Corrected mistake 2 in cloudTreeMetrics example

R/metrics.R

@@ -229,9 +229,9 @@ points2terrainStats <- function(p, centre=NULL, r=NULL)
 #'
 #' @param llasn list of LAS objects (from lidR package)
 #' @param XY a dataframe or matrix with XY coordinates of plot centers
-#' @param plot.radius numeric. plot radius in same unit as coordinates of LAS objects
+#' @param plot.radius numeric. plot radius in meters
 #' @param res numeric. resolution of canopy height model computed with \code{\link{points2DSM}} before tree segmentation
-#' @param func a function to be applied to the attributes of extracted trees (return from internal call to treeExtraction function to compute plot level metrics
+#' @param func a function to be applied to the attributes of extracted trees (return from internal call to \code{\link{treeExtraction}} function to compute plot level metrics
 #' @param ... other parameters to be passed to \code{\link{treeSegmentation}}
 #' @return a dataframe with tree metrics in columns corresponding to LAS objects of the list (lines)
 #' @seealso \code{\link{treeSegmentation}}, \code{\link{treeExtraction}}, \code{\link{stdTreeMetrics}}
@@ -255,7 +255,7 @@ points2terrainStats <- function(p, centre=NULL, r=NULL)
 #' # number of detected trees between 20 and 30 meters and their mean height
 #' user.func <- function(x, area.ha=NA)
 #' {
-#'   dummy <- x$h[which(x$h>10 & x$h<20)]
+#'   dummy <- x$h[which(x$h>20 & x$h<30)]
 #'   data.frame(Tree.between.20.30=length(dummy)/area.ha, Tree.meanH=mean(dummy))
 #' }
 #'  cloudTreeMetrics(llas, cbind(c(974350, 974390, 974350), c(6581680, 6581680, 6581640)), 10, res=0.5, func=user.func)

man/BoxcoxTr.Rd

@@ -7,7 +7,7 @@
 BoxcoxTr(x, lambda)
 }
 \arguments{
-\item{x}{vector. values to be transformed}
+\item{x}{vector or RasterLayer. values to be transformed}
 
 \item{lambda}{numeric. parameter of Box-Cox transformation}
 }

man/cloudTreeMetrics.Rd

@@ -12,11 +12,11 @@ cloudTreeMetrics(llasn, XY, plot.radius, res = 0.5,
 
 \item{XY}{a dataframe or matrix with XY coordinates of plot centers}
 
-\item{plot.radius}{numeric. plot radius in same unit as coordinates of LAS objects}
+\item{plot.radius}{numeric. plot radius in meters}
 
 \item{res}{numeric. resolution of canopy height model computed with \code{\link{points2DSM}} before tree segmentation}
 
-\item{func}{a function to be applied to the attributes of extracted trees (return from internal call to treeExtraction function to compute plot level metrics}
+\item{func}{a function to be applied to the attributes of extracted trees (return from internal call to \code{\link{treeExtraction}} function to compute plot level metrics}
 
 \item{...}{other parameters to be passed to \code{\link{treeSegmentation}}}
 }
@@ -49,7 +49,7 @@ cloudTreeMetrics(llas,
 # number of detected trees between 20 and 30 meters and their mean height
 user.func <- function(x, area.ha=NA)
 {
-  dummy <- x$h[which(x$h>10 & x$h<20)]
+  dummy <- x$h[which(x$h>20 & x$h<30)]
   data.frame(Tree.between.20.30=length(dummy)/area.ha, Tree.meanH=mean(dummy))
 }
  cloudTreeMetrics(llas, cbind(c(974350, 974390, 974350), c(6581680, 6581680, 6581640)), 10, res=0.5, func=user.func)

man/iBoxcoxTr.Rd

@@ -7,7 +7,7 @@
 iBoxcoxTr(x, lambda)
 }
 \arguments{
-\item{x}{vector. values to be transformed}
+\item{x}{vector or RasterLayer. values to be transformed}
 
 \item{lambda}{numeric. parameter of Box-Cox transformation}
 }

man/iBoxcoxTrBiasCor.Rd

@@ -7,7 +7,7 @@
 iBoxcoxTrBiasCor(x, lambda, varmod)
 }
 \arguments{
-\item{x}{vector. values to be ransformed}
+\item{x}{vector or RasterLayer. values to be ransformed}
 
 \item{lambda}{numeric. parameter of Box-Cox transformation}