smooth.construct.tecxcx.smooth.spec: Tensor product smoothing constructor for bivariate function...
In scam: Shape Constrained Additive Models

smooth.construct.tecxcx.smooth.spec

R Documentation

Tensor product smoothing constructor for bivariate function subject to double convexity constraint

Description

This is a special method function for creating tensor product bivariate smooths subject to double convexity constraint, convexity constraint wrt both the first and the second covariates. This is built by the mgcv constructor function for smooth terms, smooth.construct. It is constructed from a pair of single penalty marginal smooths which are represented using the B-spline basis functions. This tensor product is specified by model terms such as s(x1,x2,k=c(q1,q2),bs="tecxcx",m=c(2,2)), where q1 and q2 denote the basis dimensions for the marginal smooths.

Usage

## S3 method for class 'tecxcx.smooth.spec'
smooth.construct(object, data, knots)

Arguments

`object`	A smooth specification object, generated by an `s` term in a GAM formula.
`data`	A data frame or list containing the values of the elements of `object$term`, with names given by `object$term`.
`knots`	An optional list containing the knots corresponding to `object$term`. If it is `NULL` then the knot locations are generated automatically.

Value

An object of class "tecxcx.smooth". In addition to the usual elements of a smooth class documented under smooth.construct of the mgcv library, this object contains:

`p.ident`	A vector of 0's and 1's for model parameter identification: 1's indicate parameters which will be exponentiated, 0's - otherwise.
`Zc`	A matrix of identifiability constraints.

Author(s)

Natalya Pya <nat.pya@gmail.com>

References

Pya, N. and Wood, S.N. (2015) Shape constrained additive models. Statistics and Computing, 25(3), 543-559

Examples

 ## Not run: 
## tensor product `tecxcx' example 
  ## simulating data...
   set.seed(2)
   n <- 30
   x1 <- sort(2*runif(n)-1)
   x2 <- sort(2*runif(n)-1)
   f1 <- matrix(0,n,n)
   for (i in 1:n) for (j in 1:n) 
       { f1[i,j] <- 2*(x1[i]^2 + x2[j]^2)}  
   f <- as.vector(t(f1))
   y <- f+rnorm(length(f))*.05
   x11 <-  matrix(0,n,n)
   x11[,1:n] <- x1
   x11 <- as.vector(t(x11))
   x22 <- rep(x2,n)
   dat <- list(x1=x11,x2=x22,y=y)
## fit model ...
   b <- scam(y~s(x1,x2,k=c(10,10),bs="tecxcx"), data=dat)
   summary(b)
## plot results ...
   par(mfrow=c(2,2),mar=c(4,4,2,2))
   plot(b,se=TRUE)
   plot(b,pers=TRUE,theta = 30, phi = 40)
   plot(y,b$fitted.values,xlab="Simulated data",ylab="Fitted data")
   x11()
   vis.scam(b,theta=20,phi=20)
   ## plotting the truth...
   x11()
   x1 <- seq(min(x1),max(x1),length.out=30)
   x2 <- seq(min(x2),max(x2),length.out=30)
   f1 <- matrix(0,n,n)
   for (i in 1:n) for (j in 1:n) f1[i,j] <- 2*(x1[i]^2 + x2[j]^2)
   persp(x1,x2,f1,theta = 30, phi = 40)  
 
## End(Not run)

scam documentation built on June 22, 2024, 10:43 a.m.