Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/compute.logistic.score.R

A logistic regression model is fitted to the linear scores using lrm() function and the logistic scores are computed using the formula: 1/(1+exp(-(a+bX))) where a and b are the logistic coefficients.

1 2 | ```
compute.logistic.score(F_, L_, considered.features, training.samples, validating.samples,
linear.scores, report.fitting.failure = TRUE)
``` |

`F_` |
The feature matrix, each column is a feature. |

`L_` |
The vector of labels named according to the rows of F. |

`training.samples` |
The names of rows of F that should be considered as training samples. |

`validating.samples` |
The names of rows of F that should be considered as validating samples. |

`considered.features` |
The names of columns of F that determine the features of interest. |

`linear.scores` |
A vector that contains for each training or validating sample, a linear score predicted by the linear method. |

`report.fitting.failure` |
If TRUE, any failure in fitting the linear of logistic models will be printed. |

The logistic regression will be fitted to all training and validating samples.

Returns a list of:

`logistic.scores` |
A vector of predicted logistic values for all samples. |

`logistic.cofs` |
The coefficients that are computed by logistic regression. |

Logistic regression is also done on top of fitting the linear models.

Habil Zare

"Statistical Analysis of Overfitting Features", manuscript in preparation.

`FeaLect`

, `train.doctor`

, `doctor.validate`

,
`random.subset`

, `compute.balanced`

,`compute.logistic.score`

,
`ignore.redundant`

, `input.check.FeaLect`

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
library(FeaLect)
data(mcl_sll)
F <- as.matrix(mcl_sll[ ,-1]) # The Feature matrix
L <- as.numeric(mcl_sll[ ,1]) # The labels
names(L) <- rownames(F)
all.samples <- rownames(F); ts <- all.samples[5:10]; vs <- all.samples[c(1,22)]
L <- L[c(ts,vs)]
L
asymptotic.scores <- c(1,0.9,0.8,0.2,0.1,0.1,0.7,0.2)
compute.logistic.score(F_=F, L_=L, training.samples=ts, validating.samples=vs,
considered.features=colnames(F),linear.scores= asymptotic.scores)
``` |

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