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R/plot_fit.R
In OenoKPM: Modeling the Kinetics of Carbon Dioxide Production in Alcoholic Fermentation
Defines functions
plot_fit
Documented in
plot_fit
#' @title Plot graphs with observed data
#' and predicted data from models
#' @name plot_fit
#'
#' @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(s) (\strong{5PL}, \strong{Gompertz}, or/and \strong{4PL}).
#'
#' From the observed data and predicted data,
#' whether from one or all of the available
#' models, this function will plot a graph
#' for each fermentation curve evaluated.
#' The chart will have the following basic
#' structure:
#'
#' \strong{X axis}: \emph{fermentation time}
#'
#' \strong{Y axis}: \emph{CO\ifelse{html}{\out{<sub>2</sub>}}{\eqn{_2}} production}
#'
#' \strong{Observed data}: \emph{Scatterplot with dots}. Plot with geom_point function from ggplot2 package.
#'
#' \strong{Predicted data}: \emph{Smoothed line}. Plot with the stat_smooth function from the ggplot2 package.
#'
#' @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 name the graphics
#' PDF files for each
#' analyzed curve.}
#'
#' @param models Model or models to be adjusted:
#'\itemize{
#'\item \strong{Models = 1}. Only the 5PL Model.
#'\item \strong{Models = 2}. Only the Gompertz Model.
#'\item \strong{Models = 3}. Only the 4PL Model.
#'\item \strong{Models = 4}. 5PL and Gompertz Models.
#'\item \strong{Models = 5}. 5PL and 4PL Models.
#'\item \strong{Models = 6}. Gompertz and 4PL Models.
#'\item \strong{Models = 7}. 5PL, Gompertz and 4PL Models.}
#' @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.
#' @param col Plot color of observed data in points. For example, "black".
#'
#' @param col1 Plot color of predicted data from \strong{model 1} (\emph{5PL Model}). For example, "red".
#'
#' @param col2 Plot color of predicted data from \strong{model 2} (\emph{Gompertz Model}). For example, "blue".
#'
#' @param col3 Plot color of predicted data from \strong{model 3} (\emph{4PL Model}). For example, "green".
#'
#' @param axisX X Axis Title. Character vector (or expression).
#'
#' @param axisY Y Axis Title. Character vector (or expression).
#'
#' @param breaksX
#' One of:
#'
#'\itemize{
#'\item Use ggplot2::waiver() for the default X-axis breaks calculated by the transform object.
#'\item Numerical vector of X-Axis scale positions. For example, seq(0,200,20).}
#'@param limitsX
#'
#' One of:
#'\itemize{
#'\item NULL to use the default X-Axis scaling range.
#'\item A numeric vector of length two, giving the limits of the X-axis scale. For example, c(0,200).}
#' @param breaksY
#' One of:
#'\itemize{
#'\item Use ggplot2::waiver() for the default Y-axis breaks calculated by the transform object.
#'\item A numerical vector of Y-Axis scale positions. For example, seq(0,100,10).}
#'
#'@param limitsY
#'
#' One of:
#'\itemize{
#'\item NULL to use the default Y-Axis scaling range.
#'\item A numeric vector of length two, giving the limits of the Y-axis scale. For example, c(0,100).}
#' @param font Base font family
#'
#' @param font.size Base font size, given in pts.
#'
#' @param legend.position The position of the caption ("none", "left", "right", "bottom", "top", or a numeric vector of two elements (X,Y).
#'
#' @param show.R2 If TRUE, plots the R\ifelse{html}{\out{<sup>2</sup>}}{\eqn{^2}} of the plotted predicted models on the graph. If FALSE, do not plot the R\ifelse{html}{\out{<sup>2</sup>}}{\eqn{^2}} of the plotted predicted models.
#'
#' @param save.PDF If TRUE, create a folder (directory)
#' and save each graphic in PDF format. If FALSE,
#' it does not create a directory or save the
#' graphics in PDF.
#'
#'@param dir.save Path of the directory in
#' which a new folder (directory) will be
#' created for saving graphics in PDF format.
#'
#' @param dir.name Folder name (directory name)
#' to be created within the working directory
#' for saving PDF graphics. Character vector.
#'
#' @param width.PDF Width, in cm, of the graphic to be saved in a PDF file.
#'
#' @param height.PDF Height, in cm, of the graphic to be saved in a PDF
#'
#' @param width.PDF2 Width, in cm, of the multiplot graphic to be saved in a PDF file.
#'
#' @param height.PDF2 Height, in cm, of the multiplot graphic to be saved in a PDF
#'
#'@details
#'Curve fitting from the observed data is
#' performed by the nlsLM() function in
#' the 'minpack.lm' package.
#'
#' Graphs are
#' plotted using the various functions in
#' the 'ggplot2' package.
#'
#'
#' @return Elegant graphics plotted according
#' to observed and predicted data. In addition,
#' a folder (directory) can be created,
#' in which the PDF graphics will be saved,
#' if desired.
#' In this folder,
#' the graph of each analyzed
#' fermentation curve is saved in PDF format,
#' with the dimensions stipulated
#' in the \emph{width.PDF} and \emph{height.PDF}
#' arguments. The name of each PDF
#' file will be extracted from the
#' header of the dependent variable used
#' for the graph. See more in the examples.
#'
#'@author Angelo Gava
#'
#'@examples
#'
#' #################Example 1#################
#' #Using only required arguments
#'
#' #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 plot_fit function to
#' #generate elegants graphs PDF files
#' #containing both observed data and
#' #predicted data.
#'
#'
#' #Graph plotted only with Model 5PL
#' #fit (models = 1)
#'
#' plot_fit(data = df,
#' models = 1,
#' startA = 0,
#' startB = 1.5,
#' startC = 500,
#' startD = 92,
#' startG = 1500)
#'
#'
#' #Graph plotted with 5PL and Gompertz
#' #model fits (models = 4)
#'
#' plot_fit(data = df,
#' models = 4,
#' startA = 0,
#' startB = 1.5,
#' startC = 500,
#' startD = 92,
#' startG = 1500)
#'
#'
#'
#' #################Example 2#################
#' #Using the various function arguments to
#' #customize the graph.
#'
#' #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_Treatment_A' = seq(0,200, by=6.45),
#' 'Time_Treatment_B' = seq(0,200, by=6.45),
#' 'CO2_Production_Treatment_A' = c(0,0.47,0.78,3.23,19.15,22.86,
#' 26.81,29.36,36.14,52.61,55.58,
#' 61.38,66.25,71.83,74.8,78.88,
#' 83.47,84.48,85.94,87.45,87.98,
#' 88.42,88.68,89.40,89.72,89.87,
#' 90.41,90.51,90.62,90.70,91.05,
#' 91.185),
#' 'CO2_Production_Treatment_B' = c(0,0.19,0.39,1.36,9.23,11.29,
#' 13.58,15.06,19.34,30.92,33.28,
#' 37.98,42.14,47.17,50.00,54.28,
#' 60.92,62.80,65.54,69.74,71.52,
#' 73.07,73.98,76.75,77.79,78.70,
#' 80.65,81.48,82.07,82.47,84.04,
#' 84.60))
#'
#' #Using the plot_fit function to
#' #generate elegants graphs PDF files
#' #containing both observed data and
#' #predicted data.
#'
#' #Graph plotted only with Model 5PL
#' #fit (models = 1)
#' #Do not show R^2
#'
#' plot_fit(data = df,
#' startA = 0,
#' startB = 1.5,
#' startC = 500,
#' startD = 92,
#' startG = 1500,
#' models = 1,
#' col = "red", #Color of observed data (points)
#' col1 = "blue", #Predicted data color from model 1 (line). Model = 1 <- 5PL Model
#' axisX = "Fermentation time (h)", #Title X-Axis
#' axisY = "Carbon dioxide production (g/L)", #Title Y-Axis
#' breaksX = seq(0,200,20), #X-Axis scale (positions). 0,20,40,60,80,...
#' limitsX = c(0,200), #X-Axis Limits
#' breaksY = seq(0,90,5),#Y-Axis scale (positions). 0,5,10,15,20,...
#' limitsY = c(0,95), #Y-Axis Limits
#' font = "serif",
#' font.size = 12,
#' legend.position = "right",
#' show.R2 = FALSE) #Do not show R^2
#'
#'
#'
#'
#' #Graph plotted with 5PL and 4PL
#' #model fits (models = 5)
#' #Show R^2
#' \dontrun{
#' plot_fit(data = df,
#' models = 5,
#' startA = 0,
#' startB = 1.5,
#' startC = 500,
#' startD = 92,
#' startG = 1500,
#' col = "#000000", #Color of observed data (points)
#' col1 = "#FF0000", #Predicted data color from model 1 (line). Model = 1 <- 5PL Model
#' col3 = "#0B6121",#Predicted data color from model 3 (line). Model = 3 <- 4PL Model
#' axisX = "Time (h)", #Title X-Axis
#' axisY = "CO2 production (g/L)", #Title Y-Axis
#' breaksX = seq(0,200,20), #X-Axis scale (positions). 0,20,40,60,80,...
#' limitsX = c(0,200), #X-Axis Limits
#' breaksY = seq(0,90,10),#Y-Axis scale (positions). 0,10,20,30,40,...
