version 3.0.1

DESCRIPTION

@@ -2,16 +2,16 @@ Package: sidier
 Type: Package
 Title: Substitution and Indel Distances to Infer Evolutionary
         Relationships
-Version: 3.0
-Date: 2014-10-20
+Version: 3.0.1
+Date: 2015-02-27
 Author: A. Jesus Muñoz Pajares
 Maintainer: A.J. Muñoz-Pajares <ajesusmp@ugr.es>
-Depends: R (>= 2.10.1)
+Depends: R (>= 3.1.2)
 Imports: ape, network, igraph, gridBase, grid, ggmap, ggplot2
 Encoding: UTF-8
-Description: sidier is a library and R package for evolutionary reconstruction based on substitutions and insertion-deletion (indels) analyses in a distance-based framework.
+Description: Evolutionary reconstruction based on substitutions and insertion-deletion (indels) analyses in a distance-based framework.
 License: GPL-2
-Packaged: 2014-11-18 17:13:02 UTC; ajesusmp
+Packaged: 2015-02-27 11:45:53 UTC; ajesusmp
 NeedsCompilation: no
 Repository: CRAN
-Date/Publication: 2014-11-18 18:42:14
+Date/Publication: 2015-02-28 01:23:26

MD5

@@ -1,6 +1,6 @@
-643064b6b164cd775bb45e9df3afcab2 *DESCRIPTION
+06b9b775b33f9b15f873e60b492f26c8 *DESCRIPTION
 9887b0790b845201f27be77e87b3592a *NAMESPACE
-db93749b73aaea88cc30fe25a752048a *NEWS
+4c47fb2d41a6d3b0d64b0d5a2b5fd918 *NEWS
 3ae3b56ed216f175472563038ee1a871 *R/BARRIEL.R
 befd4a83910f6b28924724aa7151738b *R/FIFTH.R
 8d22dbc99309f167780ff255544bed5e *R/FindHaplo.R
@@ -11,22 +11,22 @@ c7350a1ba650dcb71d5db0e30660cb11 *R/MCIC.R
 749ccb5cb584927d1a4861f392471f38 *R/SIC.R
 1d73e61b64a2288c9feee69455b504d2 *R/colour.scheme.R
 0c241d9d86699c0dd49a34ea6a00ab71 *R/distance.comb.R
-4c378e8aafa305099aa83470467497d6 *R/double.plot.R
+56aff92966df068852e26a703dd32390 *R/double.plot.R
 ef9ad8c0ecf80072b769d5176810ec60 *R/mergeNodes.R
-5f73601975f24fdcac77d9e94f185a01 *R/mutation.network.R
+847233bc8004aadfcd660cffe47dd343 *R/mutation.network.R
 5d3d8cb60e38b85dbbe3def9f0b86d38 *R/mutationSummary.R
 5148056729d2ab5cbdeb6a84c2dc4f4e *R/nt.gap.comb.R
-6afe9c4b875e823df60fd3d8e7ed0b69 *R/perc.thr.R
-540500bd00866348007b19caceb918a0 *R/pie.network.R
+f35c54bc7b4310a2f919d09b5880bd6a *R/perc.thr.R
+bb83df8a267a4ff5069ef681c1836a86 *R/pie.network.R
 b96adbec0e52e220d2594085b9e6824b *R/pop.dist.R
 c145fcca709586ffdae41a6bfb5e093b *R/simplify.network.R
