photosynthesis (version 2.1.5)

fit_aq_response2: Fit photosynthetic light-response curves

Description

We recommend using fit_photosynthesis() with argument .photo_fun = "aq_response" rather than calling this function directly.

Usage

fit_aq_response2(
  .data,
  .model = "default",
  .method = "ls",
  usealpha_Q = FALSE,
  alpha_Q = 0.84,
  quiet = FALSE,
  brm_options = NULL
)

Value

• If .method = 'ls': an stats::nls() object.

• If .method = 'brms': a brms::brmsfit() object.

Arguments

.data

A data frame containing plant ecophysiological data. See required_variables() for the variables required for each model.

.model

A character string of model name to use. See get_all_models().

.method

A character string of the statistical method to use: 'ls' for least-squares and 'brms' for Bayesian model using brms::brm(). Default is 'ls'.

usealpha_Q

Flag. Should light intensity be multiplied by alpha_Q before fitting? Default is FALSE (i.e. assume that '.Q' is absorbed light).

alpha_Q

Number. Absorbance of incident light. Default value is 0.84. Ignored if usealpha_Q = FALSE.

quiet

Flag. Should messages be suppressed? Default is FALSE.

brm_options

A list of options passed to brms::brm() if .method = "brms". Default is NULL.

References

Marshall B, Biscoe P. 1980. A model for C3 leaves describing the dependence of net photosynthesis on irradiance. J Ex Bot 31:29-39

Examples

# \donttest{

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))

# }