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list-methods

Plot a contour view of a model, including design points

Description

Plot a contour view of a model, thus providing a better understanding of its behaviour.

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

Plot a 3-D view of a model, thus providing a better understanding of its behaviour.

Usage

## S3 method for class 'list'
contourview(
  model,
  center = NULL,
  axis = NULL,
  npoints = 20,
  nlevels = 10,
  col_points = "red",
  col_surf = "blue",
  filled = FALSE,
  bg_blend = 1,
  mfrow = NULL,
  Xname = NULL,
  yname = NULL,
  Xscale = 1,
  yscale = 1,
  xlim = NULL,
  ylim = NULL,
  title = NULL,
  add = FALSE,
  ...
)

## S3 method for class 'list'
sectionview(
  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,
  ...
)

## S3 method for class 'list'
sectionview3d(
  model,
  center = NULL,
  axis = NULL,
  npoints = 20,
  col_points = "red",
  col_surf = "blue",
  col_needles = NA,
  bg_blend = 5,
  Xname = NULL,
  yname = NULL,
  Xscale = 1,
  yscale = 1,
  xlim = NULL,
  ylim = NULL,
  title = NULL,
  add = FALSE,
  ...
)

## S4 method for signature 'list'
sectionview(
  model,
  center = 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,
  ...
)

## S4 method for signature 'list'
sectionview3d(
  model,
  center = NULL,
  axis = NULL,
  npoints = 20,
  col_points = "red",
  col_surf = "blue",
  bg_blend = 5,
  Xname = NULL,
  yname = NULL,
  Xscale = 1,
  yscale = 1,
  xlim = NULL,
  ylim = NULL,
  title = NULL,
  ...
)

## S4 method for signature 'list'
contourview(
  model,
  center = NULL,
  axis = NULL,
  npoints = 20,
  col_points = "red",
  col_surf = "blue",
  bg_blend = 1,
  nlevels = 10,
  Xname = NULL,
  yname = NULL,
  Xscale = 1,
  yscale = 1,
  xlim = NULL,
  ylim = NULL,
  title = NULL,
  ...
)

Arguments

model

a list that can be used in 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 > 2.
axis

optional matrix of 2-axis combinations to plot, one by row. The value NULL leads to all possible combinations i.e. choose(D, 2).
npoints

an optional number of points to discretize plot of response surface and uncertainties.
nlevels

number of contour levels to display.
col_points

color of points.
col_surf

color for the surface.
filled

use filled.contour
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.
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.
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.
title

an optional overload of main title.
add

to print graphics on an existing window.
...

optional arguments passed to the first call of plot3d.
col_needles

color of "needles" for the points. The default NA corresponds to no needle plotted. When a valid color is given, needles are plotted using the same fading mechanism as for points.
list

DiceEval model

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 data$X element of the model. Thus they are original data variables but not trend variables that may have been created using the model's formula.

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.

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 data$X element of the model. Thus they are original data variables but not trend variables that may have been created using the model's formula

Author(s)

Yann Richet, IRSN

Yann Richet, IRSN

Yann Richet, IRSN

See Also

sectionview.list for a 2D plot, and the modelPredict function in the DiceEval package. The sectionview3d.km produces a similar plot for km objects.

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

sectionview.list for a 2D plot, and the modelPredict function in the DiceEval package. The sectionview3d.km produces a similar plot for km objects.

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)

## linear model
m1 <- modelFit(design.fact, y[[1]], type = "Linear", formula = "Y~.")

## the same as sectionview3d.list
contourview(m1)
## 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[[1]], type = "Linear", formula = "Y~.")

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

## linear model
m1 <- modelFit(design.fact, y[[1]], type = "Linear", formula = "Y~.")

## the same as sectionview3d.list
sectionview3d(m1)
DiceView

Methods for Visualization of Computer Experiments Design and Surrogate

v2.0-1
GPL-3
Authors
Yann Richet, Yves Deville, Clement Chevalier
Initial release
2020-11-27

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