library 'pam'

Introduction

The library ‘pam’ was developed to process PAM raw data (chlorophyll fluorometry to analyze photosystem II and dual wavelength absorbance spectrometry to analyze photosystem I), for example from the DUAL PAM of the manufacturer WALZ. Four different models are provided for the regression of the light curve (Vollenweider (1965), Platt (1980), Eilers and Peeters (1988) and Walsby (1997)). To select the most suitable model for the respective data set, the models can be compared with each other. To avoid confusion in the naming of the variables and calculated factors such as $$ETR_{max}$$, it is possible to output both with publication-compliant naming and with homogenised naming. Generated control plots make it possible to check each individual regression fit and calculated data.

Install

GitHub

install.packages("remotes")
remotes::install_github("biotoolbox/pam", subdir = "src")

Examples

Examples of usage can be found in the examples directory.

Functions

read_dual_pam_data()

Description

This function reads the original CSV file as created by the DualPAM software, processes it by calculating $$ETR$$ values, and returns a cleaned dataset. Functionality with raw data from other PAM devices cannot be guaranteed. Individual customisation may be necessary when reading data.

Parameters

csv_path: A string representing the file path to the CSV file.
remove_recovery: Automatic removal of recovery measurements after the actual Pi curve for an accurate regression. Default is TRUE.
etr_factor: A numeric value used as a factor for calculating ETR. Default is 0.84.
p_ratio: A numeric value representing the ratio of PS II / PSI used in the ETR calculation formula. Default is 0.5. Calculated as:

$$\textit{P–Ratio} = \frac{PPS2}{PPS1+2}$$

Details

ETR values are calculated using the following formula:

$$ ETR = PAR \cdot \textit{ETR–Factor} \cdot P–Ratio \cdot Yield $$

The function processes the provided CSV file by:

Reading the CSV data using read.csv().
Filtering rows where the column ID equals SP.
Combining the Date and Time columns to create a new DateTime column.
Calculating the ETR values for both Y.I. and Y.II. using the function calc_etr().
Removing rows from the recovery period if remove_recovery = TRUE.

Return

A data.table containing the processed data with additional columns for recalculated ETR values.

Example

data <- read_dual_pam_data("path/to/data.csv",
remove_recovery = TRUE,
etr_factor = 0.84,
p_ratio = 0.5)

References

Heinz Walz GmbH. (2024). DUAL-PAM-100 DUAL-PAM/F MANUAL, 5th Edition, April 2024, Chapter 7 (pp. 162-172). Heinz Walz GmbH, Effeltrich, Germany. Available at: DUAL-PAM-100 Manual

vollenweider_generate_regression_ETR_I() and vollenweider_generate_regression_ETR_II()

This function generates a regression model based on Vollenweider (1965). Original naming conventions from the publication are used.

Parameters

data: A data.table containing the input data from read_dual_pam_data.
etr_type: A character string specifying the column name of the response variable (ETR I or ETR II) to be used in the model.
pmax_start_value_vollenweider: Numeric. The starting value for the parameter $$p_{max}$$ in the model. Defaults to pmax_start_values_vollenweider_default.
a_start_value_vollenweider: Numeric. The starting value for the parameter $$a$$ in the model. Defaults to a_start_values_vollenweider_default.
alpha_start_value: Numeric. The starting value for the parameter $$\alpha$$ in the model. Defaults to alpha_start_values_vollenweider_default.
n_start_value: Numeric. The starting value for the parameter $$n$$ in the model. Defaults to n_start_values_vollenweider_default.

Return

A list containing the following elements:

etr_regression_data: A data.table with the predicted values of ETR I or ETR II to each PAR based on the fitted model.
sdiff: The deviation between the actual and predicted ETR values.
pmax: The maximum electron transport rate without photoinhibition ($$p_{max}$$).
a: The obtained parameter $$a$$.
alpha: The obtained parameter $$\alpha$$.
n: The obtained parameter $$n$$.
popt: The maximum electron transport rate with photoinhibition ($$p_{opt}$$). A function computes predicted photosynthetic rates for each PAR value and tracks the maximum rate observed:

