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Calibrate new allometric equations

Source: R/est_params.R
est_params.Rd

This function calibrates new allometric equations from sampling previous ones. New allometric equations are calibrated for each species and location by resampling the original compiled equations; equations with a larger sample size, and/or higher taxonomic rank, and climatic similarity with the species and location in question are given a higher weight in this process.

Usage

est_params(
  genus,
  coords,
  species = NULL,
  new_eqtable = NULL,
  wna = 0.1,
  w95 = 500,
  nres = 10000
)

Arguments

genus

a character vector, containing the genus (e.g. "Quercus") of each tree.

coords

a numeric vector of length 2 with longitude and latitude (if all trees were measured in the same location) or a matrix with 2 numerical columns giving the coordinates of each tree.

species

a character vector (same length as genus), containing the species (e.g. "rubra") of each tree. Default is NULL, when no species identification is available.

new_eqtable

Optional. An equation table created with the new_equations() function. Default is the compiled allodb equation table.

wna

a numeric vector, this parameter is used in the weight_allom() function to determine the dbh-related and sample-size related weights attributed to equations without a specified dbh range or sample size, respectively. Default is 0.1.

w95

a numeric vector, this parameter is used in the weight_allom() function to determine the value at which the sample-size-related weight reaches 95% of its maximum value (max=1). Default is 500.

nres

number of resampled values. Default is "1e4".

Value

An object of class "data.frame" of fitted coefficients (columns) of the non-linear least-square regression: $$AGB = a * dbh ^ b + e, \space \mathit{with} \space e ~ N(0, sigma^2)$$

See also

weight_allom(), new_equations().

Examples

# calibrate new allometries for all Lauraceae species
lauraceae <- subset(scbi_stem1, Family == "Lauraceae")
est_params(
  genus = lauraceae$genus,
  species = lauraceae$species,
  coords = c(-78.2, 38.9)
)
#> # A tibble: 2 × 7
#>   genus     species  long   lat      a     b sigma
#>   <chr>     <chr>   <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1 Lindera   benzoin -78.2  38.9 0.0834  2.52  354.
#> 2 Sassafras albidum -78.2  38.9 0.0817  2.52  339.