Commit 1f903689 by Facundo Muñoz ®️

### risk_layer(): improve doc

parent 185c400d
 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 = ... ...
 #' 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. ... ...
 ... ... @@ -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 ... ...
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