View source: R/error_function_c3_aci.R
error_function_c3_aci | R Documentation |
Creates a function that returns an error value (the negative of the natural
logarithm of the likelihood) representing the amount of agreement between
modeled and measured An
values. When this function is minimized, the
likelihood is maximized.
Internally, this function uses apply_gm
to calculate Cc
,
and then uses link{calculate_c3_assimilation}
to calculate assimilation
rate values that are compared to the measured ones.
error_function_c3_aci(
replicate_exdf,
fit_options = list(),
sd_A = 1,
Wj_coef_C = 4.0,
Wj_coef_Gamma_star = 8.0,
a_column_name = 'A',
ci_column_name = 'Ci',
gamma_star_norm_column_name = 'Gamma_star_norm',
gmc_norm_column_name = 'gmc_norm',
j_norm_column_name = 'J_norm',
kc_norm_column_name = 'Kc_norm',
ko_norm_column_name = 'Ko_norm',
oxygen_column_name = 'oxygen',
rl_norm_column_name = 'RL_norm',
total_pressure_column_name = 'total_pressure',
tp_norm_column_name = 'Tp_norm',
vcmax_norm_column_name = 'Vcmax_norm',
cj_crossover_min = NA,
cj_crossover_max = NA,
hard_constraints = 0,
...
)
replicate_exdf |
An |
fit_options |
A list of named elements representing fit options to use for each parameter.
Values supplied here override the default values (see details below). Each
element must be |
sd_A |
The standard deviation of the measured values of the net CO2 assimilation
rate, expressed in units of |
Wj_coef_C |
A coefficient in the equation for RuBP-regeneration-limited carboxylation,
whose value depends on assumptions about the NADPH and ATP requirements of
RuBP regeneration; see |
Wj_coef_Gamma_star |
A coefficient in the equation for RuBP-regeneration-limited carboxylation,
whose value depends on assumptions about the NADPH and ATP requirements of
RuBP regeneration; see |
a_column_name |
The name of the column in |
ci_column_name |
The name of the column in |
gamma_star_norm_column_name |
The name of the column in |
gmc_norm_column_name |
The name of the column in |
j_norm_column_name |
The name of the column in |
kc_norm_column_name |
The name of the column in |
ko_norm_column_name |
The name of the column in |
oxygen_column_name |
The name of the column in |
rl_norm_column_name |
The name of the column in |
total_pressure_column_name |
The name of the column in |
tp_norm_column_name |
The name of the column in |
vcmax_norm_column_name |
The name of the column in |
cj_crossover_min |
The minimum value of |
cj_crossover_max |
The maximim value of |
hard_constraints |
To be passed to |
... |
Additional arguments to be passed to |
When fitting A-Ci curves using a maximum likelihood approach, it is necessary
to define a function that calculates the likelihood of a given set of
alpha_g
, alpha_old
, alpha_s
, alpha_t
,
Gamma_star_at_25
, gmc_at_25
, J_at_25
, Kc_at_25
,
Ko_at_25
, RL_at_25
, Tp_at_25
, and Vcmax_at_25
values by comparing a model prediction to a measured curve. This function will
be passed to an optimization algorithm which will determine the values that
produce the largest likelihood.
The error_function_c3_aci
returns such a function, which is based on a
particular A-Ci curve and a set of fitting options. It is possible to just fit
a subset of the available fitting parameters; by default, the fitting
parameters are alpha_old
, J_at_25
, RL_at_25
,
Tp_at_25
, and Vcmax_at_25
. This behavior can be changed via the
fit_options
argument.
For practical reasons, the function actually returns values of -ln(L)
,
where L
is the likelihood. The logarithm of L
is simpler to
calculate than L
itself, and the minus sign converts the problem from
a maximization to a minimization, which is important because most optimizers
are designed to minimize a value.
Sometimes an optimizer will choose biologically unreasonable parameter values
that nevertheless produce good fits to the supplied assimilation values. A
common problem is that the fit result may not indicate Ac-limited assimilation
at low CO2 values, which should be the case for any A-Ci curves measured at
saturating light. In this case, the optional cj_crossover_min
and
cj_crossover_max
can be used to constrain the range of Cc
values
(in ppm) where Aj
is allowed to be the overall rate limiting factor.
If the crossover from Rubisco-limited to RuBP-regeneration limited
assimilation occurs outside these bounds (when they are supplied), a heavy
penalty will be added to the error function, preventing the optimizer from
choosing those parameter values.
A penalty is also added for any parameter combination where An
is not a
number, or where calculate_c3_assimilation
produces an error.
A function with one input argument guess
, which should be a numeric
vector representing values of the parameters to be fitted (which are specified
by the fit_options
input argument.) Each element of guess
is the
value of one parameter (arranged in alphabetical order.) For example, with the
default settings, guess
should contain values of alpha_old
,
J_at_25
, RL_at_25
, Tp_at_25
, and Vcmax_at_25
(in
that order).
# 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'
)
# Calculate the total pressure in the Licor chamber
licor_file <- calculate_total_pressure(licor_file)
# Calculate temperature-dependent values of C3 photosynthetic parameters
licor_file <- calculate_temperature_response(licor_file, c3_temperature_param_bernacchi)
# Define an error function for one curve from the set
error_fcn <- error_function_c3_aci(
licor_file[licor_file[, 'species_plot'] == 'tobacco - 1', , TRUE]
)
# Evaluate the error for:
# alpha_old = 0
# J_at_25 = 236
# RL_at_25 = 4e-8
# Tp_at_25 = 22.7
# Vcmax_at_25 = 147
error_fcn(c(0, 236, 4e-8, 22.7, 147))
# Make a plot of likelihood vs. Vcmax when other parameters are fixed to the
# values above.
vcmax_error_fcn <- function(Vcmax) {error_fcn(c(0, 236, 4e-8, 22.7, Vcmax))}
vcmax_seq <- seq(135, 152, length.out = 41)
lattice::xyplot(
exp(-sapply(vcmax_seq, vcmax_error_fcn)) ~ vcmax_seq,
type = 'b',
xlab = 'Vcmax_at_25 (micromol / m^2 / s)',
ylab = 'Negative log likelihood (dimensionless)'
)
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