# Estimation of misclassification errors (generalisation errors) based on statistical and various machine learning methods

### Description

Estimates misclassification errors (generalisation errors), sensitivity and specificity using cross-validation,
bootstrap and `632plus`

bias corrected bootstrap methods based on Random Forest,
Support Vector Machines, Linear Discriminant Analysis and k-Nearest Neighbour methods.

### Usage

1 2 3 4 5 6 7 8 9 10 11 |

### Arguments

`formula` |
A formula of the form |

`data` |
A data frame containing the response (class membership) variable and the explanatory variables in the formula. |

`method` |
A character vector of length |

`errorType` |
A character vector of length |

`senSpec` |
Logical. Should sensitivity and specificity (for cross-validation estimator only)
be computed? Defaults to |

`negLevLowest` |
Logical. Is the lowest of the ordered levels of the class variable represnts
the negative control? Defaults to |

`na.action` |
Function which indicates what should happen when the data
contains |

`control` |
Control parameters of the the function |

`...` |
additional parameters to |

### Details

In the current version of the package, estimation of sensitivity and
specificity is limited to cross-validation estimator only. For LDA
sample size must be greater than the number of explanatory variables to
avoid singularity. The function `classificationError`

does not
check if this is satisfied, but the underlying function
`lda`

produces warnings if this condition is violated.

### Value

Returns an object of class `classificationError`

with components

`call` |
The call of the |

`errorRate` |
A |

`rocData` |
A |

### Author(s)

Mizanur Khondoker, Till Bachmann, Peter Ghazal

Maintainer: Mizanur Khondoker mizanur.khondoker@gmail.com.

### References

Khondoker, M. R., Till T. Bachmann, T. T., Mewissen, M., Dickinson, P. *et al.*(2010).
Multi-factorial analysis of class prediction error: estimating optimal number of biomarkers for various classification rules.
*Journal of Bioinformatics and Computational Biology*, **8**, 945-965.

Breiman, L. (2001). *Random Forests*, Machine Learning
**45**(1), 5–32.

Chang, Chih-Chung and Lin, Chih-Jen: *LIBSVM: a library for Support
Vector Machines*, http://www.csie.ntu.edu.tw/~cjlin/libsvm.

Ripley, B. D. (1996). *Pattern Recognition and Neural
Networks*.Cambridge: Cambridge University Press.

Efron, B. and Tibshirani, R. (1997). Improvements on Cross-Validation:
The .632+ Bootstrap Estimator. *Journal of the American Statistical
Association* **92**(438), 548–560.

### See Also

`simData`

### Examples

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