contourview: Plot a contour view of a prediction model or function,...

In IRSN/DiceView: Methods for Visualization of Computer Experiments Design and Surrogate

Plot a contour view of a prediction model or function, including design points if available.

Description

Plot a contour view of a prediction model or function, including design points if available.

Usage

## S3 method for class ''function''
contourview(
  fun,
  vectorized = FALSE,
  center = NULL,
  lty_center = 2,
  col_center = "black",
  axis = NULL,
  npoints = 21,
  levels = 10,
  lty_levels = 3,
  col_levels = if (!is.null(col) & length(col) == 1) col.levels(col, levels - 1) else if
    (!is.null(col) & length(col) == 2) cols.levels(col[1], col[2], levels - 1) else
    col.levels("blue", levels - 1),
  col = NULL,
  col_fading_interval = 0.5,
  mfrow = NULL,
  Xlab = NULL,
  ylab = NULL,
  Xlim = if (!add) matrix(c(0, 1), 2, 2) else NULL,
  ylim = NULL,
  title = NULL,
  title_sep = " | ",
  add = FALSE,
  ...
)

## S3 method for class 'matrix'
contourview(
  X,
  y,
  center = NULL,
  lty_center = 2,
  col_center = "black",
  axis = NULL,
  col_points = if (!is.null(col)) col else "red",
  col = NULL,
  bg_fading = 1,
  mfrow = NULL,
  Xlab = NULL,
  ylab = NULL,
  Xlim = if (!add) matrix(c(0, 1), 2, 2) else NULL,
  ylim = NULL,
  title = NULL,
  title_sep = " | ",
  add = FALSE,
  ...
)

## S3 method for class 'character'
contourview(eval_str, axis = NULL, mfrow = NULL, ...)

## S3 method for class 'km'
contourview(
  km_model,
  type = "UK",
  center = NULL,
  axis = NULL,
  npoints = 21,
  levels = pretty(km_model@y, 10),
  col_points = if (!is.null(col) & length(col) == 1) col else "red",
  col_levels = if (!is.null(col) & length(col) == 1) col.levels(col, levels) else if
    (!is.null(col) & length(col) == 2) cols.levels(col[1], col[2], levels - 1) else
    col.levels("blue", levels),
  col = NULL,
  conf_level = 0.5,
  conf_fading = 0.5,
  bg_fading = 1,
  mfrow = NULL,
  Xlab = NULL,
  ylab = NULL,
  Xlim = NULL,
  ylim = NULL,
  title = NULL,
  title_sep = " | ",
  add = FALSE,
  ...
)

## S3 method for class 'Kriging'
contourview(
  Kriging_model,
  center = NULL,
  axis = NULL,
  npoints = 21,
  levels = pretty(Kriging_model$y(), 10),
  col_points = if (!is.null(col) & length(col) == 1) col else "red",
  col_levels = if (!is.null(col) & length(col) == 1) col.levels(col, levels) else if
    (!is.null(col) & length(col) == 2) cols.levels(col[1], col[2], levels - 1) else
    col.levels("blue", levels),
  col = NULL,
  conf_level = 0.5,
  conf_fading = 0.5,
  bg_fading = 1,
  mfrow = NULL,
  Xlab = NULL,
  ylab = NULL,
  Xlim = NULL,
  ylim = NULL,
  title = NULL,
  title_sep = " | ",
  add = FALSE,
  ...
)

## S3 method for class 'NuggetKriging'
contourview(
  NuggetKriging_model,
  center = NULL,
  axis = NULL,
  npoints = 21,
  levels = pretty(NuggetKriging_model$y(), 10),
  col_points = if (!is.null(col) & length(col) == 1) col else "red",
  col_levels = if (!is.null(col) & length(col) == 1) col.levels(col, levels) else if
    (!is.null(col) & length(col) == 2) cols.levels(col[1], col[2], levels - 1) else
    col.levels("blue", levels),
  col = NULL,
  conf_level = 0.5,
  conf_fading = 0.5,
  bg_fading = 1,
  mfrow = NULL,
  Xlab = NULL,
  ylab = NULL,
  Xlim = NULL,
  ylim = NULL,
  title = NULL,
  title_sep = " | ",
  add = FALSE,
  ...
)

