OenoKPM (version 2.4.1)

pred: Get the model's predicted values

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 analyzed data, this function will provide the predicted data for each evaluated fermentation curve.

Usage

pred(
  data,
  model,
  startA,
  startB,
  startC,
  startD,
  startG,
  save.xls = FALSE,
  dir.save,
  xls.name
)

Value

The predicted values of each analyzed curve will be returned in a data.frame. In addition, a file "Predicted Values.xlsx"

can be generated, containing the predicted values of each fermentation curve studied.

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.

model

Model or models to be adjusted:

• Model = 1. 5PL Model.

• Model = 2. Gompertz Model.

• Model = 3. 4PL Model.

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.

save.xls

If TRUE, an xlsx file containing the predicted values of each curve will be saved in the working directory. If it is FALSE, the xlsx file will not be saved.

dir.save

Directory path where the xlsx file is to be saved.

xls.name

File name. Must contain the format. For example, "Predicted Values.xlsx".

Author

Angelo Gava

Details

Curve fitting from the observed data is performed by the nlsLM() function in the 'minpack.lm' package.

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 pred() function to find the 
#predicted valuesaccording to the adopted model.

pred(data = df,
model = 1, 
startA = 0,
startB = 1.5,
startC = 500,
startD = 92, 
startG = 1500,
save.xls = FALSE) #5PL Model adopted

pred(data = df,
model = 2,
startA = 92,
startB = 1.5,
startC = 0,
startD = NA, 
startG = NA, 
save.xls = FALSE) #Gompertz Model adopted

pred(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.