Group items tagged

The preceding scatterplot demonstrates that these data may not be linear.

In this tutorial, we will explore the a linear, quadratic, and cubic model.

To center a variable, simply subtract its mean from each data point and save the result into a new R variable, as demonstrated below.

Once the input variable has been centered, the higher order terms can be created.

we can simply multiply our centered term from step one and save the result into a new R variable, as demonstrated below.

Now we have all of the pieces necessary to assemble our linear and curvilinear models.

In this case, the quadratic and cubic terms are not statistically significant themselves nor are their models statistically significant beyond the linear model.

it can be helpful to summarize and compare our potential models using the summary(MODEL) and anova(MODEL1, MODEL2,… MODELi) functions.

The regression topics covered in these tutorials can be mixed and matched to create exceedingly complex models.

The good news is that more complex models can be created using the same techniques covered here. The basic principles remain the same.

Manage variables in active data set > Convert

numeric

Statistics > Summaries > Numerical summaries

R is more than a programming language. It is an interactive environment for doing statistics.

This document is a work in progress. Corrections and comments are welcome.

Conditionnals may be used inside other constructions.

While loop

Actually, R is a programming language

For loop

Switch

Repeat loop

R belongs to the family of functionnal languages

R provides a wide variety of statistical

and graphical techniques,

R is an integrated suite

of software facilities for data manipulation

storage facility,

operators for calculations on arrays,

collection of intermediate tools for data analysis,

graphical facilities

simple and effective programming language

is designed around a true computer languag

R, like S

allows users to add additional functionalit

defining new functions

For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time

R can be found on http://cran.r-project.org, the master site of comprehensive R archive network, or at one of its mirrors.

with an FAQ list, and an introduction to the language and how to use R for doing statistical analysis and graphics in PDF and other formats.

Time series data are widely seen in analytics. Some examples are stock indexes/prices, currency exchange rates and electrocardiogram (ECG)

Time Series Decomposition

Time Series Forecasting

Time Series Clustering

Time Series Classification

ANALYTICS

GRAPHICS AND VISUALIZATION

R APPLICATIONS and EXTENSIONS***

PROGRAMMING LANGUAGE FEATURES

Too Many Commands

Sloppy Control of Variables

Loop-a-phobia

Functions That Act Like Procedures

Naming and Renaming Variables is Way Too Complicated

Inability to Analyze Multiple Variables

Poor Ability to Select Variable Sets

Too Much Complexity

Lack of Graphical User Interface (GUI)

> 2 + 3 * 5      # Note the order of operations. > log (10)       # Natural logarithm with base e=2.718282 > 4^2            # 4 raised to the second power > 3/2            # Division > sqrt (16)      # Square root > abs (3-7)      # Absolute value of 3-7 > pi             # The mysterious number > exp(2)         # exponential function > 15 %/% 4       # This is the integer divide operation > # This is a comment line

x<- log(2.843432) *pi

R is a software package especially suitable for data analysis and graphical representation. Functions and results of analysis are all stored as objects, allowing easy function modification and model building. R provides the language, tool, and environment in one convenient package. It is very flexible and highly customizable. Excellent graphical tools make R an ideal environment for EDA (Exploratory Data Analysis). Since most high level functions are written in R language itself, you can learn the language by studying the function code.