 32b50883e7ff3a443bcb68150cd180c7 *R/simuEvolution.R
 599a67713f42f4bb1b776dd119278b78 *R/single.network.R
-d54889884ace07a8fb17cc9d59701912 *R/spatial.plot.R
+3e0e85a1c37a88d94dc4ed4e85e77bdb *R/spatial.plot.R
 96ef0e4246eba718daea6a8907831abb *R/zero.thr.R
 463ee712d3be151aecc85cafc1b0e2c5 *data/Example_Spatial.plot_Alignment.txt.gz
 d0fe3215091811496faa765b2485dcd3 *data/ex_Coords.txt.gz
-0ab41afdbc6649261402881576b0bed3 *data/ex_alignment1.rda
+fd4026e3179f286c4fee5dada0f20740 *data/ex_alignment1.rda
 68344bed64454a54c9a9816dea0dda1d *inst/CITATION
 6640546f0c607df1d3dc30449cfb4773 *man/BARRIEL.Rd
 354713152bc5c48a3ee1841ae16f03b4 *man/Example_Spatial.plot_Alignment.Rd
@@ -40,7 +40,7 @@ c4dbd0b111e0a182942a6c2dd9d64c1c *man/SIC.Rd
 87546425ce864d9c5d355fa0fc92e2ad *man/alignExample.Rd
 700ccacc7cff59ecc5315e7401505d9d *man/colour.scheme.Rd
 e447cb903fe99b491d78e91aa7cb0d21 *man/distance.comb.Rd
-2828281cb312ed1403c520996a10d193 *man/double.plot.Rd
+d378cd006ab6641d888a086cc3136f06 *man/double.plot.Rd
 89a1067d3a1403c8c5368b656f7b5874 *man/ex_Coords.Rd
 659ea75e6d521f86b3c6020fe43a0cb3 *man/ex_alignment1.Rd
 6e19af4eca42b7c01a7e5c765b702235 *man/mergeNodes.Rd
@@ -50,9 +50,9 @@ ca6358b5d7c00607648b35c027d8b6c1 *man/mutationSummary.Rd
 6e01efc0862707af942d68a6c62133ba *man/perc.thr.Rd
 9c80d5f694684136a9cdce9d3fb4d661 *man/pie.network.Rd
 a606d8ad6be893478f210076e48d6592 *man/pop.dist.Rd
-c89732eee1b46f62bb9d72e1acf41fad *man/sidier-package.Rd
+ae9c787a05dbf46ad908811ba6cbc47a *man/sidier-package.Rd
 094758711c58c8e8d36db7e23b61fdb3 *man/simplify.network.Rd
 52f560a4e241b00e2c0f5c34da199ee0 *man/simuEvolution.Rd
 48eb6095f0c0badb4f7ebd45b978b1fd *man/single.network.Rd
-a0745525d489242cb167a97bb0dbdbe0 *man/spatial.plot.Rd
+c72d6da9aa21a2a6eb86dc239a8c54ba *man/spatial.plot.Rd
 e4b23ea086a23d742797b91ba5e4380a *man/zero.thr.Rd

NEWS

@@ -1,3 +1,13 @@
+
+=========
+sidier 3.0.1
+=========
+
+Released February 27, 2015
+
+ .Functions giving an error for matrices showing off-diagonal zeros have changed. Now these function gives a warning only if no percolation threshold is found.
+ .double.plot now includes depicting nodes according to haplotype modules
+
 =========
 sidier 3.0
 =========