#' limitsY = c(0,95), #Y-Axis Limits
#' font = "serif",
#' font.size = 14,
#' legend.position = "bottom",
#' show.R2 = TRUE) #Show R^2
#' }
#'
#' #Graph plotted with 5PL, Gompertz and 4PL
#' #model fits (models = 7)
#' #Do not show R^2
#' \dontrun{
#' plot_fit(data = df,
#' models = 7,
#' startA = 0,
#' startB = 1.5,
#' startC = 500,
#' startD = 92,
#' startG = 1500,
#' col = "#FF0000", #Color of observed data (points)
#' col1 = "#FF00FF", #Predicted data color from model 1 (line). Model = 1 <- 5PL Model
#' col2 = "#0101DF",#Predicted data color from model 2 (line). Model = 2 <- Gompertz Model
#' col3 = "#088A08",#Predicted data color from model 3 (line). Model = 3 <- 4PL Model
#' axisX = "Time (h)", #Title X-Axis
#' axisY = "Carbon dioxide production (g/L)", #Title Y-Axis
#' breaksX = seq(0,200,20), #X-Axis scale (positions). 0,20,40,60,80,...
#' limitsX = c(0,200), #X-Axis Limits
#' breaksY = seq(0,90,10),#Y-Axis scale (positions). 0,10,20,30,40,...
#' limitsY = c(0,95), #Y-Axis Limits
#' font = "serif",
#' font.size = 14,
#' legend.position = "top",
#' show.R2 = FALSE) #Do not show R^2
#' }
#'
#' @import ggplot2
#' @import grDevices
#' @import gridExtra
#' @import minpack.lm
#' @import ggpubr
#' @import grid
#' @export
plot_fit <- function(data,
models,
startA,
startB,
startC,
startD,
startG,
col = "black",
col1 = "red",
col2 = "cornflowerblue",
col3 = "forestgreen",
axisX = "Time (hours)",
axisY = expression(paste("CO"["2"]*" Production (g L"^{"-1"}*")")),
breaksX = waiver(),
limitsX = NULL,
breaksY = waiver(),
limitsY = NULL,
font = "serif",
font.size = 14,
legend.position = "top",
show.R2 = FALSE,
save.PDF = FALSE,
dir.save,
dir.name = "Graphics",
width.PDF = 15,
height.PDF = 12,
width.PDF2 = 25,
height.PDF2 = 18
) {
graph_5PL <- function(data, var_dep, var_indep, treatment_name){
model_5PL <- 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))
r2_5PL <-stats::cor(var_dep,stats::predict(model_5PL)) * stats::cor(var_dep,stats::predict(model_5PL))
predicted_5PL <- stats::predict(model_5PL)
models <- c("5PL Model" = col1)
data$overall <- treatment_name
graph_5PL <- ggplot2::ggplot(data, ggplot2::aes_string(var_indep, var_dep))+
ggplot2::geom_point(col= col, size=2.5, pch=21, bg = col)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_5PL, color = "5PL Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::theme_bw(base_family = font)+
ggplot2::theme(legend.position = legend.position, legend.title = ggplot2::element_blank())+
ggplot2::labs(color = "Legend")+
ggplot2::scale_color_manual(values = models)+
ggplot2::xlab(axisX) +
ggplot2::ylab(axisY)+
ggplot2::facet_wrap(~overall)+
ggplot2::scale_x_continuous(breaks = breaksX, limits = limitsX) +
ggplot2::scale_y_continuous(breaks = breaksY, limits = limitsY)+
ggplot2::theme(text=ggplot2::element_text(size = font.size))
if(show.R2 == TRUE){graph_5PL <- graph_5PL + ggplot2::geom_text (x = 25, y = 90, parse = TRUE ,ggplot2::aes(label = "R2"))+
ggplot2::geom_text (x=33, y=90, ggplot2::aes(label = " = "))+
ggplot2::geom_text(x = 51, y=90, ggplot2::aes(label = round(r2_5PL, 7)))}
if(save.PDF == TRUE){
dir.create(paste(dir.save,dir.name, sep = ''))
ggplot2::ggsave(graph_5PL, filename = paste("Treatment", treatment_name, ".pdf"), width = width.PDF, height = height.PDF, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(graph_5PL)
}
graph_gompertz <- function(data, var_dep, var_indep, treatment_name){
model_gompertz <- minpack.lm::nlsLM(var_dep ~ a*exp(-exp(-c*var_indep+b)),
data = data,
start = list(a = startA, c = startC, b= startC),
control = minpack.lm::nls.lm.control(maxiter = 500))
r2_gompertz <-stats::cor(var_dep,stats::predict(model_gompertz)) * stats::cor(var_dep,stats::predict(model_gompertz))
predicted_gompertz <- stats::predict(model_gompertz)
models <- c("Gompertz Model" = col2)
data$overall <- treatment_name
graph_gompertz <- ggplot2::ggplot(data, ggplot2::aes_string(var_indep, var_dep))+
ggplot2::geom_point(col= col, size=2.5, pch=21, bg = col)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_gompertz, color="Gompertz Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::theme_bw(base_family = font)+
ggplot2::theme(legend.position = legend.position, legend.title = ggplot2::element_blank())+
ggplot2::labs(color = "Legend")+
ggplot2::scale_color_manual(values = models)+
ggplot2::xlab(axisX) +
ggplot2::ylab(axisY)+
ggplot2::facet_wrap(~overall)+
ggplot2::scale_x_continuous(breaks = breaksX, limits = limitsX) +
ggplot2::scale_y_continuous(breaks = breaksY, limits = limitsY)+
ggplot2::theme(text=ggplot2::element_text(size = font.size))
if(show.R2 == TRUE){graph_gompertz <- graph_gompertz + ggplot2::geom_text (x = 25, y = 90, parse = TRUE ,ggplot2::aes(label = paste("R^2")))+
ggplot2::geom_text (x=33, y=90, ggplot2::aes(label = " = "))+
ggplot2::geom_text(x = 51, y=90, ggplot2::aes(label = round(r2_gompertz, 7)))}
if(save.PDF == TRUE){
dir.create(paste(dir.save,dir.name, sep = ''))
ggplot2::ggsave(graph_gompertz, filename = paste("Treatment", treatment_name, ".pdf"), width = width.PDF, height = height.PDF, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(graph_gompertz)
}
graph_4PL <- function(data, var_dep, var_indep, treatment_name){
model_4PL <- 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))
r2_4PL <-stats::cor(var_dep,stats::predict(model_4PL)) * stats::cor(var_dep,stats::predict(model_4PL))
predicted_4PL <- stats::predict(model_4PL)
models <- c("4PL Model" = col3)
data$overall <- treatment_name
graph_4PL <- ggplot2::ggplot(data, ggplot2::aes_string(var_indep, var_dep))+
ggplot2::geom_point(col= col, size=2.5, pch=21, bg=col)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_4PL, color = "4PL Model"), se=FALSE, linetype = "longdash")+
ggplot2::theme_bw(base_family = font)+
ggplot2::theme(legend.position = legend.position, legend.title = ggplot2::element_blank())+
ggplot2::labs(color = "Legend")+
ggplot2::scale_color_manual(values = models)+
ggplot2::xlab(axisX) +
ggplot2::ylab(axisY)+
ggplot2::facet_wrap(~overall)+
ggplot2::scale_x_continuous(breaks = breaksX, limits = limitsX) +
ggplot2::scale_y_continuous(breaks = breaksY, limits = limitsY)+
ggplot2::theme(text=ggplot2::element_text(size = font.size))
if(show.R2 == TRUE){graph_4PL <- graph_4PL + ggplot2::geom_text (x = 25, y = 90, parse = TRUE ,ggplot2::aes(label = paste("R^2")))+
ggplot2::geom_text (x=33, y=90, ggplot2::aes(label = " = "))+
ggplot2::geom_text(x = 51, y=90, ggplot2::aes(label = round(r2_4PL, 7)))}
if(save.PDF == TRUE){
dir.create(paste(dir.save,dir.name, sep = ''))
ggplot2::ggsave(graph_4PL, filename = paste("Treatment", treatment_name, ".pdf"), width = width.PDF, height = height.PDF, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(graph_4PL)
}
graph_5PLgompertz <- function(data, var_dep, var_indep, treatment_name){
model_5PL <- 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))
model_gompertz <- minpack.lm::nlsLM(var_dep ~ a*exp(-exp(-c*var_indep+b)),
data = data,
start = list(a = max(var_dep), c = min(var_dep), b= 1.5),
control = minpack.lm::nls.lm.control(maxiter = 500))
r2_5PL <-stats::cor(var_dep,stats::predict(model_5PL)) * stats::cor(var_dep,stats::predict(model_5PL))
predicted_5PL <- stats::predict(model_5PL)
r2_gompertz <-stats::cor(var_dep,stats::predict(model_gompertz)) * stats::cor(var_dep,stats::predict(model_gompertz))
predicted_gompertz <- stats::predict(model_gompertz)
models <- c("5PL Model" = col1, "Gompertz Model" = col2)
data$overall <- treatment_name
graph_5PLgompertz <- ggplot2::ggplot(data, ggplot2::aes_string(var_indep, var_dep))+
