69. 

    70. 

  70. 

    71. 

  71. ### Begin rjMCMC

    72. 

  72.     for (i in 1:ngen) {

    73. 

  73.         lnLikelihoodRatio <- lnHastingsRatio <- lnPriorRatio <- 0

    74. 

  74. 		startparms = c(nRateProp, nRateSwapProp, nRcatProp, nRootProp)

    75. 

  75. 

    76. 

  76. ## BM IMPLEMENTATION ##

    77. 

  77. 		while(1) {

    78. 

  78. 			if (runif(1) < (2 * probMergeSplit) & !constrainK) {			# adjust rate categories

    79. 

  79. 				nr=split.or.merge(cur.delta.rates, cur.rates, ape.tre, node.des, lambdaK)

    80. 

  80. 				new.rates=nr$new.values

    81. 

  81. 				new.delta.rates=nr$new.delta

    82. 

  82. 				new.root=cur.root

    83. 

  83. 				nRcatProp=nRcatProp+1

    84. 

  84. 				lnHastingsRatio=nr$lnHastingsRatio

    85. 

  85. 				lnPriorRatio=nr$lnPriorRatio

    86. 

  86. 				break()

    87. 

  87. 			} else { 

    88. 

  88. 				if(runif(1)<probRoot) {										# adjust root

    89. 

  89. 					new.root=adjust.value(cur.root, jumpsize)

    90. 

  90. 					new.rates=cur.rates

    91. 

  91. 					new.delta.rates=cur.delta.rates

    92. 

  92. 					nRootProp=nRootProp+1

    93. 

  93. 					break()

    94. 

  94. 				} else {													

    95. 

  95. 					if(runif(1)>0.1 & length(unique(cur.rates))>1) {		# swap rates classes

    96. 

  96. 						nr=switcheroo(cur.rates)

    97. 

  97. 						new.rates=nr$new.values ##

    98. 

  98. 						new.delta.rates=cur.delta.rates 

    99. 

  99. 						new.root=cur.root

    100. 

  100. 						nRateSwapProp=nRateSwapProp+1

    101. 

  101. 						break()

    102. 

  102. 					} else {												# adjust rates

    103. 

  103. 						new.rates=adjust.rate(cur.rates, jumpsize)

    104. 

  104. 						new.delta.rates=cur.delta.rates

    105. 

  105. 						new.root=cur.root

    106. 

  106. 						nRateProp=nRateProp+1

    107. 

  107. 						break()

    108. 

  108. 					} 

    109. 

  109. 				}

    110. 

  110. 			}

    111. 

  111. 		}

    112. 

  112. 		

    113. 

  113. 		new.tre=apply.BMM.to.apetree(ape.tre, new.rates)

    114. 

  114. 		mod.new=try(new.bm.lik.fn(new.rates, new.root, new.tre, orig.dat),silent=TRUE)

    115. 

  115. 		

    116. 

  116. 		if(inherits(mod.new, "try-error")) {mod.new=as.list(mod.new); mod.new$lnL=Inf}

    117. 

  117. 		if(!is.infinite(mod.new$lnL)) {

    118. 

  118. 			lnLikelihoodRatio = mod.new$lnL - mod.cur$lnL

    119. 

  119. 		} else {

    120. 

  120. 			lnLikelihoodRatio = -Inf

    121. 

  121. 			failures=paste("./failed", model, "parameters/",sep=".")

    122. 

  122. 			failed.trees=paste(failures, "trees", sep="/")

    123. 

  123. 			failed.plots=paste(failures, "pdf", sep="/")

    124. 

  124. 			lapply(list(failures, failed.trees, failed.plots), function(x) if(!file.exists(x)) dir.create(x))

    125. 

  125. 				

    126. 

  126. 			pdf(file=paste(failed.plots, paste(i,fileBase,"failed.model.pdf",sep="."),sep="/"))

    127. 

  127. 				plot(new.tre, tip=FALSE)

    128. 

  128. 			dev.off()

    129. 

  129. 			

    130. 

  130. 			write.tree(new.tre, file=paste(failed.trees, paste(i,fileBase,"tre",sep="."),sep="/"))

    131. 

  131. 			write.table(matrix(c(i, new.rates),nrow=1),file=paste(failures,"failed.rates.txt",sep="/"),append=TRUE,col=FALSE,row=FALSE,quote=FALSE)

    132. 

  132. 			write.table(matrix(c(i, new.root),nrow=1),file=paste(failures,"failed.root.txt",sep="/"),append=TRUE,col=FALSE,row=FALSE,quote=FALSE)

    133. 

  133. 		}

    134. 

  134. 		

    135. 

  135. 	# compare likelihoods

    136. 

  136. 		endparms = c(nRateProp=nRateProp, nRateSwapProp=nRateSwapProp, nRcatProp=nRcatProp, nRootProp=nRootProp)

    137. 

  137. 

    138. 

  138. 		r=assess.lnR((heat * lnLikelihoodRatio + heat * lnPriorRatio + lnHastingsRatio)->lnR)

    139. 

  139. 

    140. 

  140. 		if (runif(1) <= r$r) {			# adopt proposal

    141. 

  141. 			decision="adopt"

    142. 

  142. 			cur.root <- new.root

    143. 

  143. 			cur.rates <- new.rates

    144. 

  144. 			cur.delta.rates <- new.delta.rates

    145. 

  145. 			curr.lnL <- mod.new$lnL

    146. 

  146. 			cur.tre <- new.tre

    147. 

  147. 			mod.cur <- mod.new

    148. 

  148. 			cOK <- determine.accepted.proposal(startparms, endparms, cOK)

    149. 

  149. 		} else {						# deny proposal	

    150. 

  150. 			decision="reject"

    151. 

  151. 			curr.lnL <- mod.cur$lnL 

    152. 

  152. 		}

    153. 

  153. 		

    154. 

  154. 		if(is.infinite(curr.lnL)) stop("starting point has exceptionally poor likelihood")

    155. 

  155. 

    156. 

  156. 		if(r$error) generate.error.message(i, mod.cur, mod.new, lnR, errorLog)

    157. 

