Nothing
R/kp.R
In OenoKPM: Modeling the Kinetics of Carbon Dioxide Production in Alcoholic Fermentation
Defines functions
kp
Documented in
kp
#' @title Calculates kinetic parameters as a
#' function of model fit for \ifelse{html}{\out{CO<sub>2</sub>}}{\eqn{CO2}} production
#' as a function of time
#' @name kp
#'@description A function that, based on the
#' observed data, the independent variable
#' (e.g. time in h) and the dependent
#' variable (e.g. CO\ifelse{html}{\out{<sub>2</sub>}}{\eqn{_2}}
#' production in g L\ifelse{html}{\out{<sup>-1</sup>}}{\eqn{^{-1}}}),
#' performs the modeling of the fermentation
#' curve based on the chosen model (\strong{5PL}, \strong{Gompertz}, or \strong{4PL}).
#'
#' Next, the coefficients are used in
#' mathematical formulas to obtain the
#' following kinetic parameters:
#'
#' \strong{t\ifelse{html}{\out{<sub>Lag</sub>}}{\eqn{_{Lag}}}} - Duration of the latency phase for CO\ifelse{html}{\out{<sub>2</sub>}}{\eqn{_2}} production;
#'
#' \strong{V\ifelse{html}{\out{<sub>max</sub>}}{\eqn{_{max}}}} - Maximum rate of production of CO\ifelse{html}{\out{<sub>2</sub>}}{\eqn{_2}};
#'
#' \strong{t\ifelse{html}{\out{<sub>Vmax</sub>}}{\eqn{_{V_{max}}}}} - Moment in which maximum fermentation rate occurs;
#'
#' \strong{CO\ifelse{html}{\out{<sub>2Vmax</sub>}}{\eqn{_{2_{Vmax}}}}} - CO\ifelse{html}{\out{<sub>2</sub>}}{\eqn{_2}} Produced until Maximum fermentation rate occurs;
#'
#' \strong{Y\ifelse{html}{\out{<sub>max</sub>}}{\eqn{_{max}}}} - Maximum production of carbon dioxide (CO\ifelse{html}{\out{<sub>2</sub>}}{\eqn{_2}});
#'
#' @param data Data frame to be analyzed.
#' The data frame must be in the
#' following order:
#' \itemize{
#' \item \strong{First}: All columns containing the independent
#' variable (e.g. \emph{time in hours})
#' \item \strong{Second}: All columns containing dependent variables
#' (e.g. \emph{CO\ifelse{html}{\out{<sub>2</sub>}}{\eqn{_2}}
#' g L\ifelse{html}{\out{<sup>-1</sup>}}{\eqn{^{-1}}}
#' production})
#' \item \strong{Header}: Columns must contain a
#' header. If the treatment \strong{ID} is in
#' the header, this \strong{ID} will be used
#' to \strong{identify} the coefficients
#' and kinetic parameters for each
#' analyzed curve.}
#'
#' @param model Model to be adjusted. Argument for model:
#' \itemize{
#' \item \strong{Model = 1}. 5PL Model (five-parameter logistic (5PL) model).
#' \item \strong{Model = 2}. Gompertz Model.
#' \item \strong{Model = 3}. 4PL Model (four-parameter logistic (4PL) model).
#' }
#' @param save.xls If TRUE, an xlsx file containing
#' the coefficients and kinetic parameters will
#' be saved in the working directory. If FALSE,
#' the xlsx file will not be saved.
#' @param dir.save Directory path where
#' the xlsx file is to be saved.
#' @param xls.name File name. Must contain the
#' format. For example, "Parameters.xlsx".
#' @param startA Starting estimate of the value of A for model.
#' @param startB Starting estimate of the value of B for model.
#' @param startC Starting estimate of the value of C for model.
#' @param startD Starting estimate of the value of D for model.
#' @param startG Starting estimate of the value of G for model.
#' @details
#'
#' Curve fitting from the observed data is
#' performed by the nlsLM() function in
#' the 'minpack.lm' package.
#'
#' You can see our article for more details on
#'the mathematical formulas used to obtain each
#'kinetic parameter (Gava \emph{et al}., 2020). In addition, feel free to
#'use it as a reference in your works.
#'
#' @return The analyzed model \strong{coefficients}
#' and the calculated \strong{kinetic parameters}
#' are returned in a data.frame. In addition,
#' a \strong{"Parameters.xlsx" file} can be generated,
#' containing the coefficients and kinetic
#' parameters of each studied fermentation curve.
