plot.tpopt {rodd} | R Documentation |
Plot of \Psi
function for resulting design
Description
Plots the \Psi(x,\xi)
function for resulting approximation \xi^{**}
of the T_{\mathrm{P}}
-optimal design achieved with the help of tpopt
. The definition of \Psi(x,\xi)
can be found in the “details” section of function's tpopt
specifications.
Usage
## S3 method for class 'tpopt'
plot(x, ...)
Arguments
x |
an object of type "tpopt". |
... |
additional graphical parameters. |
Details
We are interested in the shape of function \Psi(x,\xi^{**})
when we want to ensure the convergence of the algorithm. If algorithm had converged, then support points of \xi^{**}
(which are represented by dots) will be near local maximums of the mentioned function. Furthermore, at all local maximums \Psi(x,\xi^{**})
should have the same value. Otherwise something went wrong and the algorithm should be restarted with another parameters.
See Also
tpopt
, summary.tpopt
, print.tpopt
Examples
#List of models
eta.1 = function(x, theta.1)
theta.1[1] + theta.1[2] * x + theta.1[3] * (x ^ 2) +
theta.1[4] * (x ^ 3) + theta.1[5] * (x ^ 4)
eta.2 = function(x, theta.2)
theta.2[1] + theta.2[2] * x + theta.2[3] * (x ^ 2)
eta <- list(eta.1, eta.2)
#List of fixed parameters
theta.1 <- c(1,1,1,1,1)
theta.2 <- c(1,1,1)
theta.fix <- list(theta.1, theta.2)
#Comparison table
p <- matrix(
c(
0, 1,
0, 0
), c(length(eta), length(eta)), byrow = TRUE)
x <- seq(-1, 1, 0.1)
opt.1 <- list(method = 1, max.iter = 1)
opt.2 <- list(method = 1, max.iter = 2)
opt.3 <- list(method = 1)
res.1 <- tpopt(x = x, eta = eta, theta.fix = theta.fix, p = p, opt = opt.1)
res.2 <- tpopt(x = x, eta = eta, theta.fix = theta.fix, p = p, opt = opt.2)
res.3 <- tpopt(x = x, eta = eta, theta.fix = theta.fix, p = p, opt = opt.3)
plot(res.1)
plot(res.2)
plot(res.3)