  popt <- 0
      pars <- c()
      predictions <- c()
      for (p in min(data$PAR):max(data$PAR)) {
        pars <- c(pars, p)
        prediction <- pmax * (((a * p) / (sqrt(1 + (a * p)^2))) * (1 / (sqrt(1 + (alpha * p)^2)^n)))
        predictions <- c(
          predictions,
          prediction
        )

        if (prediction > popt) {
          popt <- prediction
        }
      }

ik: PAR where the transition point from light limitation to light saturation is achieved without photoinhibition ($$I_k$$). Calculated as:

$$I_k = \frac{1}{a}$$

iik: PAR where the transition point from light limitation to light saturation is achieved with photoinhibition ($$I_k^\prime$$). Calculated as:

$$I_k^\prime = \frac{I_k \cdot p_{opt}}{p_{max}}$$

pmax_popt_and_ik_iik_ratio: Ratio of $$p_{max}$$ to $$p_{opt}$$ and $$I_k$$ to $$I_k^\prime$$ ($$p_{max} / p_{opt}$$). Calculated as:

$$\p_max\_popt\_and\_ik\_iik\_ratio = \frac{I_k}{I_k^\prime}$$

Details

This function uses non-linear least squares fitting to estimate the parameters for the Vollenweider model, which describes the relationship between PAR and ETR. The model used is:

$$p = p_{max} \cdot \frac{a \cdot i}{\sqrt{1 + (a \cdot i)^2}} \cdot \frac{1}{\left(\sqrt{1 + (\alpha \cdot i)^2}\right)^n}$$

It is valid: $$i = PAR; p = ETR$$

Example

result_vollenweider_ETR_II <- vollenweider_generate_regression_ETR_II(data, 
    pmax_start_value_vollenweider = 40, 
    a_start_value_vollenweider = 0.1, 
    alpha_start_value = -0.0001, 
    n_start_value = 350)

References

Vollenweider, R. A. (1965). Calculation models of photosynthesis-depth curves and some implications regarding day rate estimates in primary production measurements, p. 427-457. In C. R. Goldman [ed.], Primary Productivity in Aquatic Environments. Mem. Ist. Ital. Idrobiol., 18 Suppl., University of California Press, Berkeley.

platt_generate_regression_ETR_I() and platt_generate_regression_ETR_II()

This function generates a regression model based on Platt (1980). Original naming conventions from the publication are used.

Parameters

data: A data.table containing the input data from read_dual_pam_data.
alpha_start_value_platt: Numeric. The starting value for the parameter $$\alpha$$ in the model. Defaults to alpha_start_value_platt_default.
beta_start_value_platt: Numeric. The starting value for the parameter $$\beta$$ in the model. Defaults to beta_start_value_platt_default.
ps_start_value_platt: Numeric. The starting value for the parameter $$p_s$$ in the model. Defaults to ps_start_value_platt_default.

Return

A list containing the following elements:

etr_regression_data: A data.table with the predicted values of ETR I or ETR II to each PAR based on the fitted model.
sdiff: The deviation between the actual and predicted ETR values.
ps: The maximum electron transport rate without photoinhibition ($$P_s$$).
alpha: The initial slope of the light curve ($$\alpha$$).
beta: The photoinhibition of the light curve ($$\beta$$).
pm: The maximum electron transport rate with photoinhibition ($$P_m$$). Calculated as:

$$P_m = P_s \cdot \left(\frac{\alpha}{\alpha + \beta}\right) \cdot \left(\left(\frac{\beta}{\alpha + \beta}\right)^{\frac{\beta}{\alpha}}\right)$$

ik: PAR where the transition point from light limitation to light saturation is achieved with photoinhibition ($$I_k$$). Calculated as:

$$I_k = \frac{P_m}{\alpha}$$

is: PAR where the transition point from light limitation to light saturation is achieved without photoinhibition ($$I_s$$). Calculated as:

$$I_s = \frac{P_s}{\alpha}$$

im: The PAR at which the maximum electron transport rate is achieved with photoinhibition ($$I_m$$). Calculated as:

$$I_m = \left(\frac{P_s}{\alpha}\right) \cdot \log\left(\frac{\alpha + \beta}{\beta}\right)$$

ib: ($$I_b$$) Calculated as:

$$I_b = \frac{P_s}{\beta}$$

Details

This function uses non-linear least squares fitting to estimate the parameters for the Platt model, which describes the relationship between PAR and ETR. The model used is:

$$P = P_s \cdot \left(1 - e^\frac{{-\alpha \cdot I}}{P_s}\right) \cdot e^\left(\frac{{-\beta \cdot I}}{P_s}\right)$$

