Modern applied biostatistical methods using S-Plus / Steve Selvin.

Format:

Book

Author/Creator:

Selvin, S., author.

Series:

Monographs in epidemiology and biostatistics ; v.28.

Oxford scholarship online.

Oxford scholarship online

Language:

English

Subjects (All):

S-Plus (Computer file).

Biometry.

Biology--Data processing.

Biology.

S (Computer program language).

Local Subjects:

S-Plus (Computer file).

Physical Description:

xiv, 461 p. : ill.

Edition:

1st ed.

Place of Publication:

New York ; Oxford University Press, 2023.

Language Note:

English

Summary:

Statistical analysis typically involves applying theoretically generated techniques to the description and interpretation of collected data. This text combines theory, application and interpretation to create an entire biostatistical process.

Contents:

Intro

Contents

1. S-language

In the beginning

Three data types-and some input conventions

Reading values into SPLUS

S-tools-a beginning set

S-arithmetic

More S-tools-intermediate set

S-tools for statistics

Statistical distributions in SPLUS

Arrays and tables

Matrix algebra tools

Some additional S-tools

Four S-code examples

The .Data file

Addendum: Built-in editors

Problem set I

2. Descriptive Techniques

Description of descriptive statistics

Basic statistical measures

Histogram smoothing-density estimation

Stem-and-leaf display

Comparison of groups-t-test

Comparison of groups-boxplots

Comparison of data to a theoretical distribution-quantile plots

Comparison of groups-qqplots

xy-plot

Three-dimensional plots-perspective plots

Three-dimensional plots-contour plots

Three-dimensional plots-rotation

Smoothing

Two-dimensional smoothing of spatial data

Clusters as a description of data

Additivity-"sweeping" an array

Example-geographic calculations using S-functions

Estimation of the center of a two-dimensional distribution

Addendum: S-geometry

Problem set II

3. Simulation: Random Values

Random uniform values

An example

Sampling without and with replacement

Random sample from a discrete probability distribution-acceptance/rejection sampling

Random sample from a discrete probability distribution-inverse transform method

Binomial probability distribution

Hypergeometric probability distribution

Poisson probability distribution

Geometric probability distribution

Random samples from a continuous distribution

Inverse transform method

Simulating values from the normal distribution

Four other statistical distributions

Simulating minimum and maximum values

Butler's method.

Random values over a complex region

Multivariate normal variables

Problem set III

4. General Linear Models

Simplest case-univariate linear regression

Multivariable case

Multivariable linear model

A closer look at residual values

Predict-pointwise confidence intervals

Formulas for glm( )

Polynomial regression

Discriminant analysis

Linear logistic model

Categorical data-bivariate linear logistic model

Multivariable data-linear logistic model

Goodness-of-fit

Poisson model

Multivariable Poisson model

Problem set IV

5. Estimation

Estimation: Maximum Likelihood

Estimator properties

Maximum likelihood estimator

Scoring to find maximum likelihood estimates

Multiparameter estimation

Generalized scoring

Estimation: Bootstrap

Background

General outline

Sample mean from a normal population

Confidence limits

An example-relative risk

Median

Simple linear regression

Jackknife estimation

Bias estimation

Two-sample test-bootstrap approach

Two-sample test-randomization approach

Estimation: Least Squares

Least squares properties

Non-linear least squares estimation

Problem set V

6. Analysis of Tabular Data

Two by two tables

Matched pairs-binary response

Two by k table

Measures of association-2 x 2 table

Measures of association-r x c table

Measures of association-table with ordinal variables

Loglinear model

Multidimensional-k-level variables

High dimensional tables

Problem set VI

7. Analysis of Variance and Some Other S-Functions

Analysis of variance

One-way design

Nested design

Two-way classification with one observation per cell

Matched pairs-measured response

Two-way classification with more than one observation per cell

Leaps-a model selection technique

Principal components.

Canonical correlations

Problem set VII

8. Rates, Life Tables, and Survival

Rates

Life tables

Survival analysis-an introduction

Nonparametric estimation of a survival curve

Hazard rate-estimation

Mean/median survival time

Proportional hazards model

Problem set VIII

Index

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

S

T

U

V

W

X

Y

Z.

Notes:

Includes index.

Previously issued in print: 1998.

Includes bibliographical references and index.

Derived record based on print version record and publisher information.

ISBN:

0-19-773778-1

1-280-75989-5

9786610759897

0-19-974773-3

OCLC:

922969947