R/fit_r_light.R
In cdmuir/photosynthesis: Tools for Plant Ecophysiology & Modeling
Defines functions
fit_r_light2_walker_ort_2015_ls
fit_r_light2_brms
fit_r_light2_ls
fit_r_light2
Documented in
fit_r_light2
#' Fit models to estimate light respiration (\eqn{R_\mathrm{d}})
#'
#' @description We recommend using [fit_photosynthesis()] with argument `.photo_fun = "r_light"` rather than calling this function directly.
#'
#' @inheritParams fit_photosynthesis
#' @param Q_lower Lower light intensity limit for estimating Rd using `kok_1956` and `yin_etal_2011` models.
#' @param Q_upper Upper light intensity limit for estimating Rd using `kok_1956` and `yin_etal_2011` models
#' @param Q_levels A numeric vector of light intensity levels (\eqn{\mu}mol / mol) for estimating \eqn{R_\mathrm{d}} from the linear region of the A-C curve using the `walker_ort_2015` model.
#' @param C_upper Upper C (\eqn{\mu}mol / mol) limit for estimating \eqn{R_\mathrm{d}} from the linear region of the A-C curve using the `walker_ort_2015` model.
#'
#' @return
#'
#' * If `.method = 'ls'`: an [stats::nls()] or [stats::lm()] object.
#' * If `.method = 'brms'`: a [brms::brmsfit()] object.
#'
#' @note
#'
#' Confusingly, \eqn{R_\mathrm{d}} typically denotes respiration in the light, but you might see \eqn{R_\mathrm{day}} or \eqn{R_\mathrm{light}}.
#'
#' **Models**
#'
#' *Kok (1956)*
#'
#' The `kok_1956` model estimates light respiration using the Kok method
#' (Kok, 1956). The Kok method involves looking for a breakpoint in the
#' light response of net CO2 assimilation at very low light intensities
#' and extrapolating from data above the breakpoint to estimate light
#' respiration as the y-intercept. Rd value should be negative,
#' denoting an efflux of CO2.
#'
#' *Yin et al. (2011)*
#'
#' The `yin_etal_2011` model estimates light respiration according
#' to the Yin *et al.* (2009, 2011) modifications of the Kok
#' method. The modification uses fluorescence data to get a
#' better estimate of light respiration. Rd values should be negative here to
#' denote an efflux of CO2.
#'
#' *Walker & Ort (2015)*
#'
#' The `walker_ort_2015` model estimates light respiration and
#' \eqn{\Gamma*} according to Walker & Ort (2015) using a slope-
#' intercept regression method to find the intercept of multiple
#' A-C curves run at multiple light intensities. The method estimates
#' \eqn{\Gamma*} and \eqn{R_\mathrm{d}}. If estimated \eqn{R_\mathrm{d}} is
#' positive this could indicate issues (i.e. leaks) in the gas exchange
#' measurements. \eqn{\Gamma*} is in units of umol / mol and \eqn{R_\mathrm{d}}
#' is in units of \eqn{\mu}mol m\eqn{^{-2}} s\eqn{^{-1}} of respiratory flux.
#' If using \eqn{C_\mathrm{i}}, the estimated value is technically \eqn{C_\mathrm{i}}*.
#' You need to use \eqn{C_\mathrm{c}} to get \eqn{\Gamma*} Also note, however,
#' that the convention in the field is to completely ignore this note.
#'
#'
#' @references
#' Kok B. 1956. On the inhibition of photosynthesis by intense light.
#' Biochimica et Biophysica Acta 21: 234–244
#'
#' Walker BJ, Ort DR. 2015. Improved method for measuring the apparent
#' CO2 photocompensation point resolves the impact of multiple internal
#' conductances to CO2 to net gas exchange. Plant Cell Environ 38:2462-
#' 2474
#'
#' Yin X, Struik PC, Romero P, Harbinson J, Evers JB, van der Putten
#' PEL, Vos J. 2009. Using combined measurements of gas exchange and
#' chlorophyll fluorescence to estimate parameters of a biochemical C3
#' photosynthesis model: a critical appraisal and a new integrated
#' approach applied to leaves in a wheat (Triticum aestivum) canopy.
#' Plant Cell Environ 32:448-464
#'
#' Yin X, Sun Z, Struik PC, Gu J. 2011. Evaluating a new method to
#' estimate the rate of leaf respiration in the light by analysis of
#' combined gas exchange and chlorophyll fluorescence measurements.
#' Journal of Experimental Botany 62: 3489–3499
#'
#' @examples
#' \donttest{
#'
#' # Walker & Ort (2015) model
#'
#' library(broom)
#' library(dplyr)
#' library(photosynthesis)
#'
#' acq_data = system.file("extdata", "A_Ci_Q_data_1.csv", package = "photosynthesis") |>
#' read.csv()
#'
#' fit = fit_photosynthesis(
#' .data = acq_data,
#' .photo_fun = "r_light",
#' .model = "walker_ort_2015",
#' .vars = list(.A = A, .Q = Qin, .C = Ci),
#' C_upper = 300,
#' # Irradiance levels used in experiment
#' Q_levels = c(1500, 750, 375, 125, 100, 75, 50, 25),
#' )
#'
#' # The 'fit' object inherits class 'lm' and many methods can be used
#'
#' ## Model summary:
#' summary(fit)
#'
#' ## Estimated parameters:
#' coef(fit)
#'
#' ## 95% confidence intervals:
#' ## n.b. these confidence intervals are not correct because the regression is fit
#' ## sequentially. It ignores the underlying data and uncertainty in estimates of
#' ## slopes and intercepts with each A-C curve. Use '.method = "brms"' to properly
#' ## calculate uncertainty.