It is valid: $$I = PAR; p = ETR$$

Example

result_platt_ETR_II <- platt_generate_regression_ETR_II(data, 
    alpha_start_value_platt = 0.3, 
    beta_start_value_platt = 0.01, 
    ps_start_value_platt = 30)

References

Platt, T., Gallegos, C. L., & Harrison, W. G. (1980). Photoinhibition of photosynthesis in natural assemblages of marine phytoplankton. Journal of Marine Research, 38(4). Retrieved from https://elischolar.library.yale.edu/journal_of_marine_research/1525.

eilers_peeters_generate_regression_ETR_I() and eilers_peeters_generate_regression_ETR_II()

This function generates a regression model based on Eilers-Peeters (1988). Original naming conventions from the publication are used. All parameters are calculated taking photoinhibition into account.

Parameters

data: A data.table containing the input data from read_dual_pam_data.
a_start_value: Numeric. The starting value for the parameter $$a$$ in the model. Defaults to a_start_values_eilers_peeters_default.
b_start_value: Numeric. The starting value for the parameter $$b$$ in the model. Defaults to b_start_values_eilers_peeters_default.
c_start_value: Numeric. The starting value for the parameter $$c$$ in the model. Defaults to c_start_values_eilers_peeters_default.

Return

A list containing the following elements:

etr_regression_data: A data.table with the predicted values of ETR I or ETR II to each PAR based on the fitted model.
sdiff: The deviation between the actual and predicted ETR values.
a: The obtained parameter $$a$$.
b: The obtained parameter $$b$$.
c: The obtained parameter $$c$$.
pm: The maximum electron transport rate ($$p_m$$). Calculated as:

$$p_m = \frac{1}{b + 2 \sqrt{a \cdot c}}$$

s: The initial slope of the light curve ($$s$$). Calculated as:

$$s = \frac{1}{c}$$

ik: PAR where the transition point from light limitation to light saturation is achieved ($$I_k$$). Calculated as:

$$I_k = \frac{c}{b + 2 \sqrt{a \cdot c}}$$

im: The PAR at which the maximum electron transport rate is achieved ($$I_m$$). Calculated as:

$$I_m = \sqrt{\frac{c}{a}}$$

w: The sharpness of the peak ($$w$$). Calculated as:

$$w = \frac{b}{\sqrt{a \cdot c}}$$

Details

This function uses non-linear least squares fitting to estimate the parameters for the Eilers-Peeters model, which describes the relationship between PAR and ETR. The model used is:

$$ p = \frac{I}{a \cdot I^2 + b \cdot I + c} $$

It is valid: $$I = PAR$$; $$p = ETR$$

Example

result_eilers_peeters_ETR_II <- eilers_peeters_generate_regression_ETR_II(data,
a_start_value = 0.00004,
b_start_value =  0.004,
c_start_value = 5)

References

Eilers, P. H. C., & Peeters, J. C. H. (1988). A model for the relationship between light intensity and the rate of photosynthesis in phytoplankton. Ecological Modelling, 42(3-4), 199-215. doi:10.1016/0304-3800(88)90057-9.

walsby_generate_regression_ETR_I() and walsby_generate_regression_ETR_II()

This function generates a regression model based on Walsby (1997) in a modified version without the respiration term. Naming conventions from Romoth (2019) are used. ETRmax is calculated without taking photoinhibition into account.

Parameters

data: A data.table containing the input data from read_dual_pam_data.
etr_max_start_value_walsby: Numeric. The starting value for the parameter $$ETR_{max}$$ in the model. Defaults to etr_max_start_value_walsby_default.
alpha_start_value_walsby: Numeric. The starting value for the parameter $$\alpha$$ in the model. Defaults to alpha_start_value_walsby_default.
beta_start_value_walsby: Numeric. The starting value for the parameter $$\beta$$ in the model. Defaults to beta_start_value_walsby_default.

