Learn R Programming

rrr (version 1.0.0)

threewise_plot: 3-D Reduced Rank Regression Plots

Description

Create three-dimensional, interactive plotly graphics for exploration and diagnostics.

Usage

threewise_plot(x, y, type = "pca", pair_x = 1, pair_y = 2, pair_z = 3, rank = "full", k = 0, point_size = 2.5)

Arguments

x
data frame or matrix of predictor variables
y
data frame or matrix of response variables
type
type of reduced-rank regression model to fit. type = "identity", the default, uses $\mathbf{\Gamma} = \mathbf{I}$ to fit a reduced-rank regression. type = "pca" fits a principal component analysis model as a special case of reduced-rank regression. type = "cva" fits a canonical variate analysis model as a special case of reduced-rank regression. type = "lda" fits a linear discriminant analysis model as a special case of reduced-rank regression.
pair_x
variable to be plotted on the $X$-axis
pair_y
variable to be plotted on the $Y$-axis
pair_z
variable to be plotted on the $Z$-axis
rank
rank of coefficient matrix.
k
small constant added to diagonal of covariance matrices to make inversion easier.
point_size
size of points in scatter plot.

Value

three-dimensional plot. If type = "pca" returns three principal components scores - defaulted to the first three - against each other. If type = "cva" returns three-dimensional plot of residuals. If type = "lda" returns three-dimensional plot of three linear discriminant scores plotted against each other.

Examples

Run this code
## Not run: 
# data(pendigits)
# digits_features <- pendigits[, -35:-36]
# threewise_plot(digits_features, digits_class, type = "pca", k = 0.0001)
# 
# library(dplyr)
# data(COMBO17)
# galaxy <- as_data_frame(COMBO17)
# galaxy <- select(galaxy, -starts_with("e."), -Nr, -UFS:-IFD)
# galaxy <- na.omit(galaxy)
# galaxy_x <- select(galaxy, -Rmag:-chi2red)
# galaxy_y <- select(galaxy, Rmag:chi2red)
# threewise_plot(galaxy_x, galaxy_y, type = "cva")
# 
# data(iris)
# iris_x <- iris[,1:4]
# iris_y <- iris[5]
# threewise_plot(iris_x, iris_y, type = "lda")
# ## End(Not run)

Run the code above in your browser using DataLab