Patch the extract.obiclean function

DESCRIPTION

@@ -35,4 +35,4 @@ Collate:
     'taxoDBtree.R'
     'taxonomic.resolution.R'
     'taxonomy_classic_table.R'
-RoxygenNote: 6.0.1
+RoxygenNote: 6.1.1

R/obiclean.R

@@ -56,9 +56,8 @@ setMethod("extracts.obiclean", "metabarcoding.data", function(obj) {
 			cols = colnames(obj@motus)
 			cleancols = grep(pat,cols)
 			clean.names=cols[cleancols]
-      p = grep(pat,cols)
-      d = t(as.factor.or.matrix(obj@motus[,p]))
-      n = sapply(strsplit(cols[p],':'),function(y) y[[2]])
+      d = t(as.factor.or.matrix(obj@motus[,cleancols]))
+      n = sapply(strsplit(cols[cleancols],':'),function(y) y[[2]])
       rownames(d)=n
       d = d[rownames(obj@reads),]
       obj[["obiclean_status"]]=d
@@ -68,28 +67,31 @@ setMethod("extracts.obiclean", "metabarcoding.data", function(obj) {
 			pat = "^obiclean_count:.*$"
 			cols = colnames(newmotus)
 			cleancols = grep(pat,cols)
-			clean.names=cols[cleancols]
-			p = grep(pat,cols)
-			d = t(as.factor.or.matrix(newmotus[,p]))
-			n = sapply(strsplit(cols[p],':'),function(y) y[[2]])
-			rownames(d)=n
-			d = d[rownames(obj@reads),]
-			obj[["obiclean_count"]]=d
-			
-			newmotus = newmotus[-cleancols]
+			if (length(cleancols) > 0) {
+  			clean.names=cols[cleancols]
+  			d = t(as.factor.or.matrix(newmotus[,cleancols]))
+  			n = sapply(strsplit(cols[cleancols],':'),function(y) y[[2]])
+  			rownames(d)=n
+  			d = d[rownames(obj@reads),]
+  			obj[["obiclean_count"]]=d
+  			
+  			newmotus = newmotus[-cleancols]
+			}
 			
 			pat = "^obiclean_cluster:.*$"
 			cols = colnames(newmotus)
 			cleancols = grep(pat,cols)
-			clean.names=cols[cleancols]
-			p = grep(pat,cols)
-			d = t(as.factor.or.matrix(newmotus[,p]))
-			n = sapply(strsplit(cols[p],':'),function(y) y[[2]])
-			rownames(d)=n
-			d = d[rownames(obj@reads),]
-			obj[["obiclean_cluster"]]=d
 			
-			newmotus = newmotus[-cleancols]
+			if (length(cleancols) > 0) {
+  			clean.names=cols[cleancols]
+  			d = t(as.factor.or.matrix(newmotus[,cleancols]))
+  			n = sapply(strsplit(cols[cleancols],':'),function(y) y[[2]])
+  			rownames(d)=n
+  			d = d[rownames(obj@reads),]
+  			obj[["obiclean_cluster"]]=d
+  			
+  			newmotus = newmotus[-cleancols]
+			}
 			
 			newdata = copy.metabarcoding.data(obj,motus=newmotus)
       

man/extrapol.freq.Rd

@@ -4,8 +4,8 @@
 \alias{extrapol.freq}
 \title{Read frequencies krigging}
 \usage{
-extrapol.freq(x, min.coord, max.coord, grid.grain = 100, coords, otus.table,
-  cutoff = 0.001, return.metabarcoding.data = FALSE)
+extrapol.freq(x, min.coord, max.coord, grid.grain = 100, coords,
+  otus.table, cutoff = 0.001, return.metabarcoding.data = FALSE)
 }
 \arguments{
 \item{x}{a vector or matrix from a row-normalized read table 

man/initialize-methods.Rd

@@ -7,7 +7,8 @@
 \title{metabarcoding.data constructor}
 \usage{
 \S4method{initialize}{metabarcoding.data}(.Object, reads, samples, motus,
-  taxonomy = NULL, taxid = NULL, sample.margin = NA, layers = list())
+  taxonomy = NULL, taxid = NULL, sample.margin = NA,
+  layers = list())
 }
 \description{
 metabarcoding.data constructor

man/m.univariate.test.Rd

@@ -4,7 +4,8 @@
 \alias{m.univariate.test}
 \title{Test the significance of the M statistics by Monte-Carlo}
 \usage{
-m.univariate.test(w, groups, resampling = 100, alternative = "two.sided")
+m.univariate.test(w, groups, resampling = 100,
+  alternative = "two.sided")
 }
 \arguments{
 \item{w}{the weigth matrix indicating the presence probability of each motu

man/map.extrapol.freq.Rd

@@ -4,8 +4,8 @@
 \alias{map.extrapol.freq}
 \title{Maps of krigged log10-transformed frequencies}
 \usage{
-map.extrapol.freq(x, path = NULL, col.name = NULL, index, cutoff = 0.001,
-  add.points = NULL, adj = 4)
+map.extrapol.freq(x, path = NULL, col.name = NULL, index,
+  cutoff = 0.001, add.points = NULL, adj = 4)
 }
 \arguments{
 \item{x}{an extrapol.freq output}

man/plot.PCRplate.Rd

@@ -4,8 +4,8 @@
 \alias{plot.PCRplate}
 \title{Plot PCR plates}
 \usage{
-\method{plot}{PCRplate}(x, samples = NULL, col = "cyan2", different = T,
-  ...)
+\method{plot}{PCRplate}(x, samples = NULL, col = "cyan2",
+  different = T, ...)
 }
 \arguments{
 \item{x}{a \code{\link{metabarcoding.data}} object}

man/threshold-mask-methods.Rd

@@ -6,8 +6,8 @@
 \alias{threshold.mask-methods,metabarcoding.data}
 \title{Computes a cumulatif thresold mask for filtering read aboundancies.}
 \usage{
-\S4method{threshold.mask}{metabarcoding.data}(data, MARGIN, threshold = 0.97,
-  operator = "<")
+\S4method{threshold.mask}{metabarcoding.data}(data, MARGIN,
+  threshold = 0.97, operator = "<")
 }
 \arguments{
 \item{data}{The \code{\linkS4class{metabarcoding.data}} instance