HVT (version 25.2.4)

reconcileTransitionProbability: Reconciliation of Transition Probability

Description

This is the main function for creating reconciliation plots and tables which helps in comparing the transition probabilities calculated manually and from markovchain function

Usage

reconcileTransitionProbability(
  df,
  hmap_type = NULL,
  cellid_column,
  time_column
)

Value

A list of plotly heatmap objects and tables representing the transition probability heatmaps.

Arguments

df

Data frame. The input data frame should contain two columns, cell ID from scoreHVT function and timestamp of that dataset.

hmap_type

Character. ('self_state', 'without_self_state', or 'All')

cellid_column

Character. Name of the column containing cell IDs.

time_column

Character. Name of the column containing timestamps

Author

PonAnuReka Seenivasan <[e-mail]>, Vishwavani <[e-mail]>

Examples

dataset <- data.frame(date = as.numeric(time(EuStockMarkets)),
                      DAX = EuStockMarkets[, "DAX"],
                      SMI = EuStockMarkets[, "SMI"],
                      CAC = EuStockMarkets[, "CAC"],
                      FTSE = EuStockMarkets[, "FTSE"])
hvt.results<- trainHVT(dataset,n_cells = 60, depth = 1, quant.err = 0.1,
                       distance_metric = "L1_Norm", error_metric = "max",
                       normalize = TRUE,quant_method = "kmeans")
scoring <- scoreHVT(dataset, hvt.results)
cell_id <- scoring$scoredPredictedData$Cell.ID
time_stamp <- dataset$date
dataset <- data.frame(cell_id, time_stamp)
reconcileTransitionProbability(dataset, hmap_type = "All", 
cellid_column = "cell_id", time_column = "time_stamp")