#' confint(fit)
#'
#' ## Tidy summary table using 'broom::tidy()'
#' tidy(fit, conf.int = TRUE, conf.level = 0.95)
#'
#' ## Calculate residual sum-of-squares
#' sum(resid(fit)^2)
#'
#' # Yin et al. (2011) model
#'
#' fit = fit_photosynthesis(
#' .data = acq_data,
#' .photo_fun = "r_light",
#' .model = "yin_etal_2011",
#' .vars = list(.A = A, .phiPSII = PhiPS2, .Q = Qin),
#' Q_lower = 20,
#' Q_upper = 250
#' )
#'
#' # The 'fit' object inherits class 'lm' 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)
#'
#' ## Calculate residual sum-of-squares
#' sum(resid(fit)^2)
#'
#' # Kok (1956) model
#'
#' fit = fit_photosynthesis(
#' .data = acq_data,
#' .photo_fun = "r_light",
#' .model = "kok_1956",
#' .vars = list(.A = A, .Q = Qin),
#' Q_lower = 20,
#' Q_upper = 150
#' )
#'
#' # The 'fit' object inherits class 'lm' 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)
#'
#' ## Calculate residual sum-of-squares
#' sum(resid(fit)^2)
#'
#' }
#' @md
#' @rdname fit_r_light2
#' @export
fit_r_light2 = function(
.data,
.model = "default",
.method = "ls",
Q_lower = NA,
Q_upper = NA,
Q_levels = NULL,
C_upper = NA,
quiet = FALSE,
brm_options = NULL
) {
# Checks
checkmate::assert_number(
Q_lower,
lower = 0,
na.ok = !(.model %in% c("kok_1956", "yin_etal_2011"))
)
checkmate::assert_number(
Q_upper,
lower = Q_lower,
na.ok = !(.model %in% c("kok_1956", "yin_etal_2011"))
)
checkmate::assert_numeric(
Q_levels,
lower = 0,
finite = TRUE,
any.missing = FALSE,
min.len = 2L,
null.ok = !(.model %in% c("default", "walker_ort_2015"))
)
checkmate::assert_number(
C_upper,
lower = 0,
na.ok = !(.model %in% c("default", "walker_ort_2015"))
)
# Fit model
fit = switch(
.method,
ls = fit_r_light2_ls(.data, .model, Q_lower = Q_lower, Q_upper = Q_upper,
Q_levels = Q_levels, C_upper = C_upper),
brms = fit_r_light2_brms(.data, .model, Q_lower = Q_lower, Q_upper = Q_upper,
Q_levels = Q_levels, C_upper = C_upper,
brm_options = brm_options)
)
fit
}
#' Fit models to estimate light respiration (Rd) using least-squares methods
#' @inheritParams fit_r_light2
#' @noRd
fit_r_light2_ls = function(
.data,
.model,
Q_lower,
Q_upper,
Q_levels,
C_upper
) {
do.call(
glue::glue("fit_r_light2_{.model}_ls"),
args = list(
.data = .data,
Q_lower = Q_lower,
Q_upper = Q_upper,
Q_levels = Q_levels,
C_upper = C_upper
)
)
}
#' Fit models to estimate light respiration (Rd) using Bayesian methods
#' @inheritParams fit_r_light2
#' @noRd
fit_r_light2_brms = function(
.data,
.model,
Q_lower,
Q_upper,
Q_levels,
C_upper,
brm_options
) {
do.call(
glue::glue("fit_r_light2_{.model}_brms"),
args = list(
.data = .data,
Q_lower = Q_lower,
Q_upper = Q_upper,
Q_levels = Q_levels,
C_upper = C_upper,
brm_options = brm_options
)
)
}
#' Fit models to estimate light respiration (Rd) with the Walker & Ort (2015) model using least-squares methods
#' @inheritParams fit_r_light2
#' @noRd
fit_r_light2_walker_ort_2015_ls = function(
.data,
Q_levels,
C_upper,
...
) {
.data = .data |>
dplyr::filter(.C <= C_upper) |>
dplyr::mutate(
# Group by Q_level
.Q_level = round_to_nearest(.Q, Q_levels)
)
nlme::lmList(.A ~ .C | .Q_level, data = .data) |>
coef() |>
dplyr::mutate(gamma_star = -.C) %>%
lm(`(Intercept)` ~ gamma_star, data = .)
}
#' Fit models to estimate light respiration (Rd) with the Walker & Ort (2015) model using Bayesian methods
#' @inheritParams fit_r_light2
#' @noRd
fit_r_light2_walker_ort_2015_brms = function(
.data,
Q_levels,
C_upper,
brm_options,
...
) {
.data = .data |>
dplyr::filter(.C <= C_upper) |>
dplyr::mutate(
# Group by Q_level
.Q_level = as.factor(round_to_nearest(.Q, Q_levels))
)
do.call(
brms::brm,
args = c(
brm_options,
list(
formula = .A ~ .C + (1 + .C|.Q_level),
data = .data
)
)
)
}
#' Fit models to estimate light respiration (Rd) with the Yin *et al.* (2011) model using least-squares methods
#' @inheritParams fit_r_light2
#' @noRd
fit_r_light2_yin_etal_2011_ls = function(
.data,
Q_lower,
Q_upper,
...
