DEoptim-methods {RcppDE} | R Documentation |
DEoptim-methods
Description
Methods for DEoptim objects.
Usage
## S3 method for class 'DEoptim'
summary(object, ...)
## S3 method for class 'DEoptim'
plot(x, plot.type = c("bestmemit", "bestvalit", "storepop"), ...)
Arguments
object |
an object of class |
x |
an object of class |
plot.type |
should we plot the best member at each iteration, the best value at each iteration or the intermediate populations? |
... |
further arguments passed to or from other methods. |
Details
Members of the class DEoptim
have a plot
method that
accepts the argument plot.type
. plot.type = "bestmemit"
results
in a plot of the parameter values that represent the lowest value of the objective function
each generation. plot.type = "bestvalit"
plots the best value of
the objective function each generation. Finally, plot.type = "storepop"
results in a plot of
stored populations (which are only available if these have been saved by
setting the control
argument of DEoptim
appropriately). Storing intermediate populations
allows us to examine the progress of the optimization in detail.
A summary method also exists and returns the best parameter vector, the best value of the objective function,
the number of generations optimization ran, and the number of times the
objective function was evaluated.
Note
Further details and examples of the R package DEoptim can be found in Mullen et al. (2009) and Ardia et al. (2010).
Please cite the package in publications.
Author(s)
For RcppDE: Dirk Eddelbuettel.
For DEoptim: David Ardia, Katharine Mullen katharine.mullen@nist.gov, Brian Peterson and Joshua Ulrich.
References
Mullen, K.M., Ardia, D., Gil, D.L, Windover, D., Cline, J. (2009) DEoptim: An R Package for Global Optimization by Differential Evolution. URL https://www.ssrn.com/abstract=1526466
Ardia, D., Boudt, K., Carl, P., Mullen, K.M., Peterson, B.G. (2010) Differential Evolution (DEoptim) for Non-Convex Portfolio Optimization. URL https://www.ssrn.com/abstract=1584905
See Also
DEoptim
and DEoptim.control
.
Examples
## Rosenbrock Banana function
## The function has a global minimum f(x) = 0 at the point (0,0).
## Note that the vector of parameters to be optimized must be the first
## argument of the objective function passed to DEoptim.
Rosenbrock <- function(x){
x1 <- x[1]
x2 <- x[2]
100 * (x2 - x1 * x1)^2 + (1 - x1)^2
}
lower <- c(-10, -10)
upper <- -lower
set.seed(1234)
outDEoptim <- DEoptim(Rosenbrock, lower, upper)
## print output information
summary(outDEoptim)
## plot the best members
plot(outDEoptim, type = 'b')
## plot the best values
dev.new()
plot(outDEoptim, plot.type = "bestvalit", type = 'b', col = 'blue')
## rerun the optimization, and store intermediate populations
outDEoptim <- DEoptim(Rosenbrock, lower, upper,
DEoptim.control(itermax = 500,
storepopfrom = 1, storepopfreq = 2))
summary(outDEoptim)
## plot intermediate populations
dev.new()
plot(outDEoptim, plot.type = "storepop")
## Wild function
Wild <- function(x)
10 * sin(0.3 * x) * sin(1.3 * x^2) +
0.00001 * x^4 + 0.2 * x + 80
outDEoptim = DEoptim(Wild, lower = -50, upper = 50,
DEoptim.control(trace = FALSE, storepopfrom = 50,
storepopfreq = 1))
plot(outDEoptim, type = 'b')
dev.new()
plot(outDEoptim, plot.type = "bestvalit", type = 'b')
## Not run:
## an example with a normal mixture model: requires package mvtnorm
library(mvtnorm)
## neg value of the density function
negPdfMix <- function(x) {
tmp <- 0.5 * dmvnorm(x, c(-3, -3)) + 0.5 * dmvnorm(x, c(3, 3))
-tmp
}
## wrapper plotting function
plotNegPdfMix <- function(x1, x2)
negPdfMix(cbind(x1, x2))
## contour plot of the mixture
x1 <- x2 <- seq(from = -10.0, to = 10.0, by = 0.1)
thexlim <- theylim <- range(x1)
z <- outer(x1, x2, FUN = plotNegPdfMix)
contour(x1, x2, z, nlevel = 20, las = 1, col = rainbow(20),
xlim = thexlim, ylim = theylim)
set.seed(1234)
outDEoptim <- DEoptim(negPdfMix, c(-10, -10), c(10, 10),
DEoptim.control(NP = 100, itermax = 100, storepopfrom = 1,
storepopfreq = 5))
## convergence plot
dev.new()
plot(outDEoptim)
## the intermediate populations indicate the bi-modality of the function
dev.new()
plot(outDEoptim, plot.type = "storepop")
## End(Not run)