14.2 Computer software

Ross Ihaka is one the originators of R, a software package for statistical computing that has had phenomenal uptake internationally. It can be downloaded free and easily customised for a very wide variety of applications. The package and the paper introducing it have been cited over 1700 times, by far the highest for publications in the mathematical sciences over the last ten years, worldwide. It is now disseminated from over 75 internet sites in 30 countries.

R

CRAN

The package is used both for teaching and research by hundreds of universities around the world, including Stanford, Oxford, Cambridge and Berkeley. There are over 40 books written about, or featuring, the use of R.

http://www.nytimes.com/2009/01/07/technology/business-computing/07program.html?scp=2&sq=ihaka&st=cse

Time series data.

t: time (but could be space)

0 t T (continuous)

t = 0,...,T-1 (discrete)

t = 1 ,..., N

(unique) value Y(t)

Y in R

{0,1}

Rp

N, category, [0, 2)

Useful when there is special period P

t=nP+p, p=0,...,P-1

Yn(p)=Y(nP+p)

Vector ts case matplot(type="l"

use several line types

colors

use to force comparison

Tufte (1983). 10th or 11th century movement of planets and sun

Connected. Good for smooth series

individual data points not unambiguously displayed

irregular sampling can be unclear

plot(type="l",

Symbol graph. Good for long term behavior

cannot appreciate middle and high frequency behavior

irregular sampling can be unclear

plot(type="p",...) seals T = 940

Both. Points and line segments

plot(type="b",

Both. Points on top of connected

plot(type="o",

plot(type="n"

points(pch="*"

plot(type="n"

lines(type=3

plot(type="h"

Good when need to see individual values

when series long

about centralvalue (next)

Not good when strong trend

plot(type="h",...) Amazon T = 32875

Good about central value

Box-Jenkins Model-building strategy.

 

1. model specification

2. model fitting

3. model diagnostics

 

Scientific method

EDA

I. "...three of the main strategies of data analysis are: 1. graphical presentation. 2. provision of flexibility in viewpoint and in facilities, 3. intensive search for parsimony and simplicity ..."

II. "In exploratory data analysis there can be no substitute for flexibility; for adapting what is calculated - and what we hope plotted - both to the needs of the situation and the clues that the data have already provided."

III. "I would like to convince you that the histogram is old-fashioned ..."

IV. "Exploratory data analysis ... does not need probability, significance or confidence."

V. "... I hope that I have shown that exploratory data analysis is actively incisive rather than passively descriptive, with real emphasis on the discovery of the unexpected ..."

VI. "'exploratory data analysis' is an attitude, a state of flexibility, a willingness to look for those things that we believe are not there, as well as those we believe to be there."

VII. "Exploratory data analysis isolates patterns and features of the data and reveals these forcefully to the analyst."

VIII. "If we need a short suggestion of what exploratory data analysis is, I would suggest that: 1. it is an attitude, AND 2. a flexibility, AND 3. some graph paper (or transparencies, or both)."