ggplot2::geom_point(col= col, size=2.5, pch=21, bg=col)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_gompertz, color="Gompertz Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_5PL, color="5PL Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::theme_bw(base_family = font)+
ggplot2::theme(legend.position = legend.position, legend.title = ggplot2::element_blank())+
ggplot2::xlab(axisX) +
ggplot2::ylab(axisY)+
ggplot2::facet_wrap(~overall)+
ggplot2::labs(color = "Legend")+
ggplot2::scale_color_manual(values = models)+
ggplot2::scale_x_continuous(breaks = breaksX, limits = limitsX)+
ggplot2::scale_y_continuous(breaks = breaksY, limits = limitsY)+
ggplot2::theme(text=ggplot2::element_text(size = font.size))
if(show.R2 == TRUE){graph_5PLgompertz <- graph_5PLgompertz + ggplot2::geom_text (x=6, y=90, ggplot2::aes(label = "Gompertz"), size = 4)+
ggplot2::geom_text (x = 25, y = 90, parse = TRUE ,ggplot2::aes(label = paste("R^2")), size = 4)+
ggplot2::geom_text (x=33, y=90, ggplot2::aes(label = " = "), size = 4)+
ggplot2::geom_text(x = 51, y=90, ggplot2::aes(label = round(r2_gompertz, 7)), size = 4)+
ggplot2::geom_text (x = 25, y = 85, parse = TRUE ,ggplot2::aes(label = paste("R^2")), size = 4)+
ggplot2::geom_text (x=33, y=85, ggplot2::aes(label = " = "), size = 4)+
ggplot2::geom_text (x=10, y=85, ggplot2::aes(label = "5PL"), size = 4)+
ggplot2::geom_text(x = 51, y=85, ggplot2::aes(label = round(r2_5PL, 7)), size = 4)}
if(save.PDF == TRUE){
dir.create(paste(dir.save,dir.name, sep = ''))
ggplot2::ggsave(graph_5PLgompertz, filename = paste("Treatment", treatment_name, ".pdf"), width = width.PDF, height = height.PDF, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(graph_5PLgompertz)
}
graph_5PL4PL <- function(data, var_dep, var_indep, treatment_name){
model_5PL <- 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))
model_4PL <- minpack.lm::nlsLM(var_dep ~ d+(a-d)/(1+(var_indep/c)^b),
data = data, start = list(list(a = min(var_dep), b = 2.5, c=10,d=max(var_dep))),
control = minpack.lm::nls.lm.control(maxiter = 500))
r2_5PL <-stats::cor(var_dep,stats::predict(model_5PL)) * stats::cor(var_dep,stats::predict(model_5PL))
predicted_5PL <- stats::predict(model_5PL)
r2_4PL <-stats::cor(var_dep,stats::predict(model_4PL)) * stats::cor(var_dep,stats::predict(model_4PL))
predicted_4PL <- stats::predict(model_4PL)
models <- c("5PL Model" = col1, "4PL Model" = col3)
data$overall <- treatment_name
graph_5PL4PL <- ggplot2::ggplot(data, ggplot2::aes_string(var_indep, var_dep))+
ggplot2::geom_point(col= col, size=2.5, pch=21, bg=col)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_4PL, color="4PL Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_5PL, color="5PL Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::theme_bw(base_family = font)+
ggplot2::theme(legend.position = legend.position, legend.title = ggplot2::element_blank())+
ggplot2::xlab(axisX) +
ggplot2::ylab(axisY)+
ggplot2::facet_wrap(~overall)+
ggplot2::labs(color = "Legend")+
ggplot2::scale_color_manual(values = models)+
ggplot2::scale_x_continuous(breaks = breaksX, limits = limitsX) +
ggplot2::scale_y_continuous(breaks = breaksY, limits = limitsY)+
ggplot2::theme(text=ggplot2::element_text(size = font.size))
if(show.R2 == TRUE){graph_5PL4PL <- graph_5PL4PL + ggplot2::geom_text (x=15, y=90, ggplot2::aes(label = "4PL"), size = 4)+
ggplot2::geom_text (x = 25, y = 90, parse = TRUE ,ggplot2::aes(label = paste("R^2")), size = 4)+
ggplot2::geom_text (x=33, y=90, ggplot2::aes(label = " = "), size = 4)+
ggplot2::geom_text(x = 51, y=90, ggplot2::aes(label = round(r2_4PL, 7)), size = 4)+
ggplot2::geom_text (x = 25, y = 85, parse = TRUE ,ggplot2::aes(label = paste("R^2")), size = 4)+
ggplot2::geom_text (x=33, y=85, ggplot2::aes(label = " = "), size = 4)+
ggplot2::geom_text (x=15, y=85, ggplot2::aes(label = "5PL"), size = 4)+
ggplot2::geom_text(x = 51, y=85, ggplot2::aes(label = round(r2_5PL, 7)), size = 4)
}
if(save.PDF == TRUE){
dir.create(paste(dir.save,dir.name, sep = ''))
ggplot2::ggsave(graph_5PL4PL, filename = paste("Treatment", treatment_name, ".pdf"), width = width.PDF, height = height.PDF, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(graph_5PL4PL)
}
graph_Gompertz4PL <- function(data, var_dep, var_indep, treatment_name){
model_gompertz <- minpack.lm::nlsLM(var_dep ~ a*exp(-exp(-c*var_indep+b)),
data = data,
start = list(a = max(var_dep), c = min(var_dep), b= 1.5),
control = minpack.lm::nls.lm.control(maxiter = 500))
model_4PL <- minpack.lm::nlsLM(var_dep ~ d+(a-d)/(1+(var_indep/c)^b),
data = data, start = list(a = min(var_dep), b = 2.5, c=10,d=max(var_dep)),
control = minpack.lm::nls.lm.control(maxiter = 500))
r2_gompertz <-stats::cor(var_dep,stats::predict(model_gompertz)) * stats::cor(var_dep,stats::predict(model_gompertz))
predicted_gompertz <- stats::predict(model_gompertz)
r2_4PL <-stats::cor(var_dep,stats::predict(model_4PL)) * stats::cor(var_dep,stats::predict(model_4PL))
predicted_4PL <- stats::predict(model_4PL)
models <- c("Gompertz Model" = col2, "4PL Model" = col3)
data$overall <- treatment_name
graph_Gompertz4PL <- ggplot2::ggplot(data, ggplot2::aes_string(var_indep, var_dep))+
ggplot2::geom_point(col= col, size=2.5, pch=21, bg=col)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_4PL, color="4PL Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_gompertz, color="Gompertz Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::theme_bw(base_family = font)+
ggplot2::theme(legend.position = legend.position, legend.title = ggplot2::element_blank())+
ggplot2::xlab(axisX) +
ggplot2::ylab(axisY)+
ggplot2::facet_wrap(~overall)+
ggplot2::labs(color = "Legend")+
ggplot2::scale_color_manual(values = models)+
ggplot2::scale_x_continuous(breaks = breaksX, limits = limitsX) +
ggplot2::scale_y_continuous(breaks = breaksY, limits = limitsY)+
ggplot2::theme(text=ggplot2::element_text(size = font.size))
if(show.R2 == TRUE){graph_Gompertz4PL <- graph_Gompertz4PL + ggplot2::geom_text (x=15, y=90, ggplot2::aes(label = "4PL"), size = 4)+
ggplot2::geom_text (x = 25, y = 90, parse = TRUE ,ggplot2::aes(label = paste("R^2")), size = 4)+
ggplot2::geom_text (x=33, y=90, ggplot2::aes(label = " = "), size = 4)+
ggplot2::geom_text(x = 51, y=90, ggplot2::aes(label = round(r2_4PL, 7)), size = 4)+
ggplot2::geom_text (x = 25, y = 85, parse = TRUE ,ggplot2::aes(label = paste("R^2")), size = 4)+
ggplot2::geom_text (x=33, y=85, ggplot2::aes(label = " = "), size = 4)+
ggplot2::geom_text (x=5, y=85, ggplot2::aes(label = "Gompertz"), size = 4)+
ggplot2::geom_text(x = 51, y=85, ggplot2::aes(label = round(r2_gompertz, 7)), size = 4)}
if(save.PDF == TRUE){
dir.create(paste(dir.save,dir.name, sep = ''))
ggplot2::ggsave(graph_Gompertz4PL, filename = paste("Treatment", treatment_name, ".pdf"), width = width.PDF, height = height.PDF, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(graph_Gompertz4PL)
}
graph_5PLGompertz4PL <- function(data, var_dep, var_indep, treatment_name){
model_5PL <- 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))
model_gompertz <- minpack.lm::nlsLM(var_dep ~ a*exp(-exp(-c*var_indep+b)),
data = data,
start = list(max(var_dep), c = min(var_dep), b= 1.5),
control = minpack.lm::nls.lm.control(maxiter = 500))
model_4PL <- minpack.lm::nlsLM(var_dep ~ d+(a-d)/(1+(var_indep/c)^b),
data = data, start = list(a = min(var_dep), b = 2.5, c=10,d=max(var_dep)),
control = minpack.lm::nls.lm.control(maxiter = 500))
r2_5PL <-stats::cor(var_dep,stats::predict(model_5PL)) * stats::cor(var_dep,stats::predict(model_5PL))
predicted_5PL <- stats::predict(model_5PL)
r2_gompertz <-stats::cor(var_dep,stats::predict(model_gompertz)) * stats::cor(var_dep,stats::predict(model_gompertz))
predicted_gompertz <- stats::predict(model_gompertz)