#'
#' @references Gava, A., Borsato, D., & Ficagna, E.
#' (2020). Effect of mixture of fining agents on
#' the fermentation kinetics of base wine for
#' sparkling wine production: Use of methodology
#' for modeling. \emph{LWT}, \emph{131}, 109660.
#' \doi{https://doi.org/10.1016/j.lwt.2020.109660}
#'
#'Zwietering, M. H., Jongenburger, I., Rombouts, F. M.,
#'& Van't Riet, K. J. A. E. M. (1990).
#'Modeling of the bacterial growth curve.
#'\emph{Applied and environmental microbiology}, \emph{56}(6),
#' 1875-1881. \doi{https://doi.org/10.1128/aem.56.6.1875-1881.1990}
#'
#' @author Angelo Gava
#' @examples
#'
#' #Creating a data.frame.
#' #First, columns containing independent variable.
#' #Second, columns containing dependent variable.
#' #The data frame created presents two
#' #fermentation curves for two yeasts with
#' #different times and carbon dioxide production.
#'
#' df <- data.frame('Time_Yeast_A' = seq(0,280, by=6.23),
#' 'Time_Yeast_B' = seq(0,170, by=3.7777778),
#' 'CO2_Production_Yeast_A' = c(0,0.97,4.04,9.62,13.44,17.50,
#' 24.03,27.46,33.75,36.40,40.80,
#' 44.24,48.01,50.85,54.85,57.51,
#' 61.73,65.43,66.50,72.41,75.47,
#' 77.22,78.49,79.26,80.31,81.04,
#' 81.89,82.28,82.56,83.13,83.62,
#' 84.11,84.47,85.02,85.31,85.61,
#' 86.05,86.27,85.29,86.81,86.94,
#' 87.13,87.33,87.45,87.85),
#' 'CO2_Production_Yeast_B' = c(0,0.41,0.70,3.05,15.61,18.41,
#' 21.37,23.23,28.28,41.28,43.98,
#' 49.54,54.43,60.40,63.75,69.29,
#' 76.54,78.38,80.91,83.72,84.66,
#' 85.39,85.81,86.92,87.38,87.61,
#' 88.38,88.57,88.72,88.82,89.22,
#' 89.32,89.52,89.71,89.92,90.11,
#' 90.31,90.50,90.70,90.90,91.09,
#' 91.29,91.49,91.68,91.88))
#'
#' #Using the kp() function to find the
#' #coefficients and kinetic parameters
#' #according to the adopted model.
#'
#' kp(data = df,
#' model = 1,
#' startA = 0,
#' startB = 1.5,
#' startC = 500,
#' startD = 92,
#' startG = 1500,
#' save.xls = FALSE) #5PL Model adopted
#'
#' kp(data = df,model = 2,
#' startA = 92,
#' startB = 1.5,
#' startC = 0,
#' startD = NA,
#' startG = NA,
#' save.xls = FALSE) #Gompertz Model adopted
#'
#' kp(data = df,
#' startA = 0,
#' startB = 2.5,
#' startC = 10,
#' startD = 92,
#' startG = NA,
#' model = 3,
#' save.xls = FALSE) #4PL Model adopted
#'
#' #Saving an xlsx file. In this example,
#' #we will use saving a temporary file in
#' #the temporary file directories.
