View source: R/fit_aq_response.R
| fit_aq_response2 | R Documentation | 
We recommend using fit_photosynthesis() with argument .photo_fun = "aq_response" rather than calling this function directly.
fit_aq_response2(
  .data,
  .model = "default",
  .method = "ls",
  usealpha_Q = FALSE,
  alpha_Q = 0.84,
  quiet = FALSE,
  brm_options = NULL
)
.data | 
 A data frame containing plant ecophysiological data. See   | 
.model | 
 A character string of model name to use. See   | 
.method | 
 A character string of the statistical method to use: 'ls' for least-squares and 'brms' for Bayesian model using   | 
usealpha_Q | 
 Flag. Should light intensity be multiplied by   | 
alpha_Q | 
 Number. Absorbance of incident light. Default value is 0.84. Ignored if   | 
quiet | 
 Flag. Should messages be suppressed? Default is FALSE.  | 
brm_options | 
 A list of options passed to   | 
 If .method = 'ls': an stats::nls() object.
 If .method = 'brms': a brms::brmsfit() object.
Rd fitted in this way is essentially the same as the Kok (1956) method, and represents a respiration value in the light that may not be accurate. Rd output should thus be interpreted more as a residual parameter to ensure an accurate fit of the light response parameters. Model originally from Marshall & Biscoe (1980).
Marshall B, Biscoe P. 1980. A model for C3 leaves describing the dependence of net photosynthesis on irradiance. J Ex Bot 31:29-39
library(broom)
library(dplyr)
library(photosynthesis)
# Read in your data
dat = system.file("extdata", "A_Ci_Q_data_1.csv", package = "photosynthesis") |>
  read.csv() |>
  # Set grouping variable
  mutate(group = round(CO2_s, digits = 0)) |>
  # For this example, round sequentially due to CO2_s set points
  mutate(group = as.factor(round(group, digits = -1)))
# Fit one light-response curve
fit = fit_photosynthesis(
  .data = filter(dat, group == 600),
  .photo_fun = "aq_response",
  .vars = list(.A = A, .Q = Qabs),
)
# The 'fit' object inherits class 'nls' and many methods can be used
## Model summary:
summary(fit)
## Estimated parameters:
coef(fit)
## 95% confidence intervals:
confint(fit)
## Tidy summary table using 'broom::tidy()'
tidy(fit, conf.int = TRUE, conf.level = 0.95)
# Fit multiple curves with **photosynthesis** and **purrr**
library(purrr)
fits = dat |>
  split(~ group) |>
  map(fit_photosynthesis, .photo_fun = "aq_response", .vars = list(.A = A, .Q = Qabs))
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