) {
.data |>
dplyr::filter(.Q >= Q_lower, .Q <= Q_upper) |>
dplyr::mutate(x_var = .Q * .phiPSII / 4) %>%
lm(.A ~ x_var, data = .)
}
#' Fit models to estimate light respiration (Rd) with the Yin *et al.* (2011) model using Bayesian methods
#' @inheritParams fit_r_light2
#' @noRd
fit_r_light2_yin_etal_2011_brms = function(
.data,
Q_lower,
Q_upper,
brm_options,
...
) {
.data = .data |>
dplyr::filter(.Q >= Q_lower, .Q <= Q_upper) |>
dplyr::mutate(x_var = .Q * .phiPSII / 4)
do.call(
brms::brm,
args = c(
brm_options,
list(
formula = .A ~ x_var,
data = .data
)
)
)
}
#' Fit models to estimate light respiration (Rd) with the Kok (1956) model using least-squares methods
#' @inheritParams fit_r_light2
#' @noRd
fit_r_light2_kok_1956_ls = function(
.data,
Q_lower,
Q_upper,
...
) {
.data |>
dplyr::filter(.Q >= Q_lower, .Q <= Q_upper) %>%
lm(.A ~ .Q, data = .)
}
#' Fit models to estimate light respiration (Rd) with the Kok (1956) model using Bayesian methods
#' @inheritParams fit_r_light2
#' @noRd
fit_r_light2_kok_1956_brms = function(
.data,
Q_lower,
Q_upper,
brm_options,
...
) {
.data = .data |>
dplyr::filter(.Q >= Q_lower, .Q <= Q_upper)
do.call(
brms::brm,
args = c(
brm_options,
list(
formula = .A ~ .Q,
data = .data
)
)
)
}
#' Estimating light respiration
#'
#' @description
#' `r lifecycle::badge("deprecated")`
#'
#' Please use `fit_r_light2()`.
#'
#' @param data Dataframe
#' @param varnames List of variable names
#' @param PPFD_lower Lower light intensity limit for estimating Rlight
#' (Kok & Yin)
#' @param PPFD_upper Upper light intensity limit for estimating Rlight
#' (Kok & Yin)
#'
#' @param P Atmospheric pressure in kPa (Walker & Ort, 2015)
#' @param C_i_threshold Threshold C_i (in umol / mol) to cut data to
#' linear region for fitting light respiration and gamma_star
#' (Walker & Ort, 2015)
#'
#' @return fit_r_light_kok estimates light respiration using the Kok method
#' (Kok, 1956). The Kok method involves looking for a breakpoint in the
#' light response of net CO2 assimilation at very low light intensities
#' and extrapolating from data above the breakpoint to estimate light
#' respiration as the y-intercept. r_light value should be negative,
#' denoting an efflux of CO2.
#'
#' fit_r_light_WalkerOrt estimates light respiration and
#' GammaStar according to Walk & Ort (2015) using a slope-
#' intercept regression method to find the intercept of multiple
#' ACi curves run at multiple light intensities. Output GammaStar and
#' respiration should be negative If output respiration is positive
#' this could indicate issues (i.e. leaks) in the gas exchange
#' measurements. GammaStar is output in umol mol-1, and respiration
#' is output in umol m-2 s-1 of respiratory flux. Output is a list
#' containing the slope intercept regression model, a graph of the fit,
#' and estimates of the coefficients. NOTE: if using C_i, the output value
#' is technically C_istar. You need to use Cc to get GammaStar. Also note,
#' however, that the convention in the field is to completely ignore this note.
#'
#' fit_r_light_yin estimates light respiration according
#' to the Yin et al. (2009, 2011) modifications of the Kok
#' method. The modification uses fluorescence data to get a
#' better estimate of light respiration. Note that respiration
#' output should be negative here to denote an efflux of CO2.
#'
#' @references
#' Kok B. 1956. On the inhibition of photosynthesis by intense light.
#' Biochimica et Biophysica Acta 21: 234–244
#'
#' Walker BJ, Ort DR. 2015. Improved method for measuring the apparent
#' CO2 photocompensation point resolves the impact of multiple internal
#' conductances to CO2 to net gas exchange. Plant Cell Environ 38:2462-
#' 2474
#'
#' Yin X, Struik PC, Romero P, Harbinson J, Evers JB, van der Putten
#' PEL, Vos J. 2009. Using combined measurements of gas exchange and
#' chlorophyll fluorescence to estimate parameters of a biochemical C3
#' photosynthesis model: a critical appraisal and a new integrated
#' approach applied to leaves in a wheat (Triticum aestivum) canopy.
#' Plant Cell Environ 32:448-464
#'
#' Yin X, Sun Z, Struik PC, Gu J. 2011. Evaluating a new method to
#' estimate the rate of leaf respiration in the light by analysis of
#' combined gas exchange and chlorophyll fluorescence measurements.
