gmGeostats (version 0.11.3)

constructMask: Constructs a mask for a grid

Description

Constructs a mask for a grid

Usage

constructMask(grid, method = "maxdist", maxval = NULL, x = NULL)

Value

a logical vector with as many elements as points in the grid, with TRUE for those points within the mask, and FALSE for those outside the mask.

Arguments

grid

a grid, see details for more info

method

which construction method? currently one of 'maxdist', 'sillprop' or 'point2polygon'

maxval

for maxdist and sillprop methods, maximum reference value

x

extra information for the grid construction, see details

Details

Method 'maxdist' defines the mask as all points within a maximum distance (must be given in maxval) from the reference data (given in x: this is expected to be the original complete data, with coordinates and variables). For method 'sillprop' the mask is defined by those points which total kriging variance is below a fixed proportion (given in maxval, default=0.99) of the total variogram model sill (variogram model given in x, of class "variogramModelList"). In this method, the argument grid is expected to be the output of a cokriging analysis. Finally, method 'point2poly' created the mask by taking the points internal to a "SpatialPolygon" object (given in x).

See Also

Other masking functions: getMask(), print.mask(), setMask(), unmask()

Examples

## with data.frame
x = 1:23
y = 1:29
xy = expand.grid(x=x, y=y)
xyz.df = data.frame(xy, z = rnorm(29*23)*ifelse(abs(xy$x-xy$y)<3, 1, NA)+(xy$x+xy$y)/2)
mask.df = constructMask(grid = xy, method = "maxdist", maxval = 3, x=xyz.df)
image(mask.df)
oldpar = par(mfrow = c(1,1))
mask.df
xyz.df.masked = setMask(xyz.df, mask.df)
dim(xyz.df.masked)
summary(xyz.df.masked)
xyz.df.unmasked = unmask(xyz.df.masked)
dim(xyz.df.unmasked)
length(x)*length(y)
summary(xyz.df.unmasked)
## with SpatialGrid
library(sp)
library(magrittr)
xy.sp = sp::SpatialPoints(coords = xy)
meandiff = function(x) mean(diff(x))
xy.gt = GridTopology(c(min(x),min(y)), c(meandiff(x), meandiff(y)), c(length(x),length(y)))
aux = sp::SpatialPixelsDataFrame(grid = xy.gt, data=xyz.df, points = xy.sp)
xyz.sgdf = as(aux, "SpatialGridDataFrame")
image_cokriged(xyz.sgdf, ivar="z")
## reorder the data in the grid and plot again
par(mfrow=c(1,1))
ms = function(x) sortDataInGrid(x, grid=xy.gt)
mask.gt = constructMask(grid = xy.gt, method = "maxdist", maxval = 3, x=xyz.sgdf)
image(x,y,matrix(ms(xyz.sgdf@data$z), nrow=23, ncol=29))  
image(x,y,matrix(ms(mask.gt), nrow=23, ncol=29))  
image(mask.gt)
## work with the mask and plot again
par(mfrow=c(1,1))
xyz.sgdf.masked = setMask(x = xyz.sgdf, mask = mask.gt)
getMask(xyz.sgdf.masked)
image(x,y,matrix(ms(xyz.sgdf@data$z), nrow=23, ncol=29))  
points(xyz.sgdf.masked@coords)
par(oldpar)