  157. 		

    158. 

  158. 		if(i%%tickerFreq==0) {

    159. 

  159. 			if(i==tickerFreq) cat("|",rep(" ",9),toupper("generations complete"),rep(" ",9),"|","\n")

    160. 

  160. 			cat(". ")

    161. 

  161. 		}

    162. 

  162. 		

    163. 

  163. 		if(i%%sampleFreq==0) {

    164. 

  164. 			bundled.parms=list(gen=i, mrate=exp(mean(log(cur.rates))), cats=sum(cur.delta.rates)+1, root=cur.root, lnL=curr.lnL)

    165. 

  165. 			generate.log(bundled.parms, cur.model, file=runLog)			

    166. 

  166. 			parlogger(model=model, init=FALSE, node.des=node.des, i=i, curr.lnL=curr.lnL, cur.root=cur.root, cur.rates=cur.rates, cur.delta.rates=cur.delta.rates, parmBase=parmBase)

    167. 

  167. 		}

    168. 

  168. 	}

    169. 

  169. 	

    170. 

  170. # End rjMCMC

    171. 

  171. 	close(errorLog)

    172. 

  172. 	close(runLog)

    173. 

  173. 	summarize.run(cOK, endparms, cur.model)	

    174. 

  174. }

    175. 

  175. 

    176. 

  176. 

    177. 

  177. ## AUXILIARY FUNCTIONS

    178. 

  178. 

    179. 

  179. new.bm.lik.fn <- function(rates, root, ape.tre, orig.dat) { # from LJ HARMON	

    180. 

  180. # mod 10.18.2010 JM Eastman: using determinant()$modulus rather than det() to stay in log-space

    181. 

  181. 	tree=ape.tre

    182. 

  182. 	y=orig.dat

    183. 

  183. 	

    184. 

  184. 	b <- vcv.phylo(ape.tre)

    185. 

  185. 	w <- rep(root, nrow(b))

    186. 

  186. 	num <- -t(y-w)%*%solve(b)%*%(y-w)/2

    187. 

  187. 	den <- 0.5*(length(y)*log(2*pi) + as.numeric(determinant(b)$modulus))

    188. 

  188. 	list(

    189. 

  189. 		 root=root,

    190. 

  190. 		 lnL = (num-den)[1,1]

    191. 

  191. 		 )	

    192. 

  192. }

    193. 

  193. 

    194. 

  194. split.or.merge <- function(cur.delta, cur.values, phy, node.des, lambda=lambda) { 

    195. 

  195. # mod 11.17.2010 JM Eastman

    196. 

  196. #	cur.delta=cur.delta.rates; cur.values=cur.rates; theta=FALSE; lambda=log(2); jumpsize=2

    197. 

  197. 	bb=cur.delta

    198. 

  198. 	vv=cur.values

    199. 

  199. 	names(vv)<-names(bb)<-phy$edge[,2]

    200. 

  200. 	new.bb=choose.one(bb)

    201. 

  201. 	new.vv=vv

    202. 

  202. 	

    203. 

  203. 	s=names(new.bb[bb!=new.bb])

    204. 

  204. 	all.shifts=as.numeric(names(bb[bb>0]))

    205. 

  205. 	all.D=node.des[[which(names(node.des)==s)]]

    206. 

  206. 	if(length(all.D)!=0) {

    207. 

  207. 		untouchable=unlist(lapply(all.shifts[all.shifts>s], function(x)node.des[[which(names(node.des)==x)]]))

    208. 

  208. 		remain.unchanged=union(all.shifts, untouchable)

    209. 

  209. 	} else {

    210. 

  210. 		untouchable=NULL

    211. 

  211. 		remain.unchanged=list()

    212. 

  212. 	}

    213. 

  213. 	

    214. 

  214. 	marker=match(s, names(new.vv))

    215. 

  215. 	nn=length(vv)

    216. 

  216. 	K=sum(bb)+1

    217. 

  217. 	N=Ntip(phy)

    218. 

  218. 	

    219. 

  219. 	ncat=sum(bb)

    220. 

  220. 	cur.vv=as.numeric(vv[marker])

    221. 

  221. 	ca.vv=length(which(vv==cur.vv))

    222. 

  222. 	

    223. 

  223. 	if(sum(new.bb)>sum(bb)) {			# add transition: SPLIT

    224. 

  224. #		print("s")

    225. 

  225. 

    226. 

  226. 		n.desc=sum(!all.D%in%remain.unchanged)+1

    227. 

  227. 		n.split=sum(vv==cur.vv)-n.desc

    228. 

  228. 		if(runif(1)<0.5) {

    229. 

  229. 			u=runif(1,min=-0.5*n.desc*cur.vv, max=0.5*n.split*cur.vv)

    230. 

  230. 			nr.desc=cur.vv + u/n.desc

    231. 

  231. 			nr.split=cur.vv - u/n.split

    232. 

  232. #			(n.desc*nr.desc+n.split*nr.split)/(n.desc+n.split); nr.desc; nr.split

    233. 

  233. 		} else {

    234. 

  234. 			u=runif(1,min=-0.5*n.split*cur.vv, max=0.5*n.desc*cur.vv)

    235. 

  235. 			nr.desc=cur.vv - u/n.desc

    236. 

  236. 			nr.split=cur.vv + u/n.split

    237. 

  237. #			(n.desc*nr.desc+n.split*nr.split)/(n.desc+n.split); nr.desc; nr.split

    238. 

  238. 		}

    239. 

  239. 		new.vv[vv==cur.vv]=nr.split

    240. 

  240. 		if(length(remain.unchanged)==0) {	# assign new value to all descendants

    241. 

  241. 			new.vv[match(all.D,names(new.vv))] = nr.desc

    242. 

  242. 		} else {							# assign new value to all 'open' descendants 

    243. 

  243. 			new.vv[match(all.D[!(all.D%in%remain.unchanged)],names(new.vv))] = nr.desc

    244. 

  244. 		}

    245. 

  245. 		new.vv[match(s, names(new.vv))]=nr.desc

    246. 

