# exclude.too.far: Exclude prediction grid points too far from data In mgcv: Mixed GAM Computation Vehicle with Automatic Smoothness Estimation

 exclude.too.far R Documentation

## Exclude prediction grid points too far from data

### Description

Takes two arrays defining the nodes of a grid over a 2D covariate space and two arrays defining the location of data in that space, and returns a logical vector with elements `TRUE` if the corresponding node is too far from data and `FALSE` otherwise. Basically a service routine for `vis.gam` and `plot.gam`.

### Usage

``````exclude.too.far(g1,g2,d1,d2,dist)
``````

### Arguments

 `g1` co-ordinates of grid relative to first axis. `g2` co-ordinates of grid relative to second axis. `d1` co-ordinates of data relative to first axis. `d2` co-ordinates of data relative to second axis. `dist` how far away counts as too far. Grid and data are first scaled so that the grid lies exactly in the unit square, and `dist` is a distance within this unit square.

### Details

Linear scalings of the axes are first determined so that the grid defined by the nodes in `g1` and `g2` lies exactly in the unit square (i.e. on [0,1] by [0,1]). These scalings are applied to `g1`, `g2`, `d1` and `d2`. The minimum Euclidean distance from each node to a datum is then determined and if it is greater than `dist` the corresponding entry in the returned array is set to `TRUE` (otherwise to `FALSE`). The distance calculations are performed in compiled code for speed without storage overheads.

### Value

A logical array with `TRUE` indicating a node in the grid defined by `g1`, `g2` that is ‘too far’ from any datum.

### Author(s)

Simon N. Wood simon.wood@r-project.org

### References

`vis.gam`

### Examples

``````library(mgcv)
x<-rnorm(100);y<-rnorm(100) # some "data"
n<-40 # generate a grid....
mx<-seq(min(x),max(x),length=n)
my<-seq(min(y),max(y),length=n)
gx<-rep(mx,n);gy<-rep(my,rep(n,n))
tf<-exclude.too.far(gx,gy,x,y,0.1)
plot(gx[!tf],gy[!tf],pch=".");points(x,y,col=2)
``````

mgcv documentation built on July 26, 2023, 5:38 p.m.