#' Journal of Experimental Botany 62: 3489–3499
#'
#' @importFrom nlme lmList
#' @importFrom stats coef
#' @importFrom stats lm
#'
#' @examples
#' \donttest{
#' # FITTING KOK METHOD
#' # Read in your data
#' # Note that this data is coming from data supplied by the package
#' # hence the complicated argument in read.csv()
#' # This dataset is a CO2 by light response curve for a single sunflower
#' data = read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
#' package = "photosynthesis"
#' ))
#'
#' # Fit light respiration with Kok method
#' r_light = fit_r_light_kok(
#' data = data,
#' varnames = list(
#' A_net = "A",
#' PPFD = "Qin"
#' ),
#' PPFD_lower = 20,
#' PPFD_upper = 150
#' )
#' # Return r_light
#' r_light
#'
#' # FITTING WALKER-ORT METHOD
#' # Read in your data
#' # Note that this data is coming from data supplied by the package
#' # hence the complicated argument in read.csv()
#' # This dataset is a CO2 by light response curve for a single sunflower
#' data = read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
#' package = "photosynthesis"
#' ))
#'
#' # Fit the Walker-Ort method for GammaStar and light respiration
#' walker_ort = fit_r_light_WalkerOrt(data,
#' varnames = list(
#' A_net = "A",
#' C_i = "Ci",
#' PPFD = "Qin"
#' )
#' )
#' # Extract model
#' summary(walker_ort[[1]])
#'
#' # View graph
#' walker_ort[[2]]
#'
#' # View coefficients
#' walker_ort[[3]]
#'
#' # FITTING THE YIN METHOD
#' # Read in your data
#' # Note that this data is coming from data supplied by the package
#' # hence the complicated argument in read.csv()
#' # This dataset is a CO2 by light response curve for a single sunflower
#' data = read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
#' package = "photosynthesis"
#' ))
#'
#' # Fit light respiration with Yin method
#' r_light = fit_r_light_yin(
#' data = data,
#' varnames = list(
#' A_net = "A",
#' PPFD = "Qin",
#' phi_PSII = "PhiPS2"
#' ),
#' PPFD_lower = 20,
#' PPFD_upper = 250
#' )
#' }
#'
#' @rdname fit_r_light
#' @md
#' @export
fit_r_light_kok = function(
data,
varnames = list(
A_net = "A_net",
PPFD = "PPFD"
),
PPFD_lower = 40,
PPFD_upper = 100
) {
lifecycle::deprecate_warn(
"2.1.1",
"fit_r_light_kok()",
"fit_r_light2(.model = 'kok_1956')",
always = TRUE
)
# Set variable names
data$A_net = data[, varnames$A_net]
data$PPFD = data[, varnames$PPFD]
# Reduce data to within PPFD range
data_use = data[data$PPFD < PPFD_upper, ]
data_use = data_use[data_use$PPFD > PPFD_lower, ]
# Linear regression to estimate r_light (intercept)
model = lm(A_net ~ PPFD, data = data_use)
r_light = coef(model)[1]
# Output light respiration value
return(r_light)
}
#' @rdname fit_r_light
#' @export
fit_r_light_WalkerOrt = function(
data,
varnames = list(
A_net = "A_net",
C_i = "C_i",
PPFD = "PPFD"
),
P = 100,
C_i_threshold = 300
) {
lifecycle::deprecate_warn(
"2.1.1",
"fit_r_light_WalkerOrt()",
"fit_r_light2(.model = 'walker_ort_2015')",
always = TRUE
)
# Set variable names
data$A_net = data[, varnames$A_net]
data$C_i = data[, varnames$C_i]
data$PPFD = data[, varnames$PPFD]
# Locally define slope and intercept for slope-intercept regression
# This gets rid of a note in the R CMD CHECK
Slope = NULL
Intercept = NULL
# Restrict data analysis by a threshold C_i
data_use = data[data$C_i < C_i_threshold, ]
# Convert C_i to units of Pa
data_use$C_i = data_use$C_i / 1000000 * P * 1000
# Set PPFD as factor for grouping & round
# PPFD to nearest 10s
data_use$PPFD = round(data_use$PPFD, digits = -1)
data_use$PPFD = as.factor(data_use$PPFD)
# Construct regressions on the pseudolinear portions of
# the ACi curves
model = lmList(A_net ~ C_i | PPFD, data = data_use)
# Extract coefficients
coefs = coef(model)
colnames(coefs) = c("Intercept", "Slope")
coefs$PPFD = rownames(coefs)
# Create output list
output = list(NULL)
# Run slope-intercept regression model, assign to element 1
output[[1]] = lm(Intercept ~ Slope,
data = coefs
)
# Create graph, assign to element 2
output[[2]] = ggplot(coefs, aes(x = Slope, y = Intercept)) +
labs(x = "Slope", y = "Intercept") +
geom_smooth(method = "lm", linewidth = 2) +
geom_point(size = 3) +
theme_bw()
# Extract coefficients as per Walker and Ort 2015
# But convert C_istar to umol mol-1
GammaStar = -coef(output[[1]])[2] / (P * 1000) * 1000000
r_light = coef(output[[1]])[1]
output[[3]] = as.data.frame(cbind(GammaStar, r_light))
# Return output
return(output)
}
#' @rdname fit_r_light
#' @export
fit_r_light_yin = function(
data,
varnames = list(
A_net = "A_net",
PPFD = "PPFD",
phi_PSII = "phi_PSII"
),
PPFD_lower = 40,
PPFD_upper = 100
) {
lifecycle::deprecate_warn(
"2.1.1",
"fit_r_light_yin()",
"fit_r_light2(.model = 'yin_etal_2011')",
always = TRUE
)
# Set variable names
data$A_net = data[, varnames$A_net]
data$phi_PSII = data[, varnames$phi_PSII]
data$PPFD = data[, varnames$PPFD]
# Reduce data to within PPFD range
data_use = data[data$PPFD < PPFD_upper, ]
data_use = data_use[data_use$PPFD > PPFD_lower, ]
# Calculate x-variable for Yin method
data_use$x_var = data_use$PPFD * data_use$phi_PSII / 4
# Fit linear model for Yin method
model = lm(A_net ~ x_var, data = data_use)
r_light = coef(model)[1]
return(r_light)
}
cdmuir/photosynthesis documentation built on March 5, 2024, 9:26 a.m.
