fields (version 1.2)

predict.surface: Evaluates a fitted function as a surface object

Description

Evaluates a a fitted model on a 2-D grid keeping any other variables constant. The resulting object is suitable for use with functions for viewing 3-d surfaces.

Usage

predict.surface(out, grid.list=NA, extrap=FALSE, chull.mask, model=NA,
nx=30, ny=30,...)

Arguments

out

An object from fitting a function to data. In FIELDS this is usually a Krig object.

grid.list

A list with as many components as variables describing the surface. All components should have a single value except the two that give the grid points for evaluation. If the matrix or data frame has column names, these must appear in the grid list. Se

chull.mask

Whether to restrict the fitted surface to be on a convex hull, NA's are assigned to values outside the convex hull. chull.mask should be a sequence of points defining a convex hull. Default is to form the convex hull from the observations if this argument

extrap

Extrapolation beyond the range of the data. If false function will be restricted to the convex hull of the observed data or the hull defined from the points from the argument chull.mask.

model

Model information used to evaluate the fitted surface. ( E.g. a lambda value for a Tps fit).

...

Any other arguments to pass to the predict function associated with the fit object.

nx

Number of grid points in X axis.

ny

Number of grid points in Y axis.

Value

• The usual list components for making contour and perspective plots along with the component containing the grid list.

Details

This function creates the right grid using the grid.list information or the defaults (make.surface.grid), calls the predict function for the object with these points and also adding any extra arguments passed in the ... section, and then reforms the results as a surface object ( as.surface).

See Also

Tps, Krig, predict, grid.list, make.surface.grid, as.surface, surface

Examples

fit<- Tps( BD[,1:4], BD$lnya)  # fit surface to data 

# evaluate on a grid on two 
# variables holding two fixed  
# default surface and contour plot  
# make grid list
grid.list<- list( KCl="x", MgCl2=mean(BD[,2]),KPO4="y",dNTP=mean(BD[,4]))

out.p<- predict.surface(fit,grid.list, extrap=TRUE) 

surface(out.p) 

set.panel(1,1)