Given a set of training data, this function builds the MDMEB classifier from
Srivistava and Kubokawa (2007). The MDMEB classifier is an adaptation of the
linear discriminant analysis (LDA) classifier that is designed for
small-sample, high-dimensional data. Srivastava and Kubokawa (2007) have
proposed a modification of the standard maximum likelihood estimator of the
pooled covariance matrix, where only the largest 95
their corresponding eigenvectors are kept. The resulting covariance matrix is
then shrunken towards a scaled identity matrix. The value of 95
default and can be changed via the `eigen_pct`

argument.

The MDMEB classifier is an adaptation of the linear discriminant analysis (LDA) classifier that is designed for small-sample, high-dimensional data. Srivastava and Kubokawa (2007) have proposed a modification of the standard maximum likelihood estimator of the pooled covariance matrix, where only the largest 95 are kept. The resulting covariance matrix is then shrunken towards a scaled identity matrix.

1 2 3 4 5 6 7 8 9 10 |

`x` |
matrix containing the training data. The rows are the sample observations, and the columns are the features. |

`...` |
additional arguments |

`y` |
vector of class labels for each training observation |

`prior` |
vector with prior probabilities for each class. If NULL (default), then equal probabilities are used. See details. |

`eigen_pct` |
the percentage of eigenvalues kept |

`formula` |
A formula of the form |

`data` |
data frame from which variables specified in |

`object` |
trained mdmeb object |

`newdata` |
matrix of observations to predict. Each row corresponds to a new observation. |

The matrix of training observations are given in `x`

. The rows of `x`

contain the sample observations, and the columns contain the features for each
training observation.

The vector of class labels given in `y`

are coerced to a `factor`

.
The length of `y`

should match the number of rows in `x`

.

An error is thrown if a given class has less than 2 observations because the variance for each feature within a class cannot be estimated with less than 2 observations.

The vector, `prior`

, contains the *a priori* class membership for
each class. If `prior`

is NULL (default), the class membership
probabilities are estimated as the sample proportion of observations belonging
to each class. Otherwise, `prior`

should be a vector with the same length
as the number of classes in `y`

. The `prior`

probabilties should be
nonnegative and sum to one.

`mdmeb`

object that contains the trained MDMEB classifier

list predicted class memberships of each row in newdata

Srivastava, M. and Kubokawa, T. (2007). "Comparison of Discrimination Methods for High Dimensional Data," Journal of the Japanese Statistical Association, 37, 1, 123-134.

Srivastava, M. and Kubokawa, T. (2007). "Comparison of Discrimination Methods for High Dimensional Data," Journal of the Japanese Statistical Association, 37, 1, 123-134.

1 2 3 4 5 6 7 8 | ```
n <- nrow(iris)
train <- sample(seq_len(n), n / 2)
mdmeb_out <- mdmeb(Species ~ ., data = iris[train, ])
predicted <- predict(mdmeb_out, iris[-train, -5])$class
mdmeb_out2 <- mdmeb(x = iris[train, -5], y = iris[train, 5])
predicted2 <- predict(mdmeb_out2, iris[-train, -5])$class
all.equal(predicted, predicted2)
``` |

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