A Handbook of Statistical Analyses Using R (3rd Edition)

Vignettes

- An Introduction to R
- Chapter Analysis of Variance
- Chapter Analyzing Longitudinal Data I
- Chapter Analyzing Longitudinal Data II
- Chapter Bayesian Inference
- Chapter Cluster Analysis
- Chapter Conditional Inference
- Chapter Data Analysis using Graphical Displays
- Chapter Density Estimation
- Chapter Generalized Additive Models
- Chapter Logistic Regression and Generalized Linear Models
- Chapter Meta-Analysis
- Chapter Missing Values
- Chapter Multidimensional Scaling
- Chapter Principal Component Analysis
- Chapter Quantile Regression
- Chapter Recursive Partitioning
- Chapter Simple Inference
- Chapter Simultaneous Inference and Multiple Comparisons
- Chapter Survival Analysis
- Errata
- Multiple Linear Regression

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Description Usage Format Details Source Examples

The data arise from an experiment to study the gain in weight of rats fed on four different diets, distinguished by amount of protein (low and high) and by source of protein (beef and cereal).

1 | ```
data("weightgain")
``` |

A data frame with 40 observations on the following 3 variables.

`source`

source of protein given, a factor with levels

`Beef`

and`Cereal`

.`type`

amount of protein given, a factor with levels

`High`

and`Low`

.`weightgain`

weigt gain in grams.

Ten rats are randomized to each of the four treatments. The question of interest is how diet affects weight gain.

D. J. Hand, F. Daly, A. D. Lunn, K. J. McConway and E. Ostrowski (1994).
*A Handbook of Small Datasets*, Chapman and Hall/CRC, London.

1 2 3 | ```
data("weightgain", package = "HSAUR3")
interaction.plot(weightgain$type, weightgain$source,
weightgain$weightgain)
``` |

HSAUR3 documentation built on Aug. 19, 2017, 1:02 a.m.

An Introduction to R
Chapter Analysis of Variance
Chapter Analyzing Longitudinal Data I
Chapter Analyzing Longitudinal Data II
Chapter Bayesian Inference
Chapter Cluster Analysis
Chapter Conditional Inference
Chapter Data Analysis using Graphical Displays
Chapter Density Estimation
Chapter Generalized Additive Models
Chapter Logistic Regression and Generalized Linear Models
Chapter Meta-Analysis
Chapter Missing Values
Chapter Multidimensional Scaling
Chapter Principal Component Analysis
Chapter Quantile Regression
Chapter Recursive Partitioning
Chapter Simple Inference
Chapter Simultaneous Inference and Multiple Comparisons
Chapter Survival Analysis
Errata
Multiple Linear Regression

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