| plot_fit {OenoKPM} | R Documentation |
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
)
Arguments
data |
Data frame to be analyzed. The data frame must be in the following order:
|
models |
Model or models to be adjusted:
|
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:
|
limitsX |
One of:
|
breaksY |
One of:
|
limitsY |
One of:
|
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 |
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.
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.
Author(s)
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
## Not run:
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
## End(Not run)
#Graph plotted with 5PL, Gompertz and 4PL
#model fits (models = 7)
#Do not show R^2
## Not run:
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
## End(Not run)