linear_transform {PlotNormTest} | R Documentation |
Best Linear Transformations
Description
The algorithm uses gradient descent algorithm to obtain the maximum of the square of sample skewness, of the kurtosis or of their average under any univariate linear transformation of the multivariate data.
Usage
linear_transform(
x,
l0 = rep(1, ncol(x)),
method = "both",
epsilon = 1e-10,
iter = 5000,
stepsize = 0.001
)
Arguments
x |
multivariate data matrix. |
l0 |
starting point for projection algorithm,
default is |
method |
character strings,
one of |
epsilon |
bounds on error of optimal solution, default is |
iter |
number of iteration of projection algorithm,
default is |
stepsize |
gradient descent stepsize, default is |
Value
max_result
: The maximum value after linear transformation.x_uni
: Univariate data after transformation.vector_k
: Vector of the "best" linear transformation.error
: Error of projection algorithm.iteration
: Number of iteration.
See Also
Examples
set.seed(1)
x <- MASS::mvrnorm(100, mu = rep(0, 2), diag(2))
linear_transform(x, method = "skewness")$max_result
linear_transform(x, method = "kurtosis")$max_result
linear_transform(x, method = "both")$max_result
[Package PlotNormTest version 1.0.1 Index]