Return

A list containing the following elements:

etr_regression_data: A data.table with the predicted values of ETR I or ETR II to each PAR based on the fitted model.
sdiff: The deviation between the actual and predicted ETR values.
etr_max: The maximum electron transport rate without photoinhibition ($$ETR_{max}$$).
alpha: The initial slope of the light curve ($$\alpha$$).
beta: The photoinhibition of the light curve ($$\beta$$).

Details

This function uses non-linear least squares fitting to estimate the parameters for the Walsby model, which describes the relationship between PAR and ETR I. The model used is:

$$ETR = ETR_{max} \cdot \left(1 - e^{\left(-\frac{\alpha \cdot I}{ETR_{max}}\right)}\right) + \beta \cdot I$$

It is valid: $$I = PAR$$

References

Walsby, A. E. (1997). Numerical integration of phytoplankton photosynthesis through time and depth in a water column. New Phytologist, 136(2), 189-209. https://doi.org/10.1046/j.1469-8137.1997.00736.x

Romoth, K., Nowak, P., Kempke, D., Dietrich, A., Porsche, C., & Schubert, H. (2019). Acclimation limits of Fucus evanescens along the salinity gradient of the southwestern Baltic Sea. Botanica Marina, 62(1), 1-12. https://doi.org/10.1515/bot-2018-0098

vollenweider_modified()

This function adds parameters that were not originally included in the Vollenweider (1965) model, but were introduced by other models and renames the parameters to a standardised one for all models. See the table below.

Parameters

model_result: A list containing the results of the model, including parameters such as pmax, alpha, and ik.

Return

Returns a modified model result as a list with the following elements:

etr_type: ETR Type based on the model result.
etr_regression_data: Regression data with ETR predictions based on the fitted model.
sdiff: The difference between observed and predicted ETR values.
a: obtained paramter a, here equal to etrmax_without_photoinhibition
b: obtained paramter b, transfered as a
c: obtained paramter c, here transfered as alpha
d: obtained paramter c, here transfered as n
alpha: The initial slope of the light curve, calculated as:

$${alpha} = \frac{{etrmax\_with\_photoinhibition}}{{ik\_without\_photoinhibition}}$$

beta: Not available, here set to NA_real_
etrmax_with_photoinhibition: The maximum electron transport rate with photoinhibition, transfered as popt
etrmax_without_photoinhibition: The maximum electron transport rate without photoinhibition, transfered as: pmax
ik_with_photoinhibition: PAR where the transition point from light limitation to light saturation is achieved taking photoinhibition into account, transfered as: iik
ik_without_photoinhibition: PAR where the transition point from light limitation to light saturation is achieved not taking photoinhibition into account, transfered as: ik
im_with_photoinhibition: The PAR at which the maximum electron transport rate is achieved by taking photoinhibition into account, determined as:

 etr_regression_data <- get_etr_regression_data_from_model_result(model_result)
  im_with_photoinhibition <- etr_regression_data[etr_regression_data[[prediction_name]] == max(etr_regression_data[[prediction_name]]), ][[PAR_name]]

w: Not available, here set to NA_real_
ib: Not available, here set to NA_real_
etrmax_with_without_ratio: Ratio of etrmax_with_photoinhibition to etrmax_without_photoinhibition and ik_with_photoinhibition to ik_without_photoinhibition, transfered as: pmax_popt_and_ik_iik_ratio

Details

This function validates the model_result input and processes relevant parameters for the Vollenweider model, creating a structured list using create_modified_model_result. This standardized output allows for consistent analysis and comparison across different models.

Examples

modified_result_vollenweider <- vollenweider_modified(model_result_vollenweider)

platt_modified()

This function adds parameters that were not originally included in the Platt (1980) model, but were introduced by other models and renames the parameters to a standardised one for all models. See the table below.

Parameters

model_result: A list containing the results of the model, including parameters such as etr_max, alpha, and beta.

