plot.optim_nmsa {optimization}

Plot an optim_nmsa Object

Description

Creates convergence or contour plots for visual inspection of the optimization result. Note that 'trace' must be activated for this function.
In case of a bivariate optimization, the 'contour' plot gives an overview of the parameter development over time in the entire state space. This is useful for the evaluation of the algorithm settings and therefore helps improving the performance. The development of the response can be visualized via the 'convergence' plot.

Usage

## S3 method for class 'optim_nmsa'
plot(x, type = 'convergence', lower = NA, upper = NA, ...)

Arguments

x

Object of type 'optim_nmsa' to be plotted. The 'trace' entry must not be empty.

type

Character string which determines the plot type. Either 'convergence' or 'contour' is possible.

lower

Vector containing the lower limits of the variables in the plot. Only useful for 'contour' plots.

upper

Vector containing the upper limits of the variables in the plot. Only useful for 'contour' plots.

...

Further arguments for the generic plot function.

Author(s)

Kai Husmann, Alexander Lange

See Also

optim_nm, optim_sa

Examples

# S3 method for class 'optim_nlme'

# Himmelblau's function
hi <- function(x){(x[1]**2 + x[2] - 11)**2 + (x[1] + x[2]**2 -7)**2}

out_nm <- optim_nm(hi, k = 2, trace = TRUE)
out_sa <- optim_sa(fun = hi, start = c(runif(2, min = -1, max = 1)),
                   trace = TRUE, lower = c(-4, -4) ,upper=c(4, 4),
                   control = list(t0 = 1000, nlimit = 1500,r = 0.8))

# Examples for optimization results via 'Nelder-Mead' method.
plot(out_nm)
plot(out_nm, type = "contour", lower = c(-4, -4), upper = c(4, 4))

# Examples for optimization results via 'Simulated Annealing' method.
plot(out_sa)
plot(out_sa, type = "contour")