## S3 method for class 'NoiseKriging'
contourview(
  NoiseKriging_model,
  center = NULL,
  axis = NULL,
  npoints = 21,
  levels = pretty(NoiseKriging_model$y(), 10),
  col_points = if (!is.null(col) & length(col) == 1) col else "red",
  col_levels = if (!is.null(col) & length(col) == 1) col.levels(col, levels) else if
    (!is.null(col) & length(col) == 2) cols.levels(col[1], col[2], levels - 1) else
    col.levels("blue", levels),
  col = NULL,
  conf_level = 0.5,
  conf_fading = 0.5,
  bg_fading = 1,
  mfrow = NULL,
  Xlab = NULL,
  ylab = NULL,
  Xlim = NULL,
  ylim = NULL,
  title = NULL,
  title_sep = " | ",
  add = FALSE,
  ...
)

## S3 method for class 'glm'
contourview(
  glm_model,
  center = NULL,
  axis = NULL,
  npoints = 21,
  levels = pretty(glm_model$fitted.values, 10),
  col_points = if (!is.null(col) & length(col) == 1) col else "red",
  col_levels = if (!is.null(col) & length(col) == 1) col.levels(col, levels) else if
    (!is.null(col) & length(col) == 2) cols.levels(col[1], col[2], levels - 1) else
    col.levels("blue", levels),
  col = NULL,
  conf_level = 0.5,
  conf_fading = 0.5,
  bg_fading = 1,
  mfrow = NULL,
  Xlab = NULL,
  ylab = NULL,
  Xlim = NULL,
  ylim = NULL,
  title = NULL,
  title_sep = " | ",
  add = FALSE,
  ...
)

## S3 method for class 'list'
contourview(
  modelFit_model,
  center = NULL,
  axis = NULL,
  npoints = 21,
  levels = pretty(modelFit_model$data$Y, 10),
  col_points = if (!is.null(col) & length(col) == 1) col else "red",
  col_levels = if (!is.null(col) & length(col) == 1) col.levels(col, levels) else if
    (!is.null(col) & length(col) == 2) cols.levels(col[1], col[2], levels - 1) else
    col.levels("blue", levels),
  col = NULL,
  bg_fading = 1,
  mfrow = NULL,
  Xlab = NULL,
  ylab = NULL,
  Xlim = NULL,
  ylim = NULL,
  title = NULL,
  title_sep = " | ",
  add = FALSE,
  ...
)

contourview(...)

Arguments

fun	a function or 'predict()'-like function that returns a simple numeric or mean and standard error: list(mean=...,se=...).
vectorized	is fun vectorized?
center	optional coordinates (as a list or data frame) of the center of the section view if the model's dimension is > 2.
lty_center	line type for the section center of the plot (if any).
col_center	color for the section center of the plot (if any).
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.
levels	(number of) contour levels to display.
lty_levels	contour line type.
col_levels	color for the surface.
col	color of the object (use col_* for specific objects).
col_fading_interval	an optional factor of alpha (color channel) fading used to plot function output intervals (if any).
mfrow	an optional list to force par(mfrow = ...) call. The default value NULL is automatically set for compact view.
Xlab	an optional list of string to overload names for X.
ylab	an optional string to overload name for 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.
title	an optional overload of main title.
title_sep	customize subtitle with fixed input.
add	to print graphics on an existing window.
...	arguments of the contourview.km, contourview.glm, contourview.Kriging or contourview.function function
X	the matrix of input design.
y	the array of output values (two columns means an interval).
col_points	color of points.
bg_fading	an optional factor of alpha (color channel) fading used to plot design points outside from this section.
eval_str	the expression to evaluate in each subplot.
km_model	an object of class "km".
type	the kriging type to use for model prediction.
conf_level	confidence hulls to display.
conf_fading	an optional factor of alpha (color channel) fading used to plot confidence hull.
Kriging_model	an object of class "Kriging".
NuggetKriging_model	an object of class "Kriging".
NoiseKriging_model	an object of class "Kriging".
glm_model	an object of class "glm".
modelFit_model	an object returned by DiceEval::modelFit.