Return

Returns a modified model result as a list with the following elements:

etr_type: ETR Type based on the model result.
etr_regression_data: Regression data with ETR predictions based on the fitted model.
sdiff: The difference between observed and predicted ETR values.
a: obtained paramter a, here equal to etrmax_without_photoinhibition
b: obtained paramter b, here equal to alpha
c: obtained paramter c, here equal to beta
d: not available, here set to NA_real_
alpha: The initial slope of the light curve, transfered unchanged as alpha
beta: The photoinhibition of the light curve, transfered unchanged as beta
etrmax_with_photoinhibition: The maximum electron transport rate with photoinhibition, transfered as pm
etrmax_without_photoinhibition: The maximum electron transport rate without photoinhibition, transfered as: ps
ik_with_photoinhibition: PAR where the transition point from light limitation to light saturation is achieved taking photoinhibition into account, transfered as: ik
ik_without_photoinhibition: PAR where the transition point from light limitation to light saturation is achieved not taking photoinhibition into account, transfered as: is
im_with_photoinhibition: The PAR at which the maximum electron transport rate is achieved by taking photoinhibition into account, transfered as: im
w: Not available, here set to NA_real_
ib: Transfered unchange as: ib
etrmax_with_without_ratio: Ratio of etrmax_with_photoinhibition to etrmax_without_photoinhibition and ik_with_photoinhibition to ik_without_photoinhibition. Calculated as:

$${{etrmax\_with\_without\_ratio}} = \frac{{etrmax\_with\_photoinhibition}}{{etrmax\_without\_photoinhibition}}$$

Details

This function validates the model_result input and processes relevant parameters for the Platt model, creating a structured list using create_modified_model_result. This standardized output allows for consistent analysis and comparison across different models.

Examples

modified_result_platt <- platt_modified(model_result_platt)

eilers_peeters_modified()

This function adds parameters that were not originally included in the Eilers and Peeters (1988) model, but were introduced by other models and renames the parameters to a standardised one for all models. See the table below.

Parameters

model_result: A list containing the results of the model, including parameters such as a, b, c, s, pm, ik, im, and w.

Return

Returns a modified model result as a list with the following elements:

etr_type: ETR Type based on the model result.
etr_regression_data: Regression data with ETR predictions based on the fitted model.
sdiff: The difference between observed and predicted ETR values.
a: The obtained parameter $$a$$
b: The obtained parameter $$b$$
c: The obtained parameter $$c$$
d: Not available, here set to NA_real_
alpha: The initial slope of the light curve, transfered unchanged as s
beta: Not available, here set to NA_real_
etrmax_with_photoinhibition: The maximum electron transport rate with photoinhibition, transfered as pm
etrmax_without_photoinhibition: Not available, here set to NA_real_
ik_with_photoinhibition: PAR where the transition point from light limitation to light saturation is achieved taking photoinhibition into account, transfered as ik
ik_without_photoinhibition: Not available, here set to NA_real_
im_with_photoinhibition: The PAR at which the maximum electron transport rate is achieved by taking photoinhibition into account, transfered asim
w: The sharpness of the peak, transfered as w
ib: Not available, here set to NA_real_
etrmax_with_without_ratio: Not available, here set to NA_real_

Details

This function validates the model_result input, extracts relevant parameters for the modified Eilers-Peeters model, and creates a structured list using create_modified_model_result. The list serves as a standardized output format for further analysis.

Examples

# Example usage for eilers_peeters_modified
modified_result <- eilers_peeters_modified(model_result_eilers_peeters)

walsby_modified()

This function adds parameters that were not originally included in the Walsby (1997) model, but were introduced by other models and renames the parameters to a standardised one for all models. See the table below.

Parameters

model_result: A list containing the results of the model, including parameters such as etr_max, alpha, and beta.

Return

Returns a modified model result as a list with the following elements:

etr_type: ETR Type based on the model result.
etr_regression_data: Regression data with ETR predictions based on the fitted model.
sdiff: The difference between observed and predicted ETR values.
a: obtained paramter a, here equal to etrmax_without_photoinhibition
b: obtained paramter b, here equal to alpha
c: obtained paramter c, here equal to beta
d: not available, here set to NA_real_
alpha: The initial slope of the light curve, transfered unchanged as alpha
beta: The photoinhibition of the light curve, transfered unchanged as beta
etrmax_with_photoinhibition: The maximum electron transport rate with photoinhibition, determined as:

