Plot a section view of a kriging or modelPredict model including design points, or a function.

Description

Plot one section view per dimension of a kriging, modelPredict model or function. It is useful for a better understanding of a model behaviour (including uncertainty).

Usage

sectionview(model, ...)

Arguments

Author(s)

Yann Richet, IRSN

See Also

See the documentation of sectionview.km, sectionview.list, or sectionview.fun for the arguments.

The sectionview3d method provides a 3D version.

Examples

## A 2D example - Branin-Hoo function
## 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)

sectionview(m1, center = c(.333, .333))

sectionview(branin, dim = 2, center = c(.333, .333), 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)

R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(DiceView)
Loading required package: DiceKriging
Loading required package: DiceEval
Loading required package: rgl
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/DiceView/sectionview.Rd_%03d_medium.png", width=480, height=480)
> ### Name: sectionview
> ### Title: Plot a section view of a kriging or modelPredict model including
> ###   design points, or a function.
> ### Aliases: sectionview sectionview,km-method sectionview,list-method
> ###   sectionview,function-method
> 
> ### ** Examples
> 
> ## A 2D example - Branin-Hoo function
> ## 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) :  -81.66013 

N = 2, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate     0  f=        81.66  |proj g|=       1.0441
At iterate     1  f =       81.499  |proj g|=       0.98392
At iterate     2  f =       81.154  |proj g|=        1.2805
At iterate     3  f =        81.06  |proj g|=       0.28058
At iterate     4  f =       81.058  |proj g|=      0.049742
At iterate     5  f =       81.058  |proj g|=     0.0016841
At iterate     6  f =       81.058  |proj g|=    9.6446e-06

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 9.64458e-06
final function value 81.0576

F = 81.0576
final  value 81.057643 
converged
> 
> sectionview(m1, center = c(.333, .333))
> 
> sectionview(branin, dim = 2, center = c(.333, .333), add = TRUE)
[1] 0 1
[1] 0 1
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>