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Results tagged with pca
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user 52491
Principal component analysis (PCA) is a linear dimensionality reduction technique. It reduces a multivariate dataset to a smaller set of constructed variables preserving as much information (as much variance) as possible. These variables, called principal components, are linear combinations of the input variables.
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Principal component analysis to reduce the number of observations
Principal Component Analysis (PCA) seems great to reduce the number of variables, but is it also good to reduce the number of observations? … 1 2 A y5
2 2 B y6
3 2 A y7
4 2 B y8
Then,
fm <- lme(PCA1 ~ treatment, data=big.data, random = ~ 1| Plant, method="ML")
Can PCA …
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