model_configuration2 {MSML}

model_configuration2 function

Description

This function is similar to the model_configuration function, with the added capability to maintain constant variables across models during training and prediction (see cov_train and cov_valid in page 2). Additionally, users have the option to select between linear or logistic regression models.

Usage

model_configuration2(
  data_train,
  data_valid,
  mv,
  cov_train,
  cov_valid,
  model = "lm"
)

Arguments

data_train

This includes the dataframe of the training dataset in a matrix format

data_valid

This includes the dataframe of the validation dataset in a matrix format

mv

The total number of columns in data_train/data_valid

cov_train

This includes dataframe of covariates for training dataset in a matrix format

cov_valid

This includes dataframe of covariates for validation dataset in a matrix format

model

This is the type of model (e.g. lm (default) or glm (logistic regression))

Value

This function will generate all possible model outcomes for validation and test dataset

Examples

data_train <- data_train
data_valid  <- data_valid
mv=8
cov_train <- cov_train
cov_valid  <- cov_valid
out=model_configuration2(data_train,data_valid,mv,cov_train, cov_valid, model = "lm")
#This process will produce predicted values for the validation datasets,
#corresponding to each model configuration trained on the training dataset.
#The outcome of this function will yield variables named 'predict_validation'
#and 'total_model_configurations.
#To print the outcomes run out$predict_validation and out$total_model_configurations.
#For details (see https://github.com/mommy003/MSML). 

#If a user intends to employ logistic regression without constant covariates, 
#we advise preparing a covariate file where all values are set to 1.