tidy_PCA: PCA on tidy (long-format) data
In stanstrup/Rplot.extra: Rplot.extra

Description Usage Arguments Details Value Author(s)

PCA on tidy (long-format) data. With this data you don't need to create a matrix to calculate PCA. Your data needs to be in long format with columns identifying the samples and the variables.

1 2	tidy_PCA(data, sample_col, var_col, value_col, pc_max = 2, scale = "uv", method = "nipals")

`data`	Numerical matrix with (or an object coercible to such) with samples in rows and variables as columns. Can also be a data frame in which case all numberic variables are used to fit the PCA.
`sample_col`	Bare column name for the column defining the samples
`var_col`	Bare column name for the column defining the variables
`value_col`	Bare column name for the column defining the values
`pc_max`	A scalar giving the number of principal components to calculate.
`scale`	Scaling, see `prep`. One of "none", "pareto", "vector" or "uv".
`method`	One of the methods reported by `pcaMethods::listPcaMethods()`.

This function is wrapper function for pca.

the following set of pca methods are available:

svd:: Uses classical prcomp. See documentation for svdPca.
nipals:: An iterative method capable of handling small amounts of missing values. See documentation for nipalsPca.
rnipals:: Same as nipals but implemented in R.
bpca:: An iterative method using a Bayesian model to handle missing values. See documentation for bpca.
ppca:: An iterative method using a probabilistic model to handle missing values. See documentation for ppca.
svdImpute:: Uses expectation maximation to perform SVD PCA on incomplete data. See documentation for svdImpute.