Description Usage Arguments Details Value Examples

`cnfm`

computes the confusion matrix of the clustering with
respect to an expert/reference labeling of the data. Also, it can be used
to compare the labelings of two different clusterings of the same
trajectory, (see details).

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
cnfm(obj, ref, ...)
## S4 method for signature 'binClst,numeric'
cnfm(obj, ref, ret = FALSE, ...)
## S4 method for signature 'binClstPath,missing'
cnfm(obj, ref, ret = FALSE, ...)
## S4 method for signature 'binClstStck,missing'
cnfm(obj, ref, ret = FALSE, ...)
## S4 method for signature 'binClst,binClst'
cnfm(obj, ref, ret = FALSE, ...)
``` |

`obj` |
A binClst_instance or |

`ref` |
A numeric vector with an expert/reference labeling of the data. A second binClst_instance (see details). |

`...` |
Parameters |

`ret` |
A boolean value (defaults to FALSE). If ret=TRUE the confusion matrix is returned as a matrix object. |

The confusion matrix yields marginal counts and Recall for each row, and marginal counts, Precision and class F-measure for each column. The 3x2 subset of cells at the bottom right show (in this order): the overall Accuracy, the average Recall, the average Precision, NaN, NaN, and the overall Macro-F-Measure. The number of classes (expert/reference labeling) should match or, at least not be greater than the number of clusters. The overall value of the Macro-F-Measure is an average of the class F-measure values, hence it is underestimated if the number of classes is lower than the number of clusters.

If `obj`

is a binClstPath_instance and there is a column "lbl" in
the [email protected] slot with an expert labeling, this labeling will be used by
default.

If `obj`

is a `binClstStck`

instance and, for all paths in the
stack, there is a column "lbl" in the [email protected] slot of each, this labeling
will be used to compute the confusion matrix for the whole stack.

If `obj`

and `ref`

are both a binClst_instance (e.g.
smoothed versus non-smoothed), the confusion matrix compares both labelings.

If ret=TRUE returns a matrix with the confusion matrix values.

1 2 3 4 5 6 7 8 9 10 | ```
# -- apply EMbC to the example path --
mybcp <- stbc(expth,info=-1)
# -- compute the confusion matrix --
cnfm(mybcp,expth$lbl)
# -- as we have expth$lbl the following also works --
cnfm(mybcp,mybcp@pth$lbl)
# -- or simply --
cnfm(mybcp)
# -- numerical differences with respect to the smoothed clustering --
cnfm(mybcp,smth(mybcp))
``` |

EMbC documentation built on May 7, 2018, 5:04 p.m.

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