#### Overview of this book

This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.
R for Data Science Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
Functions in R
Data Preprocessing and Preparation
Visualizing Data with ggplot2
Making Interactive Reports
Simulation from Probability Distributions
Statistical Inference in R
Time Series Mining with R
Index

## Measuring the performance of the regression model

To measure the performance of a regression model, we can calculate the distance from the predicted output and actual output as a quantifier of model performance. In this calculation, we often use root mean square error (RMSE) and relative square error (RSE) as common measurements. In the following recipe, we illustrate how to compute these measurements from a built regression model.

You need to have completed the previous recipe by fitting the house rental data into a regression model and have the fitted model assigned to the variable `lmfit`.

### How to do it…

Perform the following steps to measure the performance of the regression model:

1. Retrieve predicted values by using the predict function:

```> predicted <- predict(lmfit, data=house)
```
2. Calculate the root mean square error:

```> actual <- house\$Sqft
> rmse <- (mean((predicted - actual)^2))^0.5
> rmse
[1] 66894.34
```
3. Calculate the relative square error:

`> mu <- mean...`