ASCA.Plot: Plot ASCA results

In MetStaT: Statistical metabolomics tools

Description

This function generates several plots detailing the results of the performed ASCA

Usage

ASCA.Plot(asca)

Arguments

asca	A performed ASCA analysis

Details

ASCA.Plot takes the output of ASCA.Calculate as its input and generates scores and loading plots, including projections of the data on the first two principal components, for the complete data matrix and the effect matrices.

Value

Returns several plot windows in quick succession. Therefore, it is advised to capture by some other means (see examples below). The first two plots are a score plot and loadings plot of the performed principal component analysis (PCA) on the original data. Then, for each factor/interaction, the following plots follow: - a score plot of PC1 vs PC2 of the PCA performed on the means-matrix. - a loadings plot of PC1 and PC2 of the PCA performed on the means-matrix.

Note

Output of ASCA.Calculate is required as input.

Author(s)

Tim Dorscheidt

References

Gooitzen Zwanenburg, Huub C.J. Hoefsloot, Johan A. Westerhuis, Jeroen J. Jansen and Age K. Smilde, ANOVA principal component analysis and ANOVA simultaneous component analysis: a comparison. J Chemometrics, 25, (2011), p. 561 - 567

Examples

##Plot the results after doing ASCA.Calculate
## use the data matrix, 'data', and an experimental design matrix, 'F'.
data(ASCAdata)
ASCA <- ASCA.Calculate(ASCAX, ASCAF, equation.elements = "1,2,12", scaling = TRUE)

## plot the results to a graphical output such as R's pdf writer
pdf("ASCA_Results.pdf")
ASCA.Plot(ASCA)
dev.off()

Example output

Loading required package: MASS
Loading required package: abind
Loading required package: pls

Attaching package: 'pls'

The following object is masked from 'package:stats':

    loadings

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%

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MetStaT documentation built on May 2, 2019, 1:45 p.m.