kernPCA: kernel PCA
In Yuliaxis/kernInt: Kernel Integration of Microbiome Analysis Methods & Data

Description Usage Arguments Value Examples

kernel PCA

kernPCA(
  data,
  comb = "mean",
  kernel,
  plot = TRUE,
  H = NULL,
  domain = NULL,
  y,
  dim = c(1, 2),
  colors,
  na.col = "grey70",
  title,
  legend = TRUE,
  labels = FALSE
)

`data`	Input data: a matrix or data.frame.
`comb`	If data is a list or array: how to combine them ("mean","statis","full","sparse" or a vector of coefficients)
`kernel`	lin" or rbf" to standard Linear and RBF kernels. "clin" for compositional linear and "crbf" for Aitchison-RBF kernels. "jac" for quantitative Jaccard / Ruzicka kernel. "jsk" for Jensen-Shannon Kernel. "flin" and "frbf" for functional linear and functional RBF kernels. "matrix" if a pre-computed kernel matrix is given as input. With an array or a list of length m: Vector of m kernels to apply to each dataset.
`plot`	TRUE to return the plot, FALSE to return the projection object
`H`	Kernel gamma hyperparameter if needed (only RBF-like kernels)
`domain`	Only used in "frbf" or "flin".
`y`	Response vector; optional. Colors each dot (individual) according to its value in the response / target variable.
`dim`	The two PC that have to be displayed. Defaults to the two first PC.
`colors`	Dot fill color; optional.
`na.col`	Depends on y and colors. Dot fill color for the y missing values; defaults to grey.
`title`	Plot title
`legend`	Defaults to TRUE. A legend with the color corresponding to each y value.
`labels`	If true, each dot will be labeled with its row number.

A k-PCA plot generated with ggplot2.

kernPCA(soil$abund,kernel="clin", y=soil$metadata$phd, colors=c("orange","orchid3"),
title = "Soil kernel PCA",legend = TRUE)
## Heterogeneous data fusion case
Airway <- list()
Airway$nosel <- CSSnorm(smoker$abund$oroL)
Airway$throatl <- CSSnorm(smoker$abund$oroR)
smoking <- smoker$metadata$smoker[seq(from=1,to=62*4,by=4)]
kernPCA(data=Airway,kernel=rep("jac",2),title="Airway samples",y=smoking)#'