  246. 		lnHastingsRatio = log((K+1)/(2*N-2-K)) ### from Drummond and Suchard 2010: where N is tips, K is number of local parms in tree

    247. 

  247. 		lnPriorRatio = log(ptpois(K+1,lambda,nn)/ptpois(K,lambda,nn))

    248. 

  248. 		

    249. 

  249. 	} else {							# drop transition: MERGE

    250. 

  250. #		print("m")

    251. 

  251. 		

    252. 

  252. 		anc = get.ancestor.of.node(s, phy)

    253. 

  253. 		if(!is.root(anc, phy)) {			# base new rate on ancestral rate of selected branch

    254. 

  254. 			anc.vv=as.numeric(vv[match(anc,names(vv))])

    255. 

  255. 			na.vv=length(which(vv==anc.vv))

    256. 

  256. 			nr=(anc.vv*na.vv+cur.vv*ca.vv)/(ca.vv+na.vv)

    257. 

  257. 			new.vv[vv==cur.vv | vv==anc.vv]=nr

    258. 

  258. 		} else {							# if ancestor of selected node is root, base new rate on sister node

    259. 

  259. 			sister.tmp=get.desc.of.node(anc,phy)

    260. 

  260. 			sister=sister.tmp[sister.tmp!=s]

    261. 

  261. 			sis.vv=as.numeric(vv[match(sister,names(vv))])

    262. 

  262. 			ns.vv=length(which(vv==sis.vv))

    263. 

  263. 			nr=(sis.vv*ns.vv+cur.vv*ca.vv)/(ca.vv+ns.vv)

    264. 

  264. 			new.vv[vv==cur.vv | vv==sis.vv]=nr			

    265. 

  265. 		}

    266. 

  266. 		lnHastingsRatio = log((2*N-2-K+1)/K) ### from Drummond and Suchard 2010: where N is tips, K is number of local parms in tree

    267. 

  267. 		lnPriorRatio = log(ptpois(K-1,lambda,nn)/ptpois(K,lambda,nn))

    268. 

  268. 		

    269. 

  269. 	}

    270. 

  270. 	

    271. 

  271. 	return(list(new.delta=new.bb, new.values=new.vv, lnHastingsRatio=lnHastingsRatio, lnPriorRatio=lnPriorRatio))

    272. 

  272. }

    273. 

  273. 

    274. 

  274. switcheroo <- function(cur.values) { 

    275. 

  275. # mod 11.17.2010 JM Eastman

    276. 

  276. #	cur.delta=cur.delta.rates; cur.values=cur.rates; theta=FALSE; lambda=log(2); jumpsize=2

    277. 

  277. 

    278. 

  278. 	vv=cur.values

    279. 

  279. 	rates.sample=unique(vv)[sample(1:length(unique(vv)), 2)]

    280. 

  280. 	new.vv=vv

    281. 

  281. 	new.vv[which(vv==rates.sample[1])]=rates.sample[2]

    282. 

  282. 	new.vv[which(vv==rates.sample[2])]=rates.sample[1]

    283. 

  283. 	

    284. 

  284. 	return(list(new.values=new.vv))

    285. 

  285. }

    286. 

  286. 

    287. 

  287. generate.starting.point <- function(data, phy, node.des, theta=FALSE, K=FALSE, jumpsize) { 

    288. 

  288. # updated JME 11.14.2010

    289. 

  289. 	nn=length(phy$edge.length)

    290. 

  290. 	ntip=Ntip(phy)

    291. 

  291. 	if(!K) nshifts=rtpois(1,log(ntip),nn) else nshifts=K-1

    292. 

  292. 	if(nshifts!=0) bb=sample(c(rep(1,nshifts),rep(0,nn-nshifts)),replace=FALSE) else bb=rep(0,nn)

    293. 

  293. 	names(bb)=phy$edge[,2]

    294. 

  294. 	shifts=as.numeric(names(bb)[bb==1])

    295. 

  295. 	if(theta) {

    296. 

  296. 		min.max=c(adjust.value(min(data), jumpsize), adjust.value(max(data), jumpsize))

    297. 

  297. 		values=runif(sum(bb)+1, min=min.max[min.max==min(min.max)], max=min.max[min.max==max(min.max)])

    298. 

  298. 	} else {

    299. 

  299. 		init.rate=fitContinuous(phy,data)[[1]]$beta

    300. 

  300. 		min.max=c(adjust.rate(init.rate, jumpsize), adjust.rate(init.rate, jumpsize))

    301. 

  301. 		values=runif(sum(bb)+1, min=min.max[min.max==min(min.max)], max=min.max[min.max==max(min.max)])

    302. 

  302. 	}

    303. 

  303. 	internal.shifts<-tip.shifts<-numeric(0)

    304. 

  304. 	internal.shifts=sort(shifts[shifts>ntip])

    305. 

  305. 	tip.shifts=shifts[shifts<=ntip]

    306. 

  306. 	

    307. 

  307. 	if(length(internal.shifts)==0 & length(tip.shifts)==0) {

    308. 

  308. 		vv=rep(values, nn)

    309. 

  309. 	} else {

    310. 

  310. 		vv=bb

    311. 

  311. 		vv[]=values[length(values)]

    312. 

  312. 		i=0

    313. 

  313. 		if(length(internal.shifts)!=0) {

    314. 

  314. 			for(i in 1:length(internal.shifts)) {

    315. 

  315. 				d=node.des[which(names(node.des)==internal.shifts[i])]

    316. 

  316. 				vv[match(c(internal.shifts[i], unlist(d)), names(vv))]=values[i]

    317. 

  317. 			}

    318. 

  318. 		}

    319. 

  319. 		if(length(tip.shifts)!=0) {

    320. 

  320. 			for(j in 1:length(tip.shifts)) {

    321. 

  321. 				vv[match(tip.shifts[j], names(vv))]=values[j+i]

    322. 

  322. 			}

    323. 

  323. 		}

    324. 

  324. 	}

    325. 

  325. 	return(list(delta=unname(bb), values=unname(vv)))

    326. 

