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Generate a set of random Leslie Matrix Population Models

Source: R/rand_leslie_set.R
rand_leslie_set.Rd

Generates a set of Leslie matrix population models (MPMs) based on defined mortality and reproductive output models, and using model parameters randomly drawn from specified distributions.

Usage

rand_leslie_set(
  n_models = 5,
  mortality_model = "gompertz",
  fecundity_model = "step",
  mortality_params,
  fecundity_params,
  fecundity_maturity_params,
  dist_type = "uniform",
  output = "type1",
  scale_output = FALSE
)

Arguments

n_models

An integer indicating the number of MPMs to generate.

mortality_model

A character string specifying the name of the mortality model to be used. Options are gompertz, gompertzmakeham, exponential, siler, weibull, and weibullmakeham. See model_mortality. These names are not case-sensitive.

fecundity_model

A character string specifying the name of the model to be used for reproductive output. Options are logistic, step, vonBertalanffy, normal and hadwiger. See ?model_fecundity.

mortality_params

A two-column dataframe with a number of rows equal to the number of parameters in the mortality model. The required order of the parameters depends on the selected mortality_model (see ?model_mortality):

  • For gompertz and weibull: b_0, b_1

  • For gompertzmakeham and weibullmakeham: b_0, b_1, C

  • For exponential: C

  • For siler: a_0, a_1, C, b_0, b_1 If dist_type is uniform these rows represent the lower and upper limits of the random uniform distribution from which the parameters are drawn. If dist_type is normal, the columns represent the mean and standard deviation of a random normal distribution from which the parameter values are drawn.

fecundity_params

A two-column dataframe with a number of rows equal to the number of parameters in the fecundity model. The required order of the parameters depends on the selected fecundity_model (see ?model_fecundity):

  • For logistic: A, k, x_m

  • For step: A

  • For vonBertalanffy: A, k

  • For normal: A, mu, sd

  • For hadwiger: a, b, C If dist_type is uniform these rows represent the lower and upper limits of the random uniform distribution from which the parameters are drawn. If dist_type is normal, the columns represent the mean and standard deviation of a random normal distribution from which the parameter values are drawn.

fecundity_maturity_params

A vector with two elements defining the distribution from which age at maturity is drawn for the models. The models will coerce reproductive output to be zero before this point. If dist_type is uniform these values represent the lower and upper limits of the random uniform distribution from which the parameters are drawn. If dist_type is normal, the values represent the mean and standard deviation of a random normal distribution from which the parameter values are drawn.

dist_type

A character string specifying the type of distribution to draw parameters from. Default is uniform. Supported types are uniform and normal.

output

Character string indicating the type of output.

  • Type1 or cdb_split: A compadreDB Object containing MPMs split into the submatrices (i.e. A, U, F and C).

  • Type2 or cdb_A: A compadreDB Object containing MPMs that are not split into submatrices (i.e. only the A matrix is included).

  • Type3 or list_split1: A list of MPMs arranged so that each element of the list contains a model and associated submatrices (i.e. the nth element contains the nth A matrix alongside the nth U and F matrices).

  • Type4 or list_split2: A list of MPMs arranged so that the list contains 3 lists for the A matrix and the U and F submatrices respectively.

  • Type5 or list_A: A list of MPMs, including only the A matrix.

  • Type6 or lifetable: A list of life tables.

    Default is Type1.

scale_output

A logical argument. If TRUE the resulting MPMs or life tables are scaled by adjusting reproductive output so that the population growth rate (lambda) is 1. Default is FALSE.

Value

Returns a CompadreDB object or list containing MPMs or life tables generated using the specified model with parameters drawn from random uniform or normal distributions. The format of the output MPMs depends on the arguments output. Outputs may optionally be scaled using the argument scale_output to ensure a population growth rate (lambda) of 1.

If the output is a CompadreDB object, the parameters of the models used to produce the MPM are included in the metadata.

See also

Other Leslie matrices: make_leslie_mpm(), reorganise_matrices()

Author

Owen Jones jones@biology.sdu.dk

Examples


mortParams <- data.frame(
  minVal = c(0, 0.01, 0.1),
  maxVal = c(0.14, 0.15, 0.1)
)

fecundityParams <- data.frame(
  minVal = c(10, 0.5, 8),
  maxVal = c(11, 0.9, 10)
)

maturityParam <- c(0, 0)

rand_leslie_set(
  n_models = 5,
  mortality_model = "gompertzmakeham",
  fecundity_model = "normal",
  mortality_params = mortParams,
  fecundity_params = fecundityParams,
  fecundity_maturity_params = maturityParam,
  dist_type = "uniform",
  output = "Type1"
)
#> A COM(P)ADRE database ('CompadreDB') object with ?? SPECIES and 5 MATRICES.
#> 
#> # A tibble: 5 × 10
#>   mat        mortality_model    b_0    b_1     C fecundity_model     A    mu
#>   <list>     <chr>            <dbl>  <dbl> <dbl> <chr>           <dbl> <dbl>
#> 1 <CompdrMt> gompertzmakeham 0.0250 0.0753   0.1 normal           10.2  10.4
#> 2 <CompdrMt> gompertzmakeham 0.101  0.106    0.1 normal           10.2  10.2
#> 3 <CompdrMt> gompertzmakeham 0.0275 0.0956   0.1 normal           10.0  10.7
#> 4 <CompdrMt> gompertzmakeham 0.120  0.0303   0.1 normal           10.6  10.8
#> 5 <CompdrMt> gompertzmakeham 0.0341 0.0305   0.1 normal           10.8  10.7
#> # ℹ 2 more variables: sd <dbl>, fecundity_scaling <dbl>