Last data update: 2014.03.03

R: ASCA scores plot with projected data.
ASCA.PlotScoresPerLevelR Documentation

ASCA scores plot with projected data.

Description

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

Usage

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

Arguments

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")

Value

Only the plot is returned

Note

Output of PerformAsca is required as input.

Author(s)

Tim Dorscheidt, Gooitzen Zwanenburg

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 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")

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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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.PlotScoresPerLevel.Rd_%03d_medium.png", width=480, height=480)
> ### Name: ASCA.PlotScoresPerLevel
> ### Title: ASCA scores plot with projected data.
> ### Aliases: ASCA.PlotScoresPerLevel
> ### Keywords: ASCA PCA
> 
> ### ** Examples
> 
> ##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)
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 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")
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>