#'
#'
#' @import minpack.lm
#' @import openxlsx
#'
#' @export
kp <- function(data,
model,
save.xls = FALSE,
dir.save,
xls.name,
startA,
startB,
startC,
startD,
startG) {
fit_5PL <- function(data, var_dep, var_indep){
minpack.lm::nlsLM(var_dep ~ d + ((a-d)/((1+((var_indep/c)^b))^g)),
data = data, start = list(a = startA, b = startB, c=startC,d=startD, g = startG),
control = minpack.lm::nls.lm.control(maxiter = 500))
}
fit_gompertz <- function(data, var_dep, var_indep) {
minpack.lm::nlsLM(var_dep ~ a*exp(-exp(-c*var_indep+b)),
data = data,
start = list(a = startA, b= startB,c = startC),
control = minpack.lm::nls.lm.control(maxiter = 500))
}
fit_4PL <- function(data, var_dep, var_indep){
minpack.lm::nlsLM(var_dep ~ d+(a-d)/(1+(var_indep/c)^b),
data = data, start = list(a = startA, b = startB, c=startC,d=startD),
control = minpack.lm::nls.lm.control(maxiter = 500))
}
if(model == 1){
Coeff_5PL <- data.frame(a = rep(NA, ncol(data)/2),b = rep(NA, ncol(data)/2),c = rep(NA, ncol(data)/2),d = rep(NA, ncol(data)/2), g = rep(NA, ncol(data)/2))
for (i in 1:(ncol(data)/2)) {
model_5PL <- fit_5PL(data = data,
var_indep = data[,i],
var_dep = data[,i + ((ncol(data))/2)])
Coeff_5PL[i,] <- as.data.frame(t(stats::coefficients(model_5PL)))
row.names(Coeff_5PL) <- colnames(data[(((ncol(data))/2))+ 1:(ncol(data)/2)])
}
Parameters_5PL <- Coeff_5PL
Parameters_5PL[] <- lapply(Parameters_5PL, function(x) as.numeric(as.character(x)))
Parameters_5PL$'tVmax (h)' <- with(Parameters_5PL, exp(log(((-1+b))/(b*g+1))/b)*c)
Parameters_5PL$'tLag (h)' <- with(Parameters_5PL,(d+(a-d)/((1+exp(log((-1+b)/(b*g+1))/b)^b)^g))/(a-d)/g/(exp(log((-1+b)/(b*g+1))/b)^b)/b*(1+exp(log((-1+b)/(b*g+1))/b)^b)^g*exp(log((-1+b)/(b*g+1))/b)*c*(1+exp(log((-1+b)/(b*g+1))/b)^b)+exp(log((-1+b)/(b*g+1))/b)*c)
Parameters_5PL$'Vmax (g/L/h)'<- with(Parameters_5PL,-(a-d)*g*exp(log((-1+b)/(b*g+1))/b)^b*b/((1+exp(log((-1+b)/(b*g+1))/b)^b)^g)/exp(log((-1+b)/(b*g+1))/b)/c/(1+exp(log((-1+b)/(b*g+1))/b)^b))
Parameters_5PL$'CO2Vmax (g/L)'<-with(Parameters_5PL, d+(a-d)/((1+exp(log((-1+b)/(b*g+1))/b)^b)^g))
Parameters_5PL$'Ymax (g/L)'<- Parameters_5PL$d
if (save.xls == TRUE){
openxlsx::write.xlsx(Parameters_5PL ,file = paste(dir.save,xls.name, sep = ''), sheetName = "Sheet1",
col.names = TRUE, row.names = TRUE, append = FALSE)}
Parameters_5PL
print(Parameters_5PL)}
else if(model == 2){
Coeff_gompertz <- data.frame(a = rep(NA, ncol(data)/2),b = rep(NA, ncol(data)/2),c = rep(NA, ncol(data)/2), check.names = FALSE)
for (i in 1:(ncol(data)/2)) {
model_gompertz <- fit_gompertz(data = data,
var_indep = data[,i],
var_dep = data[,i + ((ncol(data))/2)])
Coeff_gompertz[i,] <- as.data.frame(t(stats::coefficients(model_gompertz)))
row.names(Coeff_gompertz) <- colnames(data[(((ncol(data))/2))+ 1:(ncol(data)/2)])
}
Parameters_gompertz <- Coeff_gompertz
Parameters_gompertz[] <- lapply(Parameters_gompertz, function(x) as.numeric(as.character(x)))
Parameters_gompertz$'tVmax (h)' <- with(Parameters_gompertz, b/c)
Parameters_gompertz$'tLag (h)' <- with(Parameters_gompertz, (b-1)/c)
Parameters_gompertz$'Vmax (g/L/h)'<- with(Parameters_gompertz,a*c*exp(-1))
Parameters_gompertz$'CO2Vmax (g/L)'<- with(Parameters_gompertz, a*exp(-1))
