Define a non-linear relationship

sim_nlf(
  formula = ~alpha - ((depth - mu)^2)/(2 * sigma^2),
  coeff = list(alpha = 0, mu = 200, sigma = 70)
)

Arguments

formula

A formula describing parametric relationships between the data and coefficients. The data used in sim_distribution() consist of grid coordinates expanded across ages and years, and include columns such as "x", "y", "depth", "cell", "division", "strat", "age", and "year". Coefficient values referenced in the formula must be provided in the coeff argument as a named list.

coeff

A named list of coefficient values used in formula.

Value

A function that can be passed to sim_distribution().

Details

Closure to be used in sim_distribution().

Examples

## Make a grid and replicate data for 5 ages and 5 years
## (This mimics what happens inside sim_distribution)
grid <- make_grid(shelf_width = 10)
grid_xy <- data.frame(grid)
i <- rep(seq(nrow(grid_xy)), times = 5)
a <- rep(1:5, each = nrow(grid_xy))
grid_xy <- grid_xy[i, ]
grid_xy$age <- a
i <- rep(seq(nrow(grid_xy)), times = 5)
y <- rep(1:5, each = nrow(grid_xy))
grid_xy <- grid_xy[i, ]
grid_xy$year <- y

## Define a non-linear function to apply to the expanded grid
## This example imposes ontogenetic deepening via a parabolic depth effect
nlf <- sim_nlf(
  formula = ~ alpha - ((depth - mu + beta * age)^2) / (2 * sigma^2),
  coeff = list(alpha = 0, mu = 200, sigma = 70, beta = -70)
)
grid_xy$depth_effect <- nlf(grid_xy)

library(plotly)
#> Loading required package: ggplot2
#> 
#> Attaching package: ‘plotly’
#> The following object is masked from ‘package:ggplot2’:
#> 
#>     last_plot
#> The following object is masked from ‘package:stats’:
#> 
#>     filter
#> The following object is masked from ‘package:graphics’:
#> 
#>     layout
grid_xy |>
  filter(year == 1) |>
  plot_ly(x = ~depth, y = ~depth_effect, split = ~age) |>
  add_lines()