pca_report: Compute an analysis report using principal component analysis...
In mcanouil/rain: Reference-based Ancestry INference

View source: R/pca_report.R

pca_report

R Documentation

Compute an analysis report using principal component analysis from flashpca tool.

Description

The function can be used in a chunk within a Rmarkdown document/script with results="asis" to render the report.

Usage

pca_report(
  data,
  design,
  id_var = "Sample_ID",
  technical_vars,
  n_comp = 10,
  fig_n_comp = n_comp,
  outliers_threshold = 1.5,
  outliers_quantile = 0.75,
  title_level = 3
)

Arguments

`data`	A `vector` or `data.frame`. The numeric data on which the PCA has to be performed.
`design`	A `data.frame`. Additional variables to be used with factorial planes.
`id_var`	A `character`. The identifier column used to merge the data.
`technical_vars`	A `vector(character)`. Variables from design to be used with factorial planes.
`n_comp`	A `numeric`. The number of principal components to be computed.
`fig_n_comp`	A `numeric`. The number of principal components to be used for figures.
`outliers_threshold`	A `numeric`. The threshold to define outliers.
`outliers_quantile`	A `numeric`. The upper quantile percentile to use to define outliers, with `n x > quantile(x, outliers_quantile) + outliers_threshold * IQR(x)`.
`title_level`	A `numeric`. The markdown title level, i.e., the number of `#` preceding the section.

Value

A data.frame.

Examples


if (interactive()) {
  pca_report(
    data = t(mtcars),
    design = as.data.table(mtcars, keep.rownames = "Sample_ID"),
    id_var = "Sample_ID",
    technical_vars = c("cyl", "gear", "vs"),
    n_comp = 5,
    fig_n_comp = 5,
    outliers_threshold = 3,
    outliers_quantile = 0.75,
    title_level = 0
  )
}

mcanouil/rain documentation built on Nov. 28, 2022, 10:40 a.m.