Parameters_gompertz$'Ymax (g/L)'<- Parameters_gompertz$a
if (save.xls == TRUE){
openxlsx::write.xlsx(Parameters_gompertz, file = paste(dir.save,xls.name, sep = ''), sheetName = "Sheet1",
col.names = TRUE, row.names = TRUE, append = FALSE)}
Parameters_gompertz
print(Parameters_gompertz)
}
else if(model == 3){
Coeff_4PL <- data.frame(a = rep(NA, ncol(data)/2),b = rep(NA, ncol(data)/2),c = rep(NA, ncol(data)/2), d=rep(NA, ncol(data)/2), check.names = FALSE)
for (i in 1:(ncol(data)/2)) {
model_4PL <- fit_4PL(data = data,
var_indep = data[,i],
var_dep = data[,i + ((ncol(data))/2)])
Coeff_4PL[i,] <- as.data.frame(t(stats::coefficients(model_4PL)))
row.names(Coeff_4PL) <- colnames(data[(((ncol(data))/2))+ 1:(ncol(data)/2)])
}
Parameters_4PL <- Coeff_4PL
Parameters_4PL[] <- lapply(Parameters_4PL, function(x) as.numeric(as.character(x)))
Parameters_4PL$'tVmax (h)' <- with(Parameters_4PL, exp(log((-1+b)/(b+1))/b)*c)
Parameters_4PL$'tLag (h)' <- with(Parameters_4PL, c*(d*(exp(log((-1+b)/(b+1))/b)^b)^2+((b+1)*a-d*(-1+b))*exp(log((-1+b)/(b+1))/b)^b+a)*exp(log((-1+b)/(b+1))/b)/(a-d)/(exp(log((-1+b)/(b+1))/b)^b)/b)
Parameters_4PL$'Vmax (g/L/h)'<- with(Parameters_4PL, -(a-d)/(1+exp(log((-1+b)/(b+1))/b)^b)^2*exp(log((-1+b)/(b+1))/b)^b*b/exp(log((-1+b)/(b+1))/b)/c)
Parameters_4PL$'CO2Vmax (g/L)'<- with(Parameters_4PL, d+(a-d)/(1+exp(log((-1+b)/(b+1))/b)^b))
Parameters_4PL$'Ymax (g/L)'<- Parameters_4PL$d
if (save.xls == TRUE){
openxlsx::write.xlsx(Parameters_4PL, file = paste(dir.save,xls.name, sep = ''), sheetName = "Sheet1",
col.names = TRUE, row.names = TRUE, append = FALSE)}
Parameters_4PL
print(Parameters_4PL)
} else {print ("Model not found!!")}
}
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Defines functions kp
Documented in kp
#' @title Calculates kinetic parameters as a
#' function of model fit for \ifelse{html}{\out{CO<sub>2</sub>}}{\eqn{CO2}} production
#' as a function of time
#' @name kp
#'@description A function that, based on the
#' observed data, the independent variable
#' (e.g. time in h) and the dependent
#' variable (e.g. CO\ifelse{html}{\out{<sub>2</sub>}}{\eqn{_2}}
#' production in g L\ifelse{html}{\out{<sup>-1</sup>}}{\eqn{^{-1}}}),
#' performs the modeling of the fermentation
#' curve based on the chosen model (\strong{5PL}, \strong{Gompertz}, or \strong{4PL}).
#'
#' Next, the coefficients are used in
#' mathematical formulas to obtain the
#' following kinetic parameters:
#'
#' \strong{t\ifelse{html}{\out{<sub>Lag</sub>}}{\eqn{_{Lag}}}} - Duration of the latency phase for CO\ifelse{html}{\out{<sub>2</sub>}}{\eqn{_2}} production;
#'
#' \strong{V\ifelse{html}{\out{<sub>max</sub>}}{\eqn{_{max}}}} - Maximum rate of production of CO\ifelse{html}{\out{<sub>2</sub>}}{\eqn{_2}};
#'
#' \strong{t\ifelse{html}{\out{<sub>Vmax</sub>}}{\eqn{_{V_{max}}}}} - Moment in which maximum fermentation rate occurs;
#'
#' \strong{CO\ifelse{html}{\out{<sub>2Vmax</sub>}}{\eqn{_{2_{Vmax}}}}} - CO\ifelse{html}{\out{<sub>2</sub>}}{\eqn{_2}} Produced until Maximum fermentation rate occurs;
#'
#' \strong{Y\ifelse{html}{\out{<sub>max</sub>}}{\eqn{_{max}}}} - Maximum production of carbon dioxide (CO\ifelse{html}{\out{<sub>2</sub>}}{\eqn{_2}});
#'
#' @param data Data frame to be analyzed.
