Make an initial guess of C4 hyperbola parameter values for one curve

Creates a function that makes an initial guess of C4 hyperbola model parameter values for one curve. This function is used internally by fit_c4_aci_hyperbola.

Values estimated by this guessing function should be considered inaccurate, and should always be improved upon by an optimizer.

Usage

initial_guess_c4_aci_hyperbola(
    a_column_name = 'A'
  )

Arguments

a_column_name

The name of the column in rc_exdf that contains the net assimilation rate in micromol m^(-2) s^(-1).

Details

Here we estimate values of c4_curvature, c4_slope, rL, and Vmax from a measured C4 CO2 response curve. For more information about these parameters, see the documentation for calculate_c4_assimilation_hyperbola.

Here we take a very simple approach to forming the initial guess. We always choose c4_curvature = 0.5, c4_slope = 1.0, and rL = 0.0. For Vmax, we use Vmax = max{A} - rL_guess, where max{A} is the largest observed net CO2 assimilation rate and rL_guess is the guess for rL.

Value

A function with one input argument rc_exdf, which should be an exdf object representing one C4 CO2 response curve. The return value of this function will be a numeric vector with four elements, representing the values of c4_curvature, c4_slope, rL, and Vmax (in that order).

Examples

# Read an example Licor file included in the PhotoGEA package
licor_file <- read_gasex_file(
  PhotoGEA_example_file_path('c4_aci_1.xlsx')
)

# Define a new column that uniquely identifies each curve
licor_file[, 'species_plot'] <-
  paste(licor_file[, 'species'], '-', licor_file[, 'plot'] )

# Organize the data
licor_file <- organize_response_curve_data(
    licor_file,
    'species_plot',
    c(9, 10, 16),
    'CO2_r_sp'
)

# Create the guessing function
guessing_func <- initial_guess_c4_aci_hyperbola()

# Apply it and see the initial guesses for each curve
str(by(licor_file, licor_file[, 'species_plot'], guessing_func))
#> List of 3
#>  $ maize - 5  : num [1:4] 0.5 1 0 67.3
#>  $ sorghum - 2: num [1:4] 0.5 1 0 71.2
#>  $ sorghum - 3: num [1:4] 0.5 1 0 69.6

# Calculate simulated A-Ci curves based on the guesses and compare them to the
# actual data
calculated_aci <- do.call(rbind, by(
  licor_file,
  licor_file[, 'species_plot'],
  function(x) {
    param <- guessing_func(x)
    calculate_c4_assimilation_hyperbola(
      x,
      param[1], param[2], param[3], param[4]
    )
  }
))

lattice::xyplot(
  A + Ainitial + Amax + An ~ Ci | species_plot,
  data = cbind(licor_file, calculated_aci)$main_data,
  type = 'b',
  auto = TRUE,
  grid = TRUE,
  ylim = c(0, 100),
  par.settings = list(
    superpose.line = list(col = multi_curve_line_colors()),
    superpose.symbol = list(col = multi_curve_point_colors(), pch = 16)
  )
)