  326. }

    327. 

  327. 

    328. 

  328. assign.root.theta <- function(cur.theta, phy) {

    329. 

  329. 	root=phy$edge[1,1]

    330. 

  330. 	desc=phy$edge[which(phy$edge[,1]==root),2]

    331. 

  331. 	root.theta=cur.theta[sample(which(phy$edge[,2]==desc),1)]

    332. 

  332. 	root.theta

    333. 

  333. }

    334. 

  334. 

    335. 

  335. get.descendants.of.node <- function(node, phy) {

    336. 

  336. 	storage=list()

    337. 

  337. 	if(is.null(node)) node=phy$edge[1,1]

    338. 

  338. 	if(node%in%phy$edge[,1]) {

    339. 

  339. 		desc=c(sapply(node, function(x) {return(phy$edge[which(phy$edge[,1]==x),2])}))

    340. 

  340. 		while(any(desc%in%phy$edge[,1])) {

    341. 

  341. 			desc.true=desc[which(desc%in%phy$edge[,1])]

    342. 

  342. 			desc.desc=lapply(desc.true, function(x) return(get.desc.of.node(x, phy)))

    343. 

  343. 			

    344. 

  344. 			storage=list(storage,union(desc,desc.desc))

    345. 

  345. 			

    346. 

  346. 			desc=unlist(desc.desc)

    347. 

  347. 		}

    348. 

  348. 		storage=sort(unique(unlist(union(desc,storage))))

    349. 

  349. 	}

    350. 

  350. 	return(storage)

    351. 

  351. }

    352. 

  352. 

    353. 

  353. get.desc.of.node <- function(node, phy) {

    354. 

  354. 	return(phy$edge[which(phy$edge[,1]==node),2])

    355. 

  355. }

    356. 

  356. 

    357. 

  357. get.ancestor.of.node<-function(node, phy) {

    358. 

  358. 	anc=phy$edge[which(phy$edge[,2]==node),1]

    359. 

  359. 	anc

    360. 

  360. }

    361. 

  361. 

    362. 

  362. choose.one <- function(cur.delta)

    363. 

  363. {

    364. 

  364. 	bb=cur.delta

    365. 

  365. 	s=sample(1:length(bb),1)

    366. 

  366. 	bb[s]=1-bb[s]

    367. 

  367. 	bb

    368. 

  368. }

    369. 

  369. 

    370. 

  370. is.root<-function(node,phy) {

    371. 

  371. 	if(node==phy$edge[1,1]) return(TRUE) else return(FALSE)

    372. 

  372. }

    373. 

  373. 

    374. 

  374. assess.lnR <- function(lnR) {

    375. 

  375. 	if(is.na(lnR) || abs(lnR)==Inf) {

    376. 

  376. 		error=TRUE

    377. 

  377. 		r=0

    378. 

  378. 	} else {

    379. 

  379. 		if(lnR < -20) {

    380. 

  380. 			r=0 

    381. 

  381. 		} else if(lnR > 0) {

    382. 

  382. 			r=1 

    383. 

  383. 		} else {

    384. 

  384. 			r=exp(lnR)

    385. 

  385. 		}

    386. 

  386. 		error=FALSE

    387. 

  387. 	}

    388. 

  388. 	return(list(r=r, error=error))

    389. 

  389. }

    390. 

  390. 

    391. 

  391. apply.BMM.to.apetree<-function(apetree, rates) {

    392. 

  392. 	apetree$edge.length=apetree$edge.length*rates

    393. 

  393. 	return(apetree)

    394. 

  394. }

    395. 

  395. 

    396. 

  396. adjust.value <- function(value, jumpsize) {

    397. 

  397. # mod 10.20.2010 JM Eastman

    398. 

  398. 	vv=value

    399. 

  399. 	rch <- runif(1, min = -jumpsize, max = jumpsize)

    400. 

  400. 	return(vv[]+rch)

    401. 

  401. }

    402. 

  402. 

    403. 

  403. adjust.rate <- function(rate, jumpsize) {

    404. 

  404. # mod 10.20.2010 JM Eastman

    405. 

  405. 	vv=rate

    406. 

  406. 	v=log(vv)

    407. 

  407. 	rch <- runif(1, min = -jumpsize, max = jumpsize)

    408. 

  408. 	v=exp(v[]+rch)

    409. 

  409. 	v

    410. 

  410. }

    411. 

  411. 

    412. 

  412. prepare.data <- function(phy, data) {

    413. 

  413. 	td <- treedata(phy, data, sort = T)	

    414. 

  414. 	return(list(ape.tre=td$phy, orig.dat=td$data[,1]))

    415. 

  415. }

    416. 

  416. 

    417. 

  417. summarize.run<-function(cOK, endparms, cur.model) {

    418. 

  418. #	end = c(nRateProp, nRateSwapProp, nRcatProp, nRootProp, nAlphaProp, nThetaProp, nThetaSwapProp, nTcatProp)

    419. 

  419. 

    420. 

  420. 	if(cur.model=="BM") {

    421. 

  421. 		names(endparms)<-names(cOK)<-c("rates", "rate.swap", "rate.catg", "root")

    422. 

  422. 	} else {

    423. 

  423. 		names(endparms)<-names(cOK)<-c("rates", "rate.swap", "rate.catg", "root", "alpha", "theta", "theta.swap", "theta.catg")

    424. 

  424. 	}

    425. 

  425. 	names(endparms)=paste("pr.",names(endparms),sep="")

    426. 

  426. 	names(cOK)=paste("ok.",names(cOK),sep="")

    427. 

  427. 	cat("\n")

    428. 

  428. 	if(cur.model=="BM") {

    429. 

  429. 		print(endparms[paste("pr.", c("rates", "rate.swap", "rate.catg", "root"),sep="")])

    430. 

  430. 		print(cOK[paste("ok.", c("rates", "rate.swap", "rate.catg", "root"),sep="")])

    431. 

  431. 	} else {

    432. 

  432. 		print(endparms[paste("pr.", c("rates", "rate.swap", "rate.catg", "alpha", "theta", "theta.swap", "theta.catg"),sep="")])

    433. 