#' The data frame must be in the
#' following order:
#' \itemize{
#' \item \strong{First}: All columns containing the independent
#' variable (e.g. \emph{time in hours})
#' \item \strong{Second}: All columns containing dependent variables
#' (e.g. \emph{CO\ifelse{html}{\out{<sub>2</sub>}}{\eqn{_2}}
#' g L\ifelse{html}{\out{<sup>-1</sup>}}{\eqn{^{-1}}}
#' production})
#' \item \strong{Header}: Columns must contain a
#' header. If the treatment \strong{ID} is in
#' the header, this \strong{ID} will be used
#' to \strong{identify} the coefficients
#' and kinetic parameters for each
#' analyzed curve.}
#'
#' @param model Model to be adjusted. Argument for model:
#' \itemize{
#' \item \strong{Model = 1}. 5PL Model (five-parameter logistic (5PL) model).
#' \item \strong{Model = 2}. Gompertz Model.
#' \item \strong{Model = 3}. 4PL Model (four-parameter logistic (4PL) model).
#' }
#' @param save.xls If TRUE, an xlsx file containing
#' the coefficients and kinetic parameters will
#' be saved in the working directory. If FALSE,
#' the xlsx file will not be saved.
#' @param dir.save Directory path where
#' the xlsx file is to be saved.
#' @param xls.name File name. Must contain the
#' format. For example, "Parameters.xlsx".
#' @param startA Starting estimate of the value of A for model.
#' @param startB Starting estimate of the value of B for model.
#' @param startC Starting estimate of the value of C for model.
#' @param startD Starting estimate of the value of D for model.
#' @param startG Starting estimate of the value of G for model.
#' @details
#'
#' Curve fitting from the observed data is
#' performed by the nlsLM() function in
#' the 'minpack.lm' package.
#'
#' You can see our article for more details on
#'the mathematical formulas used to obtain each
#'kinetic parameter (Gava \emph{et al}., 2020). In addition, feel free to
#'use it as a reference in your works.
#'
#' @return The analyzed model \strong{coefficients}
#' and the calculated \strong{kinetic parameters}
#' are returned in a data.frame. In addition,
#' a \strong{"Parameters.xlsx" file} can be generated,
#' containing the coefficients and kinetic
#' parameters of each studied fermentation curve.
#'
#' @references Gava, A., Borsato, D., & Ficagna, E.
#' (2020). Effect of mixture of fining agents on
#' the fermentation kinetics of base wine for
#' sparkling wine production: Use of methodology
#' for modeling. \emph{LWT}, \emph{131}, 109660.
#' \doi{https://doi.org/10.1016/j.lwt.2020.109660}
#'
#'Zwietering, M. H., Jongenburger, I., Rombouts, F. M.,
#'& Van't Riet, K. J. A. E. M. (1990).
#'Modeling of the bacterial growth curve.
#'\emph{Applied and environmental microbiology}, \emph{56}(6),
#' 1875-1881. \doi{https://doi.org/10.1128/aem.56.6.1875-1881.1990}
#'
#' @author Angelo Gava
#' @examples
#'
#' #Creating a data.frame.
#' #First, columns containing independent variable.
#' #Second, columns containing dependent variable.
#' #The data frame created presents two
#' #fermentation curves for two yeasts with
#' #different times and carbon dioxide production.
#'
#' df <- data.frame('Time_Yeast_A' = seq(0,280, by=6.23),
#' 'Time_Yeast_B' = seq(0,170, by=3.7777778),
#' 'CO2_Production_Yeast_A' = c(0,0.97,4.04,9.62,13.44,17.50,
#' 24.03,27.46,33.75,36.40,40.80,
#' 44.24,48.01,50.85,54.85,57.51,
#' 61.73,65.43,66.50,72.41,75.47,
#' 77.22,78.49,79.26,80.31,81.04,
#' 81.89,82.28,82.56,83.13,83.62,
#' 84.11,84.47,85.02,85.31,85.61,
#' 86.05,86.27,85.29,86.81,86.94,
#' 87.13,87.33,87.45,87.85),
#' 'CO2_Production_Yeast_B' = c(0,0.41,0.70,3.05,15.61,18.41,
#' 21.37,23.23,28.28,41.28,43.98,
#' 49.54,54.43,60.40,63.75,69.29,
#' 76.54,78.38,80.91,83.72,84.66,
#' 85.39,85.81,86.92,87.38,87.61,
#' 88.38,88.57,88.72,88.82,89.22,
#' 89.32,89.52,89.71,89.92,90.11,
#' 90.31,90.50,90.70,90.90,91.09,
#' 91.29,91.49,91.68,91.88))
#'
#' #Using the kp() function to find the
#' #coefficients and kinetic parameters
#' #according to the adopted model.
