contourmap.mc {excursions} | R Documentation |
Contour maps and contour map quality measures using Monte Carlo samples
Description
contourmap.mc
is used for calculating contour maps and quality measures for contour maps based on Monte Carlo samples of a model.
Usage
contourmap.mc(
samples,
n.levels,
ind,
levels,
type = c("standard", "equalarea", "P0-optimal", "P1-optimal", "P2-optimal"),
compute = list(F = TRUE, measures = NULL),
alpha,
verbose = FALSE
)
Arguments
samples |
Matrix with model Monte Carlo samples. Each column contains a sample of the model. |
n.levels |
Number of levels in contour map. |
ind |
Indices of the nodes that should be analyzed (optional). |
levels |
Levels to use in contour map. |
type |
Type of contour map. One of:
|
compute |
A list with quality indices to compute
|
alpha |
Maximal error probability in contour map function (default=0.1). |
verbose |
Set to TRUE for verbose mode (optional). |
Details
The contour map is computed for the empirical mean of the samples.
See contourmap
and contourmap.inla
for further details.
Value
contourmap
returns an object of class "excurobj" with the following elements
u |
Contour levels used in the contour map. |
n.levels |
The number of contours used. |
u.e |
The values associated with the level sets G_k. |
G |
A vector which shows which of the level sets G_k each node belongs to. |
map |
Representation of the contour map with map[i]=u.e[k] if i is in G_k. |
F |
The contour map function (if computed). |
M |
Contour avoiding sets (if |
P0/P1/P2 |
Calculated quality measures (if computed). |
P0bound/P1bound/P2bound |
Calculated upper bounds quality measures (if computed). |
meta |
A list containing various information about the calculation. |
Author(s)
David Bolin davidbolin@gmail.com
References
Bolin, D. and Lindgren, F. (2017) Quantifying the uncertainty of contour maps, Journal of Computational and Graphical Statistics, 26:3, 513-524.
Bolin, D. and Lindgren, F. (2018), Calculating Probabilistic Excursion Sets and Related Quantities Using excursions, Journal of Statistical Software, 86(5), 1–20.
See Also
contourmap
, contourmap.inla
, contourmap.colors
Examples
n <- 100
Q <- Matrix(toeplitz(c(1, -0.5, rep(0, n - 2))))
mu <- seq(-5, 5, length = n)
## Sample the model 100 times (increase for better estimate)
X <- mu + solve(chol(Q), matrix(rnorm(n = n * 100), nrow = n, ncol = 100))
lp <- contourmap.mc(X, n.levels = 2, compute = list(F = FALSE, measures = c("P1", "P2")))
# plot contourmap
plot(lp$map)
# display quality measures
c(lp$P1, lp$P2)