OptimClassifier (version 0.1.5)

Optim.LMM: Discover what is the best random variable for your data set

Description

This function allows to find best LMM for a specific data.

Usage

Optim.LMM(response, data, p, criteria = c("success_rate", "error_ti",
  "error_tii"), randomatributtecandidate = NULL, includedata = FALSE,
  seed = NULL, ...)

Arguments

response

A character object that contain the name of response variable about which a researcher is asking a question. "Y"

data

Data frame from which variables specified in formula are preferentially to be taken.

p

A percentage of training elements

criteria

This variable selects the criteria to select the best threshold. The default value is success_rate

randomatributtecandidate

a character vector, or NULL. The default value is NULL,the function tests with all those categorical variables in the data. The default option is nor recommended. Because the decision must be made according to the objective of statistical modeling. But it can serve as orientation.

includedata

logicals. If TRUE the training and testing datasets are returned.

seed

a single value, interpreted as an integer, or NULL. The default value is NULL, but for future checks of the model or models generated it is advisable to set a random seed to be able to reproduce it.

...

arguments passed to lmer

Value

An object of class Optim. See Optim.object

Examples

# NOT RUN {
if(interactive()){
## Load a Dataset
data(AustralianCredit)
## Generate a model
modelFit <- Optim.LMM("Y", AustralianCredit, p = 0.7, seed=2018)
modelFit
}

# }