Multivariate Techniques
These are the available functions for multivariate techniques. See each function's help topic in the TERR Language Reference for more information.
Function name | Title description |
---|---|
[[.dendrogram
|
General Tree Structures |
as.dendrogram
|
General Tree Structures |
as.dendrogram.dendrogram
|
General Tree Structures |
as.dendrogram.hclust
|
General Tree Structures |
as.dist
|
Distance Matrix Calculation |
as.dist.default
|
Distance Matrix Calculation |
as.hclust
|
Converts Objects to Class hclust |
as.hclust.default
|
Converts Objects to Class hclust |
as.hclust.dendrogram
|
Converts Objects to Class hclust |
as.hclust.twins
|
Converts Objects to Class hclust |
as.matrix.dist
|
Distance Matrix Calculation |
bartlett.test
|
Bartlett Test of Homogeneity of Variances |
cancor
|
Canonical Correlation Analysis |
cmdscale
|
Classical Metric Multi-Dimensional Scaling |
cophenetic
|
Cophenetic Distances for a Hierarchical Clustering |
cophenetic.default
|
Cophenetic Distances for a Hierarchical Clustering |
cophenetic.dendrogram
|
Cophenetic Distances for a Hierarchical Clustering |
cor
|
Correlation, Variance, and Covariance (Matrices) |
cov
|
Correlation, Variance, and Covariance (Matrices) |
cov.wt
|
Weighted Covariance Estimation |
cov2cor
|
Correlation, Variance, and Covariance (Matrices) |
cut.dendrogram
|
General Tree Structures |
cutree
|
Create Groups from Hierarchical Clustering |
dendrogram
|
General Tree Structures |
dist
|
Distance Matrix Calculation |
estVar
|
SSD Matrix and Estimated Variance Matrix in Multivariate Models |
estVar.mlm
|
SSD Matrix and Estimated Variance Matrix in Multivariate Models |
estVar.SSD
|
SSD Matrix and Estimated Variance Matrix in Multivariate Models |
factanal
|
Estimate a Factor Analysis Model |
fft
|
Fast Fourier Transform |
fitted.kmeans
|
K-Means Clustering |
format.dist
|
Distance Matrix Calculation |
hclust
|
Hierarchical Clustering |
is.leaf
|
General Tree Structures |
kmeans
|
K-Means Clustering |
labels.dist
|
Distance Matrix Calculation |
loadings
|
Extract Loadings from an Object |
loglin
|
Contingency Table Analysis |
mahalanobis
|
Mahalanobis Distance |
mvfft
|
Fast Fourier Transform |
plot.dendrogram
|
General Tree Structures |
prcomp
|
Principal Components Analysis |
prcomp.default
|
Principal Components Analysis |
prcomp.formula
|
Principal Components Analysis |
predict.prcomp
|
Principal Component Scores |
predict.princomp
|
Principal Component Scores |
princomp
|
Principal Components Analysis |
princomp.default
|
Principal Components Analysis |
princomp.formula
|
Principal Components Analysis |
print.dendrogram
|
General Tree Structures |
print.dist
|
Distance Matrix Calculation |
print.factanal
|
Estimate a Factor Analysis Model |
print.hclust
|
Hierarchical Clustering |
print.kmeans
|
K-Means Clustering |
print.loadings
|
Print a Loadings Matrix |
print.prcomp
|
Print a Principal Components Object |
print.princomp
|
Print a Principal Components Object |
print.summary.prcomp
|
Print a Principal Component Summary |
print.summary.princomp
|
Print a Principal Component Summary |
SSD
|
SSD Matrix and Estimated Variance Matrix in Multivariate Models |
SSD.mlm
|
SSD Matrix and Estimated Variance Matrix in Multivariate Models |
str.dendrogram
|
General Tree Structures |
summary.prcomp
|
Summary of a Principal Components Object |
summary.princomp
|
Summary of a Principal Components Object |
var
|
Correlation, Variance, and Covariance (Matrices) |
Parent topic: Statistics
Related reference
- Clustering
- Computations Related to Plotting (Statistics)
- Curve (and Surface) Smoothing
- Designed Experiments
- Loess Objects
- Non-Linear Regression
- Nonparametric Statistics
- Probability Distributions and Random Numbers
- Regression
- Regression and Classification Trees
- Robust and Resistant Techniques
- Simple Univariate Statistics
- Statistical Inference
- Statistical Models
- Time Series