In finding optimal parameters in nonlinear optimization and nonlinear least squares problems, we frequently wish to fix one or more parameters while allowing the rest to be adjusted to explore or optimize an objective function.

This vignette discusses some ideas about specifying the fixed parameters. A
lot of the material is drawn from Nash J C (2014) **Nonlinear parameter
optimization using R tools** Chichester UK: Wiley, in particular chapters
11 and 12. There is, however, additional material concerning ways to
manage extensible models, as well as some update to the package `nlsr`

.
The algorithm has been marginally altered to allow for
different sub-variants to be used, and mechanisms
for specifying parameter constraints and providing Jacobian
approximations have been changed.

Here are some of the ways fixed parameters may be specified in R packages.

Function `nlxb()`

in package `nlsr`

has argument `masked`

:

| Character vector of quoted parameter names. These parameters will NOT be | altered by the algorithm.

This approach has a simplicity that is attractive, but introduces an extra argument to
calling sequences. (This approach was previously in defunct package `nlmrt`

.)

Simlarly, function `nlfb()`

in `nlsr`

has argument `maskidx`

:

| Vector of indices of the parameters to be masked. These parameters will NOT
| be altered by the algorithm. Note that the mechanism here is different from

| that in nlxb which uses the names of the parameters.

From `Rvmmin`

and `Rcgmin`

in package `optimx`

the argument `bdmsk`

:

| An indicator vector, having 1 for each parameter that is "free" or | unconstrained, and 0 for any parameter that is fixed or MASKED for the | duration of the optimization.

Note that the function `bmchk()`

in package `optimx`

contains a much more
extensive examination of the bounds on parameters. In particular, it considers
the issues of inadmissible bounds (lower > upper), when to convert a pair of bounds
where upper["parameter"] - lower["parameter"] < tol to a
fixed or masked parameter (`maskadded`

) and whether parameters outside of bounds should be
moved to the nearest bound (`parchanged`

). It may be useful to use **inadmissible**
to refer to situations where a lower bound is higher than an upper bound and
**infeasible** where a parameter value, especially in a given starting vector,
is outside the bounds.

Further in package `optimx`

, the function `optimr()`

can call many different
"optimizers" (actually
function minimization methods that may include bounds and possibly masks).
These may be specified by setting the lower and upper bounds equal for
the parameters to be fixed. This seems a simple method for specifying
masks, but does pose some issues. For example, what happens when the
upper bound is only very slightly greater than the lower bound? Also
should we stop or declare an error if starting values are NOT on the
fixed value?

Of these methods, my preference is now to use the last one -- setting lower and upper bounds equal, and furthermore requiring the starting value of such a parameter to this fixed value, otherwise declaring an error. The approach does not add any special argument for masking, and is relatively obvious to novice users. However, such users may be tempted to put in narrow bounds rather than explicit equalities, and this could have deleterious consequences.

In the revision to package `nlsr`

, package `nlsr`

, I have stopped using
`masked`

in `nlxb()`

and `maskidx`

in `nlfb()`

(though the latter is a
returned value). This is because I feel the use of equal lower and upper
bounds is a better approach. Moreover, though it is not documented, it
appears to "mostly work" for the base R function `nls()`

with the `algorithm="port"`

option and with `minpack.lm::nlsLM()`

.

`bdmsk`

is the internal structure used in `Rcgmin`

, `Rvmmin`

and `nlfb`

to handle bounds
constraints as well as masks.
There is one element of `bdmsk`

for each parameter, and in `Rcgmin`

and `Rvmmin`

,
this is used on input to
specify parameter i as fixed or masked by setting `bdmsk[i] <- 0`

. Free
parameters have their `bdmsk`

element `1`

,
but during optimization in the presence of bounds, we can set other values.
The full set is as follows

- 1 for a free or unconstrained parameter
- 0 for a masked or fixed parameter
- -0.5 for a parameter that is out of bounds high (> upper bound)
- -1 for a parameter at its upper bound
- -3 for a parameter at its lower bound
- -3.5 for a parameter that is out of bounds low (< lower bound)

Not all these possibilities will be used by all methods that use `bdmsk`

.

