A function to check the sensitivity of cnp_model predictions based on the variation of input parameters

This function runs the cnp_model fixing all parameters SD's but one to test for sensitivity

Usage

sensitivity(
  TL,
  param,
  iter = 1000,
  par,
  out = c("Ic", "In", "Ip", "Gc", "Gn", "Gp", "Fc", "Fn", "Fp", "Wc", "Wn", "Wp"),
  ...
)

Arguments

TL: total length of a fish in cm
param: list of all parameter means ("_m") and standard deviations ("_sd") Default parameters are set with very low sd's. See cnp_model_mcmc for a list of all requested parameters
iter: A positive integer specifying the number of iterations. The default is 1000
par: Charachter vector specifying which input parameter sd's should be used for sensitivity.
out: Charachter vector specifying which output parameter sd's should be returned.
...: Other arguments that can be used from cnp_model_mcmc

Value

Returns a dataframe with sd's of model predictions. Row names indicate the variable, who's sd was used for the model run. Plots a heatplot with width of the 95%CI of output predictions.

Examples

library(fishflux)
sensitivity(TL = 10, param = list(k_sd = 0.2, Dn_sd = 0.2, Dc_sd = 0.1),
            par = c("k_sd","Dn_sd","Dc_sd"), out = c("Ic", "In", "Ip", "Gc"))
#> Joining with `by = join_by(key)`