R/double.plot.R

@@ -1,17 +1,19 @@
 double.plot <-
-function(align=NA,indel.method="MCIC",substitution.model="raw", pairwise.deletion=TRUE,network.method.mut="percolation",network.method.pie="percolation",range=seq(0,1,0.01), addExtremes=FALSE,alpha.mut="info",alpha.pie="info",combination.method.mut="Corrected", combination.method.pie="Corrected",na.rm.row.col.mut=FALSE,na.rm.row.col.pie=FALSE, save.distance.mut=FALSE, save.distance.name.mut="DistanceMatrix_threshold_Mutations.txt", save.distance.pie=FALSE, save.distance.name.pie="DistanceMatrix_threshold_Pies.txt", bgcol=NA, label.col.mut="black", label.col.pie="black",label.mut=NA,label.pie=NA,label.sub.str.mut=NA,label.sub.str.pie=NA,colInd="red", colSust="black",lwd.mut=1,InScale=1, SuScale=1, lwd.edge=1.5,cex.mut=1,cex.label.mut=1,cex.label.pie=1,cex.vertex=1, main=c("Haplotypes","Populations"),NameIniPopulations=NA, NameEndPopulations=NA, NameIniHaplotypes=NA,NameEndHaplotypes=NA, cex.pie=1, HaplosNames=NA,offset.label=1.5)
+function(align=NA,indel.method="MCIC",substitution.model="raw", pairwise.deletion=TRUE,network.method.mut="percolation",network.method.pie="percolation",range=seq(0,1,0.01), addExtremes=FALSE,alpha.mut="info",alpha.pie="info",combination.method.mut="Corrected", combination.method.pie="Corrected",na.rm.row.col.mut=FALSE,na.rm.row.col.pie=FALSE, save.distance.mut=FALSE, save.distance.name.mut="DistanceMatrix_threshold_Mutations.txt", save.distance.pie=FALSE, save.distance.name.pie="DistanceMatrix_threshold_Pies.txt", modules=FALSE, modules.col=NA, bgcol=NA, label.col.mut="black", label.col.pie="black",label.mut=NA,label.pie=NA,label.sub.str.mut=NA,label.sub.str.pie=NA,colInd="red", colSust="black",lwd.mut=1,InScale=1, SuScale=1, lwd.edge=1.5,cex.mut=1,cex.label.mut=1,cex.label.pie=1,cex.vertex=1, main=c("Haplotypes","Populations"),NameIniPopulations=NA, NameEndPopulations=NA, NameIniHaplotypes=NA,NameEndHaplotypes=NA, cex.pie=1, HaplosNames=NA,offset.label=1.5)
 {
-
-if(is.na(bgcol[1]))
-	{
-	Aux<-GetHaplo(align=align, saveFile =FALSE, format = "fasta", seqsNames = NA,silent=T)
-	if(is.na(bgcol))
-	bgcol<-colour.scheme(def=bgcol,N=length(dimnames(Aux)[[1]]))
-	col.pie<-NA
-	}
-
-if(is.na(bgcol[1])==F)
-	bgcol->col.pie
+if (modules==FALSE)
+{
+	if(is.na(bgcol[1]))
+		{
+		Aux<-GetHaplo(align=align, saveFile =FALSE, format = "fasta", seqsNames = NA,silent=T)
+		if(is.na(bgcol))
+		bgcol<-colour.scheme(def=bgcol,N=length(dimnames(Aux)[[1]]))
+		col.pie<-NA
+		}
+
+	if(is.na(bgcol[1])==FALSE)
+		bgcol->col.pie
+}
 
 if(length(main)==1 & main[1]=="summary")
 {
@@ -30,9 +32,13 @@ main[2]<-paste("Haplotypes network (left) \nPopulations network (right)\n")
 
 layout(matrix(c(1:2),ncol=2))
 
-mutation.network (align=align,indel.method=indel.method,substitution.model=substitution.model,pairwise.deletion=pairwise.deletion, network.method=network.method.mut,range=range,addExtremes=addExtremes,alpha=alpha.mut,combination.method=combination.method.mut, bgcol=bgcol, label.col=label.col.mut,modules=FALSE,colInd=colInd, colSust=colSust,lwd.mut=lwd.mut,lwd.edge=lwd.edge,cex.mut=cex.mut,label=label.mut, cex.label=cex.label.mut,cex.vertex =cex.vertex, na.rm.row.col=na.rm.row.col.mut,save.distance=save.distance.mut, save.distance.name=save.distance.name.mut ,label.sub.str=label.sub.str.mut,silent=TRUE,InScale=InScale, SuScale=SuScale)
+a<-mutation.network (align=align,indel.method=indel.method,substitution.model=substitution.model,pairwise.deletion=pairwise.deletion, network.method=network.method.mut,range=range,addExtremes=addExtremes,alpha=alpha.mut,combination.method=combination.method.mut, bgcol=bgcol, label.col=label.col.mut,modules=modules, moduleCol= modules.col,colInd=colInd, colSust=colSust,lwd.mut=lwd.mut,lwd.edge=lwd.edge,cex.mut=cex.mut,label=label.mut, cex.label=cex.label.mut,cex.vertex =cex.vertex, na.rm.row.col=na.rm.row.col.mut,save.distance=save.distance.mut, save.distance.name=save.distance.name.mut ,label.sub.str=label.sub.str.mut,silent=TRUE,InScale=InScale, SuScale=SuScale)
 mtext(paste(main[1],"\n"),font=2)
 
