prod_optim | R Documentation |
This is the optimizer-function that is minimized for the inverse, production based model. It takes as input a vector of the influx, as well as the values of the production to be optimized.
This function is embedded in pro_flux()
and is not intended to be
used manually.
prod_optim(
X,
height,
DS,
D0 = NA,
C0,
pmap,
cmap,
conc,
dstor = 0,
zero_flux = TRUE,
F0 = 0,
known_flux = NA,
known_flux_factor = 0,
DSD0_optim = FALSE,
layer_couple,
wmap,
evenness_factor
)
X |
(numeric vector) specifying the production rates to be optimized |
height |
(numeric vector) giving the height of each step |
DS |
(numeric vector) giving the DS of each step |
D0 |
RESERVED FOR FUTURE EXPANSION |
C0 |
(numeric) The concentration at the bottom of the lowermost step. |
pmap |
(integer vector) assigning a production from X to each step |
cmap |
(integer vector) assigning the modeled concentrations to the observed concentrations as there can be multiple observations per depth |
conc |
(numeric) the observed concentrations (in the same unit as the modelled concentrations). |
dstor |
RESERVED FOR FUTURE EXPANSION |
zero_flux |
(logical) Applies the zero-flux boundary
condition( |
F0 |
(numeric) flux into lowest layer. |
known_flux |
RESERVED FOR FUTURE EXPANSION |
known_flux_factor |
RESERVED FOR FUTURE EXPANSION |
DSD0_optim |
RESERVED FOR FUTURE EXPANSION |
layer_couple |
(numeric vector) A vector defining the weights that bind the different layers together. If all is zero, no penalisation for stark differences between the optimized production rates of adjacent layers takes place |
wmap |
(numeric) A vector defining the weights of the different concentration measurements in the RMSE calculation. |
evenness_factor |
(numeric) Defines strong should stark differences between the production rates and very small production rates be penalized. |
A modified RMSE root mean square error of the modeled and measured concentration.
Other proflux:
prod_mod_conc()
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