  433. 		print(cOK[paste("ok.", c("rates", "rate.swap", "rate.catg", "alpha", "theta", "theta.swap", "theta.catg"),sep="")])

    434. 

  434. 	}

    435. 

  435. }

    436. 

  436. 

    437. 

  437. generate.log<-function(bundled.parms, cur.model, file, init=FALSE) {

    438. 

  438. #	bundled.parms=list(gen=i, mrate=mean(cur.rates), cats=sum(cur.delta.rates)+1, root=cur.root, alpha=cur.alpha, reg=sum(cur.delta.theta)+1, theta=mean(cur.theta), lnL=curr.lnL)

    439. 

  439. 	res=bundled.parms

    440. 

  440. 

    441. 

  441. 	if(init) {

    442. 

  442. 		if(cur.model=="BM") msg=paste("gen", "model", "mean.rate", "rates", "root", "lnL", sep="\t") else msg=paste("gen", "model", "mean.rate", "rates", "alpha", "regimes", "mean.theta", "lnL", sep="\t")

    443. 

  443. 	} else {

    444. 

  444. 		if(cur.model=="BM") {

    445. 

  445. 			msg<-paste(res$gen, sQuote(cur.model), sprintf("%.3f", res$mrate), res$cats, sprintf("%.3f", res$root), sprintf("%.3f", res$lnL),sep="\t")

    446. 

  446. 		} else {

    447. 

  447. 			msg<-paste(res$gen, sQuote(cur.model), sprintf("%.3f", res$mrate), res$cats, sprintf("%.4f", res$alpha), res$reg, sprintf("%.3f", res$theta), sprintf("%.3f", res$lnL),sep="\t")

    448. 

  448. 		}

    449. 

  449. 	}

    450. 

  450. 	write(msg, file=file, append=TRUE)

    451. 

  451. }

    452. 

  452. 

    453. 

  453. parlogger<-function(model, init=FALSE, node.des, i, curr.lnL, cur.root=NULL, cur.rates=NULL, cur.delta.rates=NULL, cur.alpha=NULL, cur.theta=NULL, cur.delta.theta=NULL, parmBase) {	

    454. 

  454. 	if(init) {

    455. 

  455. 		msg.long=names(node.des[-1])

    456. 

  456. 		if(model=="BM") {

    457. 

  457. 			msg.short=paste("gen", "model", "lnL", sep="\t") 

    458. 

  458. 			parlogs=paste(parmBase, paste(c("summary", "root", "rates", "rate.shifts"), "txt", sep="."), sep="")

    459. 

  459. 			sapply(parlogs[1], function(x) write.table(msg.short, x, quote=FALSE, col.names=FALSE, row.names=FALSE))

    460. 

  460. 			sapply(parlogs[2], function(x) write.table(NULL, x, quote=FALSE, col.names=FALSE, row.names=FALSE))

    461. 

  461. 			sapply(parlogs[3:length(parlogs)], function(x) write.table(paste(msg.long,collapse=" "), x, quote=FALSE, col.names=FALSE, row.names=FALSE))

    462. 

  462. 		} else {

    463. 

  463. 			msg.short=paste("gen", "model", "lnL", sep="\t")

    464. 

  464. 			parlogs=paste(parmBase, paste(c("summary", "root", "alpha", "rates", "rate.shifts", "optima", "optima.shifts"), "txt", sep="."), sep="")

    465. 

  465. 			sapply(parlogs[1], function(x) write.table(msg.short, x, quote=FALSE, col.names=FALSE, row.names=FALSE))

    466. 

  466. 			sapply(parlogs[2:3], function(x) write.table(NULL, x, quote=FALSE, col.names=FALSE, row.names=FALSE))

    467. 

  467. 			sapply(parlogs[4:length(parlogs)], function(x) write.table(paste(msg.long,collapse=" "), x, quote=FALSE, col.names=FALSE, row.names=FALSE))

    468. 

  468. 		}

    469. 

  469. 	} else {

    470. 

  470. 		if(model=="BM") {

    471. 

  471. 			parlogs=paste(parmBase, paste(c("summary", "root", "rates", "rate.shifts"), "txt", sep="."), sep="")

    472. 

  472. 			outlist=list(paste(i, model, curr.lnL, sep="\t"), cur.root, cur.rates, cur.delta.rates) 

    473. 

  473. 			sapply(1:length(outlist), function(x) write.table(paste(outlist[[x]],collapse=" "), parlogs[[x]], quote=FALSE, col.names=FALSE, row.names=FALSE, append=TRUE)) 

    474. 

  474. 		} else {

    475. 

  475. 			parlogs=paste(parmBase, paste(c("summary", "root", "alpha", "rates", "rate.shifts", "optima", "optima.shifts"), "txt", sep="."), sep="")

    476. 

  476. 			outlist=list(paste(i, model, curr.lnL, sep="\t"), cur.root, cur.alpha, cur.rates, cur.delta.rates, cur.theta, cur.delta.theta) 

    477. 

  477. 			sapply(1:length(outlist), function(x) write.table(paste(outlist[[x]],collapse=" "), parlogs[[x]], quote=FALSE, col.names=FALSE, row.names=FALSE, append=TRUE)) 

    478. 

  478. 		}

    479. 

  479. 	}

    480. 

  480. }

    481. 

  481. 

    482. 

  482. generate.error.message<-function(i, mod.cur, mod.new, lnR, errorLog, init=FALSE) {

    483. 

  483. 	if(init) {

    484. 

  484. 		initmsg = write(paste("gen", "curr.lnL", "new.lnL", "lnR", sep="\t"), file=errorLog)

    485. 

  485. 	} else {

    486. 

  486. 		write(paste(i, sprintf("%.3f", mod.cur$lnL), sprintf("%.3f", mod.new$lnL), sprintf("%.3f", lnR), sep="\t"), file=errorLog)

    487. 

  487. 	}

    488. 

  488. }

    489. 

  489. 

    490. 

  490. determine.accepted.proposal<-function(startparms, endparms, cOK) {

    491. 