r2_4PL <-stats::cor(var_dep,stats::predict(model_4PL)) * stats::cor(var_dep,stats::predict(model_4PL))
predicted_4PL <- stats::predict(model_4PL)
models <- c("5PL Model" = col1, "Gompertz Model" = col2, "4PL Model" = col3)
data$overall <- treatment_name
graph_5PLGompertz4PL <- ggplot2::ggplot(data, ggplot2::aes_string(var_indep, var_dep))+
ggplot2::geom_point(col= col, size=2.5, pch=21, bg=col)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_5PL, color="5PL Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_gompertz, color="Gompertz Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_4PL, color="4PL Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::theme_bw(base_family = font)+
ggplot2::theme(legend.position = legend.position, legend.title = ggplot2::element_blank())+
ggplot2::xlab(axisX) +
ggplot2::ylab(axisY)+
ggplot2::facet_wrap(~overall)+
ggplot2::labs(color = "Legend")+
ggplot2::scale_color_manual(values = models)+
ggplot2::scale_x_continuous(breaks = breaksX, limits = limitsX) +
ggplot2::scale_y_continuous(breaks = breaksY, limits = limitsY)+
ggplot2::theme(text=ggplot2::element_text(size = font.size))
if(show.R2 == TRUE){graph_5PLGompertz4PL <- graph_5PLGompertz4PL + ggplot2::geom_text (x=15, y=90, ggplot2::aes(label = "5PL"), size = 4)+
ggplot2::geom_text (x = 25, y = 90, parse = TRUE ,ggplot2::aes(label = paste("R^2")), size = 4)+
ggplot2::geom_text (x=33, y=90, ggplot2::aes(label = " = "), size = 4)+
ggplot2::geom_text(x = 51, y=90, ggplot2::aes(label = round(r2_5PL, 7)), size = 4)+
ggplot2::geom_text (x = 25, y = 85, parse = TRUE ,ggplot2::aes(label = paste("R^2")), size = 4)+
ggplot2::geom_text (x=33, y=85, ggplot2::aes(label = " = "), size = 4)+
ggplot2::geom_text (x=5, y=85, ggplot2::aes(label = "Gompertz"), size = 4)+
ggplot2::geom_text(x = 51, y=85, ggplot2::aes(label = round(r2_gompertz, 7)), size = 4)+
ggplot2::geom_text (x=15, y=80, ggplot2::aes(label = "4PL"), size = 4)+
ggplot2::geom_text (x = 25, y = 80, parse = TRUE ,ggplot2::aes(label = paste("R^2")), size = 4)+
ggplot2::geom_text (x=33, y=80, ggplot2::aes(label = " = "), size = 4)+
ggplot2::geom_text(x = 51, y=80, ggplot2::aes(label = round(r2_4PL, 7)), size = 4)}
if(save.PDF == TRUE){
dir.create(paste(dir.save,dir.name, sep = ''))
ggplot2::ggsave(graph_5PLGompertz4PL, filename = paste("Treatment", treatment_name, ".pdf"), width = width.PDF, height = height.PDF, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(graph_5PLGompertz4PL)
}
if(models == 1){
p <- list()
for (i in 1:(ncol(data)/2)) { p[[i]] <- graph_5PL(data = data,
var_dep = data[,i + (ncol(data)/2)],
var_indep = data[,i],
treatment_name = colnames(data)[i + (ncol(data)/2)])
p[[i]] <- p[[i]] + ggplot2::ylab(NULL) +ggplot2::xlab(NULL) + ggplot2::theme(legend.position = "none")
}
plot <- do.call(gridExtra::grid.arrange,c(p, labels = NULL, ncol = 4))
plot <- ggpubr::annotate_figure(plot, left = grid::textGrob(axisX, rot = 90, vjust = 1, gp = grid::gpar(cex = 1.3)),
bottom = grid::textGrob(axisY, gp = grid::gpar(cex = 1.3)))
if(save.PDF == TRUE){ggplot2::ggsave(plot, filename = "Multiplot.pdf", width = width.PDF2, height = height.PDF2, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(plot)
}
else if (models == 2){
p <- list()
for (i in 1:(ncol(data)/2)) {p[[i]] <- graph_gompertz(data = data,
var_dep = data[,i + (ncol(data)/2)],
var_indep = data[,i],
treatment_name = colnames(data)[i + (ncol(data)/2)])
p[[i]] <- p[[i]] + ggplot2::ylab(NULL) +ggplot2::xlab(NULL) + ggplot2::theme(legend.position = "none")}
plot <- do.call(gridExtra::grid.arrange,c(p, labels = NULL, ncol = 4))
plot <- ggpubr::annotate_figure(plot, left = grid::textGrob(axisX, rot = 90, vjust = 1, gp = grid::gpar(cex = 1.3)),
bottom = grid::textGrob(axisY, gp = grid::gpar(cex = 1.3)))
if(save.PDF == TRUE){ggplot2::ggsave(plot, filename = "Multiplot.pdf", width = width.PDF2, height = height.PDF2, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(plot)
}
else if (models == 3){
p <- list()
for (i in 1:(ncol(data)/2)) {p[[i]] <- graph_4PL(data = data,
var_dep = data[,i + (ncol(data)/2)],
var_indep = data[,i],
treatment_name = colnames(data)[i + (ncol(data)/2)])
p[[i]] <- p[[i]] + ggplot2::ylab(NULL) +ggplot2::xlab(NULL) + ggplot2::theme(legend.position = "none")}
plot <- do.call(gridExtra::grid.arrange,c(p, labels = NULL, ncol = 4))
plot <- ggpubr::annotate_figure(plot, left = grid::textGrob(axisX, rot = 90, vjust = 1, gp = grid::gpar(cex = 1.3)),
bottom = grid::textGrob(axisY, gp = grid::gpar(cex = 1.3)))
if(save.PDF == TRUE){ggplot2::ggsave(plot, filename = "Multiplot.pdf", width = width.PDF2, height = height.PDF2, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(plot)
}
else if (models == 4){
p <- list()
for (i in 1:(ncol(data)/2)) {p[[i]] <- graph_5PLgompertz(data = data,
var_dep = data[,i + (ncol(data)/2)],
var_indep = data[,i],
treatment_name = colnames(data)[i + (ncol(data)/2)])
p[[i]] <- p[[i]] + ggplot2::ylab(NULL) +ggplot2::xlab(NULL) + ggplot2::theme(legend.position = "none")}
plot <- do.call(gridExtra::grid.arrange,c(p, labels = NULL, ncol = 4))
plot <- ggpubr::annotate_figure(plot, left = grid::textGrob(axisX, rot = 90, vjust = 1, gp = grid::gpar(cex = 1.3)),
bottom = grid::textGrob(axisY, gp = grid::gpar(cex = 1.3)))
if(save.PDF == TRUE){ggplot2::ggsave(plot, filename = "Multiplot.pdf", width = width.PDF2, height = height.PDF2, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(plot)
}
else if (models == 5){
p <- list()
for (i in 1:(ncol(data)/2)) {p[[i]] <- graph_5PL4PL(data = data,
var_dep = data[,i + (ncol(data)/2)],
var_indep = data[,i],
treatment_name = colnames(data)[i + (ncol(data)/2)
])
p[[i]] <- p[[i]] + ggplot2::ylab(NULL) +ggplot2::xlab(NULL) + ggplot2::theme(legend.position = "none")}
plot <- do.call(gridExtra::grid.arrange,c(p, labels = NULL, ncol = 4))
plot <- ggpubr::annotate_figure(plot, left = grid::textGrob(axisX, rot = 90, vjust = 1, gp = grid::gpar(cex = 1.3)),
bottom = grid::textGrob(axisY, gp = grid::gpar(cex = 1.3)))
if(save.PDF == TRUE){ggplot2::ggsave(plot, filename = "Multiplot.pdf", width = width.PDF2, height = height.PDF2, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(plot)
}
else if (models == 6){
p <- list()
for (i in 1:(ncol(data)/2)) {p[[i]] <- graph_Gompertz4PL(data = data,
var_dep = data[,i + (ncol(data)/2)],
var_indep = data[,i],
treatment_name = colnames(data)[i + (ncol(data)/2)])
p[[i]] <- p[[i]] + ggplot2::ylab(NULL) +ggplot2::xlab(NULL) + ggplot2::theme(legend.position = "none")}
}
else if (models == 7){
p <- list()
for (i in 1:(ncol(data)/2)) {p[[i]] <- graph_5PLGompertz4PL(data = data,
var_dep = data[,i + (ncol(data)/2)],
var_indep = data[,i],
treatment_name = colnames(data)[i + (ncol(data)/2)])
p[[i]] <- p[[i]] + ggplot2::ylab(NULL) +ggplot2::xlab(NULL) + ggplot2::theme(legend.position = "none")}
plot <- do.call(gridExtra::grid.arrange,c(p, labels = NULL, ncol = 4))
plot <- ggpubr::annotate_figure(plot, left = grid::textGrob(axisX, rot = 90, vjust = 1, gp = grid::gpar(cex = 1.3)),
bottom = grid::textGrob(axisY, gp = grid::gpar(cex = 1.3)))
if(save.PDF == TRUE){ggplot2::ggsave(plot, filename = "Multiplot.pdf", width = width.PDF2, height = height.PDF2, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(plot)}
else {print("Models not found!!")}}
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OenoKPM documentation built on May 29, 2024, 9:13 a.m.
R Package Documentation
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Defines functions plot_fit
Documented in plot_fit
#' @title Plot graphs with observed data
#' and predicted data from models
#' @name plot_fit
#'
#' @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(s) (\strong{5PL}, \strong{Gompertz}, or/and \strong{4PL}).
