diagPlot | R Documentation |

Function to construct Diagnosis Plots for HVT Model

diagPlot( hvt.results, data, level, quant.err, distance_metric = "L1_Norm", error_metric = "max", ... )

`data` |
Data frame. A data frame with different columns is given as input. The dataframe should be the same dataframe used to train the HVT Model |

`level` |
Numeric. Indicating the level for which the heat map is to be plotted. |

`quant.err` |
Numeric. A number indicating the quantization error threshold. |

`...` |
The ellipsis is passed to it as additional argument. (Used internally) |

`hvt.results.model` |
A list of hvt.results.model obtained from HVT function while performing hierarchical vector quantization on train data |

This function creates Diagnosis Plots for HVT Model. The output of the functions contains a Minimum Intra-Centroid distance plot, a Minimum Intra-DataPoint Distance Plot, Distribution of number of cells, a Minimum Intra-DataPoint Distance Plot, Distribution of number of cells and count of singletons(segments with single observation)

A list that contains a Minimum Inter-Centroid distance plot, a Minimum Intra-DataPoint Distance Plot, Distribution of number of cells and count of singletons(segments with single observation)

`cent_plot ` |
Plot. a Minimum Intra-Centroid distance plot |

`datapoint_plot ` |
Plot. a Minimum Intra-Datapoints distance plot |

`number_plot ` |
Plot. a Distribution of number of cells |

`singleton_count` |
Numeric. Count of singletons(segments with single observation) |

Shubhra Prakash <shubhra.prakash@mu-sigma.com>

`predictHVT`

data(USArrests) hvt.results <- list() hvt.results <- HVT(USArrests, n_cells = 15, depth = 1, quant.err = 0.2, distance_metric = "L1_Norm", error_metric = "mean", projection.scale = 10, normalize = TRUE, quant_method="kmeans",diagnose=TRUE) diagPlot(hvt.results = hvt.results, data = USArrests, level = 1, quant.err = 0.2)

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