  491. 	which(startparms!=endparms)->marker

    492. 

  492. 	cOK[marker]=cOK[marker]+1

    493. 

  493. 	cOK

    494. 

  494. }

    495. 

  495. 

    496. 

  496. rtpois<-function(N, lambda, k) {

    497. 

  497. 	p=ppois(k, lambda, lower.tail=FALSE)

    498. 

  498. 	out=qpois(runif(N, min=0, max=1-p), lambda)

    499. 

  499. 	out

    500. 

  500. }

    501. 

  501. 				 

    502. 

  502. ptpois<-function(x, lambda, k) {

    503. 

  503. 	p.k=ppois(k, lambda, lower.tail=FALSE)

    504. 

  504. 	p.x=ppois(x, lambda, lower.tail=FALSE)

    505. 

  505. 	ptp=p.x/(1-p.k)

    506. 

  506. 	return(ptp)

    507. 

  507. }

    508. 

  508. 				 

    509. 

  509. ## SUMMARIZING MCMC -- PLOTTING FUNCTIONS ##

    510. 

  510. shifts.plot=function(phy, base.dir, burnin=0, level=0.03, internal.only=FALSE, paint.branches=TRUE, pdf.base=NULL, verbosity.base=NULL, ...) {

    511. 

  511. 	color.length=21

    512. 

  512. 	require(ape)

    513. 

  513. 	oldwd=getwd()

    514. 

  514. 	setwd(base.dir)

    515. 

  515. 	files=dir()

    516. 

  516. #	if(optima) {

    517. 

  517. #		shifts.file=files[grep("optima.shifts.txt", files)]

    518. 

  518. #		estimates.file=files[grep("optima.txt", files)]

    519. 

  519. #	} else {

    520. 

  520. 		shifts.file=files[grep("rate.shifts.txt", files)]

    521. 

  521. 		estimates.file=files[grep("rates.txt", files)]

    522. 

  522. #	}

    523. 

  523. 	if(!is.null(pdf.base)) lab=paste(pdf.base, paste("bi", burnin, sep=""), paste("fq", level, sep=""), sep=".")

    524. 

  524. 

    525. 

  525. 	cat("READING shifts...\n")

    526. 

  526. 	results=read.table(shifts.file)

    527. 

  527. 	names(results)=as.numeric(results[1,])

    528. 

  528. 	results=results[-c(1:(burnin+1)),]

    529. 

  529. 	if(!all(names(results)%in%phy$edge[,2])) stop("Cannot process results: check that tree matches posterior summaries")

    530. 

  530. 	hits.tmp=apply(results,1,sum)

    531. 

  531. 	hits=length(hits.tmp[hits.tmp>0])

    532. 

  532. 	aa.tmp=apply(results, 2, function(x) sum(x))

    533. 

  533. 	aa.tmp=aa.tmp/hits

    534. 

  534. 	aa=aa.tmp[aa.tmp>=level]

    535. 

  535. 	aa=aa[order(aa, decreasing=TRUE)]

    536. 

  536. 	aa.nodes=aa[which(as.numeric(names(aa))>Ntip(phy))]

    537. 

  537. 	aa.tips=aa[which(as.numeric(names(aa))<=Ntip(phy))]

    538. 

  538. 	aa=aa.nodes

    539. 

  539. 	print(aa.tips)

    540. 

  540. 	nodes=as.numeric(names(aa))

    541. 

  541. 	nodes=nodes[nodes>Ntip(phy)]

    542. 

  542. 	desc=lapply(nodes, function(x) {

    543. 

  543. 				foo=get.descendants.of.node(x,phy)

    544. 

  544. 				if(length(foo)) return(phy$tip.label[foo[foo<=Ntip(phy)]]) else return(NULL)

    545. 

  545. 				}

    546. 

  546. 				)

    547. 

  547. 	shifts=apply(results, 1, sum)

    548. 

  548. 	cat("READING estimates...\n")

    549. 

  549. 	ests=read.table(estimates.file)

    550. 

  550. 	names(ests)=ests[1,]

    551. 

  551. 	ests=ests[-c(1:(burnin+1)),]

    552. 

  552. 	average.ests.tmp=apply(ests,2,mean)

    553. 

  553. 	average.ests=average.ests.tmp-median(average.ests.tmp)

    554. 

  554. 	if(paint.branches) {

    555. 

  555. 		require(colorspace)

    556. 

  556. #		cce=diverge_hcl(color.length, c = c(100, 0), l = c(50, 90), power = 1.1)

    557. 

  557. 		cce=diverge_hcl(2*color.length, power = 0.5)

    558. 

  558. 		e.seq=seq(min(average.ests),max(average.ests),length=color.length)

    559. 

  559. 		color.gamut=seq(-max(abs(e.seq)), max(abs(e.seq)), length=length(cce))

    560. 

  560. 		colors.branches=sapply(average.ests, function(x) cce[which(min(abs(color.gamut-x))==abs(color.gamut-x))])

    561. 

  561. 		colors.branches=colors.branches[match(as.numeric(names(colors.branches)), phy$edge[,2])]

    562. 

  562. 	} else {

    563. 

  563. 		colors.branches=1

    564. 

  564. 	}

    565. 

  565. 	if(length(nodes)) {

    566. 

  566. 		require(colorspace)

    567. 

  567. 		cols=sapply(nodes, function(x) {

    568. 

  568. 						 a=get.ancestor.of.node(x, phy)

    569. 

  569. 						 comp=ests[,which(names(ests)==a)]

    570. 

  570. 						 this=ests[,which(names(ests)==x)]

    571. 

  571. 						 if(!length(comp)) { # dealing with first descendant of root

    572. 

  572. 						 d=get.desc.of.node(a, phy)

    573. 

  573. 						 d=d[which(d!=x)]

    574. 

  574. 						 

    575. 

  575. 						# find if sister descendant also experienced rate shift 

    576. 

  576. 						 d.shifts=results[, which(names(results)==d)]

    577. 

  577. 						 comp=ests[,which(names(ests)==d)]

    578. 

