optional coordinates (as a list or data
frame) of the center of the section view if the model's
dimension is > 1.
axis
optional matrix of 1-axis combinations to
plot, one by row. The value NULL leads to all
possible combinations i.e. 1:D.
npoints
an optional number of points to discretize
plot of response surface and uncertainties.
col_points
color of points.
col_surf
color for the section.
conf_lev
an optional list of confidence interval
values to display.
conf_blend
an optional factor of alpha (color
channel) blending used to plot confidence intervals.
bg_blend
an optional factor of alpha (color
channel) blending used to plot design points outside from
this section.
mfrow
an optional list to force par(mfrow =
...) call. The default value NULL is
automatically set for compact view.
xlim
an optional list to force x range for all
plots. The default value NULL is automatically set
to include all design points.
ylim
an optional list to force y range for all
plots. The default value NULL is automatically set
to include all design points (and their 1-99
percentiles).
Xname
an optional list of string to overload names
for X.
yname
an optional string to overload name for y.
Xscale
an optional factor to scale X.
yscale
an optional factor to scale y.
title
an optional overload of main title.
add
to print graphics on an existing window.
...
further arguments passed to the first call
of plot.
Details
A multiple rows/columns plot is produced. 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.
Author(s)
Yann Richet, IRSN
See Also
The function sectionview3d.km produces a 3D
version. For more information on the km class, see
the km function in the
DiceKriging package.
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))
## Display reference function
sectionview(branin,dim=2,center=c(.333, .333),add=TRUE,col='red')
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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Type 'demo()' for some demos, 'help()' for on-line help, or
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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.km.Rd_%03d_medium.png", width=480, height=480)
> ### Name: sectionview.km
> ### Title: Plot section views of a kriging model, including design points
> ### Aliases: sectionview.km
> ### Keywords: models
>
> ### ** 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) : -81.3824
N = 2, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= 81.382 |proj g|= 1.0001
At iterate 1 f = 81.293 |proj g|= 0.9569
At iterate 2 f = 81.085 |proj g|= 0.974
At iterate 3 f = 81.059 |proj g|= 0.16292
At iterate 4 f = 81.058 |proj g|= 0.016096
At iterate 5 f = 81.058 |proj g|= 0.00030849
At iterate 6 f = 81.058 |proj g|= 5.687e-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 5.68701e-07
final function value 81.0576
F = 81.0576
final value 81.057643
converged
>
> sectionview(m1, center = c(.333, .333))
>
> ## Display reference function
> sectionview(branin,dim=2,center=c(.333, .333),add=TRUE,col='red')
[1] 0 1
[1] 0 1
>
>
>
>
>
> dev.off()
null device
1
>