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
>