wine

This dataset is about chemical analysis of 178 wines in a particular region of Italy. There are three cultivars with 59, 71, and 48, respectively.

datasets

Provides tools to implement both unsupervised and supervised nonlinear dimension reduction methods. Principal Component Analysis (PCA), Sliced Inverse Regression (SIR), and Sliced Average Variance Estimation (SAVE) are useful methods to reduce the dimensionality of covariates. However, they produce linear combinations of covariates. Kernel PCA, generalized SIR, and generalized SAVE address this problem by extending the applicability of the dimension reduction problem to nonlinear settings. This package includes a comprehensive algorithm for  kernel PCA, generalized SIR, and generalized SAVE, including methods for choosing tuning parameters and some essential functions.

Kyongwon Kim

nsdr

Nonlinear Sufficient Dimension Reduction

Bing Li

wine function

A data frame with 178 observations on 14 variables.
Attributes
1. cultivar
2. alcohol
3. malic acid
4. ash
5. alcalinity of ash
6. magnesium
7. total phenols
8. flavanoids
9. nonflavanoid phenols
10. oroanthocyanins
11. color intensity
12. hue
13. OD280/OD315 of diluted wines
14. proline.

wine: Chemical ingredients of wine dataset

Description

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References