Understanding Hierarchical Linear Regression

Hierarchical Linear Regression is used when the researchers want to study the relationship between the variables at different levels. These levels have different coefficients and they vary with each other. In R Programming Language this is used to understand the trend of the data in an overall manner as well as at different levels.
A hierarchical model is generally used when the data is divided into different groups at different levels. This kind of model helps in understanding the hierarchy at multiple levels. Sectors like education, psychology, and sociology use this model where data is taken in a multilevel structure.

To implement hierarchical linear regression in R, we can use various packages such as:

• lme4: The lme4 package stands for “Linear Mixed-Effects Models. This package uses linear algebra to deal with hierarchical data. It handles large datasets with complex nature and random effects structures. The observations are not independent in such datasets and are influenced by multiple sources of variations. It also provides us with functions of model fitting and model diagnostic which helps users in checking the efficiency of the model. It also provides the formula function helping in specifying the model formula in R.
• nlme: It stands for “Nonlinear Mixed-Effects Models. This is one of the fundamental packages in R for fitting various types of nonlinear mixed-effects models. This deals with data where the relation between the variables is nonlinear. It supports the inclusion of both random and fixed effects. It can also handle missing values from our datasets using different techniques. This package also provides extensions to check model accuracy and visualization.
• tidyverse: This is a collection of packages with a set of tools for data manipulation, visualization, and analysis. It has packages like ggplot2, dplyr, readr packages, etc.
• ggplot2: ggplot2 library stands for the grammar of graphics, popular because of its declarative syntax used to visualize and plot our data into graphs for better understanding.
  Syntax to install and load these packages:

Linear Regression model is used to establish a connection between two or more variables. These variables are either dependent or independent. Linear Regression In R Programming Language is used to give predictions based on the given data about a particular topic, It helps us to have valuable insights and give conclusions that help us in many future decisions. Hierarchical linear regression is an extension of the standard linear regression that allows for the analysis of such hierarchical data(grouped data or data at different levels).