Description Usage Arguments Value Author(s) Examples

View source: R/fit.only.model.R

Applies models to high-dimensional data for classification.

1 2 3 4 |

`X` |
A scaled matrix or dataframe containing numeric values of each feature |

`Y` |
A factor vector containing group membership of samples |

`method` |
A vector listing models to be fit.
Available options are |

`p` |
Percent of data to by 'trained' |

`optimize` |
Logical argument determining if each model should be
optimized. Default |

`tuning.grid` |
Optional list of grids containing parameters to optimize
for each algorithm. Default |

`k.folds` |
Number of folds generated during cross-validation.
Default |

`repeats` |
Number of times cross-validation repeated.
Default |

`resolution` |
Resolution of model optimization grid.
Default |

`metric` |
Criteria for model optimization.
Available options are |

`allowParallel` |
Logical argument dictating if parallel processing
is allowed via foreach package.
Default |

`verbose` |
Logical argument if should output progress |

`...` |
Extra arguments that the user would like to apply to the models |

`Methods` |
Vector of models fit to data |

`performance` |
Performance metrics of each model and bootstrap iteration |

`specs` |
List with the following elements: |

total.samples: Number of samples in original dataset

number.features: Number of features in orginal dataset

number.groups: Number of groups

group.levels: The specific levels of the groups

number.observations.group: Number of observations in each group

Charles Determan Jr

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
dat.discr <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10
)
vars <- dat.discr$discr.mat
groups <- dat.discr$classes
fit <- fit.only.model(X=vars,
Y=groups,
method="plsda",
p = 0.9)
``` |

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