Plot a 3-D (using RGL) view of a kriging or modelPredict model, including design points

Description

Plot a 3-D view of a kriging or modelPredict model. It is useful for a better understanding of a model behaviour.

Usage

sectionview3d(model, ...)

Arguments

Author(s)

Yann Richet, IRSN

See Also

sectionview

Examples

## A 2D example - Branin-Hoo function. See DiceKriging package manual 
## a 16-points factorial design, and the corresponding response
d <- 2; n <- 16
design.fact <- expand.grid(seq(0, 1, length = 4), seq(0, 1, length = 4))
design.fact <- data.frame(design.fact); names(design.fact)<-c("x1", "x2")
y <- branin(design.fact) 

## kriging model 1 : matern5_2 covariance structure, no trend, no nugget effect

m1 <- km(design = design.fact, response = y)

## the same as sectionview3d.km
sectionview3d(m1)

sectionview3d(branin, dim = 2, add = TRUE)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(DiceView)
Loading required package: DiceKriging
Loading required package: DiceEval
Loading required package: rgl
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/DiceView/sectionview3d.Rd_%03d_medium.png", width=480, height=480)
> ### Name: sectionview3d
> ### Title: Plot a 3-D (using RGL) view of a kriging or modelPredict model,
> ###   including design points
> ### Aliases: sectionview3d sectionview3d,km-method
> ###   sectionview3d,list-method sectionview3d,function-method
> ### Keywords: models
> 
> ### ** Examples
> 
> ## A 2D example - Branin-Hoo function. See DiceKriging package manual 
> ## a 16-points factorial design, and the corresponding response
> d <- 2; n <- 16
> design.fact <- expand.grid(seq(0, 1, length = 4), seq(0, 1, length = 4))
> design.fact <- data.frame(design.fact); names(design.fact)<-c("x1", "x2")
> y <- branin(design.fact) 
> 
> ## kriging model 1 : matern5_2 covariance structure, no trend, no nugget effect
> 
> m1 <- km(design = design.fact, response = y)

optimisation start
------------------
* estimation method   : MLE 
* optimisation method : BFGS 
* analytical gradient : used
* trend model : ~1
* covariance model : 
  - type :  matern5_2 
  - nugget : NO
  - parameters lower bounds :  1e-10 1e-10 
  - parameters upper bounds :  2 2 
  - best initial criterion value(s) :  -81.66836 

N = 2, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate     0  f=       81.668  |proj g|=      0.91677
At iterate     1  f =       81.572  |proj g|=       0.87438
At iterate     2  f =       81.082  |proj g|=       0.92316
At iterate     3  f =       81.059  |proj g|=       0.19545
At iterate     4  f =       81.058  |proj g|=      0.017482
At iterate     5  f =       81.058  |proj g|=    0.00037744
At iterate     6  f =       81.058  |proj g|=    7.5163e-07

iterations 6
function evaluations 8
segments explored during Cauchy searches 8
BFGS updates skipped 0
active bounds at final generalized Cauchy point 1
norm of the final projected gradient 7.51629e-07
final function value 81.0576

F = 81.0576
final  value 81.057643 
converged
> 
> ## the same as sectionview3d.km
> sectionview3d(m1)
> 
> sectionview3d(branin, dim = 2, add = TRUE)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>