| pred {OenoKPM} | R Documentation |
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
)
Arguments
data |
Data frame to be analyzed. The data frame must be in the following order:
|
model |
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. |
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". |
Details
Curve fitting from the observed data is performed by the nlsLM() function in the 'minpack.lm' package.
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.
Author(s)
Angelo Gava
Examples
#Creating a data.frame.
#First, columns containing independent variable.
#Second, columns containing dependent variable.
#The data frame created presents two
#fermentation curves for two yeasts with
#different times and carbon dioxide production.
df <- data.frame('Time_Yeast_A' = seq(0,280, by=6.23),
'Time_Yeast_B' = seq(0,170, by=3.7777778),
'CO2_Production_Yeast_A' = c(0,0.97,4.04,9.62,13.44,17.50,
24.03,27.46,33.75,36.40,40.80,
44.24,48.01,50.85,54.85,57.51,
61.73,65.43,66.50,72.41,75.47,
77.22,78.49,79.26,80.31,81.04,
81.89,82.28,82.56,83.13,83.62,
84.11,84.47,85.02,85.31,85.61,
86.05,86.27,85.29,86.81,86.94,
87.13,87.33,87.45,87.85),
'CO2_Production_Yeast_B' = c(0,0.41,0.70,3.05,15.61,18.41,
21.37,23.23,28.28,41.28,43.98,
49.54,54.43,60.40,63.75,69.29,
76.54,78.38,80.91,83.72,84.66,
85.39,85.81,86.92,87.38,87.61,
88.38,88.57,88.72,88.82,89.22,
89.32,89.52,89.71,89.92,90.11,
90.31,90.50,90.70,90.90,91.09,
91.29,91.49,91.68,91.88))
#Using the 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.