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)