#'
#' kp(data = df,
#' model = 1,
#' startA = 0,
#' startB = 1.5,
#' startC = 500,
#' startD = 92,
#' startG = 1500,
#' save.xls = FALSE) #5PL Model adopted
#'
#' kp(data = df,model = 2,
#' startA = 92,
#' startB = 1.5,
#' startC = 0,
#' startD = NA,
#' startG = NA,
#' save.xls = FALSE) #Gompertz Model adopted
#'
#' kp(data = df,
#' startA = 0,
#' startB = 2.5,
#' startC = 10,
#' startD = 92,
#' startG = NA,
#' model = 3,
#' save.xls = FALSE) #4PL Model adopted
#'
#' #Saving an xlsx file. In this example,
#' #we will use saving a temporary file in
#' #the temporary file directories.
#'
#'
#' @import minpack.lm
#' @import openxlsx
#'
#' @export
kp <- function(data,
model,
save.xls = FALSE,
dir.save,
xls.name,
startA,
startB,
startC,
startD,
startG) {
fit_5PL <- function(data, var_dep, var_indep){
minpack.lm::nlsLM(var_dep ~ d + ((a-d)/((1+((var_indep/c)^b))^g)),
data = data, start = list(a = startA, b = startB, c=startC,d=startD, g = startG),
control = minpack.lm::nls.lm.control(maxiter = 500))
}
fit_gompertz <- function(data, var_dep, var_indep) {
minpack.lm::nlsLM(var_dep ~ a*exp(-exp(-c*var_indep+b)),
data = data,
start = list(a = startA, b= startB,c = startC),
control = minpack.lm::nls.lm.control(maxiter = 500))
}
fit_4PL <- function(data, var_dep, var_indep){
minpack.lm::nlsLM(var_dep ~ d+(a-d)/(1+(var_indep/c)^b),
data = data, start = list(a = startA, b = startB, c=startC,d=startD),
control = minpack.lm::nls.lm.control(maxiter = 500))
}
if(model == 1){
Coeff_5PL <- data.frame(a = rep(NA, ncol(data)/2),b = rep(NA, ncol(data)/2),c = rep(NA, ncol(data)/2),d = rep(NA, ncol(data)/2), g = rep(NA, ncol(data)/2))
for (i in 1:(ncol(data)/2)) {
model_5PL <- fit_5PL(data = data,
var_indep = data[,i],
var_dep = data[,i + ((ncol(data))/2)])
Coeff_5PL[i,] <- as.data.frame(t(stats::coefficients(model_5PL)))
row.names(Coeff_5PL) <- colnames(data[(((ncol(data))/2))+ 1:(ncol(data)/2)])
}
Parameters_5PL <- Coeff_5PL
Parameters_5PL[] <- lapply(Parameters_5PL, function(x) as.numeric(as.character(x)))
Parameters_5PL$'tVmax (h)' <- with(Parameters_5PL, exp(log(((-1+b))/(b*g+1))/b)*c)
Parameters_5PL$'tLag (h)' <- with(Parameters_5PL,(d+(a-d)/((1+exp(log((-1+b)/(b*g+1))/b)^b)^g))/(a-d)/g/(exp(log((-1+b)/(b*g+1))/b)^b)/b*(1+exp(log((-1+b)/(b*g+1))/b)^b)^g*exp(log((-1+b)/(b*g+1))/b)*c*(1+exp(log((-1+b)/(b*g+1))/b)^b)+exp(log((-1+b)/(b*g+1))/b)*c)
Parameters_5PL$'Vmax (g/L/h)'<- with(Parameters_5PL,-(a-d)*g*exp(log((-1+b)/(b*g+1))/b)^b*b/((1+exp(log((-1+b)/(b*g+1))/b)^b)^g)/exp(log((-1+b)/(b*g+1))/b)/c/(1+exp(log((-1+b)/(b*g+1))/b)^b))
Parameters_5PL$'CO2Vmax (g/L)'<-with(Parameters_5PL, d+(a-d)/((1+exp(log((-1+b)/(b*g+1))/b)^b)^g))
Parameters_5PL$'Ymax (g/L)'<- Parameters_5PL$d
if (save.xls == TRUE){
