Plot a contour view of a kriging model, including design points
Plot a contour view of a kriging model: mean response surface, fitted points and confidence surfaces. Provide a better understanding of the kriging model behaviour.
Plot one section view per dimension of a kriging model thus providing a better understanding of the model behaviour including uncertainty.
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.
## S3 method for class 'km' contourview( model, type = "UK", 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 'km' sectionview( model, type = "UK", center = NULL, axis = NULL, npoints = 100, col_points = "red", col_surf = "blue", conf_lev = c(0.5, 0.8, 0.9, 0.95, 0.99), conf_blend = NULL, 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 'km' sectionview3d( 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, ... ) ## S4 method for signature 'km' sectionview( model, type = "UK", center = NULL, npoints = 100, col_points = "red", col_surf = "blue", conf_lev = c(0.5, 0.8, 0.9, 0.95, 0.99), conf_blend = NULL, bg_blend = 5, mfrow = NULL, Xname = NULL, yname = NULL, Xscale = 1, yscale = 1, xlim = NULL, ylim = NULL, title = NULL, ... ) ## S4 method for signature 'km' sectionview3d( 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, ... ) ## S4 method for signature 'km' contourview( model, type = "UK", 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, ... )
model |
an object of class |
type |
the kriging type to use for model prediction. |
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 |
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 |
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 |
ylim |
an optional list to force y range for all plots. The default value |
title |
an optional overload of main title. |
add |
to print graphics on an existing window. |
... |
further arguments passed to the first call of |
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. |
col_needles |
color of "needles" for the points. The default |
km |
kriging model |
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'.
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 X slot of the model. Thus they are 'spatial dimensions' but not 'trend variables'.
The confidence bands are computed using normal quantiles and the standard error given by predict.km.
The confidence bands are computed using normal quantiles and the standard error given by predict.km.
Yann Richet, IRSN
Yann Richet, IRSN
Yann Richet, IRSN
See sectionview3d.km and the km function in the DiceKriging package.
The function sectionview3d.km produces a 3D version. For more information on the km class, see the km function in the DiceKriging package.
See sectionview.km and the km function in the DiceKriging package.
## 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
if (!exists("m1"))
m1 <- km(design = design.fact, response = y)
## the same as contourview.km
contourview(m1)
## change colors
contourview(m1, col_points = "firebrick", col_surf = "SpringGreen2")
## change colors, use finer grid and add needles
contourview(m1, npoints = c(50, 30), col_points = "orange",
col_surf = "SpringGreen2")
## Display reference function
contourview(branin,dim=2,add=TRUE,col='red')
## 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
if (!exists("m1"))
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')
## 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
if (!exists("m1"))
m1 <- km(design = design.fact, response = y)
## the same as sectionview3d.km
sectionview3d(m1)
## Not run:
## 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")
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.