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Defines functions fit_r_light2_walker_ort_2015_ls fit_r_light2_brms fit_r_light2_ls fit_r_light2
Documented in fit_r_light2
#' Fit models to estimate light respiration (\eqn{R_\mathrm{d}})
#'
#' @description We recommend using [fit_photosynthesis()] with argument `.photo_fun = "r_light"` rather than calling this function directly.
#'
#' @inheritParams fit_photosynthesis
#' @param Q_lower Lower light intensity limit for estimating Rd using `kok_1956` and `yin_etal_2011` models.
#' @param Q_upper Upper light intensity limit for estimating Rd using `kok_1956` and `yin_etal_2011` models
#' @param Q_levels A numeric vector of light intensity levels (\eqn{\mu}mol / mol) for estimating \eqn{R_\mathrm{d}} from the linear region of the A-C curve using the `walker_ort_2015` model.
#' @param C_upper Upper C (\eqn{\mu}mol / mol) limit for estimating \eqn{R_\mathrm{d}} from the linear region of the A-C curve using the `walker_ort_2015` model.
#'
#' @return
#'
#' * If `.method = 'ls'`: an [stats::nls()] or [stats::lm()] object.
#' * If `.method = 'brms'`: a [brms::brmsfit()] object.
#'
#' @note
#'
#' Confusingly, \eqn{R_\mathrm{d}} typically denotes respiration in the light, but you might see \eqn{R_\mathrm{day}} or \eqn{R_\mathrm{light}}.
#'
#' **Models**
#'
#' *Kok (1956)*
#'
#' The `kok_1956` model estimates light respiration using the Kok method
#' (Kok, 1956). The Kok method involves looking for a breakpoint in the
#' light response of net CO2 assimilation at very low light intensities
#' and extrapolating from data above the breakpoint to estimate light
#' respiration as the y-intercept. Rd value should be negative,
#' denoting an efflux of CO2.
#'
#' *Yin et al. (2011)*
#'
#' The `yin_etal_2011` model estimates light respiration according
#' to the Yin *et al.* (2009, 2011) modifications of the Kok
#' method. The modification uses fluorescence data to get a
#' better estimate of light respiration. Rd values should be negative here to
#' denote an efflux of CO2.
#'
#' *Walker & Ort (2015)*
#'
#' The `walker_ort_2015` model estimates light respiration and
#' \eqn{\Gamma*} according to Walker & Ort (2015) using a slope-
#' intercept regression method to find the intercept of multiple
#' A-C curves run at multiple light intensities. The method estimates
#' \eqn{\Gamma*} and \eqn{R_\mathrm{d}}. If estimated \eqn{R_\mathrm{d}} is
#' positive this could indicate issues (i.e. leaks) in the gas exchange
#' measurements. \eqn{\Gamma*} is in units of umol / mol and \eqn{R_\mathrm{d}}
#' is in units of \eqn{\mu}mol m\eqn{^{-2}} s\eqn{^{-1}} of respiratory flux.
#' If using \eqn{C_\mathrm{i}}, the estimated value is technically \eqn{C_\mathrm{i}}*.
#' You need to use \eqn{C_\mathrm{c}} to get \eqn{\Gamma*} Also note, however,
#' that the convention in the field is to completely ignore this note.
#'
#'
#' @references
#' Kok B. 1956. On the inhibition of photosynthesis by intense light.
#' Biochimica et Biophysica Acta 21: 234–244
#'
#' Walker BJ, Ort DR. 2015. Improved method for measuring the apparent
#' CO2 photocompensation point resolves the impact of multiple internal
#' conductances to CO2 to net gas exchange. Plant Cell Environ 38:2462-
#' 2474
#'
#' Yin X, Struik PC, Romero P, Harbinson J, Evers JB, van der Putten
#' PEL, Vos J. 2009. Using combined measurements of gas exchange and
#' chlorophyll fluorescence to estimate parameters of a biochemical C3
#' photosynthesis model: a critical appraisal and a new integrated
#' approach applied to leaves in a wheat (Triticum aestivum) canopy.
#' Plant Cell Environ 32:448-464
#'
#' Yin X, Sun Z, Struik PC, Gu J. 2011. Evaluating a new method to
#' estimate the rate of leaf respiration in the light by analysis of
#' combined gas exchange and chlorophyll fluorescence measurements.
#' Journal of Experimental Botany 62: 3489–3499
#'
#' @examples
#' \donttest{
#'
#' # Walker & Ort (2015) model
#'
#' library(broom)
#' library(dplyr)
#' library(photosynthesis)
#'
#' acq_data = system.file("extdata", "A_Ci_Q_data_1.csv", package = "photosynthesis") |>
#' read.csv()
#'
#' fit = fit_photosynthesis(
#' .data = acq_data,
#' .photo_fun = "r_light",
#' .model = "walker_ort_2015",
#' .vars = list(.A = A, .Q = Qin, .C = Ci),
#' C_upper = 300,
#' # Irradiance levels used in experiment
#' Q_levels = c(1500, 750, 375, 125, 100, 75, 50, 25),
#' )
#'
#' # The 'fit' object inherits class 'lm' and many methods can be used
#'
#' ## Model summary:
#' summary(fit)
#'
#' ## Estimated parameters:
#' coef(fit)
#'
#' ## 95% confidence intervals:
#' ## n.b. these confidence intervals are not correct because the regression is fit
#' ## sequentially. It ignores the underlying data and uncertainty in estimates of
#' ## slopes and intercepts with each A-C curve. Use '.method = "brms"' to properly
#' ## calculate uncertainty.
