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

Description

Plot a 3-D view of a kriging model: mean response surface, fitted points and confidence surfaces. Provide a better understanding of the kriging model behaviour.

Usage

  sectionview3d.km(model, type = "UK", center = NULL,
    axis = NULL, npoints = 20, col_points = "red",
    col_surf = "blue", col_needles = NA,
    conf_lev = c(0.95), conf_blend = NULL, bg_blend = 5,
    Xname = NULL, yname = NULL, Xscale = 1, yscale = 1,
    xlim = NULL, ylim = NULL, title = NULL, add = FALSE,
    ...)

Arguments

Details

Experimental points are plotted with fading colors. Points that fall in the specified section (if any) have the color specified col_points while points far away from the center have shaded versions of the same color. The amount of fading is determined using the Euclidean distance between the plotted point and center. The variables chosen with their number are to be found in the X slot of the model. Thus they are 'spatial dimensions' but not 'trend variables'.

Note

The confidence bands are computed using normal quantiles and the standard error given by predict.km.

Author(s)

Yann Richet, IRSN

See Also

See sectionview.km and the km function in the DiceKriging package.

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)

## change colors
sectionview3d(m1, col_points = "firebrick", col_surf = "SpringGreen2")

## change colors,  use finer grid and add needles
sectionview3d(m1, npoints = c(50, 30), col_points = "orange",
  col_surf = "SpringGreen2", col_needles = "firebrick")

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.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
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/sectionview3d.km.Rd_%03d_medium.png", width=480, height=480)
> ### Name: sectionview3d.km
> ### Title: Plot a 3-D (using RGL) view of a kriging model, including design
> ###   points
> ### Aliases: sectionview3d.km
> ### 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) :  -82.95986 

N = 2, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate     0  f=        82.96  |proj g|=       1.0103
At iterate     1  f =       82.405  |proj g|=       0.89916
At iterate     2  f =       81.573  |proj g|=        1.3764
At iterate     3  f =       81.074  |proj g|=       0.74282
At iterate     4  f =       81.059  |proj g|=       0.23601
At iterate     5  f =       81.058  |proj g|=      0.017306
At iterate     6  f =       81.058  |proj g|=    0.00044838
At iterate     7  f =       81.058  |proj g|=    8.8395e-07

iterations 7
function evaluations 9
segments explored during Cauchy searches 8
BFGS updates skipped 0
active bounds at final generalized Cauchy point 1
norm of the final projected gradient 8.83952e-07
final function value 81.0576

F = 81.0576
final  value 81.057643 
converged
> 
> ## the same as sectionview3d.km
> sectionview3d(m1)
> 
> ## change colors
> sectionview3d(m1, col_points = "firebrick", col_surf = "SpringGreen2")
> 
> ## change colors,  use finer grid and add needles
> sectionview3d(m1, npoints = c(50, 30), col_points = "orange",
+   col_surf = "SpringGreen2", col_needles = "firebrick")
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>