#'
#' From the observed data and predicted data,
#' whether from one or all of the available
#' models, this function will plot a graph
#' for each fermentation curve evaluated.
#' The chart will have the following basic
#' structure:
#'
#' \strong{X axis}: \emph{fermentation time}
#'
#' \strong{Y axis}: \emph{CO\ifelse{html}{\out{<sub>2</sub>}}{\eqn{_2}} production}
#'
#' \strong{Observed data}: \emph{Scatterplot with dots}. Plot with geom_point function from ggplot2 package.
#'
#' \strong{Predicted data}: \emph{Smoothed line}. Plot with the stat_smooth function from the ggplot2 package.
#'
#' @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 name the graphics
#' PDF files for each
#' analyzed curve.}
#'
#' @param models Model or models to be adjusted:
#'\itemize{
#'\item \strong{Models = 1}. Only the 5PL Model.
#'\item \strong{Models = 2}. Only the Gompertz Model.
#'\item \strong{Models = 3}. Only the 4PL Model.
#'\item \strong{Models = 4}. 5PL and Gompertz Models.
#'\item \strong{Models = 5}. 5PL and 4PL Models.
#'\item \strong{Models = 6}. Gompertz and 4PL Models.
#'\item \strong{Models = 7}. 5PL, Gompertz and 4PL Models.}
#' @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.
#' @param col Plot color of observed data in points. For example, "black".
#'
#' @param col1 Plot color of predicted data from \strong{model 1} (\emph{5PL Model}). For example, "red".
#'
#' @param col2 Plot color of predicted data from \strong{model 2} (\emph{Gompertz Model}). For example, "blue".
#'
#' @param col3 Plot color of predicted data from \strong{model 3} (\emph{4PL Model}). For example, "green".
#'
#' @param axisX X Axis Title. Character vector (or expression).
#'
#' @param axisY Y Axis Title. Character vector (or expression).
#'
#' @param breaksX
#' One of:
#'
#'\itemize{
#'\item Use ggplot2::waiver() for the default X-axis breaks calculated by the transform object.
#'\item Numerical vector of X-Axis scale positions. For example, seq(0,200,20).}
#'@param limitsX
#'
#' One of:
#'\itemize{
#'\item NULL to use the default X-Axis scaling range.
#'\item A numeric vector of length two, giving the limits of the X-axis scale. For example, c(0,200).}
#' @param breaksY
#' One of:
#'\itemize{
#'\item Use ggplot2::waiver() for the default Y-axis breaks calculated by the transform object.
#'\item A numerical vector of Y-Axis scale positions. For example, seq(0,100,10).}
#'
#'@param limitsY
#'
#' One of:
#'\itemize{
#'\item NULL to use the default Y-Axis scaling range.
#'\item A numeric vector of length two, giving the limits of the Y-axis scale. For example, c(0,100).}
#' @param font Base font family
#'
#' @param font.size Base font size, given in pts.
#'
#' @param legend.position The position of the caption ("none", "left", "right", "bottom", "top", or a numeric vector of two elements (X,Y).
#'
#' @param show.R2 If TRUE, plots the R\ifelse{html}{\out{<sup>2</sup>}}{\eqn{^2}} of the plotted predicted models on the graph. If FALSE, do not plot the R\ifelse{html}{\out{<sup>2</sup>}}{\eqn{^2}} of the plotted predicted models.
#'
#' @param save.PDF If TRUE, create a folder (directory)
#' and save each graphic in PDF format. If FALSE,
#' it does not create a directory or save the
#' graphics in PDF.
#'
#'@param dir.save Path of the directory in
#' which a new folder (directory) will be
#' created for saving graphics in PDF format.
#'
#' @param dir.name Folder name (directory name)
#' to be created within the working directory
#' for saving PDF graphics. Character vector.
#'
#' @param width.PDF Width, in cm, of the graphic to be saved in a PDF file.
#'
#' @param height.PDF Height, in cm, of the graphic to be saved in a PDF
#'
#' @param width.PDF2 Width, in cm, of the multiplot graphic to be saved in a PDF file.
#'
#' @param height.PDF2 Height, in cm, of the multiplot graphic to be saved in a PDF
#'
#'@details
#'Curve fitting from the observed data is
#' performed by the nlsLM() function in
#' the 'minpack.lm' package.
#'
#' Graphs are
#' plotted using the various functions in
#' the 'ggplot2' package.
#'
#'
#' @return Elegant graphics plotted according
#' to observed and predicted data. In addition,
#' a folder (directory) can be created,
#' in which the PDF graphics will be saved,
#' if desired.
#' In this folder,
#' the graph of each analyzed
#' fermentation curve is saved in PDF format,
#' with the dimensions stipulated
#' in the \emph{width.PDF} and \emph{height.PDF}
#' arguments. The name of each PDF
#' file will be extracted from the
#' header of the dependent variable used
#' for the graph. See more in the examples.
#'
#'@author Angelo Gava
#'
#'@examples
#'
#' #################Example 1#################
#' #Using only required arguments
#'
#' #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 plot_fit function to
#' #generate elegants graphs PDF files
#' #containing both observed data and
#' #predicted data.
#'
#'
#' #Graph plotted only with Model 5PL
#' #fit (models = 1)
#'
#' plot_fit(data = df,
#' models = 1,
#' startA = 0,
#' startB = 1.5,
#' startC = 500,
#' startD = 92,
#' startG = 1500)
#'
#'
#' #Graph plotted with 5PL and Gompertz
#' #model fits (models = 4)
#'
#' plot_fit(data = df,
#' models = 4,
#' startA = 0,
#' startB = 1.5,
#' startC = 500,
#' startD = 92,
#' startG = 1500)
#'
#'
#'
#' #################Example 2#################
#' #Using the various function arguments to
#' #customize the graph.
#'
#' #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_Treatment_A' = seq(0,200, by=6.45),
#' 'Time_Treatment_B' = seq(0,200, by=6.45),
#' 'CO2_Production_Treatment_A' = c(0,0.47,0.78,3.23,19.15,22.86,
#' 26.81,29.36,36.14,52.61,55.58,
#' 61.38,66.25,71.83,74.8,78.88,
#' 83.47,84.48,85.94,87.45,87.98,
#' 88.42,88.68,89.40,89.72,89.87,
#' 90.41,90.51,90.62,90.70,91.05,
#' 91.185),
#' 'CO2_Production_Treatment_B' = c(0,0.19,0.39,1.36,9.23,11.29,
#' 13.58,15.06,19.34,30.92,33.28,
#' 37.98,42.14,47.17,50.00,54.28,
#' 60.92,62.80,65.54,69.74,71.52,
#' 73.07,73.98,76.75,77.79,78.70,
#' 80.65,81.48,82.07,82.47,84.04,
#' 84.60))
#'
#' #Using the plot_fit function to
#' #generate elegants graphs PDF files
#' #containing both observed data and
#' #predicted data.
#'
#' #Graph plotted only with Model 5PL
#' #fit (models = 1)
#' #Do not show R^2
#'
#' plot_fit(data = df,
#' startA = 0,
#' startB = 1.5,
#' startC = 500,
#' startD = 92,
#' startG = 1500,
#' models = 1,
#' col = "red", #Color of observed data (points)
#' col1 = "blue", #Predicted data color from model 1 (line). Model = 1 <- 5PL Model
#' axisX = "Fermentation time (h)", #Title X-Axis
#' axisY = "Carbon dioxide production (g/L)", #Title Y-Axis
#' breaksX = seq(0,200,20), #X-Axis scale (positions). 0,20,40,60,80,...
#' limitsX = c(0,200), #X-Axis Limits
#' breaksY = seq(0,90,5),#Y-Axis scale (positions). 0,5,10,15,20,...
#' limitsY = c(0,95), #Y-Axis Limits
#' font = "serif",
#' font.size = 12,
#' legend.position = "right",
#' show.R2 = FALSE) #Do not show R^2
#'
#'
#'
#'
#' #Graph plotted with 5PL and 4PL
#' #model fits (models = 5)
#' #Show R^2
#' \dontrun{
#' plot_fit(data = df,
#' models = 5,
#' startA = 0,
#' startB = 1.5,
#' startC = 500,
#' startD = 92,
#' startG = 1500,
#' col = "#000000", #Color of observed data (points)
#' col1 = "#FF0000", #Predicted data color from model 1 (line). Model = 1 <- 5PL Model
#' col3 = "#0B6121",#Predicted data color from model 3 (line). Model = 3 <- 4PL Model
#' axisX = "Time (h)", #Title X-Axis
#' axisY = "CO2 production (g/L)", #Title Y-Axis
#' breaksX = seq(0,200,20), #X-Axis scale (positions). 0,20,40,60,80,...
#' limitsX = c(0,200), #X-Axis Limits
#' breaksY = seq(0,90,10),#Y-Axis scale (positions). 0,10,20,30,40,...
