Last data update: 2014.03.03

R: Plot a section view of a model, including design points
sectionview.listR Documentation

Plot a section view of a model, including design points

Description

Plot one section view per dimension of a surrogate model. It is useful for a better understanding of a model behaviour.

Usage

  sectionview.list(model, center = NULL, axis = NULL,
    npoints = 100, col_points = "red", col_surf = "blue",
    bg_blend = 5, mfrow = NULL, Xname = NULL, yname = NULL,
    Xscale = 1, yscale = 1, xlim = NULL, ylim = NULL,
    title = NULL, add = FALSE, ...)

Arguments

model

a list that can be used as model with the modelPredict function of the DiceEval package.

center

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.

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. Default (NULL value) 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.

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.

...

optional 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

See sectionview3d.list for a 3d version, and the modelPredict function in the DiceEval package.

Examples

## A 2D example: Branin-Hoo function. See the 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)

## linear model
m1 <- modelFit(design.fact, y$x1, type = "Linear", formula = "Y~.")

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

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 '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.list.Rd_%03d_medium.png", width=480, height=480)
> ### Name: sectionview.list
> ### Title: Plot a section view of a model, including design points
> ### Aliases: sectionview.list
> ### Keywords: models
> 
> ### ** Examples
> 
> ## A 2D example: Branin-Hoo function. See the 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)
> 
> ## linear model
> m1 <- modelFit(design.fact, y$x1, type = "Linear", formula = "Y~.")
> 
> sectionview.list(m1, center = c(.333,.333))
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>