Details

If available, 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

sectionview.function for a section plot, and sectionview3d.function for a 2D section plot.

sectionview.matrix for a section plot, and sectionview3d.matrix for a 2D section plot.

contourview.matrix for a section plot.

sectionview.km for a section plot, and sectionview3d.km for a 2D section plot.

sectionview.Kriging for a section plot, and sectionview3d.Kriging for a 2D section plot.

sectionview.NuggetKriging for a section plot, and sectionview3d.NuggetKriging for a 2D section plot.

sectionview.NoiseKriging for a section plot, and sectionview3d.NoiseKriging for a 2D section plot.

sectionview.glm for a section plot, and sectionview3d.glm for a 2D section plot.

sectionview.glm for a section plot, and sectionview3d.glm for a 2D section plot.

Examples

x1 <- rnorm(15)
x2 <- rnorm(15)

y <- x1 + x2 + rnorm(15)
model <- lm(y ~ x1 + x2)

contourview(function(x) sum(x),
                     Xlim=cbind(range(x1),range(x2)), col='black')
points(x1,x2)

contourview(function(x) {
                      x = as.data.frame(x)
                      colnames(x) <- all.vars(model$call)[-1]
                      predict.lm(model, newdata=x, se.fit=FALSE)
                    }, vectorized=TRUE, add=TRUE)

X = matrix(runif(15*2),ncol=2)
y = apply(X,1,branin)

contourview(X, y)

x1 <- rnorm(15)
x2 <- rnorm(15)

y <- x1 + x2^2 + rnorm(15)
model <- glm(y ~ x1 + I(x2^2))

contourview(model)

contourview("abline(h=0.25,col='red')")
if (requireNamespace("DiceKriging")) { library(DiceKriging)

X = matrix(runif(15*2),ncol=2)
y = apply(X,1,branin)

model <- km(design = X, response = y, covtype="matern3_2")

contourview(model)

}

if (requireNamespace("rlibkriging")) { library(rlibkriging)

X = matrix(runif(15*2),ncol=2)
y = apply(X,1,branin)

model <- Kriging(X = X, y = y, kernel="matern3_2")

contourview(model)

}

if (requireNamespace("rlibkriging")) { library(rlibkriging)

X = matrix(runif(15*2),ncol=2)
y = apply(X,1,branin) + 5*rnorm(15)

model <- NuggetKriging(X = X, y = y, kernel="matern3_2")

contourview(model)

}

if (requireNamespace("rlibkriging")) { library(rlibkriging)

X = matrix(runif(15*2),ncol=2)
y = apply(X,1,branin) + 5*rnorm(15)

model <- NoiseKriging(X = X, y = y, kernel="matern3_2", noise=rep(5^2,15))

contourview(model)

}

x1 <- rnorm(15)
x2 <- rnorm(15)

y <- x1 + x2^2 + rnorm(15)
model <- glm(y ~ x1 + I(x2^2))

contourview(model)

if (requireNamespace("DiceEval")) { library(DiceEval)

X = matrix(runif(15*2),ncol=2)
y = apply(X,1,branin)

model <- modelFit(X, y, type = "StepLinear")

contourview(model)

}

## A 2D example - Branin-Hoo function
contourview(branin, levels=30, col='black')

## Not run: 
## 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); names(y) <- "y"

if (requireNamespace("DiceKriging")) { library(DiceKriging)
## model: km
model <- DiceKriging::km(design = design.fact, response = y)
contourview(model, levels=30)
contourview(branin, levels=30, col='red', add=TRUE)
}

if (requireNamespace("rlibkriging")) { library(rlibkriging)
## model: Kriging
model <- Kriging(X = as.matrix(design.fact), y = as.matrix(y), kernel="matern3_2")
contourview(model, levels=30)
contourview(branin, levels=30, col='red', add=TRUE)
}

## model: glm
model <- glm(y ~ 1+ x1 + x2 + I(x1^2) + I(x2^2) + x1*x2, data=cbind(y,design.fact))
contourview(model, levels=30)
contourview(branin, levels=30, col='red', add=TRUE)

if (requireNamespace("DiceEval")) { library(DiceEval)
## model: StepLinear
model <- modelFit(design.fact, y, type = "StepLinear")
contourview(model, levels=30)
contourview(branin, levels=30, col='red', add=TRUE)
}

## End(Not run)

IRSN/DiceView documentation built on Dec. 28, 2024, 11:07 a.m.