Description Usage Arguments Details Value Author(s) References See Also Examples
All ANISOSTATIS steps are combined in this function. It enables preparation of the data, processing and graphing.
1 2 |
data |
Data Matrix |
anisostatis.option |
ANISOSTATIS string ptions: 'ANISOSTATIS_Type1' or 'ANISOSTATIS_Type2' |
column.design |
Matrix used to identify tables of data matrix |
make.columndesign.nominal |
a boolean. If TRUE (default), table is a vector that indicates groups (and will be dummy-coded). If FALSE, table is a dummy-coded matrix. |
DESIGN |
a design matrix to indicate if rows belong to groups. |
make.design.nominal |
Boolean option. If TRUE (default), table is a vector that indicates groups (and will be dummy-coded). If FALSE, table is a dummy-coded matrix. |
graphs |
Boolean option. If TRUE (default), graphs are displayed |
mpANISOSTATIS
computes Anisotropic STATIS, where the one weight is assigned per variable.
Returns a large list of items which are divided into four categories:
$Overview |
Overview of Results |
$InnerProduct |
Results for the Inner Product |
$Compromise |
Results for the Compromise |
$Table |
Results for the Tables |
The results for Overview are bundled inside of $Overview.
$Overview$data |
Data Matrix |
$Overview$groupmatrix |
Matrix used to identify the different tables of the data matrix |
$Overview$preprocess.data |
Preprocessed data matrix |
$Overview$num.groups |
Number of Tables |
$Overview$num.obs |
Number of Observations |
$Overview$row.preprocess |
Row Preprocess Option used |
$Overview$column.preprocess |
Column Preprocess Option used |
$Overview$Table.preprocess |
Table Preprocess Option used |
The results for InnerProduct are bundled inside of $InnerProduct
$InnerProduct$S |
Inner Product: Scalar Product Matrices |
$InnerProduct$C |
Inner Product: C Matrix |
$InnerProduct$RVMatrix |
Inner Product: RV Matrix |
$InnerProduct$eigs.vector |
Inner Product: Eigen Vectors |
$InnerProduct$eigs |
Inner Product: Eigen Values |
$InnerProduct$fi |
Inner Product: Factor Scores |
$InnerProduct$t |
Inner Product: Percent Variance Explained |
$InnerProduct$ci |
Inner Product: Contribution of the Rows |
$InnerProduct$cj |
Inner Product: Contribution of the Columns |
$InnerProduct$alphaWeights |
Alpha Weights |
The results for the Compromise are bundled inside of $Compromise
compromise |
Compromise Matrix |
compromise.eigs |
Compromise: Eigen Values |
compromise.eigs.vector |
Compromise: Eigen Vector |
compromise.fi |
Compromise: Factor Scores |
Compromise.t |
Compromise: Percent Variance Explained |
compromise.ci |
Compromise: Contributions of the rows |
compromise.cj |
Compromise: Contributions of the Columns |
The results for the Tables are bundled inside of $Table.
$m |
Table: masses |
$Table$eigs |
Table: Eigen Values |
$Table$eigs.vector |
Table: Eigen Vectors |
$Table$Q |
Table: Loadings |
$Table$fi |
Table: Factor Scores |
$Table$partial.fi |
Table: Partial Factor Scores |
$Table$partial.fi.array |
Table: Arrray of Partial Factor Scores |
Table$ci |
Table: Contribition of the Rows |
$Table$cj |
Table: Contribution of the Columns |
$Table$t |
Table: Percent Variance Explained |
Cherise R. Chin Fatt cherise.chinfatt@utdallas.edu
Abdi, H., Williams, L.J., Valentin, D., & Bennani-Dosse, M. (2012). STATIS and DISTATIS: Optimum multi-table principal component analysis and three way metric multidimensional scaling. Wiley Interdisciplinary Reviews: Computational Statistics, 4, 124-167.
Abdi, H., Valentin, D., Chollet, S., & Chrea, C. (2007). Analyzing assessors and products in sorting tasks: DISTATIS, theory and applications. Food Quality and Preference, 18, 627-640.
Abdi, H., & Valentin, D. (2005). DISTATIS: the analysis of multiple distance matrices. In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage. pp. 284-290.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | # ANOISTATIS Type 1
data('wines2012')
data = wines2012$data
column.design = wines2012$table
row.design= c('NZ','NZ','NZ','NZ','FR','FR','FR','FR','CA','CA','CA','CA')
demo.anisostatis1 <- mpANISOSTATIS(data,anisostatis.option='ANISOSTATIS_Type1',
column.design = column.design)
# ANISOSTATISType 2
data('wines2012')
data = wines2012$data
column.design = wines2012$table
row.design = c('NZ','NZ','NZ','NZ','FR','FR','FR','FR','CA','CA','CA','CA')
demo.anisostatis2 <- mpANISOSTATIS(data,anisostatis.option='ANISOSTATIS_Type2',
column.design = column.design)
|
Loading required package: prettyGraphs
Loading required package: ExPosition
[1] "Preprocessed the Rows of the data matrix using: None"
[1] "Preprocessed the Columns of the data matrix using: Center_1Norm"
[1] "Preprocessed the Tables of the data matrix using: Sum_PCA"
[1] "Preprocessing Completed"
[1] "Optimizing using: ANISOSTATIS_Type1"
[1] "Processing Complete"
dev.new(): using pdf(file="Rplots1.pdf")
dev.new(): using pdf(file="Rplots2.pdf")
dev.new(): using pdf(file="Rplots3.pdf")
[1] "Preprocessed the Rows of the data matrix using: None"
[1] "Preprocessed the Columns of the data matrix using: Center_1Norm"
[1] "Preprocessed the Tables of the data matrix using: Sum_PCA"
[1] "Preprocessing Completed"
[1] "Optimizing using: ANISOSTATIS_Type2"
[1] "Processing Complete"
dev.new(): using pdf(file="Rplots4.pdf")
dev.new(): using pdf(file="Rplots5.pdf")
dev.new(): using pdf(file="Rplots6.pdf")
dev.new(): using pdf(file="Rplots7.pdf")
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