+if(modules==TRUE)
+#col.pie<-unique(a[[3]][,3])
+col.pie<-a[[3]][,3]
+
 pie.network (align=align,indel.method=indel.method,substitution.model=substitution.model,pairwise.deletion=pairwise.deletion, network.method=network.method.pie,range=range,addExtremes=addExtremes,alpha=alpha.pie,combination.method=combination.method.pie, label=label.pie,label.col=label.col.mut,cex.label=cex.label.pie ,label.sub.str=label.sub.str.pie, na.rm.row.col=na.rm.row.col.pie, NameIniPopulations=NameIniPopulations, NameEndPopulations=NameEndPopulations, NameIniHaplotypes=NameIniHaplotypes, NameEndHaplotypes=NameEndHaplotypes,save.distance=save.distance.pie,save.distance.name=save.distance.name.pie, col.pie=col.pie,cex.pie=cex.pie,HaplosNames=HaplosNames,offset.label=offset.label)
 mtext(paste(main[2],"\n"),font=2)
 }

R/mutation.network.R

@@ -87,9 +87,6 @@ dis<-mergeNodes(dis)
 	colnames(salida)<-c("Threshold","#Clusters")
 		for (j in range)
 		{
-		if(length(which(dis==0))!=nrow(dis))
-		stop("\n\nSome of the off-diagonal elements in your matrix are zero and percolation threshold can not be estimated. Your distance matrix seems to provide low resolution. You may:\n\n1.- Redefine populations by meging those showing distance values of 0 before percolation threshold estimation. For that use the 'merge=TRUE' option \n\n2.- Represent your original distance matrix using the 'No Isolated Nodes Allowed' method. For that use the 'network.method=\"NINA\"' option.\n\n3.- Represent your original distance matrix using the 'zero' method. For that use the 'network.method=\"zero\"' option.")
-
 		dis2<-matrix(1,nrow=nrow(dis),ncol=ncol(dis))
 		lim<-max(dis)*j
 		fuera<-which(dis>lim)
@@ -191,6 +188,7 @@ if(network.method=="zero")
 			M[zero1,zero2]<-1
 			}
 		dis2<-M
+		j<-0
 		}
 	}
 ## 3- END ZERO THRESHOLD
@@ -218,8 +216,12 @@ if(network.method=="zero")
 ### END THRESHOLD ESTIMATION ###
 #
 #
-write.table(dis2,file="kk_control_combined_dis2.txt")
+#write.table(dis2,file="kk_control_combined_dis2.txt")
 #
+## WARNING IF percolation threshold is not found:
+	if(is.na(j) & length(which(dis==0))!=nrow(dis))
+	warning("\n\nPercolation threshold can not be estimated and some of the off-diagonal elements in your matrix are zero. Your distance matrix seems to provide low resolution. You may:\n\n1.- Redefine populations by meging those showing distance values of 0 before percolation threshold estimation. For that use the 'merge=TRUE' option \n\n2.- Represent your original distance matrix using the 'No Isolated Nodes Allowed' method. For that use the 'network.method=\"NINA\"' option.\n\n3.- Represent your original distance matrix using the 'zero' method. For that use the 'network.method=\"zero\"' option.")
+
 ## GETTING NETWORKS ###
 G<-graph.adjacency(dis2)
 A<-as.network.matrix(dis2)

