View source: R/confidence_intervals_c3_aci.R
confidence_intervals_c3_aci | R Documentation |
Calculates confidence intervals for parameters estimated by a C3 A-Ci curve
fit. It is rare for users to call this function directly, because it can be
automatically applied to each curve when calling fit_c3_aci
.
confidence_intervals_c3_aci(
replicate_exdf,
best_fit_parameters,
lower = list(),
upper = list(),
fit_options = list(),
sd_A = 1,
relative_likelihood_threshold = 0.147,
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 |
best_fit_parameters |
An |
lower |
The same value that was passed to |
upper |
The same value that was passed to |
fit_options |
The same value that was passed to |
sd_A |
The same value that was passed to |
relative_likelihood_threshold |
The threshold value of relative likelihood used to define the boundaries of the confidence intervals; see details below. |
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 |
In maximum likelihood fitting, each set of parameter values has an associated
likelihood value. If the maximum likelihood is known, then it is also possible
to define a relative likelihood p
according to p = L / L_max
.
The set of all parameter values where p
exceeds a threshold value
p_0
defines a region in parameter space called like a "relative
likelihood region." When taking one-dimensional cuts through parameter space,
the boundaries of the relative likelihood region define a relative likelihood
interval.
Here we calculate the upper and lower limits of the relative likelihood
intervals for each parameter. This is done by fixing the other parameters to
their best-fit values, and varying a single parameter to find the interval
where the relative likelihood is above the threshold value (set by the
relative_likelihood_threshold
input argument). If the threshold
is set to 0.147, then these intervals are equivalent to 95% confidence
intervals in most situations. See the Wikipedia page about
relative likelihood
for more information.
Internally, this function uses error_function_c3_aci
to
calculate the negative logarithm of the likelihood (-ln(L)
). It varies
each fitting parameter independendently to find values where
ln(L) - ln(p_0) - ln(L_max) = 0
.
If the upper limit of a confidence interval is found to exceed ten times the upper limit specified when fitting that parameter, then the upper limit of the condfidence interval is taken to be infinity.
An exdf
object based on best_fit_parameters
that contains lower
and upper bounds for each parameter; for example, if Vcmax_at_25
was
fit, best_fit_parameters
will contain new columns called
Vcmax_at_25_lower
and Vcmax_at_25_upper
.
# 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)
# Fit just one curve from the data set
one_result <- fit_c3_aci(
licor_file[licor_file[, 'species_plot'] == 'tobacco - 1', , TRUE],
calculate_confidence_intervals = FALSE
)
# Calculate confidence limits for the fit parameters
parameters_with_limits <- confidence_intervals_c3_aci(
licor_file[licor_file[, 'species_plot'] == 'tobacco - 1', , TRUE],
one_result$parameters
)
# View confidence limits and best estimate for Vcmax_at_25
parameters_with_limits[, c('Vcmax_at_25_lower', 'Vcmax_at_25', 'Vcmax_at_25_upper')]
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