gjamPoints2Grid: Incidence point pattern to grid counts
In dbystrova/GJAM_clust: Generalized Joint Attribute Modeling

Description Usage Arguments Details Value Author(s) References See Also Examples

From point pattern data in (x, y) generates counts on a lattice supplied by the user or specified by lattice size or density. For analysis in gjam as counts (known effort) or count composition (unknown effort) data.

1 2	gjamPoints2Grid(specs, xy, nxy = NULL, dxy = NULL, predGrid = NULL, effortOnly = TRUE)

`specs`	`character vector` of species names or codes.
`xy`	`matrix` with rows = `length(specs)` and columns for (x, y).
`nxy`	length-2 `numeric vector` with numbers of points evenly spaced on (x, y).
`dxy`	length-2 `numeric vector` with distances for points evenly spaced on (x, y).
`predGrid`	`matrix` with 2 columns for (x, y).
`effortOnly`	`logical` to return only points where counts are positive (e.g., effort is unknown).

For incidence data with species names specs and locations (x, y) constructs a lattice based a prediction grid predGrid, at a density of (dx, dy), or with numbers of lattice points (nx, ny). If effortOnly = T, returns only points with non-zero values.

A prediction grid predGrid would be passed when counts by locations of known effort are required or where multiple groups should be assign to the same lattice points.

The returned gridBySpec can be analyzed in gjam with known effort as count data "DA" or with unknown effort as count composition data "CC".

`gridBySpec`	`matrix` with rows for grid locations, columns for counts by species.
`predGrid`	`matrix` with columns for (x, y) and rows matching `gridBySpec`.

James S Clark, jimclark@duke.edu

Clark, J.S., D. Nemergut, B. Seyednasrollah, P. Turner, and S. Zhang. 2016. Generalized joint attribute modeling for biodiversity analysis: Median-zero, multivariate, multifarious data. Ecological Monographs 87, 34-56.

gjam A more detailed vignette is can be obtained with:

browseVignettes('gjam')

## Not run: 
## random data
n  <- 100
s  <- sample( letters[1:3], n, replace = TRUE)
xy <- cbind( rnorm(n,0,.2), rnorm(n,10,2) )

nx <- ny <- 5                                    # uniform 5 X 5 lattice
f  <- gjamPoints2Grid(s, xy, nxy = c(nx, ny))
plot(f$predGrid[,1], f$predGrid[,2], cex=.1, xlim=c(-1,1), ylim=c(0,20),
     xlab = 'x', ylab = 'y')
text(f$predGrid[,1], f$predGrid[,2], rowSums(f$gridBySpec))

dx <- .2                                          # uniform density
dy <- 1.5
g  <- gjamPoints2Grid(s, xy, dxy = c(dx, dy))
text(g$predGrid[,1], g$predGrid[,2], rowSums(g$gridBySpec), col='brown')

p  <- cbind( runif(30, -1, 1), runif(30, 0, 20) ) # irregular lattice
h  <- gjamPoints2Grid(s, xy, predGrid = p)
text(h$predGrid[,1], h$predGrid[,2], rowSums(h$gridBySpec), col='blue')

## End(Not run)