R: Loadings plot for a specific factor/interaction of the ASCA
ASCA.PlotLoadings
R Documentation
Loadings plot for a specific factor/interaction of the ASCA
Description
Allows the user to plot a single loadings plot for one factor or interaction (or for the SVD on the original data)
Usage
ASCA.PlotLoadings(asca, ee= "", pcs = c(1,2))
Arguments
asca
Results of a performed ASCA analysis
ee
Which factor or interaction to use (eg. "1", or "12", or leave empty to use the original data)
pcs
Which PCs (Principal Components) to use for plotting (eg. c1,2)
Value
Only the plot is returned.
Note
Output of ASCA.Calculate is required.
Author(s)
Tim Dorscheidt
Examples
##Plot selected loadings after doing PerformAsca
## use the data matrix, ASCAX, and an experimental design matrix, ASCAF.
data(ASCAdata)
ASCA <- ASCA.Calculate(ASCAX, ASCAF, equation.elements = "1,2,12", scaling = TRUE)
## plot the loadings of the first two principal components of the first factor
ASCA.PlotLoadings(ASCA, ee = "1", pcs="1,2")
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
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> library(MetStaT)
Loading required package: MASS
Loading required package: abind
Loading required package: pls
Attaching package: 'pls'
The following object is masked from 'package:stats':
loadings
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/MetStaT/ASCA.PlotLoadings.Rd_%03d_medium.png", width=480, height=480)
> ### Name: ASCA.PlotLoadings
> ### Title: Loadings plot for a specific factor/interaction of the ASCA
> ### Aliases: ASCA.PlotLoadings
>
> ### ** Examples
>
> ##Plot selected loadings after doing PerformAsca
> ## use the data matrix, ASCAX, and an experimental design matrix, ASCAF.
> data(ASCAdata)
> ASCA <- ASCA.Calculate(ASCAX, ASCAF, equation.elements = "1,2,12", scaling = TRUE)
Variance explained per principal component (if >1%):
Whole data set PC1: 52.84% PC2: 22.89% PC3: 18.92% PC4: 5.34%
Factor 1 PC1: 100.00% PC2: NA% PC3: NA% PC4: NA%
Factor 2 PC1: 91.34% PC2: 8.66% PC3: NA% PC4: NA%
Interaction 12 PC1: 88.72% PC2: 11.28% PC3: NA% PC4: NA%
Percentage each effect contributes to the total sum of squares:
Overall means 0.96%
Factor 1 0.00%
Factor 2 0.00%
Interaction 12 0.00%
Residuals 0.00%
Percentage each effect contributes to the sum of squares of the centered data:
Factor 1 0.00%
Factor 2 0.00%
Interaction 12 0.00%
Residuals 0.00%
>
> ## plot the loadings of the first two principal components of the first factor
> ASCA.PlotLoadings(ASCA, ee = "1", pcs="1,2")
>
>
>
>
>
> dev.off()
null device
1
>