View source: R/plot_laisk_fit.R
| plot_laisk_fit | R Documentation |
Plots the output from fit_laisk.
plot_laisk_fit(
fit_results,
identifier_column_name,
plot_type,
cols = multi_curve_colors(),
a_column_name = 'A',
ci_column_name = 'Ci',
ppfd_column_name = 'PPFD',
...
)
fit_results |
A list of four |
identifier_column_name |
The name of a column in each element of |
plot_type |
Must be either |
cols |
A vector of color specifications to use for each light level when plotting. |
a_column_name |
The name of the columns in the elements of |
ci_column_name |
The name of the column in the elements of |
ppfd_column_name |
The name of the column in the elements of |
... |
Additional arguments to be passed to |
This is a convenience function for plotting the results of a Laisk 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_laisk.
Because the Laisk fitting process involves two sets of linear fits, there are
two possible graphs that can be created. When plot_type is
'first', this function will plot the individual A-Ci curves at each
PPFD, along with the linear fits and the estimated intersection point. When
plot_type is 'second', this function will plot the Laisk
intercept vs. Laisk slope from the results of the first fits, along with a
linear fit of Laisk intercept vs. Laisk slope. See
fit_laisk for more details.
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_laisk 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('c3_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'
)
# Apply the Laisk method. Note: this is a bad example because these curves were
# measured at the same light intensity, but from different species. Because of
# this, the results are not meaningful.
laisk_results <- fit_laisk(
licor_file, 20, 150,
ppfd_column_name = 'species_plot'
)
# Plot the linear fits of A vs. Ci
plot_laisk_fit(laisk_results, 'instrument', 'first', ppfd_column_name = 'species_plot')
# Plot the linear fits of Laisk intercept vs. Laisk slope
plot_laisk_fit(laisk_results, 'instrument', 'second', ppfd_column_name = 'species_plot')
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