View source: R/plot_c4_aci_fit.R
plot_c4_aci_fit | R Documentation |
Plots the output from fit_c4_aci
.
plot_c4_aci_fit(
fit_results,
identifier_column_name,
x_name,
plot_operating_point = TRUE,
a_column_name = 'A',
ci_column_name = 'Ci',
pcm_column_name = 'PCm',
...
)
fit_results |
A list of three |
identifier_column_name |
The name of a column in each element of |
x_name |
The name of the column that should be used for the x-axis in the plot. This
should refer to either |
plot_operating_point |
A logical value indicating whether to plot the operating point. |
a_column_name |
The name of the columns in the elements of |
ci_column_name |
The name of the columns in the elements of |
pcm_column_name |
The name of the columns in the elements of |
... |
Additional arguments to be passed to |
This is a convenience function for plotting the results of a C4 A-Ci curve
fit. It is typically used for displaying several fits at once, in which case
fit_results
is actually the output from calling
consolidate
on a list created by calling by.exdf
with FUN = fit_c4_aci
.
The resulting plot will show curves for the fitted rates An
,
Apr
, Apc
, and Ar
, along with points for the measured
values of A
, and (optionally) the estimated operating point. The x-axis
can be set to either Ci
or PCm
.
Internally, this function uses xyplot
to perform the
plotting. Optionally, additional arguments can be passed to xyplot
.
These should typically be limited to things like xlim
, ylim
,
main
, and grid
, since many other xyplot
arguments will be
set internally (such as xlab
, ylab
, auto
, and others).
See the help file for fit_c4_aci
for an example using this
function.
A trellis
object created by lattice::xyplot
.
# 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'
)
# Calculate temperature-dependent values of C4 photosynthetic parameters
licor_file <- calculate_temperature_response(licor_file, c4_temperature_param_vc)
# Calculate the total pressure in the Licor chamber
licor_file <- calculate_total_pressure(licor_file)
# For these examples, we will use a faster (but sometimes less reliable)
# optimizer so they run faster
optimizer <- optimizer_nmkb(1e-7)
# Fit all curves in the data set
aci_results <- consolidate(by(
licor_file,
licor_file[, 'species_plot'],
fit_c4_aci,
Ca_atmospheric = 420,
optim_fun = optimizer
))
# View the fits for each species / plot
plot_c4_aci_fit(aci_results, 'species_plot', 'Ci', ylim = c(0, 100))
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