R/perc.thr.R

@@ -31,9 +31,6 @@ dis<-mergeNodes(dis)
 
 ## END merging nodes ##
 
-if(length(which(dis==0))!=nrow(dis))
-warning("\n\nSome of the off-diagonal elements in your matrix are zero. Percolation threshold may not be estimated if the input distance matrix provides low resolution (that is, if there are many off-diagonal zeros). In that case you may:\n\n1.- Redefine populations by meging those showing distance values of 0 before percolation threshold estimation. For that use the 'merge=TRUE' option \n\n2.- Represent your original distance matrix using the 'No Isolated Nodes Allowed' method. For that use the 'NINA.thr' function.\n\n3.- Represent your original distance matrix using the 'zero' method. For that use the 'zero.thr' function.\n\n4.- Use another distance matrix or combine this matrix with other more informative matrix")
-
 for (j in range)
 {
 
@@ -175,7 +172,11 @@ A<-as.network.matrix(dis2)
 vertis<-plot.network(A)
 plot.network(A,coord=vertis,vertex.col=as.matrix(bgcol),label=label,usearrows=0,vertex.cex=2.5*cex.vertex,interactive=F, label.pos=5,label.col=label.col,label.cex=0.8*cex.label,main=paste("Percolation threshold=",round(j,ncs),sep=" "))
 
-if(is.na(j)) stop("\n\nNo percolation threshold found.\n\n")
+#if(is.na(j)) stop("\n\nNo percolation threshold found.\n\n")
+
+## WARNING IF percolation threshold is not found:
+	if(is.na(j) & length(which(dis==0))!=nrow(dis))
+	warning("\n\nPercolation threshold can not be estimated and some of the off-diagonal elements in your matrix are zero. Your distance matrix seems to provide low resolution. You may:\n\n1.- Redefine populations by meging those showing distance values of 0 before percolation threshold estimation. For that use the 'merge=TRUE' option \n\n2.- Represent your original distance matrix using the 'No Isolated Nodes Allowed' method. For that use the 'network.method=\"NINA\"' option.\n\n3.- Represent your original distance matrix using the 'zero' method. For that use the 'network.method=\"zero\"' option.")
 
 if(ptPDF==TRUE)
 {

R/pie.network.R

@@ -158,9 +158,6 @@ dis<-mergeNodes(dis)
 	colnames(salida)<-c("Threshold","#Clusters")
 		for (j in range)
 		{
-		if(length(which(dis==0))!=nrow(dis))
-		stop("\n\nSome of the off-diagonal elements in your matrix are zero and percolation threshold can not be estimated. Your distance matrix seems to provide low resolution. You may:\n\n1.- Redefine populations by meging those showing distance values of 0 before percolation threshold estimation. For that use the 'merge=TRUE' option \n\n2.- Represent your original distance matrix using the 'No Isolated Nodes Allowed' method. For that use the 'network.method=\"NINA\"' option.\n\n3.- Represent your original distance matrix using the 'zero' method. For that use the 'network.method=\"zero\"' option.")
-
 #		print(paste("Threshold value:",j,"  Range to test: from ",min(range)," to ",max(range),sep=""))
 
 		dis2<-matrix(1,nrow=nrow(dis),ncol=ncol(dis))
@@ -268,6 +265,7 @@ if(network.method=="zero")
 			}
 		dis2<-M
 		}
+	j<-0
 	}
 ## 3- END ZERO THRESHOLD
 
@@ -285,6 +283,7 @@ if(network.method=="zero")
 		M[zero1,zero2]<-1
 		}
 	dis2<-M
+	j<-0
 	}
 