#' limitsY = c(0,95), #Y-Axis Limits
#' font = "serif",
#' font.size = 14,
#' legend.position = "bottom",
#' show.R2 = TRUE) #Show R^2
#' }
#'
#' #Graph plotted with 5PL, Gompertz and 4PL
#' #model fits (models = 7)
#' #Do not show R^2
#' \dontrun{
#' plot_fit(data = df,
#' models = 7,
#' startA = 0,
#' startB = 1.5,
#' startC = 500,
#' startD = 92,
#' startG = 1500,
#' col = "#FF0000", #Color of observed data (points)
#' col1 = "#FF00FF", #Predicted data color from model 1 (line). Model = 1 <- 5PL Model
#' col2 = "#0101DF",#Predicted data color from model 2 (line). Model = 2 <- Gompertz Model
#' col3 = "#088A08",#Predicted data color from model 3 (line). Model = 3 <- 4PL Model
#' axisX = "Time (h)", #Title X-Axis
#' axisY = "Carbon dioxide production (g/L)", #Title Y-Axis
#' breaksX = seq(0,200,20), #X-Axis scale (positions). 0,20,40,60,80,...
#' limitsX = c(0,200), #X-Axis Limits
#' breaksY = seq(0,90,10),#Y-Axis scale (positions). 0,10,20,30,40,...
#' limitsY = c(0,95), #Y-Axis Limits
#' font = "serif",
#' font.size = 14,
#' legend.position = "top",
#' show.R2 = FALSE) #Do not show R^2
#' }
#'
#' @import ggplot2
#' @import grDevices
#' @import gridExtra
#' @import minpack.lm
#' @import ggpubr
#' @import grid
#' @export
plot_fit <- function(data,
models,
startA,
startB,
startC,
startD,
startG,
col = "black",
col1 = "red",
col2 = "cornflowerblue",
col3 = "forestgreen",
axisX = "Time (hours)",
axisY = expression(paste("CO"["2"]*" Production (g L"^{"-1"}*")")),
breaksX = waiver(),
limitsX = NULL,
breaksY = waiver(),
limitsY = NULL,
font = "serif",
font.size = 14,
legend.position = "top",
show.R2 = FALSE,
save.PDF = FALSE,
dir.save,
dir.name = "Graphics",
width.PDF = 15,
height.PDF = 12,
width.PDF2 = 25,
height.PDF2 = 18
) {
graph_5PL <- function(data, var_dep, var_indep, treatment_name){
model_5PL <- 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))
r2_5PL <-stats::cor(var_dep,stats::predict(model_5PL)) * stats::cor(var_dep,stats::predict(model_5PL))
predicted_5PL <- stats::predict(model_5PL)
models <- c("5PL Model" = col1)
data$overall <- treatment_name
graph_5PL <- ggplot2::ggplot(data, ggplot2::aes_string(var_indep, var_dep))+
ggplot2::geom_point(col= col, size=2.5, pch=21, bg = col)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_5PL, color = "5PL Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::theme_bw(base_family = font)+
ggplot2::theme(legend.position = legend.position, legend.title = ggplot2::element_blank())+
ggplot2::labs(color = "Legend")+
ggplot2::scale_color_manual(values = models)+
ggplot2::xlab(axisX) +
ggplot2::ylab(axisY)+
ggplot2::facet_wrap(~overall)+
ggplot2::scale_x_continuous(breaks = breaksX, limits = limitsX) +
ggplot2::scale_y_continuous(breaks = breaksY, limits = limitsY)+
ggplot2::theme(text=ggplot2::element_text(size = font.size))
if(show.R2 == TRUE){graph_5PL <- graph_5PL + ggplot2::geom_text (x = 25, y = 90, parse = TRUE ,ggplot2::aes(label = "R2"))+
ggplot2::geom_text (x=33, y=90, ggplot2::aes(label = " = "))+
ggplot2::geom_text(x = 51, y=90, ggplot2::aes(label = round(r2_5PL, 7)))}
if(save.PDF == TRUE){
dir.create(paste(dir.save,dir.name, sep = ''))
ggplot2::ggsave(graph_5PL, filename = paste("Treatment", treatment_name, ".pdf"), width = width.PDF, height = height.PDF, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(graph_5PL)
}
graph_gompertz <- function(data, var_dep, var_indep, treatment_name){
model_gompertz <- minpack.lm::nlsLM(var_dep ~ a*exp(-exp(-c*var_indep+b)),
data = data,
start = list(a = startA, c = startC, b= startC),
control = minpack.lm::nls.lm.control(maxiter = 500))
r2_gompertz <-stats::cor(var_dep,stats::predict(model_gompertz)) * stats::cor(var_dep,stats::predict(model_gompertz))
predicted_gompertz <- stats::predict(model_gompertz)
models <- c("Gompertz Model" = col2)
data$overall <- treatment_name
graph_gompertz <- ggplot2::ggplot(data, ggplot2::aes_string(var_indep, var_dep))+
ggplot2::geom_point(col= col, size=2.5, pch=21, bg = col)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_gompertz, color="Gompertz Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::theme_bw(base_family = font)+
ggplot2::theme(legend.position = legend.position, legend.title = ggplot2::element_blank())+
ggplot2::labs(color = "Legend")+
ggplot2::scale_color_manual(values = models)+
ggplot2::xlab(axisX) +
ggplot2::ylab(axisY)+
ggplot2::facet_wrap(~overall)+
ggplot2::scale_x_continuous(breaks = breaksX, limits = limitsX) +
ggplot2::scale_y_continuous(breaks = breaksY, limits = limitsY)+
ggplot2::theme(text=ggplot2::element_text(size = font.size))
if(show.R2 == TRUE){graph_gompertz <- graph_gompertz + ggplot2::geom_text (x = 25, y = 90, parse = TRUE ,ggplot2::aes(label = paste("R^2")))+
ggplot2::geom_text (x=33, y=90, ggplot2::aes(label = " = "))+
ggplot2::geom_text(x = 51, y=90, ggplot2::aes(label = round(r2_gompertz, 7)))}
if(save.PDF == TRUE){
dir.create(paste(dir.save,dir.name, sep = ''))
ggplot2::ggsave(graph_gompertz, filename = paste("Treatment", treatment_name, ".pdf"), width = width.PDF, height = height.PDF, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(graph_gompertz)
}
graph_4PL <- function(data, var_dep, var_indep, treatment_name){
model_4PL <- minpack.lm::nlsLM(var_dep ~ d+(a-d)/(1+(var_indep/c)^b),
data = data, start = list(a = startA, b = startB, c=startC,d=startD),
control = minpack.lm::nls.lm.control(maxiter = 500))
r2_4PL <-stats::cor(var_dep,stats::predict(model_4PL)) * stats::cor(var_dep,stats::predict(model_4PL))
predicted_4PL <- stats::predict(model_4PL)
models <- c("4PL Model" = col3)
data$overall <- treatment_name
graph_4PL <- ggplot2::ggplot(data, ggplot2::aes_string(var_indep, var_dep))+
ggplot2::geom_point(col= col, size=2.5, pch=21, bg=col)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_4PL, color = "4PL Model"), se=FALSE, linetype = "longdash")+
ggplot2::theme_bw(base_family = font)+
ggplot2::theme(legend.position = legend.position, legend.title = ggplot2::element_blank())+
ggplot2::labs(color = "Legend")+
ggplot2::scale_color_manual(values = models)+
ggplot2::xlab(axisX) +
ggplot2::ylab(axisY)+
ggplot2::facet_wrap(~overall)+
ggplot2::scale_x_continuous(breaks = breaksX, limits = limitsX) +
ggplot2::scale_y_continuous(breaks = breaksY, limits = limitsY)+
ggplot2::theme(text=ggplot2::element_text(size = font.size))
if(show.R2 == TRUE){graph_4PL <- graph_4PL + ggplot2::geom_text (x = 25, y = 90, parse = TRUE ,ggplot2::aes(label = paste("R^2")))+
ggplot2::geom_text (x=33, y=90, ggplot2::aes(label = " = "))+
ggplot2::geom_text(x = 51, y=90, ggplot2::aes(label = round(r2_4PL, 7)))}
if(save.PDF == TRUE){
dir.create(paste(dir.save,dir.name, sep = ''))
ggplot2::ggsave(graph_4PL, filename = paste("Treatment", treatment_name, ".pdf"), width = width.PDF, height = height.PDF, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(graph_4PL)
}
graph_5PLgompertz <- function(data, var_dep, var_indep, treatment_name){
model_5PL <- 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))
model_gompertz <- minpack.lm::nlsLM(var_dep ~ a*exp(-exp(-c*var_indep+b)),
data = data,
start = list(a = max(var_dep), c = min(var_dep), b= 1.5),
control = minpack.lm::nls.lm.control(maxiter = 500))
r2_5PL <-stats::cor(var_dep,stats::predict(model_5PL)) * stats::cor(var_dep,stats::predict(model_5PL))
predicted_5PL <- stats::predict(model_5PL)
r2_gompertz <-stats::cor(var_dep,stats::predict(model_gompertz)) * stats::cor(var_dep,stats::predict(model_gompertz))
predicted_gompertz <- stats::predict(model_gompertz)
models <- c("5PL Model" = col1, "Gompertz Model" = col2)
data$overall <- treatment_name
