moa-class: plot moa object

In mengchen18/mogsa: Multiple omics data integrative clustering and gene set analysis

Description

moa class object

Usage

## S4 method for signature 'moa,missing'
plot(
  x,
  value,
  type = 1,
  axes = NULL,
  n = NULL,
  tol = 1e-05,
  legend = NULL,
  col = NULL,
  lty = 1,
  pch = NULL,
  lg.x = "topright",
  lg.y = NULL,
  xlim = NULL,
  ylim = NULL,
  data.pch = 20,
  label = FALSE,
  label.cex = 1,
  layout = NULL,
  ...
)

Arguments

x	an moa object
value	which value to be plotted, see details
type	which type of plot to be plotted, see details
axes	which axes to be plotted
n	Numbe of eigenvalues to shown
tol	Only eigenvalues greater than the tol will be plotted
legend	legend
col	color
lty	line type
pch	point shape
lg.x	legend position x
lg.y	legend position y
xlim	coordinate limit of x axis
ylim	coordinate limit of y axis
data.pch	the pch (shape) used to distinguish shapes
label	point label
label.cex	size of labels
layout	layout passed to par()
...	other parameters

Details

value options: eig - plot the eigen values ... could be: type=1 - the type of plot axes=NULL - the axes selected to plot n=NULL - n eigenvalues to be drawn tol=1e-5 - the tolerance of eigenvalue, eigenvalues lower than this value wont be considered. legend=NULL - legend to put col=NULL - the color of each partial eigenvalue lty=1 - the line type used in the matplot, when type =4, used pch=NULL - the pch to draw 2D partial eigen plot, when type = 5 used lg.x="topright" - the position of legend lg.y=NULL - poistion argument passed to legend(...) ... - other arguemnts passed to functions, see below for: type 1: the eigen value ... are passed to barplot type 2: barplot show, partial eigenvalue, beside=FALSE ... are passed to barplot type 3: barplot show, partial eigenvalue, beside =TRUE ... are passed to barplot type 4: matplot show ... are passed to matplot type 5: the two dimensional plot, axes need to be specified ... are passed to heatmap tau - the same with eig, but in the percentage view ... could be (same with eig, but in the percentage) obs - the observation ... could be: axes=1:2 - which axes should be draw type=1 - which type, see below data.pch=20 - the pch of dataset, if type=1, the first one is used col=1 - the color of observations, recycled used by data.frame label=FALSE - should be labeled? lg.x="topright" - position of legend lg.y=NULL - position of legend xlim=NULL - the xlimit ylim=NULL - the ylimit label.cex=1 - the cex of text ... for: type 1: the center points draw ... passed to points type 2: the separate factor scores linked by lines ... passed to points var - the separate gene view, layout can be specified RV - the heatmap of RV coefficient

Objects from the Class

Objects can be created by calls of the form new("moa", ...).

Author(s)

Chen Meng

References

Herve Abdi, Lynne J. Williams, Domininique Valentin and Mohammed Bennani-Dosse. STATIS and DISTATIS: optimum multitable principal component analysis and three way metric multidimensional scaling. WIREs Comput Stat 2012. Volume 4, Issue 2, pages 124-167

Herve Abdi, Lynne J. Williams, Domininique Valentin. Multiple factor analysis: principal component analysis for multitable and multiblock data sets. WIREs Comput Stat 2013

Examples

    showClass("moa")
    # load("R/mogsa/data/NCI60_4arrays.rda")
    data(NCI60_4arrays)
    ana <- moa(NCI60_4arrays, proc.row = "center_ssq1", w.data = "inertia", statis = TRUE)

    plot(ana, value="eig")
    plot(ana, value="tau", type=2)

mengchen18/mogsa documentation built on June 7, 2020, 6:05 p.m.