  etr_regression_data <- get_etr_regression_data_from_model_result(model_result)
  etr_max_row <- etr_regression_data[etr_regression_data[[prediction_name]] == max(etr_regression_data[[prediction_name]]), ]
  etrmax_with_photoinhibition <- etr_max_row[[prediction_name]]

etrmax_without_photoinhibition: The maximum electron transport rate without photoinhibition, transfered as: etr_max
ik_with_photoinhibition: PAR where the transition point from light limitation to light saturation is achieved taking photoinhibition into account, calculated as:

$$ik\_with\_photoinhibition = \frac{etrmax\_with\_photoinhibition}{alpha}$$

ik_without_photoinhibition: PAR where the transition point from light limitation to light saturation is achieved not taking photoinhibition into account, calculated as:

$$ik\_without\_photoinhibition = \frac{etrmax\_without\_photoinhibition}{alpha}$$

im_with_photoinhibition: The PAR at which the maximum electron transport rate is achieved by taking photoinhibition into account, calculated as:

  etr_regression_data <- get_etr_regression_data_from_model_result(model_result)
  etr_max_row <- etr_regression_data[etr_regression_data[[prediction_name]] == max(etr_regression_data[[prediction_name]]), ]
  im_with_photoinhibition <- etr_max_row[[PAR_name]]

w: Not available, here set to NA_real_
ib: Not available, here set to NA_real_
etrmax_with_without_ratio: Ratio of etrmax_with_photoinhibition to etrmax_without_photoinhibition and ik_with_photoinhibition to ik_without_photoinhibition. Calculated as:

$${{etrmax\_with\_without\_ratio}} = \frac{{etrmax\_with\_photoinhibition}}{{etrmax\_without\_photoinhibition}}$$

Details

This function validates the model_result input and processes relevant parameters for the Walsby model, creating a structured list using create_modified_model_result. This standardized output allows for consistent analysis and comparison across different photosynthesis models.

Examples

modified_result <- walsby_modified(model_result_walsby)

Naming overview

Publication-accurate naming and the respective modified naming

modified	Eilers and Peeters	Platt	Walsby	Vollenweider
a	a	ps	etr_max	pmax
b	b	alpha	alpha	a
c	c	beta	beta	alpha
d	NA	NA	NA	n
alpha	s	alpha	alpha	NA
beta	NA	beta	beta	NA
etrmax_with_photoinhibition	pm	pm	NA	popt
etrmax_without_photoinhibition	NA	ps	etr_max	pmax
ik_with_photoinhibition	ik	ik	NA	iik
ik_without_photoinhibition	NA	is	NA	ik
im_with_photoinhibition	im	im	NA	NA
w	w	NA	NA	NA
ib	NA	ib	NA	NA
etrmax_with_without_ratio	NA	NA	NA	pmax_popt_and_ik_iik_ratio
sdiff	sdiff	sdiff	sdiff	sdiff

Publication-accurate naming and the respective modified naming with additional calculations not included in the original publication

modified	Eilers and Peeters	Platt	Walsby	Vollenweider
sdiff	sdiff	sdiff	sdiff	sdiff
a	a	ps	etr_max	pmax
b	b	alpha	alpha	a
c	c	beta	beta	alpha
d	NA	NA	NA	n
alpha	s	alpha	alpha	real_alpha
beta	NA	beta	beta	NA
etrmax_with_photoinhibition	pm	pm	etrmax_with_photoinhibition	popt
etrmax_without_photoinhibition	NA	ps	etr_max	pmax
ik_with_photoinhibition	ik	ik	ik_with_photoinhibition	iik
ik_without_photoinhibition	NA	is	ik_without_photoinhibition	ik
im_with_photoinhibition	im	im	im_with_photoinhibition	im_with_photoinhibition
w	w	NA	NA	NA
ib	NA	ib	NA	NA
etrmax_with_without_ratio	NA	etrmax_with_without_ratio	etrmax_with_without_ratio	pmax_popt_and_ik_iik_ratio

compare_regression_models_ETR_I() and compare_regression_models_ETR_II()

This function compares different regression models.

Parameters

data_dir: A character string specifying the directory where the input data files are located.