graph_5PLgompertz <- ggplot2::ggplot(data, ggplot2::aes_string(var_indep, var_dep))+
ggplot2::geom_point(col= col, size=2.5, pch=21, bg=col)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_gompertz, color="Gompertz Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_5PL, color="5PL Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::theme_bw(base_family = font)+
ggplot2::theme(legend.position = legend.position, legend.title = ggplot2::element_blank())+
ggplot2::xlab(axisX) +
ggplot2::ylab(axisY)+
ggplot2::facet_wrap(~overall)+
ggplot2::labs(color = "Legend")+
ggplot2::scale_color_manual(values = models)+
ggplot2::scale_x_continuous(breaks = breaksX, limits = limitsX)+
ggplot2::scale_y_continuous(breaks = breaksY, limits = limitsY)+
ggplot2::theme(text=ggplot2::element_text(size = font.size))
if(show.R2 == TRUE){graph_5PLgompertz <- graph_5PLgompertz + ggplot2::geom_text (x=6, y=90, ggplot2::aes(label = "Gompertz"), size = 4)+
ggplot2::geom_text (x = 25, y = 90, parse = TRUE ,ggplot2::aes(label = paste("R^2")), size = 4)+
ggplot2::geom_text (x=33, y=90, ggplot2::aes(label = " = "), size = 4)+
ggplot2::geom_text(x = 51, y=90, ggplot2::aes(label = round(r2_gompertz, 7)), size = 4)+
ggplot2::geom_text (x = 25, y = 85, parse = TRUE ,ggplot2::aes(label = paste("R^2")), size = 4)+
ggplot2::geom_text (x=33, y=85, ggplot2::aes(label = " = "), size = 4)+
ggplot2::geom_text (x=10, y=85, ggplot2::aes(label = "5PL"), size = 4)+
ggplot2::geom_text(x = 51, y=85, ggplot2::aes(label = round(r2_5PL, 7)), size = 4)}
if(save.PDF == TRUE){
dir.create(paste(dir.save,dir.name, sep = ''))
ggplot2::ggsave(graph_5PLgompertz, filename = paste("Treatment", treatment_name, ".pdf"), width = width.PDF, height = height.PDF, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(graph_5PLgompertz)
}
graph_5PL4PL <- function(data, var_dep, var_indep, treatment_name){
model_5PL <- 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))
model_4PL <- minpack.lm::nlsLM(var_dep ~ d+(a-d)/(1+(var_indep/c)^b),
data = data, start = list(list(a = min(var_dep), b = 2.5, c=10,d=max(var_dep))),
control = minpack.lm::nls.lm.control(maxiter = 500))
r2_5PL <-stats::cor(var_dep,stats::predict(model_5PL)) * stats::cor(var_dep,stats::predict(model_5PL))
predicted_5PL <- stats::predict(model_5PL)
r2_4PL <-stats::cor(var_dep,stats::predict(model_4PL)) * stats::cor(var_dep,stats::predict(model_4PL))
predicted_4PL <- stats::predict(model_4PL)
models <- c("5PL Model" = col1, "4PL Model" = col3)
data$overall <- treatment_name
graph_5PL4PL <- ggplot2::ggplot(data, ggplot2::aes_string(var_indep, var_dep))+
ggplot2::geom_point(col= col, size=2.5, pch=21, bg=col)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_4PL, color="4PL Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_5PL, color="5PL Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::theme_bw(base_family = font)+
ggplot2::theme(legend.position = legend.position, legend.title = ggplot2::element_blank())+
ggplot2::xlab(axisX) +
ggplot2::ylab(axisY)+
ggplot2::facet_wrap(~overall)+
ggplot2::labs(color = "Legend")+
ggplot2::scale_color_manual(values = models)+
ggplot2::scale_x_continuous(breaks = breaksX, limits = limitsX) +
ggplot2::scale_y_continuous(breaks = breaksY, limits = limitsY)+
ggplot2::theme(text=ggplot2::element_text(size = font.size))
if(show.R2 == TRUE){graph_5PL4PL <- graph_5PL4PL + ggplot2::geom_text (x=15, y=90, ggplot2::aes(label = "4PL"), size = 4)+
ggplot2::geom_text (x = 25, y = 90, parse = TRUE ,ggplot2::aes(label = paste("R^2")), size = 4)+
ggplot2::geom_text (x=33, y=90, ggplot2::aes(label = " = "), size = 4)+
ggplot2::geom_text(x = 51, y=90, ggplot2::aes(label = round(r2_4PL, 7)), size = 4)+
ggplot2::geom_text (x = 25, y = 85, parse = TRUE ,ggplot2::aes(label = paste("R^2")), size = 4)+
ggplot2::geom_text (x=33, y=85, ggplot2::aes(label = " = "), size = 4)+
ggplot2::geom_text (x=15, y=85, ggplot2::aes(label = "5PL"), size = 4)+
ggplot2::geom_text(x = 51, y=85, ggplot2::aes(label = round(r2_5PL, 7)), size = 4)
}
if(save.PDF == TRUE){
dir.create(paste(dir.save,dir.name, sep = ''))
ggplot2::ggsave(graph_5PL4PL, filename = paste("Treatment", treatment_name, ".pdf"), width = width.PDF, height = height.PDF, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(graph_5PL4PL)
}
graph_Gompertz4PL <- function(data, var_dep, var_indep, treatment_name){
model_gompertz <- minpack.lm::nlsLM(var_dep ~ a*exp(-exp(-c*var_indep+b)),
data = data,
start = list(a = max(var_dep), c = min(var_dep), b= 1.5),
control = minpack.lm::nls.lm.control(maxiter = 500))
model_4PL <- minpack.lm::nlsLM(var_dep ~ d+(a-d)/(1+(var_indep/c)^b),
data = data, start = list(a = min(var_dep), b = 2.5, c=10,d=max(var_dep)),
control = minpack.lm::nls.lm.control(maxiter = 500))
r2_gompertz <-stats::cor(var_dep,stats::predict(model_gompertz)) * stats::cor(var_dep,stats::predict(model_gompertz))
predicted_gompertz <- stats::predict(model_gompertz)
r2_4PL <-stats::cor(var_dep,stats::predict(model_4PL)) * stats::cor(var_dep,stats::predict(model_4PL))
predicted_4PL <- stats::predict(model_4PL)
models <- c("Gompertz Model" = col2, "4PL Model" = col3)
data$overall <- treatment_name
graph_Gompertz4PL <- ggplot2::ggplot(data, ggplot2::aes_string(var_indep, var_dep))+
ggplot2::geom_point(col= col, size=2.5, pch=21, bg=col)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_4PL, color="4PL Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_gompertz, color="Gompertz Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::theme_bw(base_family = font)+
ggplot2::theme(legend.position = legend.position, legend.title = ggplot2::element_blank())+
ggplot2::xlab(axisX) +
ggplot2::ylab(axisY)+
ggplot2::facet_wrap(~overall)+
ggplot2::labs(color = "Legend")+
ggplot2::scale_color_manual(values = models)+
ggplot2::scale_x_continuous(breaks = breaksX, limits = limitsX) +
ggplot2::scale_y_continuous(breaks = breaksY, limits = limitsY)+
ggplot2::theme(text=ggplot2::element_text(size = font.size))
if(show.R2 == TRUE){graph_Gompertz4PL <- graph_Gompertz4PL + ggplot2::geom_text (x=15, y=90, ggplot2::aes(label = "4PL"), size = 4)+
ggplot2::geom_text (x = 25, y = 90, parse = TRUE ,ggplot2::aes(label = paste("R^2")), size = 4)+
ggplot2::geom_text (x=33, y=90, ggplot2::aes(label = " = "), size = 4)+
ggplot2::geom_text(x = 51, y=90, ggplot2::aes(label = round(r2_4PL, 7)), size = 4)+
ggplot2::geom_text (x = 25, y = 85, parse = TRUE ,ggplot2::aes(label = paste("R^2")), size = 4)+
ggplot2::geom_text (x=33, y=85, ggplot2::aes(label = " = "), size = 4)+
ggplot2::geom_text (x=5, y=85, ggplot2::aes(label = "Gompertz"), size = 4)+
ggplot2::geom_text(x = 51, y=85, ggplot2::aes(label = round(r2_gompertz, 7)), size = 4)}
if(save.PDF == TRUE){
dir.create(paste(dir.save,dir.name, sep = ''))
ggplot2::ggsave(graph_Gompertz4PL, filename = paste("Treatment", treatment_name, ".pdf"), width = width.PDF, height = height.PDF, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(graph_Gompertz4PL)
}
graph_5PLGompertz4PL <- function(data, var_dep, var_indep, treatment_name){
model_5PL <- 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))
model_gompertz <- minpack.lm::nlsLM(var_dep ~ a*exp(-exp(-c*var_indep+b)),
data = data,
start = list(max(var_dep), c = min(var_dep), b= 1.5),
control = minpack.lm::nls.lm.control(maxiter = 500))
model_4PL <- minpack.lm::nlsLM(var_dep ~ d+(a-d)/(1+(var_indep/c)^b),
data = data, start = list(a = min(var_dep), b = 2.5, c=10,d=max(var_dep)),
control = minpack.lm::nls.lm.control(maxiter = 500))
r2_5PL <-stats::cor(var_dep,stats::predict(model_5PL)) * stats::cor(var_dep,stats::predict(model_5PL))
predicted_5PL <- stats::predict(model_5PL)