#' confint(fit)
#'
#' ## Tidy summary table using 'broom::tidy()'
#' tidy(fit, conf.int = TRUE, conf.level = 0.95)
#'
#' ## Calculate residual sum-of-squares
#' sum(resid(fit)^2)
#'
#' # Yin et al. (2011) model
#'
#' fit = fit_photosynthesis(
#' .data = acq_data,
#' .photo_fun = "r_light",
#' .model = "yin_etal_2011",
#' .vars = list(.A = A, .phiPSII = PhiPS2, .Q = Qin),
#' Q_lower = 20,
#' Q_upper = 250
#' )
#'
#' # The 'fit' object inherits class 'lm' 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)
#'
#' ## Calculate residual sum-of-squares
#' sum(resid(fit)^2)
#'
#' # Kok (1956) model
#'
#' fit = fit_photosynthesis(
#' .data = acq_data,
#' .photo_fun = "r_light",
#' .model = "kok_1956",
#' .vars = list(.A = A, .Q = Qin),
#' Q_lower = 20,
#' Q_upper = 150
#' )
#'
#' # The 'fit' object inherits class 'lm' 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)
#'
#' ## Calculate residual sum-of-squares
#' sum(resid(fit)^2)
#'
#' }
#' @md
#' @rdname fit_r_light2
#' @export
fit_r_light2 = function(
.data,
.model = "default",
.method = "ls",
Q_lower = NA,
Q_upper = NA,
Q_levels = NULL,
C_upper = NA,
quiet = FALSE,
brm_options = NULL
) {
# Checks
checkmate::assert_number(
Q_lower,
lower = 0,
na.ok = !(.model %in% c("kok_1956", "yin_etal_2011"))
)
checkmate::assert_number(
Q_upper,
lower = Q_lower,
na.ok = !(.model %in% c("kok_1956", "yin_etal_2011"))
)
checkmate::assert_numeric(
Q_levels,
lower = 0,
finite = TRUE,
any.missing = FALSE,
min.len = 2L,
null.ok = !(.model %in% c("default", "walker_ort_2015"))
)
checkmate::assert_number(
C_upper,
lower = 0,
na.ok = !(.model %in% c("default", "walker_ort_2015"))
)
# Fit model
fit = switch(
.method,
ls = fit_r_light2_ls(.data, .model, Q_lower = Q_lower, Q_upper = Q_upper,
Q_levels = Q_levels, C_upper = C_upper),
brms = fit_r_light2_brms(.data, .model, Q_lower = Q_lower, Q_upper = Q_upper,
Q_levels = Q_levels, C_upper = C_upper,
brm_options = brm_options)
)
fit
}
#' Fit models to estimate light respiration (Rd) using least-squares methods
#' @inheritParams fit_r_light2
#' @noRd
fit_r_light2_ls = function(
.data,
.model,
Q_lower,
Q_upper,
Q_levels,
C_upper
) {
do.call(
glue::glue("fit_r_light2_{.model}_ls"),
args = list(
.data = .data,
Q_lower = Q_lower,
Q_upper = Q_upper,
Q_levels = Q_levels,
C_upper = C_upper
)
)
}
#' Fit models to estimate light respiration (Rd) using Bayesian methods
#' @inheritParams fit_r_light2
#' @noRd
fit_r_light2_brms = function(
.data,
.model,
Q_lower,
Q_upper,
Q_levels,
C_upper,
brm_options
) {
do.call(
glue::glue("fit_r_light2_{.model}_brms"),
args = list(
.data = .data,
Q_lower = Q_lower,
Q_upper = Q_upper,
Q_levels = Q_levels,
C_upper = C_upper,
brm_options = brm_options
)
)
}
#' Fit models to estimate light respiration (Rd) with the Walker & Ort (2015) model using least-squares methods
#' @inheritParams fit_r_light2
#' @noRd
fit_r_light2_walker_ort_2015_ls = function(
.data,
Q_levels,
C_upper,
...
) {
.data = .data |>
dplyr::filter(.C <= C_upper) |>
dplyr::mutate(
# Group by Q_level
.Q_level = round_to_nearest(.Q, Q_levels)
)
nlme::lmList(.A ~ .C | .Q_level, data = .data) |>
coef() |>
dplyr::mutate(gamma_star = -.C) %>%
lm(`(Intercept)` ~ gamma_star, data = .)
}
#' Fit models to estimate light respiration (Rd) with the Walker & Ort (2015) model using Bayesian methods
#' @inheritParams fit_r_light2
#' @noRd
fit_r_light2_walker_ort_2015_brms = function(
.data,
Q_levels,
C_upper,
brm_options,
...
) {
.data = .data |>
dplyr::filter(.C <= C_upper) |>
dplyr::mutate(
# Group by Q_level
.Q_level = as.factor(round_to_nearest(.Q, Q_levels))
)
do.call(
brms::brm,
args = c(
brm_options,
list(
formula = .A ~ .C + (1 + .C|.Q_level),
data = .data
)
)
)
}
#' Fit models to estimate light respiration (Rd) with the Yin *et al.* (2011) model using least-squares methods
#' @inheritParams fit_r_light2
#' @noRd
fit_r_light2_yin_etal_2011_ls = function(
.data,
Q_lower,
Q_upper,
...
) {
.data |>
dplyr::filter(.Q >= Q_lower, .Q <= Q_upper) |>
dplyr::mutate(x_var = .Q * .phiPSII / 4) %>%
lm(.A ~ x_var, data = .)
