sim_R: Simulate starting abundance, random recruitment and total...

In PaulRegular/SimSurvey: Test Surveys by Simulating Spatially-Correlated Populations

Simulate starting abundance, random recruitment and total mortality

Description

These functions return a function to use inside sim_abundance. Given parameters, it generates N0, R and Z values.

Usage

sim_R(log_mean = log(3e+07), log_sd = 0.5, random_walk = TRUE, plot = FALSE)

sim_Z(
  log_mean = log(0.5),
  log_sd = 0.2,
  phi_age = 0.9,
  phi_year = 0.5,
  plot = FALSE
)

sim_N0(N0 = "exp", plot = FALSE)

Arguments

log_mean	One mean value or a vector of means, in log scale, of length equal to years for sim_R or a matrix of means with rows equaling the number of ages and columns equaling the number of years for sim_Z.
log_sd	Standard deviation of the variable in the log scale.
random_walk	Simulate recruitment as a random walk?
plot	produce a simple plot of the simulated values?
phi_age	Autoregressive parameter for the age dimension.
phi_year	Autoregressive parameter for the year dimension.
N0	Either specify "exp" or numeric vector of starting abundance excluding the first age. If "exp" is specified using sim_N0, then abundance at age are calculated using exponential decay.

Details

sim_R generates uncorrelated recruitment values or random walk values from a log normal distribution. sim_Z does the same as sim_R when phi_age and phi_year are both 0, otherwise values are correlated in the age and/or year dimension. The covariance structure follows that described in Cadigan (2015).

Value

Returns a function for use inside sim_abundance.

References

Cadigan, Noel G. 2015. A State-Space Stock Assessment Model for Northern Cod, Including Under-Reported Catches and Variable Natural Mortality Rates. Canadian Journal of Fisheries and Aquatic Sciences 73 (2): 296-308.

Examples

R_fun <- sim_R(log_mean = log(100000), log_sd = 0.1, random_walk = TRUE, plot = TRUE)
R_fun(years = 1:100)
sim_abundance(R = sim_R(log_mean = log(100000), log_sd = 0.5))
sim_abundance(years = 1:20,
              R = sim_R(log_mean = log(c(rep(100000, 10), rep(10000, 10))), plot = TRUE))

Z_fun <- sim_Z(log_mean = log(0.5), log_sd = 0.1, phi_age = 0.9, phi_year = 0.9, plot = TRUE)
Z_fun(years = 1:100, ages = 1:20)
sim_abundance(Z = sim_Z(log_mean = log(0.5), log_sd = 0.1, plot = TRUE))
Za_dev <- c(-0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.3, 0.2, 0.1, 0)
Zy_dev <- c(-0.2, -0.2, -0.2, -0.2, -0.2, 2, 2, 2, 2, 0.2, 0.2, 0.2, 0.2, 0.2, 0, 0, 0, 0, 0, 0)
Z_mat <- outer(Za_dev, Zy_dev, "+") + 0.5
sim_abundance(ages = 1:10, years = 1:20,
              Z = sim_Z(log_mean = log(Z_mat), plot = TRUE))
sim_abundance(ages = 1:10, years = 1:20,
              Z = sim_Z(log_mean = log(Z_mat), log_sd = 0, phi_age = 0, phi_year = 0, plot = TRUE))

N0_fun <- sim_N0(N0 = "exp", plot = TRUE)
N0_fun(R0 = 1000, Z0 = rep(0.5, 20), ages = 1:20)
sim_abundance(N0 = sim_N0(N0 = "exp", plot = TRUE))

PaulRegular/SimSurvey documentation built on Sept. 21, 2023, 7:42 p.m.