OenoKPM (version 2.4.1)

plot_fit: Plot graphs with observed data and predicted data from models

Description

A function that, based on the observed data, the independent variable (e.g. time in h) and the dependent variable (e.g. CO₂ production in g L^-1), performs the modeling of the fermentation curve based on the chosen model(s) (5PL, Gompertz, or/and 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:

X axis: fermentation time

Y axis: CO₂ production

Observed data: Scatterplot with dots. Plot with geom_point function from ggplot2 package.

Predicted data: Smoothed line. Plot with the stat_smooth function from the ggplot2 package.

Usage

plot_fit(
  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
)

Value

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 width.PDF and 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.

Arguments

data

Data frame to be analyzed. The data frame must be in the following order:

• First: All columns containing the independent variable (e.g. time in hours)

• Second: All columns containing dependent variables (e.g. CO₂ g L^-1 production)

• Header: Columns must contain a header. If the treatment ID is in the header, this ID will be used to name the graphics PDF files for each analyzed curve.

models

Model or models to be adjusted:

• Models = 1. Only the 5PL Model.

• Models = 2. Only the Gompertz Model.

• Models = 3. Only the 4PL Model.

• Models = 4. 5PL and Gompertz Models.

• Models = 5. 5PL and 4PL Models.

• Models = 6. Gompertz and 4PL Models.

• Models = 7. 5PL, Gompertz and 4PL Models.

startA

Starting estimate of the value of A for model.

startB

Starting estimate of the value of B for model.

startC

Starting estimate of the value of C for model.

startD

Starting estimate of the value of D for model.

startG

Starting estimate of the value of G for model.

col

Plot color of observed data in points. For example, "black".

col1

Plot color of predicted data from model 1 (5PL Model). For example, "red".

col2

Plot color of predicted data from model 2 (Gompertz Model). For example, "blue".

col3

Plot color of predicted data from model 3 (4PL Model). For example, "green".

axisX

X Axis Title. Character vector (or expression).

axisY

Y Axis Title. Character vector (or expression).

breaksX

One of:

• Use ggplot2::waiver() for the default X-axis breaks calculated by the transform object.

• Numerical vector of X-Axis scale positions. For example, seq(0,200,20).

limitsX

One of:

• NULL to use the default X-Axis scaling range.

• A numeric vector of length two, giving the limits of the X-axis scale. For example, c(0,200).

breaksY

One of:

• Use ggplot2::waiver() for the default Y-axis breaks calculated by the transform object.

• A numerical vector of Y-Axis scale positions. For example, seq(0,100,10).

limitsY

One of:

• NULL to use the default Y-Axis scaling range.

• A numeric vector of length two, giving the limits of the Y-axis scale. For example, c(0,100).

font

Base font family

font.size

Base font size, given in pts.

legend.position

The position of the caption ("none", "left", "right", "bottom", "top", or a numeric vector of two elements (X,Y).

show.R2

If TRUE, plots the R² of the plotted predicted models on the graph. If FALSE, do not plot the R² of the plotted predicted models.

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.

dir.save

Path of the directory in which a new folder (directory) will be created for saving graphics in PDF format.

dir.name

Folder name (directory name) to be created within the working directory for saving PDF graphics. Character vector.

width.PDF

Width, in cm, of the graphic to be saved in a PDF file.

height.PDF

Height, in cm, of the graphic to be saved in a PDF

width.PDF2

Width, in cm, of the multiplot graphic to be saved in a PDF file.

height.PDF2

Height, in cm, of the multiplot graphic to be saved in a PDF

Author

Angelo Gava

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.

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
if (FALSE) {
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
if (FALSE) {
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
 }