ASCA.PlotScoresPerLevel: ASCA scores plot with projected data.
In MetStaT: Statistical metabolomics tools

Description Usage Arguments Value Note Author(s) References Examples

Plots the ASCA scores with projected data for a selected factor or interaction.

1	ASCA.PlotScoresPerLevel(asca, ee, pcs = "1,2")

`asca`	results of a performed ASCA analysis
`ee`	which factor/interaction to use (eg. "1" or "12")
`pcs`	which PCs to use for plotting (eg. "1,2")

Only the plot is returned

Output of PerformAsca is required as input.

Tim Dorscheidt, Gooitzen Zwanenburg

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

##Plot the results 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 scores for the first two principal components and the projections of 
## the data for the second factor
ASCA.PlotScoresPerLevel(ASCA, ee = "2", pcs = "1,2")

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%