openxlsx::write.xlsx(Parameters_5PL ,file = paste(dir.save,xls.name, sep = ''), sheetName = "Sheet1",
col.names = TRUE, row.names = TRUE, append = FALSE)}
Parameters_5PL
print(Parameters_5PL)}
else if(model == 2){
Coeff_gompertz <- data.frame(a = rep(NA, ncol(data)/2),b = rep(NA, ncol(data)/2),c = rep(NA, ncol(data)/2), check.names = FALSE)
for (i in 1:(ncol(data)/2)) {
model_gompertz <- fit_gompertz(data = data,
var_indep = data[,i],
var_dep = data[,i + ((ncol(data))/2)])
Coeff_gompertz[i,] <- as.data.frame(t(stats::coefficients(model_gompertz)))
row.names(Coeff_gompertz) <- colnames(data[(((ncol(data))/2))+ 1:(ncol(data)/2)])
}
Parameters_gompertz <- Coeff_gompertz
Parameters_gompertz[] <- lapply(Parameters_gompertz, function(x) as.numeric(as.character(x)))
Parameters_gompertz$'tVmax (h)' <- with(Parameters_gompertz, b/c)
Parameters_gompertz$'tLag (h)' <- with(Parameters_gompertz, (b-1)/c)
Parameters_gompertz$'Vmax (g/L/h)'<- with(Parameters_gompertz,a*c*exp(-1))
Parameters_gompertz$'CO2Vmax (g/L)'<- with(Parameters_gompertz, a*exp(-1))
Parameters_gompertz$'Ymax (g/L)'<- Parameters_gompertz$a
if (save.xls == TRUE){
openxlsx::write.xlsx(Parameters_gompertz, file = paste(dir.save,xls.name, sep = ''), sheetName = "Sheet1",
col.names = TRUE, row.names = TRUE, append = FALSE)}
Parameters_gompertz
print(Parameters_gompertz)
}
else if(model == 3){
Coeff_4PL <- data.frame(a = rep(NA, ncol(data)/2),b = rep(NA, ncol(data)/2),c = rep(NA, ncol(data)/2), d=rep(NA, ncol(data)/2), check.names = FALSE)
for (i in 1:(ncol(data)/2)) {
model_4PL <- fit_4PL(data = data,
var_indep = data[,i],
var_dep = data[,i + ((ncol(data))/2)])
Coeff_4PL[i,] <- as.data.frame(t(stats::coefficients(model_4PL)))
row.names(Coeff_4PL) <- colnames(data[(((ncol(data))/2))+ 1:(ncol(data)/2)])
}
Parameters_4PL <- Coeff_4PL
Parameters_4PL[] <- lapply(Parameters_4PL, function(x) as.numeric(as.character(x)))
Parameters_4PL$'tVmax (h)' <- with(Parameters_4PL, exp(log((-1+b)/(b+1))/b)*c)
Parameters_4PL$'tLag (h)' <- with(Parameters_4PL, c*(d*(exp(log((-1+b)/(b+1))/b)^b)^2+((b+1)*a-d*(-1+b))*exp(log((-1+b)/(b+1))/b)^b+a)*exp(log((-1+b)/(b+1))/b)/(a-d)/(exp(log((-1+b)/(b+1))/b)^b)/b)
Parameters_4PL$'Vmax (g/L/h)'<- with(Parameters_4PL, -(a-d)/(1+exp(log((-1+b)/(b+1))/b)^b)^2*exp(log((-1+b)/(b+1))/b)^b*b/exp(log((-1+b)/(b+1))/b)/c)
Parameters_4PL$'CO2Vmax (g/L)'<- with(Parameters_4PL, d+(a-d)/(1+exp(log((-1+b)/(b+1))/b)^b))
Parameters_4PL$'Ymax (g/L)'<- Parameters_4PL$d
if (save.xls == TRUE){
openxlsx::write.xlsx(Parameters_4PL, file = paste(dir.save,xls.name, sep = ''), sheetName = "Sheet1",
col.names = TRUE, row.names = TRUE, append = FALSE)}
Parameters_4PL
print(Parameters_4PL)
} else {print ("Model not found!!")}
}
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