 
@@ -294,6 +293,9 @@ if(network.method=="zero")
 ### END THRESHOLD ESTIMATION ###
 #
 #
+## WARNING IF percolation threshold is not found:
+	if(is.na(j) & length(which(dis==0))!=nrow(dis))
+	warning("\n\nPercolation threshold can not be estimated and some of the off-diagonal elements in your matrix are zero. Your distance matrix seems to provide low resolution. You may:\n\n1.- Redefine populations by meging those showing distance values of 0 before percolation threshold estimation. For that use the 'merge=TRUE' option \n\n2.- Represent your original distance matrix using the 'No Isolated Nodes Allowed' method. For that use the 'network.method=\"NINA\"' option.\n\n3.- Represent your original distance matrix using the 'zero' method. For that use the 'network.method=\"zero\"' option.")
 #
 ## GETTING NETWORKS ###
 G<-graph.adjacency(dis2)

R/spatial.plot.R

@@ -1,5 +1,5 @@
 spatial.plot <-
-function(dis=NA, align=NA, X=NULL, Y=NULL, indel.method="MCIC", substitution.model="raw",
+function(dis=NULL, align=NA, X=NULL, Y=NULL, indel.method="MCIC", substitution.model="raw",
 pairwise.deletion=TRUE, alpha="info", combination.method="Corrected",
 na.rm.row.col=FALSE, addExtremes=FALSE, NameIniPopulations=NA, NameEndPopulations=NA,
 NameIniHaplotypes=NA, NameEndHaplotypes=NA, HaplosNames=NA, save.distance=FALSE,
@@ -9,7 +9,11 @@ bgcol="white", label.col="black", label=NA, label.sub.str=NA, label.pos= "b",
 cex.label=1,cex.vertex=1,vertex.size="equal", plot.edges=TRUE, lwd.edge=1,to.ggmap=FALSE,
 plot.ggmap=FALSE, zoom.ggmap=6, maptype.ggmap="satellite", label.size.ggmap=3)
 {
-	if(length(which(is.na(dis)))>0)
+
+	if(is.null(dis)==FALSE)
+	pieSize<-rep(1,nrow(dis))
+
+	if(is.null(dis))
 	{
 	#### ALIGNMENT OF UNIQUE HAPLOTYPES:
 	#
@@ -122,10 +126,18 @@ plot.ggmap=FALSE, zoom.ggmap=6, maptype.ggmap="satellite", label.size.ggmap=3)
 			}
 
 
-####### PROBAR A REDEFINIR LOS NOMBRES DE LAS SECUENCIAS NADA MÁS EMPEZAR Y VER SI ASÍ LO HACE BIEN...
+		dis<-pop.dist(distances=dis,Haplos=HaplosPop[[1]], logfile=FALSE,saveFile=FALSE,NameIniHaplotypes=NameIniHaplotypes, NameEndHaplotypes=NameEndHaplotypes,NameIniPopulations=NameIniPopulations,NameEndPopulations=NameEndPopulations)
 
+### vertex.size
 
-		dis<-pop.dist(distances=dis,Haplos=HaplosPop[[1]], logfile=FALSE,saveFile=FALSE,NameIniHaplotypes=NameIniHaplotypes, NameEndHaplotypes=NameEndHaplotypes,NameIniPopulations=NameIniPopulations,NameEndPopulations=NameEndPopulations)
+	HP<-as.matrix(HaplosPop[[1]])
+	pieSize<-rowSums(HP)/max(rowSums(HP)) # radius
+	if(vertex.size=="area")
+	pieSize<-sqrt(pieSize/pi)/max(sqrt(pieSize/pi)) # area
+	if(vertex.size=="equal")
+	pieSize<-rep(1,nrow(HP))
+	if(vertex.size!="radius" & vertex.size!="area" & vertex.size!="equal")
+	stop("wrong pie.size defined")
 