  578. 						 x.res=sapply(1:length(d.shifts), function(y) {

    579. 

  579. 									  if(d.shifts[y]==0) {

    580. 

  580. 									  zz=this[y]-comp[y]

    581. 

  581. 									  if(zz>0) {

    582. 

  582. 									  return(1) 

    583. 

  583. 									  } else {

    584. 

  584. 									  if(zz<0) return(-1) else return(0)

    585. 

  585. 									  }

    586. 

  586. 									  } else {

    587. 

  587. 									  return(0)

    588. 

  588. 									  }

    589. 

  589. 									  }

    590. 

  590. 									  )

    591. 

  591. 						 x.res=mean(x.res[x.res!=0])

    592. 

  592. 						 } else {

    593. 

  593. 						 yy=this-comp

    594. 

  594. 						 zz=yy

    595. 

  595. 						 zz[yy>0]=1

    596. 

  596. 						 zz[yy<0]=-1

    597. 

  597. 						 zz[yy==0]=0

    598. 

  598. 						 x.res=mean(zz[zz!=0])

    599. 

  599. 						 }

    600. 

  600. 						 return(x.res)

    601. 

  601. 						 }

    602. 

  602. 						 )

    603. 

  603. 		ccx=diverge_hcl(21, c = c(100, 0), l = c(50, 90), power = 1.1)

    604. 

  604. 		c.seq=round(seq(-1,1,by=0.1),digits=1)

    605. 

  605. 		colors.nodes=ccx[match(round(cols,1),c.seq)]

    606. 

  606. 		names(colors.nodes)=nodes

    607. 

  607. 		colors.nodes=colors.nodes[which(as.numeric(names(colors.nodes))>Ntip(phy))]

    608. 

  608. 	} else {

    609. 

  609. 		colors.nodes=NULL

    610. 

  610. 		cols=NULL

    611. 

  611. 	}

    612. 

  612. 		

    613. 

  613. # send to pdf 

    614. 

  614. 	if(!is.null(pdf.base)) pdf(file=paste(oldwd, paste(lab,"pdf",sep="."),sep="/"))

    615. 

  615. 	plot(phy, cex=0.1, lwd=0.4, edge.width=0.5, edge.color=colors.branches, ...)

    616. 

  616. 	nn=(Ntip(phy)+1):max(phy$edge[,2])

    617. 

  617. 	ll<-cc<-rr<-rep(0,length(nn))

    618. 

  618. 	ll[nn%in%nodes]=1

    619. 

  619. 	if(length(nodes)) cc[nn%in%nodes]=aa[match(nn[nn%in%nodes],nodes)]/max(aa) 

    620. 

  620. 	if(is.null(colors.nodes)) {

    621. 

  621. 		nodelabels(pch=ifelse(ll==1, 21, NA), bg=ifelse(ll==1, "black", "white"), cex=2*cc)

    622. 

  622. 	} else {

    623. 

  623. 		rr[nn%in%nodes]=colors.nodes[order(names(colors.nodes))]

    624. 

  624. 		nodelabels(pch=ifelse(ll==1, 21, NA), cex=2*cc, col=rr, bg=rr, lwd=0.5)

    625. 

  625. 		if(length(aa.tips) & !internal.only) {

    626. 

  626. 			tt=rep(0,Ntip(phy))

    627. 

  627. 			tt[(1:Ntip(phy))[as.numeric(names(aa.tips))]]=1

    628. 

  628. 			tt.cex=ifelse(tt==1, aa.tips/max(aa.tmp), 0)

    629. 

  629. 			tt.col=sapply(names(aa.tips), function(x) {

    630. 

  630. 						a=get.ancestor.of.node(x, phy)

    631. 

  631. 						comp=ests[,which(names(ests)==a)]

    632. 

  632. 						this=ests[,which(names(ests)==x)]

    633. 

  633. 						if(!length(comp)) { # dealing with first descendant of root

    634. 

  634. 						d=get.desc.of.node(a, phy)

    635. 

  635. 						d=d[which(d!=x)]

    636. 

  636. 						

    637. 

  637. 						# find if sister descendant also experienced rate shift 

    638. 

  638. 						d.shifts=results[, which(names(results)==d)]

    639. 

  639. 						comp=ests[,which(names(ests)==d)]

    640. 

  640. 						x.res=sapply(1:length(d.shifts), function(y) {

    641. 

  641. 									 if(d.shifts[y]==0) {

    642. 

  642. 									 zz=this[y]-comp[y]

    643. 

  643. 									 if(zz>0) {

    644. 

  644. 									 return(1) 

    645. 

  645. 									 } else {

    646. 

  646. 									 if(zz<0) return(-1) else return(0)

    647. 

  647. 									 }

    648. 

  648. 									 } else {

    649. 

  649. 									 return(0)

    650. 

  650. 									 }

    651. 

  651. 									 }

    652. 

  652. 									 )

    653. 

  653. 						x.res=mean(x.res[x.res!=0])

    654. 

  654. 						} else {

    655. 

  655. 						yy=this-comp

    656. 

  656. 						zz=yy

    657. 

  657. 						zz[yy>0]=1

    658. 

  658. 						zz[yy<0]=-1

    659. 

  659. 						zz[yy==0]=0

    660. 

  660. 						x.res=mean(zz[zz!=0])

    661. 

  661. 						}

    662. 

  662. 						return(x.res)

    663. 

  663. 						}

    664. 

  664. 						)

    665. 

  665. 			tt.ccx=diverge_hcl(21, c = c(100, 0), l = c(50, 90), power = 1.1)

    666. 

  666. 			tt.c.seq=round(seq(-1,1,by=0.1),digits=1)

    667. 

  667. 			tt.colors.nodes=tt.ccx[match(round(tt.col,1),tt.c.seq)]

    668. 

  668. 			names(tt.colors.nodes)=as.numeric(names(aa.tips))

    669. 

  669. 			colors.tips.tmp=tt.colors.nodes[order(as.numeric(names(tt.colors.nodes)))]

    670. 

  670. 			colors.tips=rep(NA, Ntip(phy))

    671. 