r2_gompertz <-stats::cor(var_dep,stats::predict(model_gompertz)) * stats::cor(var_dep,stats::predict(model_gompertz))
predicted_gompertz <- stats::predict(model_gompertz)
r2_4PL <-stats::cor(var_dep,stats::predict(model_4PL)) * stats::cor(var_dep,stats::predict(model_4PL))
predicted_4PL <- stats::predict(model_4PL)
models <- c("5PL Model" = col1, "Gompertz Model" = col2, "4PL Model" = col3)
data$overall <- treatment_name
graph_5PLGompertz4PL <- ggplot2::ggplot(data, ggplot2::aes_string(var_indep, var_dep))+
ggplot2::geom_point(col= col, size=2.5, pch=21, bg=col)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_5PL, color="5PL Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_gompertz, color="Gompertz Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::stat_smooth(ggplot2::aes(var_indep, predicted_4PL, color="4PL Model"), se=FALSE, linetype = "longdash", show.legend = TRUE)+
ggplot2::theme_bw(base_family = font)+
ggplot2::theme(legend.position = legend.position, legend.title = ggplot2::element_blank())+
ggplot2::xlab(axisX) +
ggplot2::ylab(axisY)+
ggplot2::facet_wrap(~overall)+
ggplot2::labs(color = "Legend")+
ggplot2::scale_color_manual(values = models)+
ggplot2::scale_x_continuous(breaks = breaksX, limits = limitsX) +
ggplot2::scale_y_continuous(breaks = breaksY, limits = limitsY)+
ggplot2::theme(text=ggplot2::element_text(size = font.size))
if(show.R2 == TRUE){graph_5PLGompertz4PL <- graph_5PLGompertz4PL + ggplot2::geom_text (x=15, y=90, ggplot2::aes(label = "5PL"), size = 4)+
ggplot2::geom_text (x = 25, y = 90, parse = TRUE ,ggplot2::aes(label = paste("R^2")), size = 4)+
ggplot2::geom_text (x=33, y=90, ggplot2::aes(label = " = "), size = 4)+
ggplot2::geom_text(x = 51, y=90, ggplot2::aes(label = round(r2_5PL, 7)), size = 4)+
ggplot2::geom_text (x = 25, y = 85, parse = TRUE ,ggplot2::aes(label = paste("R^2")), size = 4)+
ggplot2::geom_text (x=33, y=85, ggplot2::aes(label = " = "), size = 4)+
ggplot2::geom_text (x=5, y=85, ggplot2::aes(label = "Gompertz"), size = 4)+
ggplot2::geom_text(x = 51, y=85, ggplot2::aes(label = round(r2_gompertz, 7)), size = 4)+
ggplot2::geom_text (x=15, y=80, ggplot2::aes(label = "4PL"), size = 4)+
ggplot2::geom_text (x = 25, y = 80, parse = TRUE ,ggplot2::aes(label = paste("R^2")), size = 4)+
ggplot2::geom_text (x=33, y=80, ggplot2::aes(label = " = "), size = 4)+
ggplot2::geom_text(x = 51, y=80, ggplot2::aes(label = round(r2_4PL, 7)), size = 4)}
if(save.PDF == TRUE){
dir.create(paste(dir.save,dir.name, sep = ''))
ggplot2::ggsave(graph_5PLGompertz4PL, filename = paste("Treatment", treatment_name, ".pdf"), width = width.PDF, height = height.PDF, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(graph_5PLGompertz4PL)
}
if(models == 1){
p <- list()
for (i in 1:(ncol(data)/2)) { p[[i]] <- graph_5PL(data = data,
var_dep = data[,i + (ncol(data)/2)],
var_indep = data[,i],
treatment_name = colnames(data)[i + (ncol(data)/2)])
p[[i]] <- p[[i]] + ggplot2::ylab(NULL) +ggplot2::xlab(NULL) + ggplot2::theme(legend.position = "none")
}
plot <- do.call(gridExtra::grid.arrange,c(p, labels = NULL, ncol = 4))
plot <- ggpubr::annotate_figure(plot, left = grid::textGrob(axisX, rot = 90, vjust = 1, gp = grid::gpar(cex = 1.3)),
bottom = grid::textGrob(axisY, gp = grid::gpar(cex = 1.3)))
if(save.PDF == TRUE){ggplot2::ggsave(plot, filename = "Multiplot.pdf", width = width.PDF2, height = height.PDF2, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(plot)
}
else if (models == 2){
p <- list()
for (i in 1:(ncol(data)/2)) {p[[i]] <- graph_gompertz(data = data,
var_dep = data[,i + (ncol(data)/2)],
var_indep = data[,i],
treatment_name = colnames(data)[i + (ncol(data)/2)])
p[[i]] <- p[[i]] + ggplot2::ylab(NULL) +ggplot2::xlab(NULL) + ggplot2::theme(legend.position = "none")}
plot <- do.call(gridExtra::grid.arrange,c(p, labels = NULL, ncol = 4))
plot <- ggpubr::annotate_figure(plot, left = grid::textGrob(axisX, rot = 90, vjust = 1, gp = grid::gpar(cex = 1.3)),
bottom = grid::textGrob(axisY, gp = grid::gpar(cex = 1.3)))
if(save.PDF == TRUE){ggplot2::ggsave(plot, filename = "Multiplot.pdf", width = width.PDF2, height = height.PDF2, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(plot)
}
else if (models == 3){
p <- list()
for (i in 1:(ncol(data)/2)) {p[[i]] <- graph_4PL(data = data,
var_dep = data[,i + (ncol(data)/2)],
var_indep = data[,i],
treatment_name = colnames(data)[i + (ncol(data)/2)])
p[[i]] <- p[[i]] + ggplot2::ylab(NULL) +ggplot2::xlab(NULL) + ggplot2::theme(legend.position = "none")}
plot <- do.call(gridExtra::grid.arrange,c(p, labels = NULL, ncol = 4))
plot <- ggpubr::annotate_figure(plot, left = grid::textGrob(axisX, rot = 90, vjust = 1, gp = grid::gpar(cex = 1.3)),
bottom = grid::textGrob(axisY, gp = grid::gpar(cex = 1.3)))
if(save.PDF == TRUE){ggplot2::ggsave(plot, filename = "Multiplot.pdf", width = width.PDF2, height = height.PDF2, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(plot)
}
else if (models == 4){
p <- list()
for (i in 1:(ncol(data)/2)) {p[[i]] <- graph_5PLgompertz(data = data,
var_dep = data[,i + (ncol(data)/2)],
var_indep = data[,i],
treatment_name = colnames(data)[i + (ncol(data)/2)])
p[[i]] <- p[[i]] + ggplot2::ylab(NULL) +ggplot2::xlab(NULL) + ggplot2::theme(legend.position = "none")}
plot <- do.call(gridExtra::grid.arrange,c(p, labels = NULL, ncol = 4))
plot <- ggpubr::annotate_figure(plot, left = grid::textGrob(axisX, rot = 90, vjust = 1, gp = grid::gpar(cex = 1.3)),
bottom = grid::textGrob(axisY, gp = grid::gpar(cex = 1.3)))
if(save.PDF == TRUE){ggplot2::ggsave(plot, filename = "Multiplot.pdf", width = width.PDF2, height = height.PDF2, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(plot)
}
else if (models == 5){
p <- list()
for (i in 1:(ncol(data)/2)) {p[[i]] <- graph_5PL4PL(data = data,
var_dep = data[,i + (ncol(data)/2)],
var_indep = data[,i],
treatment_name = colnames(data)[i + (ncol(data)/2)
])
p[[i]] <- p[[i]] + ggplot2::ylab(NULL) +ggplot2::xlab(NULL) + ggplot2::theme(legend.position = "none")}
plot <- do.call(gridExtra::grid.arrange,c(p, labels = NULL, ncol = 4))
plot <- ggpubr::annotate_figure(plot, left = grid::textGrob(axisX, rot = 90, vjust = 1, gp = grid::gpar(cex = 1.3)),
bottom = grid::textGrob(axisY, gp = grid::gpar(cex = 1.3)))
if(save.PDF == TRUE){ggplot2::ggsave(plot, filename = "Multiplot.pdf", width = width.PDF2, height = height.PDF2, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(plot)
}
else if (models == 6){
p <- list()
for (i in 1:(ncol(data)/2)) {p[[i]] <- graph_Gompertz4PL(data = data,
var_dep = data[,i + (ncol(data)/2)],
var_indep = data[,i],
treatment_name = colnames(data)[i + (ncol(data)/2)])
p[[i]] <- p[[i]] + ggplot2::ylab(NULL) +ggplot2::xlab(NULL) + ggplot2::theme(legend.position = "none")}
}
else if (models == 7){
p <- list()
for (i in 1:(ncol(data)/2)) {p[[i]] <- graph_5PLGompertz4PL(data = data,
var_dep = data[,i + (ncol(data)/2)],
var_indep = data[,i],
treatment_name = colnames(data)[i + (ncol(data)/2)])
p[[i]] <- p[[i]] + ggplot2::ylab(NULL) +ggplot2::xlab(NULL) + ggplot2::theme(legend.position = "none")}
plot <- do.call(gridExtra::grid.arrange,c(p, labels = NULL, ncol = 4))
plot <- ggpubr::annotate_figure(plot, left = grid::textGrob(axisX, rot = 90, vjust = 1, gp = grid::gpar(cex = 1.3)),
bottom = grid::textGrob(axisY, gp = grid::gpar(cex = 1.3)))
if(save.PDF == TRUE){ggplot2::ggsave(plot, filename = "Multiplot.pdf", width = width.PDF2, height = height.PDF2, units = 'cm', device = grDevices::cairo_pdf, path = paste(dir.save,dir.name, sep = ''))}
print(plot)}
else {print("Models not found!!")}}
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