Return

A vector containing the total points assigned to each regression model based on their performance. Models are ranked based on the calculated deviation of the difference between observed and predicted values. Rating:

1st: 3 points
2nd: 2 points
3rd: 1 point
4th: 0 points

Details

This function allows a straightforward comparison of the models: Eilers-Peeters (1988), Platt (1980), Vollenweider (1965), and Walsby (1997). The results can guide users in selecting the most appropriate model for their data. If regression is not possible for a model, no points are awarded for the file for any of the models. Start values cannot be adjusted in this function.

Example

#raw data file directory
data_dir_compare <- file.path(getwd(), "data")

#compare regression models
compare_regression_models_ETR_II <- compare_regression_models_ETR_II(data_dir_compare)
print(compare_regression_models_ETR_II)

References

Eilers, P. H. C., & Peeters, J. C. H. (1988). A model for the relationship between light intensity and the rate of photosynthesis in phytoplankton. Ecological Modelling, 42(3-4), 199-215. doi:10.1016/0304-3800(88)90057-9.

Platt, T., Gallegos, C. L., & Harrison, W. G. (1980). Photoinhibition of photosynthesis in natural assemblages of marine phytoplankton. Journal of Marine Research, 38(4). Retrieved from https://elischolar.library.yale.edu/journal_of_marine_research/1525.

Romoth, K., Nowak, P., Kempke, D., Dietrich, A., Porsche, C., & Schubert, H. (2019). Acclimation limits of Fucus evanescens along the salinity gradient of the southwestern Baltic Sea. Botanica Marina, 62(1), 1-12. https://doi.org/10.1515/bot-2018-0098

Vollenweider, R. A. (1965). Calculation models of photosynthesis-depth curves and some implications regarding day rate estimates in primary production measurements, p. 427-457. In C. R. Goldman [ed.], Primary Productivity in Aquatic Environments. Mem. Ist. Ital. Idrobiol., 18 Suppl., University of California Press, Berkeley.

Walsby, A. E. (1997). Numerical integration of phytoplankton photosynthesis through time and depth in a water column. New Phytologist, 136(2), 189-209. https://doi.org/10.1046/j.1469-8137.1997.00736.x

plot_control()

This function creates a control plot for the used model based on the provided data and model results.

Parameters

data: A data.table containing the original ETR and yield data for the plot.
model_result: A list containing the fitting results of the used model and the calculated parameters (alpha, ik, etc.).
title: A character string that specifies the title of the plot.
color: A color specification for the regression line in the plot.

Return

A plot displaying the original ETR and Yield values and the regression data. A table below the plot shows the calculated data (alpha, ik, etc.).

Example

plot_control_eilers_peeters_ETR_II <- plot_control(
  data = pam_data,
  model_result = modified_model_result_eilers_peeters_ETR_II,
  title = "eilers_peeters ETR II modified 20231122_01.csv",
  color = "purple"
)
print(plot_control_eilers_peeters_ETR_II)

combo_control_plot()

The combo_control_plot function generates a combined plot of electron transport rate (ETR) data and regression model predictions, along with a customized table summarizing the parameters for each model.

Parameters

title: A character string specifying the title for the plot.
data: A data frame containing the raw input data for ETR and Photosynthetically Active Radiation (PAR).
model_results: A list of model results, where each model result is a list containing regression data and parameters for ETR.
name_list: A list of names corresponding to each model result. These names will be used in the legend and table.
color_list: A list of color values for each model result. Colors are used to differentiate lines on the plot.

Return

A plot displaying the original ETR and Yield values and the regression data from different models. A table below the plot shows the calculated data (alpha, ik, etc.).

Examples

test_data_file <- file.path(getwd(), "data", "20231122_01.csv")
    data <- read_dual_pam_data(test_data_file)

    eilers_peeters <- eilers_peeters_modified(eilers_peeters_generate_regression_ETR_II(data))
    platt <- platt_modified(platt_generate_regression_ETR_II(data))
    walsby <- walsby_modified(walsby_generate_regression_ETR_II(data))
    vollenweider <- vollenweider_modified(vollenweider_generate_regression_ETR_II(data))

    plot <- combo_control_plot(
      "etr II test-combo_plot_control_20231122_01.csv",
      data,
      list(eilers_peeters, platt, walsby, vollenweider),
      list("eilers_peeters", "platt", "walsby", "vollenweider"),
      list("purple", "blue", "green", "red")
    )

write_model_result_csv()

This function exports the raw input data, regression data, and model parameters into separate CSV files for easy access and further analysis.

