pdr_optimize {PoolDilutionR}

Optimize production and consumption parameters for pool dilution data

Description

Optimize production and consumption parameters for pool dilution data

Usage

pdr_optimize(
  time,
  m,
  n,
  m_prec,
  ap_prec,
  P,
  k,
  params_to_optimize = c("P", "k"),
  pool = "CH4",
  frac_P = NULL,
  frac_k = NULL,
  other_params = list(),
  cost_fn = pdr_cost,
  prediction_fn = pdr_predict,
  include_progress = FALSE,
  quiet = FALSE
)

Arguments

time

Vector of numeric time values (e.g. days); first should be zero

m

Observed total pool size (as a volume), same length as time

n

Observed heavy isotope (as a volume), same length as time

m_prec

Instrument precision for pool size, expressed as a standard deviation

ap_prec

Instrument precision for atom percent, expressed as a standard deviation

P

production rate, unit gas/unit time

k

first-order rate constant for consumption, 1/unit time

params_to_optimize

Named vector of parameters ("P", "k", "frac_P", and/or "frac_k") to optimize against observations

pool

Name of pool to use when looking up fractionation values if they are not supplied; see pdr_fractionation

frac_P

Fractionation value for production; see pdr_fractionation

frac_k

Fractionation value for consumption; see pdr_fractionation

other_params

Other parameters pass on to optim

cost_fn

Cost function to use; the default is pdr_cost

prediction_fn

Prediction function that the cost function will use; the default is pdr_predict

include_progress

Include detailed optimizer progress data in output?

quiet

Suppress output messages, logical

Value

The output of optim.

Note

Currently there is only one set of fractionation values available in pdr_fractionation, from von Fischer and Hedin (2002, 10.1029/2001GB001448).

See Also

pdr_optimize_df

Examples

tm <- 0:5
m <- c(10, 8, 6, 5, 4, 3)
n <- c(1, 0.7, 0.6, 0.4, 0.3, 0.2)
m_prec <- 0.001
ap_prec <- 0.01

# Optimize values for P (production) and k (consumption), provide starting values for P and k
pdr_optimize(time = tm, m, n, m_prec, ap_prec, P = 0.5, k = 0.3)
# If we don't provide a value for k, it can be estimated from the data
pdr_optimize(tm, m, n, m_prec, ap_prec, P = 0.5)
# Hold k and frac_k constant (ie., k = estimated k0, frac_k = default value), optimize P and frac_P
pdr_optimize(tm, m, n, m_prec, ap_prec, P = 0.5, params_to_optimize = c("P", "frac_P"))
# Optimize only k (provide P and exclude from params_to_optimize)
pdr_optimize(tm, m, n, m_prec, ap_prec, P = 0.5, params_to_optimize = "k")
# Optimize only k, bounding its possible values
op <- list(lower = c("k" = 0.2), upper = c("k" = 0.3))
pdr_optimize(tm, m, n, m_prec, ap_prec, 0.5, 0.27, params_to_optimize = "k", other_params = op)