 	}
 #
@@ -139,9 +151,6 @@ plot.ggmap=FALSE, zoom.ggmap=6, maptype.ggmap="satellite", label.size.ggmap=3)
 	colnames(salida)<-c("Threshold","#Clusters")
 		for (j in range)
 		{
-		if(length(which(dis==0))!=nrow(dis))
-		stop("\n\nSome of the off-diagonal elements in your matrix are zero and percolation threshold can not be estimated. Your distance matrix seems to provide low resolution. You may:\n\n1.- Redefine populations by meging those showing distance values of 0 before percolation threshold estimation. For that use the 'merge=TRUE' option \n\n2.- Represent your original distance matrix using the 'No Isolated Nodes Allowed' method. For that use the 'network.method=\"NINA\"' option.\n\n3.- Represent your original distance matrix using the 'zero' method. For that use the 'network.method=\"zero\"' option.")
-
 #		print(paste("Threshold value:",j,"  Range to test: from ",min(range)," to ",max(range),sep=""))
 
 		dis2<-matrix(1,nrow=nrow(dis),ncol=ncol(dis))
@@ -193,6 +202,8 @@ plot.ggmap=FALSE, zoom.ggmap=6, maptype.ggmap="satellite", label.size.ggmap=3)
 	lim<-max(dis)*j
 	fuera<-which(dis>lim)
 	dis2[fuera]<-0
+
+
 	}
 ## 1- END PERCOLATION THRESHOLD
 #
@@ -249,6 +260,7 @@ if(network.method=="zero")
 			}
 		dis2<-M
 		}
+	j<-0
 	}
 ## 3- END ZERO THRESHOLD
 
@@ -266,6 +278,7 @@ if(network.method=="zero")
 		M[zero1,zero2]<-1
 		}
 	dis2<-M
+	j<-0
 	}
 
 
@@ -275,6 +288,11 @@ if(network.method=="zero")
 ### END THRESHOLD ESTIMATION ###
 #
 #
+## WARNING IF percolation threshold is not found:
+	if(is.na(j) & length(which(dis==0))!=nrow(dis))
+	warning("\n\nPercolation threshold can not be estimated and some of the off-diagonal elements in your matrix are zero. Your distance matrix seems to provide low resolution. You may:\n\n1.- Redefine populations by meging those showing distance values of 0 before percolation threshold estimation. For that use the 'merge=TRUE' option \n\n2.- Represent your original distance matrix using the 'No Isolated Nodes Allowed' method. For that use the 'network.method=\"NINA\"' option.\n\n3.- Represent your original distance matrix using the 'zero' method. For that use the 'network.method=\"zero\"' option.")
+#
+#
 #
 ## GETTING NETWORKS ###
 G<-graph.adjacency(dis2)
@@ -319,17 +337,6 @@ if(modules==FALSE)
 	colores<-bgcol
 
 
-### vertex.size
-
-HP<-as.matrix(HaplosPop[[1]])
-pieSize<-rowSums(HP)/max(rowSums(HP)) # radius
-if(vertex.size=="area")
-pieSize<-sqrt(pieSize/pi)/max(sqrt(pieSize/pi)) # area
-if(vertex.size=="equal")
-pieSize<-rep(1,nrow(HP))
-if(vertex.size!="radius" & vertex.size!="area" & vertex.size!="equal")
-stop("wrong pie.size defined")
-
 #### label names
 
 if(is.na(label[1])==F & is.na(label.sub.str[1])==F)
@@ -360,6 +367,14 @@ POS<-4
 
 ### BEGIN PLOT
 
+if (is.null(X)|is.null(Y))
+{
+warning("Geographic coordinates not provided. Populations plotted according to the Fruchterman-Reingold algorithm.")
+vertis1<-plot.network(A)
+X<-vertis1[,1]
+Y<-vertis1[,2]
+}
+
 rangeX<-max(X)-min(X)
 rangeY<-max(Y)-min(Y)