Fit photosynthetic light-response curves — fit_aq

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
)

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.

Value

If .method = 'ls': an stats::nls() object.
If .method = 'brms': a brms::brmsfit() object.

Note

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).

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)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
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)
#> 
#> Formula: .A ~ marshall_biscoe_1980(Q_abs = .data[[".Qabs"]], k_sat, phi_J, 
#>     theta_J) - Rd
#> 
#> Parameters:
#>          Estimate Std. Error t value Pr(>|t|)    
#> k_sat   21.170337   0.154428  137.09 1.70e-08 ***
#> phi_J    0.061543   0.001218   50.52 9.19e-07 ***
#> theta_J  0.775752   0.014526   53.40 7.36e-07 ***
#> Rd       0.666320   0.063490   10.49 0.000466 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 0.0539 on 4 degrees of freedom
#> 
#> Number of iterations to convergence: 5 
#> Achieved convergence tolerance: 1.49e-08
#> 

## Estimated parameters:
coef(fit)
#>       k_sat       phi_J     theta_J          Rd 
#> 21.17033721  0.06154348  0.77575157  0.66631987 

## 95% confidence intervals:
confint(fit)
#> Waiting for profiling to be done...
#>                2.5%       97.5%
#> k_sat   20.75337772 21.60612987
#> phi_J    0.05835624  0.06501655
#> theta_J  0.73285635  0.81306156
#> Rd       0.49523165  0.84311606

## Tidy summary table using 'broom::tidy()'
tidy(fit, conf.int = TRUE, conf.level = 0.95)
#> # A tibble: 4 × 7
#>   term    estimate std.error statistic      p.value conf.low conf.high
#>   <chr>      <dbl>     <dbl>     <dbl>        <dbl>    <dbl>     <dbl>
#> 1 k_sat    21.2      0.154       137.  0.0000000170  20.8      21.6   
#> 2 phi_J     0.0615   0.00122      50.5 0.000000919    0.0584    0.0650
#> 3 theta_J   0.776    0.0145       53.4 0.000000736    0.733     0.813 
#> 4 Rd        0.666    0.0635       10.5 0.000466       0.495     0.843 

# Fit multiple curves with **photosynthesis** and **purrr**

library(purrr)
#> 
#> Attaching package: ‘purrr’
#> The following object is masked from ‘package:magrittr’:
#> 
#>     set_names

fits = dat |>
  split(~ group) |>
  map(fit_photosynthesis, .photo_fun = "aq_response", .vars = list(.A = A, .Q = Qabs))

# }