DCEM (version 1.0.0)

dcem_test: dcem_test: Part of DCEM package.

Description

For demonstrating the execution on the bundled dataset.

Usage

dcem_test()

Accessing the output parameters

The function dcem_test() calls dcem_train() that returns a list of objects. This list contains parameters associated with the Gaussian (posterior probabilities, mean, covariance/standard-deviation and priors). The parameters can be accessed as follows where sample_out is the list containing the output:

1. (1) Posterior Probabilities: sample_out$prob A matrix of posterior-probabilities

2. (2) Mean(s): sample_out$mean

   For multivariate data: It is a matrix of means for the Gaussian(s). Each row in the matrix corresponds to a mean for the Gaussian.

   For univariate data: It is a vector of means. Each element of the vector corresponds to one Gaussian.

3. (3) Co-variance matrices: sample_out$cov

   For multivariate data: List of co-variance matrices for the Gaussian(s).

   Standard-deviation: sample_out$sd

   For univariate data: Vector of standard deviation for the Gaussian(s))

4. (4) Priors: sample_out$prior A vector of priors for the Gaussian(s).

Details

The dcem_test performs the following steps in order:

1. Read the data from the disk (from the file data/ionosphere_data.csv). The data folder is under the package installation folder.

2. The dataset details can be see by typing ionosphere_data in R-console or at https://archive.ics.uci.edu/ml/datasets/ionosphere.

3. Clean the data (by removing the columns). The data should be cleaned before use. Refer trim_data to see what columns should be removed and how. The package provides the basic interface for removing columns.

4. Call the dcem_train on the cleaned data.

References

Using data to build a better EM: EM* for big data.

Hasan Kurban, Mark Jenne, Mehmet M. Dalkilic (2016) <https://doi.org/10.1007/s41060-017-0062-1>.