risk_layer(): improve doc

DESCRIPTION

 Package: mapMCDA
 Title: Produce an epidemiological risk map by weighting multiple risk
         factors
-Version: 0.4.11
-Date: 2019-06-05
+Version: 0.4.12
+Date: 2019-06-12
 Authors@R: c( person("Andrea", "Apolloni", email =
         "andrea.apolloni@cirad.fr", role = c("ctb"), comment = "Animal
         mobility algorithm"), person("Elena", "Arsevska", email =

R/risk_layer.R

 #' Compute risk layer
 #' 
-#' Rescale a raster with a linear relationship.
+#' Compute a raster map from the input layer and rescale with a linear
+#' relationship.
 #' 
-#' If you need an inverse relationship, just reverse the target scale.
+#' For Spatial* objects (geometries such as point, lines or polygons),
+#' compute the \code{distance_map()}, which gives a RasterLayer. For
+#' \code{igraph} objects (from network data), compute a RasterLayer 
+#' with the relative importance of the nearest node. For a RasterLayer
+#' mask, extend or crop to the \code{boundaries} as needed.
+#' 
+#' Finally, scale the RasterLayer outcome of any of the three input
+#' types. If you need an inverse relationship, just reverse the target
+#' scale.
 #'
-#' @param x a RasterLayer object
+#' @param x a Spatial*, RasterLayer or igraph object
 #' @param boundaries a Spatial* object, used to determine the boundaries of the
 #'   computed risk layer.
 #' @param scale_target numeric vector of length 2. New scale.

man/risk_layer.Rd

@@ -7,7 +7,7 @@
 risk_layer(x, boundaries, scale_target = c(0, 100))
 }
 \arguments{
-\item{x}{a RasterLayer object}
+\item{x}{a Spatial*, RasterLayer or igraph object}
 
 \item{boundaries}{a Spatial* object, used to determine the boundaries of the
 computed risk layer.}
@@ -18,10 +18,19 @@ computed risk layer.}
 A RasterLayer object in the new scale.
 }
 \description{
-Rescale a raster with a linear relationship.
+Compute a raster map from the input layer and rescale with a linear
+relationship.
 }
 \details{
-If you need an inverse relationship, just reverse the target scale.
+For Spatial* objects (geometries such as point, lines or polygons),
+compute the \code{distance_map()}, which gives a RasterLayer. For
+\code{igraph} objects (from network data), compute a RasterLayer 
+with the relative importance of the nearest node. For a RasterLayer
+mask, extend or crop to the \code{boundaries} as needed.
+
+Finally, scale the RasterLayer outcome of any of the three input
+types. If you need an inverse relationship, just reverse the target
+scale.
 }
 \examples{
   ad <- mapMCDA_datasets()$animal.density