}
#' Fit models to estimate light respiration (Rd) with the Yin *et al.* (2011) model using Bayesian methods
#' @inheritParams fit_r_light2
#' @noRd
fit_r_light2_yin_etal_2011_brms = function(
.data,
Q_lower,
Q_upper,
brm_options,
...
) {
.data = .data |>
dplyr::filter(.Q >= Q_lower, .Q <= Q_upper) |>
dplyr::mutate(x_var = .Q * .phiPSII / 4)
do.call(
brms::brm,
args = c(
brm_options,
list(
formula = .A ~ x_var,
data = .data
)
)
)
}
#' Fit models to estimate light respiration (Rd) with the Kok (1956) model using least-squares methods
#' @inheritParams fit_r_light2
#' @noRd
fit_r_light2_kok_1956_ls = function(
.data,
Q_lower,
Q_upper,
...
) {
.data |>
dplyr::filter(.Q >= Q_lower, .Q <= Q_upper) %>%
lm(.A ~ .Q, data = .)
}
#' Fit models to estimate light respiration (Rd) with the Kok (1956) model using Bayesian methods
#' @inheritParams fit_r_light2
#' @noRd
fit_r_light2_kok_1956_brms = function(
.data,
Q_lower,
Q_upper,
brm_options,
...
) {
.data = .data |>
dplyr::filter(.Q >= Q_lower, .Q <= Q_upper)
do.call(
brms::brm,
args = c(
brm_options,
list(
formula = .A ~ .Q,
data = .data
)
)
)
}
#' Estimating light respiration
#'
#' @description
#' `r lifecycle::badge("deprecated")`
#'
#' Please use `fit_r_light2()`.
#'
#' @param data Dataframe
#' @param varnames List of variable names
#' @param PPFD_lower Lower light intensity limit for estimating Rlight
#' (Kok & Yin)
#' @param PPFD_upper Upper light intensity limit for estimating Rlight
#' (Kok & Yin)
#'
#' @param P Atmospheric pressure in kPa (Walker & Ort, 2015)
#' @param C_i_threshold Threshold C_i (in umol / mol) to cut data to
#' linear region for fitting light respiration and gamma_star
#' (Walker & Ort, 2015)
#'
#' @return fit_r_light_kok estimates light respiration using the Kok method
#' (Kok, 1956). The Kok method involves looking for a breakpoint in the
#' light response of net CO2 assimilation at very low light intensities
#' and extrapolating from data above the breakpoint to estimate light
#' respiration as the y-intercept. r_light value should be negative,
#' denoting an efflux of CO2.
#'
#' fit_r_light_WalkerOrt estimates light respiration and
#' GammaStar according to Walk & Ort (2015) using a slope-
#' intercept regression method to find the intercept of multiple
#' ACi curves run at multiple light intensities. Output GammaStar and
#' respiration should be negative If output respiration is positive
#' this could indicate issues (i.e. leaks) in the gas exchange
#' measurements. GammaStar is output in umol mol-1, and respiration
#' is output in umol m-2 s-1 of respiratory flux. Output is a list
#' containing the slope intercept regression model, a graph of the fit,
#' and estimates of the coefficients. NOTE: if using C_i, the output value
#' is technically C_istar. You need to use Cc to get GammaStar. Also note,
#' however, that the convention in the field is to completely ignore this note.
#'
#' fit_r_light_yin estimates light respiration according
#' to the Yin et al. (2009, 2011) modifications of the Kok
#' method. The modification uses fluorescence data to get a
#' better estimate of light respiration. Note that respiration
#' output should be negative here to denote an efflux of CO2.
#'
#' @references
#' Kok B. 1956. On the inhibition of photosynthesis by intense light.
#' Biochimica et Biophysica Acta 21: 234–244
#'
#' Walker BJ, Ort DR. 2015. Improved method for measuring the apparent
#' CO2 photocompensation point resolves the impact of multiple internal
#' conductances to CO2 to net gas exchange. Plant Cell Environ 38:2462-
#' 2474
#'
#' Yin X, Struik PC, Romero P, Harbinson J, Evers JB, van der Putten
#' PEL, Vos J. 2009. Using combined measurements of gas exchange and
#' chlorophyll fluorescence to estimate parameters of a biochemical C3
#' photosynthesis model: a critical appraisal and a new integrated
#' approach applied to leaves in a wheat (Triticum aestivum) canopy.
#' Plant Cell Environ 32:448-464
#'
#' Yin X, Sun Z, Struik PC, Gu J. 2011. Evaluating a new method to
#' estimate the rate of leaf respiration in the light by analysis of
#' combined gas exchange and chlorophyll fluorescence measurements.
