These functions create labels from FLSR models and params to be used, for example, on the legend of the ggplot-based plot method for FLSRs
eqlabel(model, param)
modlabel(model, param)a list of model formulas
a list of FLPar objects for the params slot
data(nsher)
srs <- FLSRs(sapply(c('ricker', 'bevholt'), function(x) {
y <- nsher
model(y) <- x
return(fmle(y))
}))
#> Nelder-Mead direct search function minimizer
#> function value for initial parameters = -15.862252
#> Scaled convergence tolerance is 2.36366e-07
#> Stepsize computed as 11.939303
#> BUILD 3 10000000000000000159028911097599180468360808563945281389781327557747838772170381060813469985856815104.000000 -15.862252
#> SHRINK 7 10000000000000000159028911097599180468360808563945281389781327557747838772170381060813469985856815104.000000 -15.862252
#> HI-REDUCTION 9 267.632807 -15.862252
#> HI-REDUCTION 11 236.438352 -15.862252
#> HI-REDUCTION 13 205.241529 -15.862252
#> HI-REDUCTION 15 174.041331 -15.862252
#> HI-REDUCTION 17 142.839861 -15.862252
#> HI-REDUCTION 19 111.657775 -15.862252
#> HI-REDUCTION 21 80.601711 -15.862252
#> HI-REDUCTION 23 50.133420 -15.862252
#> HI-REDUCTION 25 21.954719 -15.862252
#> HI-REDUCTION 27 0.340450 -15.862252
#> HI-REDUCTION 29 -10.920787 -15.862252
#> HI-REDUCTION 31 -14.696800 -15.862252
#> HI-REDUCTION 33 -15.639120 -15.862252
#> HI-REDUCTION 35 -15.676851 -15.862252
#> HI-REDUCTION 37 -15.816477 -15.862252
#> HI-REDUCTION 39 -15.825739 -15.862252
#> LO-REDUCTION 41 -15.847989 -15.862252
#> LO-REDUCTION 43 -15.852490 -15.862252
#> LO-REDUCTION 45 -15.857566 -15.862252
#> LO-REDUCTION 47 -15.860412 -15.862252
#> LO-REDUCTION 49 -15.861062 -15.862252
#> LO-REDUCTION 51 -15.861647 -15.862252
#> LO-REDUCTION 53 -15.862025 -15.862252
#> LO-REDUCTION 55 -15.862108 -15.862252
#> LO-REDUCTION 57 -15.862175 -15.862252
#> LO-REDUCTION 59 -15.862224 -15.862252
#> LO-REDUCTION 61 -15.862235 -15.862252
#> LO-REDUCTION 63 -15.862243 -15.862252
#> LO-REDUCTION 65 -15.862249 -15.862252
#> LO-REDUCTION 67 -15.862250 -15.862252
#> LO-REDUCTION 69 -15.862251 -15.862252
#> LO-REDUCTION 71 -15.862252 -15.862252
#> LO-REDUCTION 73 -15.862252 -15.862252
#> Exiting from Nelder Mead minimizer
#> 75 function evaluations used
#> Nelder-Mead direct search function minimizer
#> function value for initial parameters = -10.336211
#> Scaled convergence tolerance is 1.54022e-07
#> Stepsize computed as 501.110000
#> BUILD 3 44.842344 -11.603908
#> Warning: NaNs produced
#> HI-REDUCTION 5 31.685209 -11.603908
#> Warning: NaNs produced
#> HI-REDUCTION 7 17.913114 -11.603908
#> Warning: NaNs produced
#> HI-REDUCTION 9 5.415279 -11.603908
#> Warning: NaNs produced
#> HI-REDUCTION 11 -3.412974 -11.603908
#> HI-REDUCTION 13 -8.018030 -11.603908
#> LO-REDUCTION 15 -10.336211 -11.603908
#> LO-REDUCTION 17 -11.081040 -11.603908
#> EXTENSION 19 -11.295930 -12.061705
#> LO-REDUCTION 21 -11.603908 -12.061705
#> REFLECTION 23 -11.813826 -12.087620
#> REFLECTION 25 -12.061705 -12.199591
#> LO-REDUCTION 27 -12.087620 -12.199591
#> LO-REDUCTION 29 -12.158184 -12.199591
#> LO-REDUCTION 31 -12.191726 -12.199591
#> HI-REDUCTION 33 -12.192269 -12.199591
#> HI-REDUCTION 35 -12.197784 -12.199591
#> LO-REDUCTION 37 -12.198015 -12.199591
#> HI-REDUCTION 39 -12.199555 -12.199776
#> REFLECTION 41 -12.199591 -12.200058
#> HI-REDUCTION 43 -12.199776 -12.200092
#> HI-REDUCTION 45 -12.200058 -12.200142
#> HI-REDUCTION 47 -12.200092 -12.200155
#> HI-REDUCTION 49 -12.200142 -12.200160
#> HI-REDUCTION 51 -12.200155 -12.200177
#> HI-REDUCTION 53 -12.200160 -12.200177
#> LO-REDUCTION 55 -12.200171 -12.200179
#> HI-REDUCTION 57 -12.200177 -12.200179
#> HI-REDUCTION 59 -12.200178 -12.200179
#> HI-REDUCTION 61 -12.200179 -12.200179
#> HI-REDUCTION 63 -12.200179 -12.200179
#> HI-REDUCTION 65 -12.200179 -12.200179
#> Exiting from Nelder Mead minimizer
#> 67 function evaluations used
eqlabel(model=lapply(srs, model),
param=lapply(srs, params))
#> $ricker
#> expression(rec %~% 119.4 %.% ssb %.% exp(-0.009451 %.% ssb))
#>
#> $bevholt
#> expression(rec %~% 6736 %.% ssb/(52.2 + ssb))
#>
modlabel(model=lapply(srs, model),
param=lapply(srs, params))
#> $ricker
#> expression(ricker(119.4, 0.009451))
#>
#> $bevholt
#> expression(bevholt(6736.4, 52.2))
#>