PCAbiplot {bipl5}

Construct a rank-2 PCA biplot

Description

Rank-2 PCA biplots are constructed based on a combination of the first three principal components.

Usage

PCAbiplot(
  x,
  group = NULL,
  scale = TRUE,
  basis = 1:2,
  symbol = "circle",
  color = NULL,
  build_plot = TRUE
)

Arguments

x

A numeric matrix or data frame of size n x p

group

Vector of size n representing the class of each observation in x

scale

Boolean: indicating whether the data matrix should be standardized before SVD is performed. Similar to the cor argument in princomp

basis

A vector specifying which eigenvectors serve as basis for the plot. Currently only a biplot of rank 2 is supported

symbol

Plotting symbol to be used per class group

color

Colors to be utilized per class group

build_plot

Boolean, indicating whether the biplot should be drawn or not. Mostly used in internal function calls serve as basis for the biplot. Currently only a biplot of rank 2 is supported

Details

The method performs Principal Component Analysis (PCA) on the input data and constructs both a traditional biplot using vector representation and with calibrated axes. The data is clustered together on the display by the group parameter. The scale parameter determines if SVD is performed on the covariance matrix or correlation of x. It is highly recommended to set scale=TRUE as the rendered display is sensitive to the scaling in x.

By default three sets of principal components are used for the scaffolding axes, namely: 1 and 2, 1 and 3, and 2 and 3. The function constructs these biplots in the plot_ly graphing library with reactivity embedded on the display. The following features are available on the display:

• A dropdown menu to change the principal components used to construct the display. Currently only the first three pairwise are supported.

• A button to give fit statistics of the biplot. Once clicked, a table is added to give the adequacy and predictivity of each axis for the display.

• A button that inserts a graph depicting the cumulative predictivity of each axis against the dimension of the biplot. See FMbiplot for the coordinates.

• A button that changes the display from vector representation of the variables, to calibrated axes in their original scale. The vector representation includes a unit circle around the origin.

• Prediction lines are inserted onto the display if an observation is clicked. The prediction lines can be removed by clicking on the legend entry.

Value

A named list of class bipl5 with the following attributes

x

A data frame which is the original input data

Z

A matrix of n x 2 representing the coordinates of each observation on the biplot

rank

The rank of the approximated data

scale

Whether the data is standardized prior to performing dimension reduction

group

The grouping vector of the data

mu

The vector of column means of the input data

stddev

Vector of column standard deviations if the scale parameter is set to TRUE.

PCA

The singular value decomposition of the covariance/correlation matrix, see svd

bipl

The plotly graph displaying the biplot, see plot_ly

Adequacy

The adequacy of each axis displayed for each set of principal components

Predictivity

The predictivity of each axis displayed for each set of principal components

See Also

print.bipl5 to display the biplot graph and further see TDAbiplot to draw a biplot with calibrated density axes. Fit measures can be obtained by FMbiplot

Examples

## Consruct a biplot with
x<-PCAbiplot(iris[,1:4],group=iris[,5])
#alternatively
print(x)

## Construct a biplot that preserves the correlation structure among the variables
y<-PCAbiplot(iris[,-5],group=iris[,5],scale=TRUE)