请问R中如何做PLS-DA？

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

请问R中如何做PLS-DA？  偏最小二乘法判别分析

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏0 回帖

关键词：PLS 偏最小二乘法 最小二乘法 偏最小二乘 最小二乘 如何

Classification with O-PLS-DA
September 29, 2013
By dgrapov

(This article was first published on imDEV » r-bloggers, and kindly contributed to R-bloggers)
unnamed-chunk-5Partial least squares (PLS) is a versatile algorithm which can be used to predict either continuous or discrete/categorical variables. Classification with PLS is termed PLS-DA, where the DA stands for discriminant analysis.  The PLS-DA algorithm has many favorable properties for dealing with multivariate data; one of the most important of which is how variable collinearity is dealt with, and the model’s ability to rank variables’ predictive capacities within a multivariate context. Orthogonal signal correction PLS-DA or O-PLS-DA is an extension of PLS-DA which seeks to maximize the explained variance between groups in a single dimension or the first latent variable (LV), and separate the within group variance (orthogonal to classification goal) into orthogonal LVs. The variable loadings and/or coefficient weights from a validated O-PLS-DA model can be used to rank all variables with respect to their performance for discriminating between groups. This can be used part of a dimensional reduction or feature selection task which seek to identify the top predictors for a given model.

Like with most predictive modeling or forecasting tasks, model validation is a critical requirement. Otherwise the produced models maybe overfit or perform no better than coin flips. Model validation is the process of defining the models performance, and thus ensuring that the model’s internal variable rankings are actually informative.

Below is a demonstration of the development and validation of an O-PLS-DA multivariate classification model for the famous Iris data set. This example describes the classification of the famous Iris data set.

O-PLS-DA model validation  Tutorial

Data pretreatment and preparation
Model optimization
Permutation testing
Internal cross-validation
External cross-validation
The Iris data only contains 4 variables, but the sample sizes are favorable for demonstrating a two tiered testing and training scheme (internal and external cross-validation). However O-PLS really shines when building models with many correlated variables (coming soon).