  671. 			colors.tips[(1:Ntip(phy))[as.numeric(names(aa.tips))]]=colors.tips.tmp

    672. 

  672. 

    673. 

  673. 			tiplabels(pch=ifelse(tt==1, 21, NA), cex=2*tt.cex, col=colors.tips, bg=colors.tips, lwd=0.5)

    674. 

  674. 		}

    675. 

  675. 		legend("bottomleft", title=toupper("direction"), cex=0.5, pt.cex=1, text.col="darkgray", legend = sprintf("%6.1f", rev(c.seq[seq(1,length(c.seq),by=2)])), pch=21, ncol=1, col = "darkgray", pt.bg = rev(ccx[seq(1,length(c.seq),by=2)]), box.lty="blank", border="white")

    676. 

  676. 		if(length(nodes)) legend("bottomright", title=toupper("frequency"), cex=0.5, pt.cex=2*(seq(min(cc), max(cc), length=6)), text.col="darkgray", legend = sprintf("%10.3f", seq(round(min(aa),2), round(max(aa),2), length=6)), pch=21, ncol=1, col = "darkgray", pt.bg = "white", box.lty="blank", border="white")

    677. 

  677. 	}

    678. 

  678. 	if(!is.null(pdf.base)) dev.off()

    679. 

  679. 		

    680. 

  680. 	if(length(nodes)) names(cols)=names(aa)

    681. 

  681. 	setwd(oldwd)

    682. 

  682. 	names(desc)=paste("node",nodes,sep=".")

    683. 

  683. 	if(!is.null(verbosity.base)) {

    684. 

  684. 		summary.table=c(hits/nrow(results), mean(shifts), median(shifts))

    685. 

  685. 		names(summary.table)=c("shift.prob", "mean.shift","median.shifts")

    686. 

  686. 		write.table(summary.table, file=paste(verbosity.base, "run.summary.txt", sep="."), quote=FALSE, col.names=FALSE)

    687. 

  687. 		if(length(nodes)) {

    688. 

  688. 			for(nn in 1:length(nodes)) {

    689. 

  689. 				write.table(c(nodes[nn], desc[[nn]]), file=paste(verbosity.base, "shift.descendants.txt", sep="."), append=TRUE, quote=FALSE, row.names=FALSE, col.names=FALSE)

    690. 

  690. 			}

    691. 

  691. 		}

    692. 

  692. 		allres=merge(as.data.frame(aa),as.data.frame(cols),by=0)

    693. 

  693. 		allres=allres[order(allres$aa, decreasing=TRUE),]

    694. 

  694. 		names(allres)=c("node", "conditional.shift.probability", "relative.shift.direction")

    695. 

  695. 		write.table(allres, file=paste(verbosity.base, "estimates.summary.txt", sep="."), quote=FALSE, row.names=FALSE)

    696. 

  696. 	}

    697. 

  697. 	return(list(desc=desc, shift.prob=hits/nrow(results), mean.shifts=mean(shifts), median.shifts=median(shifts), shift.prop=aa, shift.direction=cols, mean.rates=average.ests.tmp, mean.shifts=aa.tmp/nrow(results)))

    698. 

  698. }

    699. 

  699. 

    700. 

  700. 

    701. 

  701. plot.quant<-function(phy, data, data.names, shrinker=1, relative=TRUE, cex=0.5, adj=c(15,5), lwd=0.5, font=1, ...){

    702. 

  702. 	std=function(x,max,min){return((x-min)/(max-min))}

    703. 

  703. 	if(relative) {

    704. 

  704. 		data=sapply(data, function(x) std(x, max(data), min(data)))

    705. 

  705. 	}

    706. 

  706. 	

    707. 

  707. 	f=match(phy$tip.label, data.names)

    708. 

  708. 	if(is.na(sum(f))) {

    709. 

  709. 		stop("tips cannot be matched to tree")

    710. 

  710. 	}

    711. 

  711. 	else {

    712. 

  712. 		data=data[f]

    713. 

  713. 		phy$trait=data

    714. 

  714. 		plot.phylo(phy, show.tip.label=T, label.offset=adj[1], cex=cex, font=font, ...)

    715. 

  715. 		tiplabels(text=NULL, pch=21, adj=adj[2], cex=c(phy$trait/shrinker), bg="white",lwd=lwd)

    716. 

  716. 		} 

    717. 

  717. }

    718. 

  718. 

    719. 

  719. branchcol.plot=function(phy, cur.rates, color.length=4, ...) {

    720. 

  720. 	color.length=color.length

    721. 

  721. 	require(ape)

    722. 

  722. 	require(colorspace)

    723. 

  723. 	ests=cur.rates

    724. 

  724. 	names(ests)=phy$edge[,2]

    725. 

  725. 	cce=diverge_hcl(color.length, power = 0.5)

    726. 

  726. 	e.seq=seq(min(ests),max(ests),length=color.length)

    727. 

  727. 	colors.branches=sapply(ests, function(x) cce[which(min(abs(e.seq-x))==abs(e.seq-x))])

    728. 

  728. 	names(colors.branches)=names(ests)

    729. 

  729. 	colors.branches=colors.branches[match(as.numeric(names(colors.branches)), phy$edge[,2])]	

    730. 

  730. 	plot(phy, cex=0.1, lwd=0.4, edge.width=0.5, edge.color=colors.branches, ...)

    731. 

  731. }

    732. 

  732. 

    733. 

  733. 

    734. 

  734. prior.posterior.plot=function(data, rpois.lambda) {

    735. 

  735. # data should be a vector of number of shifts, for instance, for all interations across the MCMC sampling (post-burnin)

    736. 

  736. 	require(ggplot2)

    737. 

  737. 	dataset <- data.frame(variable = gl(2, length(data), labels = c("posterior", "prior")), value = c(data, rpois(length(data),rpois.lambda))) 

    738. 

  738. 	ggplot(data = dataset, aes(x = value, fill = variable)) + geom_histogram(position = "dodge") + scale_fill_manual(values = c("gray", "black"))	

    739. 

  739. }
    740.