#' Journal of Experimental Botany 62: 3489–3499
#'
#' @importFrom nlme lmList
#' @importFrom stats coef
#' @importFrom stats lm
#'
#' @examples
#' \donttest{
#' # FITTING KOK METHOD
#' # Read in your data
#' # Note that this data is coming from data supplied by the package
#' # hence the complicated argument in read.csv()
#' # This dataset is a CO2 by light response curve for a single sunflower
#' data = read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
#' package = "photosynthesis"
#' ))
#'
#' # Fit light respiration with Kok method
#' r_light = fit_r_light_kok(
#' data = data,
#' varnames = list(
#' A_net = "A",
#' PPFD = "Qin"
#' ),
#' PPFD_lower = 20,
#' PPFD_upper = 150
#' )
#' # Return r_light
#' r_light
#'
#' # FITTING WALKER-ORT METHOD
#' # Read in your data
#' # Note that this data is coming from data supplied by the package
#' # hence the complicated argument in read.csv()
#' # This dataset is a CO2 by light response curve for a single sunflower
#' data = read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
#' package = "photosynthesis"
#' ))
#'
#' # Fit the Walker-Ort method for GammaStar and light respiration
#' walker_ort = fit_r_light_WalkerOrt(data,
#' varnames = list(
#' A_net = "A",
#' C_i = "Ci",
#' PPFD = "Qin"
#' )
#' )
#' # Extract model
#' summary(walker_ort[[1]])
#'
#' # View graph
#' walker_ort[[2]]
#'
#' # View coefficients
#' walker_ort[[3]]
#'
#' # FITTING THE YIN METHOD
#' # Read in your data
#' # Note that this data is coming from data supplied by the package
#' # hence the complicated argument in read.csv()
#' # This dataset is a CO2 by light response curve for a single sunflower
#' data = read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
#' package = "photosynthesis"
#' ))
#'
#' # Fit light respiration with Yin method
#' r_light = fit_r_light_yin(
#' data = data,
#' varnames = list(
#' A_net = "A",
#' PPFD = "Qin",
#' phi_PSII = "PhiPS2"
#' ),
#' PPFD_lower = 20,
#' PPFD_upper = 250
#' )
#' }
#'
#' @rdname fit_r_light
#' @md
#' @export
fit_r_light_kok = function(
data,
varnames = list(
A_net = "A_net",
PPFD = "PPFD"
),
PPFD_lower = 40,
PPFD_upper = 100
) {
lifecycle::deprecate_warn(
"2.1.1",
"fit_r_light_kok()",
"fit_r_light2(.model = 'kok_1956')",
always = TRUE
)
# Set variable names
data$A_net = data[, varnames$A_net]
data$PPFD = data[, varnames$PPFD]
# Reduce data to within PPFD range
data_use = data[data$PPFD < PPFD_upper, ]
data_use = data_use[data_use$PPFD > PPFD_lower, ]
# Linear regression to estimate r_light (intercept)
model = lm(A_net ~ PPFD, data = data_use)
r_light = coef(model)[1]
# Output light respiration value
return(r_light)
}
#' @rdname fit_r_light
#' @export
fit_r_light_WalkerOrt = function(
data,
varnames = list(
A_net = "A_net",
C_i = "C_i",
PPFD = "PPFD"
),
P = 100,
C_i_threshold = 300
) {
lifecycle::deprecate_warn(
"2.1.1",
"fit_r_light_WalkerOrt()",
"fit_r_light2(.model = 'walker_ort_2015')",
always = TRUE
)
# Set variable names
data$A_net = data[, varnames$A_net]
data$C_i = data[, varnames$C_i]
data$PPFD = data[, varnames$PPFD]
# Locally define slope and intercept for slope-intercept regression
# This gets rid of a note in the R CMD CHECK
Slope = NULL
Intercept = NULL
# Restrict data analysis by a threshold C_i
data_use = data[data$C_i < C_i_threshold, ]
# Convert C_i to units of Pa
data_use$C_i = data_use$C_i / 1000000 * P * 1000
# Set PPFD as factor for grouping & round
# PPFD to nearest 10s
data_use$PPFD = round(data_use$PPFD, digits = -1)
data_use$PPFD = as.factor(data_use$PPFD)
# Construct regressions on the pseudolinear portions of
# the ACi curves
model = lmList(A_net ~ C_i | PPFD, data = data_use)
# Extract coefficients
coefs = coef(model)
colnames(coefs) = c("Intercept", "Slope")
coefs$PPFD = rownames(coefs)
# Create output list
output = list(NULL)
# Run slope-intercept regression model, assign to element 1
output[[1]] = lm(Intercept ~ Slope,
data = coefs
)
# Create graph, assign to element 2
output[[2]] = ggplot(coefs, aes(x = Slope, y = Intercept)) +
labs(x = "Slope", y = "Intercept") +
geom_smooth(method = "lm", linewidth = 2) +
geom_point(size = 3) +
theme_bw()
# Extract coefficients as per Walker and Ort 2015
# But convert C_istar to umol mol-1
GammaStar = -coef(output[[1]])[2] / (P * 1000) * 1000000
r_light = coef(output[[1]])[1]
output[[3]] = as.data.frame(cbind(GammaStar, r_light))
# Return output
return(output)
}
#' @rdname fit_r_light
#' @export
fit_r_light_yin = function(
data,
varnames = list(
A_net = "A_net",
PPFD = "PPFD",
phi_PSII = "phi_PSII"
),
PPFD_lower = 40,
PPFD_upper = 100
) {
lifecycle::deprecate_warn(
"2.1.1",
"fit_r_light_yin()",
"fit_r_light2(.model = 'yin_etal_2011')",
always = TRUE
)
# Set variable names
data$A_net = data[, varnames$A_net]
data$phi_PSII = data[, varnames$phi_PSII]
data$PPFD = data[, varnames$PPFD]
# Reduce data to within PPFD range
data_use = data[data$PPFD < PPFD_upper, ]
data_use = data_use[data_use$PPFD > PPFD_lower, ]
# Calculate x-variable for Yin method
data_use$x_var = data_use$PPFD * data_use$phi_PSII / 4
# Fit linear model for Yin method
model = lm(A_net ~ x_var, data = data_use)
r_light = coef(model)[1]
return(r_light)
}
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