This section is still under construction. Sorry for the inconvenience

In general, OptimClassifier functions have an intuitive syntax, although some examples are shown below.

LM

First step charging the dataset and package

# Load the package 
library(OptimClassifier)
 
# Load the dataset, AustralianCredit in this example  
data("AustralianCredit")

# Let's go with the model  
linearcreditscoring <- Optim.LM(Y~., AustralianCredit, p = 0.7, seed=2018)

print(linearcreditscoring)

GLM

# Load the package, if you still do not have it loaded
library(OptimClassifier)
# Load the dataset, AustralianCredit in this example  
data("AustralianCredit")
# Let's go with the model, if you had seen the LM example, this is very similar  
creditscoring <- Optim.GLM(Y~., AustralianCredit, p = 0.7, seed=2018)

print(creditscoring)

LMM

First step charging the dataset and package

# Load the package, if you still do not have it loaded
library(OptimClassifier)
# Load the dataset, AustralianCredit in this example  
data("AustralianCredit")
# Let's go with the model, if you had seen the LM example, this is very similar  
modelChooser <- Optim.LMM("Y", AustralianCredit, seed=2018)

print(modelChooser)

DA

# Load the package, if you still do not have it loaded
library(OptimClassifier)
# Load the dataset, AustralianCredit in this example  
data("AustralianCredit")
# Let's go with the model, if you had seen the LM example, this is very similar  
fit <- Optim.DA("Y~.", AustralianCredit,p=0.7 ,seed=2018)

print(fit)

CART

# Load the package, if you still do not have it loaded
library(OptimClassifier)
# Load the dataset, AustralianCredit in this example  
data("AustralianCredit")
# Let's go with the model, if you had seen the LM example, this is very similar  
fit <- Optim.CART("Y~.", AustralianCredit,p=0.7 ,seed=2018)

print(fit)

NN

# Load the package, if you still do not have it loaded
library(OptimClassifier)
# Load the dataset, AustralianCredit in this example  
data("AustralianCredit")
# Let's go with the model, if you had seen the LM example, this is very similar  
fit <- Optim.NN("Y~.", AustralianCredit,p=0.7 ,seed=2018)

print(fit)

SVM

# Load the package, if you still do not have it loaded
library(OptimClassifier)
# Load the dataset, AustralianCredit in this example  
data("AustralianCredit")
# Let's go with the model, if you had seen the LM example, this is very similar  
fit <- Optim.SVM("Y~.", AustralianCredit,p=0.7 ,seed=2018)

print(fit)