In prpatil/tdsm: Templated Deterministic Statistical Machines

`r title`

We have provided a generic data analysis template format with descriptions and examples of what general items should appear in each section. Please edit this template to fit with your expected data inputs and analysis.

Introduction

Provide an overview of what your analysis will accomplish and how.

This template performs analysis XXX. With data input Y, the following steps are run... Resulting figures A, B, and tables C, D are provided to summarize the analysis.

Environment and data preparation

Add chunks which prepare the computing environment and the data input for analysis. For example, setting random seeds, defining training and testing datasets, normalization and pre-processing. For ease of reading, split up different core steps into different chunks.

set.seed(seed)
library(library)

Training and Testing Data

If validation data have been supplied, we will build our model on the training data and make predictions the validation data. If not, we will split the provided dataset into training and testing sets, build the model on the training set, and predict on the testing set. These procedures ensure that our model does not overfit to our training data.

...

Main Data Analysis

Here, the prepared data are analyzed. A wide range of steps may be applicable in this section depending on the goal of the analysis. Include any and all data manipulation, statistical modeling, etc., but do not present results. Clearly define steps in different chunks.

Feature selection steps X, Y, Z are applied.

...

Model-fitting steps A, B, C, are applied using method W.

...

Report results

Describe the results of the data analysis. Figures, tables, metrics, etc., should go in this section. Include descriptions of the different result quantities, how to interpret them, what to expect.

We present these important attributes of the analysis in figures A and B.