Call fnFull with variable and fixed parameters

Combine variable parameters with with fixed parameters and pass to fnFull. Useful for optimizing over a subset of parameters without writing a separate function. Values are combined by name if available. Otherwise, xFull is constructed by position (the default).

fnSubset(x, fnFull, xFixed, xFull=c(x, xFixed), ...)

Arguments

x

Variable parameters to be passed to fnFull.

fnFull

Function whose first argument has length = length(xFull).

xFixed

Parameter values to be combined with x to construct the first argument for a call to fnFull.

xFull

Prototype initial argument for fnFull.

...

Optional arguments passed to fnFull.

Details

This function first confirms that length(x) + length(xFixed) == length(xFull). Next,

  • If xFull has names, match at least xFixed by name.

  • Else xFull = c(x, xFixes), the default.

Finally, call fnFull(xFull, ...).

Value

value returned by fnFull

Author

Spencer Graves

Examples

##
## Example with 'optim'
##
fn <- function(x) (x[2]-2*x[1])^2
# note: true minimum is 0 on line 2*x[1] == x[2]
fullEst <- optim(par=c(1,1), method="BFGS", fn=fn)
fullEst$par
#> [1] 0.6 1.2
# par = c(0.6, 1.2) at minimum (not convex)

# Fix the last component to 4 
est4 <- optim(par=1, fn=fnSubset, method="BFGS", fnFull=fn, xFixed=4)
est4$par
#> [1] 2
# now there is a unique minimun x[1] = 2

# Fix the first component
fnSubset(x=1, fnFull=fn, xFixed=c(a=4), xFull=c(a=1, b=2))
#>  b 
#> 49 
# After substitution:  xFull = c(a=4, b=1),
# so fn = (1 - 2*4)^2 = (-7)^2 = 49

est4. <- optim(par=1, fn=fnSubset, method="BFGS",
               fnFull=fn, xFixed=c(a=4), 
               xFull=c(a=1, b=2))
est4.$par
#> [1] 8
# At optimum: xFull=c(a=4, b=8),
# so fn = (8 - 2*4)^2 = 0

##
## Example with 'maxLik'
##
fn2max <- function(x) -(x[2]-2*x[1])^2
# -> need to have a maximum
max4 <- maxLik(fnSubset, start=1, fnFull=fn2max, xFixed=4)
summary(max4)
#> --------------------------------------------
#> Maximum Likelihood estimation
#> Newton-Raphson maximisation, 2 iterations
#> Return code 1: gradient close to zero (gradtol)
#> Log-Likelihood: -4.173074e-28 
#> 1  free parameters
#> Estimates:
#>      Estimate Std. error t value  Pr(> t)    
#> [1,]   2.0000     0.3536   5.657 1.54e-08 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> --------------------------------------------
# Similar result using fixed parameters in maxNR, called by maxLik 
max4. <- maxLik(fn2max, start=c(1, 4), fixed=2)
summary(max4.)
#> --------------------------------------------
#> Maximum Likelihood estimation
#> Newton-Raphson maximisation, 2 iterations
#> Return code 1: gradient close to zero (gradtol)
#> Log-Likelihood: -4.173074e-28 
#> 1  free parameters
#> Estimates:
#>      Estimate Std. error t value  Pr(> t)    
#> [1,]   2.0000     0.3536   5.657 1.54e-08 ***
#> [2,]   4.0000     0.0000      NA       NA    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> --------------------------------------------