The -1 and -3 are historical, and arose in the development of BASIC
codes for @jnmws87 (This is now available for free download
from archive.org. (https://archive.org/details/NLPE87plus).
In particular, adding 2 to the `bdmsk`

element gives 1 for an upper bound
and -1 for a lower bound,
simplifying the expression to decide if an optimization trial step
will move away from a bound.

Because masks (fixed parameters) reduce the dimensionality of the optimization problem, we can consider modifying the problem to the lower dimension space. This is Duncan Murdoch's suggestion, using

`fn0(par0)`

to be the initial user function of the full dimension parameter vector`par0`

`fn1(par1)`

to be the reduced or internal functin of the reduced dimension vector`par1`

`par1 <- forward(par0)`

`par0 <- inverse(par1)`

The major advantage of this approach is explicit dimension reduction. The main disadvantage is the effort of transformation at every step of an optimization.

An alternative is to use the `bdmsk`

vector to **mask**
the optimization search or adjustment vector,
including gradients and (approximate) Hessian or Jacobian matrices. A 0 element of `bdmsk`

"multiplies" any
adjustment. The principal difficulty is to ensure we do not essentially divide by
zero in applying
any inverse Hessian. This approach avoids `forward`

, `inverse`

and `fn1`

. However,
it may hide the
reduction in dimension, and caution is necessary in using the function and its
derived gradient,
Hessian and derived information.

require(optimx) sq<-function(x){ nn<-length(x) yy<-1:nn f<-sum((yy-x)^2) f } sq.g <- function(x){ nn<-length(x) yy<-1:nn gg<- 2*(x - yy) } xx <- c(.3, 4) uncans <- Rvmmin(xx, sq, sq.g) proptimr(uncans) mybm <- c(0,1) # fix parameter 1 cans <- Rvmmin(xx, sq, sq.g, bdmsk=mybm) proptimr(cans) require(nlsr) weed <- c(5.308, 7.24, 9.638, 12.866, 17.069, 23.192, 31.443, 38.558, 50.156, 62.948, 75.995, 91.972) ii <- 1:12 wdf <- data.frame(weed, ii) weedux <- nlxb(weed~b1/(1+b2*exp(-b3*ii)), start=c(b1=200, b2=50, b3=0.3)) weedux weedcx <- nlxb(weed~b1/(1+b2*exp(-b3*ii)), start=c(b1=200, b2=50, b3=0.3), masked=c("b1")) weedcx rfn <- function(bvec, weed=weed, ii=ii){ res <- rep(NA, length(ii)) for (i in ii){ res[i]<- bvec[1]/(1+bvec[2]*exp(-bvec[3]*i))-weed[i] } res } weeduf <- nlfb(start=c(200, 50, 0.3),resfn=rfn,weed=weed, ii=ii, control=list(japprox="jacentral")) weeduf weedcf <- nlfb(start=c(200, 50, 0.3),resfn=rfn,weed=weed, ii=ii, lower=c(200, 0, 0), upper=c(200, 100,100), control=list(japprox="jacentral")) weedcf

Package `nlraa`

has a selfStart model `SSbell`

(@ArchMiguez2013) of which the formula is

$$ y \approx ymax * exp(a *(x - xc)^2 + b*(x-xc)^3) $$

This is essentially the Gaussian bell curve with an additional cubic element in
the exponential
function. If we **fix** $b = 0$, then we have the usual Gaussian, and we can use
the standard
deviation $sigma$ of the variable $x$ with $xc$ equal to its mean and our
parameters will be given approximately by

$$ ymax = max(y)$$ $$ a = -0.5/sigma^2$$ $$ xc = mean(y) $$

We illustrate this in the following example.

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