Description Usage Arguments Value References See Also Examples

Performs linear discriminant analysis on matrix variate data.
This works slightly differently from the LDA function in MASS:
it does not sphere the data or otherwise normalize it. It presumes
equal variance matrices and probabilities are given as if
the data are from a matrix variate normal distribution.
The estimated variance matrices are weighted by the prior. However,
if there are not enough members of a class to estimate a variance,
this may be a problem.
The function does not take the formula interface. If `method = 't'`

is selected, this performs discrimination using the matrix variate t
distribution, presuming equal covariances between classes.

1 2 3 4 5 6 7 8 9 10 |

`x` |
3-D array of matrix data indexed by the third dimension |

`grouping` |
vector |

`prior` |
a vector of prior probabilities of the same length as the number of classes |

`tol` |
by default, |

`method` |
whether to use the normal distribution ( |

`nu` |
If using the t-distribution, the degrees of freedom parameter. By default, 10. |

`...` |
Arguments passed to or from other methods, such
as additional parameters to pass to |

`subset` |
An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.) |

Returns a list of class `matrixlda`

containing
the following components:

`prior`

the prior probabilities used.

`counts`

the counts of group membership

`means`

the group means.

`scaling`

the scalar variance parameter

`U`

the between-row covariance matrix

`V`

the between-column covariance matrix

`lev`

levels of the grouping factor

`N`

The number of observations used.

`method`

The method used.

`nu`

The degrees of freedom parameter if the t distribution was used.

`call`

The (matched) function call.

1 2 3 4 5 6 7 | ```
G Z Thompson, R Maitra, W Q Meeker, A Bastawros (2019),
"Classification with the matrix-variate-t distribution", arXiv
e-prints arXiv:1907.09565 <https://arxiv.org/abs/1907.09565>
Ming Li, Baozong Yuan, "2D-LDA: A statistical linear discriminant
analysis for image matrix", Pattern Recognition Letters, Volume 26,
Issue 5, 2005, Pages 527-532, ISSN 0167-8655.
``` |

Aaron Molstad & Adam J. Rothman (2019), "A Penalized Likelihood Method for Classification With Matrix-Valued Predictors", Journal of Computational and Graphical Statistics, 28:1, 11-22, doi: 10.1080/10618600.2018.1476249 MatrixLDA

Venables, W. N. & Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth Edition. Springer, New York. ISBN 0-387-95457-0 MASS

`predict.matrixlda()`

, `MASS::lda()`

,
`MLmatrixnorm()`

and `MLmatrixt()`

`matrixqda()`

, and `matrixmixture()`

1 2 3 4 5 6 7 8 9 10 | ```
set.seed(20180221)
# construct two populations of 3x4 random matrices with different means
A <- rmatrixnorm(30, mean = matrix(0, nrow = 3, ncol = 4))
B <- rmatrixnorm(30, mean = matrix(1, nrow = 3, ncol = 4))
C <- array(c(A, B), dim = c(3, 4, 60)) # combine together
groups <- c(rep(1, 30), rep(2, 30)) # define groups
prior <- c(.5, .5) # set prior
D <- matrixlda(C, groups, prior) # fit model
logLik(D)
print(D)
``` |

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