Parameters

dest_dir: A character string specifying the directory where the CSV files will be saved.
name: A character string specifying the base name for the output files.
data: A data frame containing the raw input data used in the model.
model_result: A list containing the model results, including parameter values and regression data.

Details

This function creates three CSV files:

name_raw_data.csv: Contains the original raw data used in the model.
name_regression_data.csv: Contains the regression data with predictions for electron transport rate (ETR).
name_model_result.csv: Contains the parameter values from the model results (excluding regression data), including parameters like alpha, beta, and etr_max.

Each file will be named using the name parameter as a prefix, followed by a specific suffix for clarity.

Examples

write_model_result_csv(
  dest_dir = "output",
  name = "eilers_peeters_experiment_001",
  data = raw_data,
  model_result = model_result_eilers_peeters
)

known issues

subscript out of bounds

Skipped file: 20231214_14_W6_T5_ML.csv because of error: Error in eilers_peeters[["sdiff"]]: subscript out of bounds

This could indicate that Pm lable in the Action column is at the wrong position in the csv raw data file. Error could be caused by WALZ-Software.

Removed rows in combo_plot_control

Warnings:
1: Removed 1 row containing missing values or values outside the scale range (`geom_point()`). 
2: Removed 1 row containing missing values or values outside the scale range (`geom_point()`). 
3: Removed 1 row containing missing values or values outside the scale range (`geom_line()`).

All points and lines present. Reason for warning messages unknown. Possibly a problem in the library ggplot2.

test all

library(devtools);
devtools::test();

test specific file

library(devtools);
devtools::load_all();
library(testthat);
test_file('$$path')"

Linux dependencies for devtools

Ubuntu: libxml2-dev libssl-dev libcurl4-openssl-dev libfontconfig1-dev libharfbuzz-dev libfribidi-dev libfreetype6-dev libpng-dev libtiff5-dev libjpeg-dev

Debian: libxml2-dev libssl-dev libcurl4-openssl-dev libfontconfig1-dev libharfbuzz-dev libfribidi-dev libfreetype6-dev libpng-dev libjpeg-dev libfreetype6-dev libpng-dev libtiff5-dev libjpeg-dev

install.packages("data.table") install.packages("dplyr") install.packages("ggplot2") install.packages("minpack.lm") install.packages("SciViews") install.packages("ggthemes") install.packages("gridExtra")

Name		Name	Last commit message	Last commit date
Latest commit History 93 Commits
examples		examples
src		src
.editorconfig		.editorconfig
.gitignore		.gitignore
CITATION.cff		CITATION.cff
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
eilers_peeters_ETR_II_modified.jpg		eilers_peeters_ETR_II_modified.jpg
test_combo_plot_control_doku.jpg		test_combo_plot_control_doku.jpg

License

biotoolbox/pam

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library 'pam'

Introduction

Install

GitHub

Examples

Functions

read_dual_pam_data()

Description

Parameters

Details

Return

Example

References

vollenweider_generate_regression_ETR_I() and vollenweider_generate_regression_ETR_II()

Parameters

Return

Details

Example

References

platt_generate_regression_ETR_I() and platt_generate_regression_ETR_II()

Parameters

Return

Details

Example

References

eilers_peeters_generate_regression_ETR_I() and eilers_peeters_generate_regression_ETR_II()

Parameters

Return

Details

Example

References

walsby_generate_regression_ETR_I() and walsby_generate_regression_ETR_II()

Parameters

Return

Details

References

vollenweider_modified()

Parameters

Return

Details

Examples

platt_modified()

Parameters

Return

Details

Examples

eilers_peeters_modified()

Parameters

Return

Details

Examples

walsby_modified()

Parameters

Return

Details

Examples

Naming overview

Publication-accurate naming and the respective modified naming

Publication-accurate naming and the respective modified naming with additional calculations not included in the original publication

compare_regression_models_ETR_I() and compare_regression_models_ETR_II()

Parameters

Return

Details

Example

References

plot_control()

Parameters

Return

Example

combo_control_plot()

Parameters

Return

Examples

write_model_result_csv()

Packages