Chemometrics with R: Multivariate Data Analysis in the Natural Sciences and Life Sciences
"Chemometrics with R" offers readers an accessible introduction to the world of multivariate statistics in the life sciences, providing a complete description of the general data analysis paradigm, from exploratory analysis to modeling to validation. Several more specific topics from the area of chemometrics are included in a special section. The corresponding R code is provided for all the examples in the book; scripts, functions and data are available in a separate, publicly available R package. For researchers working in the life sciences, the book can also serve as an easy-to-use primer on R.
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Chemometrics with R: Multivariate Data Analysis in the Natural Sciences and ...
No preview available - 2011
algorithm applied assess autoscaling Barolo Barolo Barolo bootstrap bootstrap samples calculate Chemometrics classification codebook vectors comps 2 comps confidence intervals covariance matrix criterion crossvalidation data matrix data set default dendrogram discriminant analysis distance error estimate example flavonoids function(x gasoline data Grignolino Grignolino Grignolino implementation indicated iterations k-medoids kmax latent variables leads left plot mean-centered measured multivariate ncomp newdata number of clusters number of components number of variables obtained optimal overfitting package parameters peak phenols plot in Figure PLS model prediction error proline prostate data q q q q q q q q q q qq qq q qqqq Rand index random forests regression coefficients result is shown ridge regression right plot rows scaling segment shown in Figure shows solution subset SVMs techniques test set training data training set usually validation values variable selection variance warping function wavelengths wine data xlab ylab