Functions to perform plus-minus classifier with a formula interface. Leave one out crossvalidation also implemented. Model extraction, plot, print and summary methods are also implemented.

1 2 3 4 5 6 |

`formula` |
a model formula (see below). |

`data` |
an optional data frame containing the variables in the model. |

`subset` |
an optional vector specifying a subset of observations to be used in the fitting process. |

`na.action` |
a function which indicates what should happen when the data contain |

`method` |
the classification algorithm to be used. |

`n_cores` |
Number of cores to run for parallel processing. Currently set to 2 with the max being 4. |

`validation` |
character. What kind of (internal) validation to use. See below. |

`model` |
an optional data frame containing the variables in the model. |

`x` |
a logical. If TRUE, the model matrix is returned. |

`y` |
a logical. If TRUE, the response is returned. |

`object` |
an object of class |

`...` |
additional arguments, passed to the underlying fit functions, and |

The function fits a Plus-Minus classifier.

The formula argument should be a symbolic formula of the form response ~ terms, where response is the name of the response vector and terms is the name of one or more predictor matrices, usually separated by +, e.g., y ~ X + Z. See `lm`

for a detailed description. The named variables should exist in the supplied data data frame or in the global environment. The chapter Statistical models in R of the manual An Introduction to R distributed with R is a good reference on formulas in R.

If `validation = "loo"`

, leave-one-out cross-validation is performed. If `validation = "none"`

, no cross-validation is performed.

An object of class `plusminus`

is returned. The object contains all components returned by the underlying fit function. In addition, it contains the following:

`coefficients` |
Plus-Minus regression coefficients |

`X` |
X matrix |

`Y` |
actual response values (class labels) |

`val.method` |
validation method |

`call` |
model call |

`terms` |
model terms |

`mm` |
model matrix |

`model` |
fitted model |

Richard Baumgartner (richard_baumgartner@merck.com), Nelson Lee Afanador (nelson.afanador@mvdalab.com)

Zhao et al.: Mas-o-menos: a simple sign averaging method for discriminationin genomic data analysis. Bioinformatics, 30(21):3062-3069,2014.

1 2 3 4 5 6 7 8 9 | ```
### Plus-Minus CLASSIFIER WITH validation = 'loo', i.e. leave-one-out CV ###
data(plusMinusDat)
mod1 <- plusminusFit(Y ~., data = plusMinusDat, validation = "loo", n_cores = 2)
summary(mod1)
### PLUS-Minus CLASSIFIER WITH validation = 'none', i.e. no CV ###
mod2 <- plusminusFit(Y ~., data = plusMinusDat, validation = "none", n_cores = 2)
summary(mod2)
``` |

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