getTransitionProbability: Creating Transition Probability Matrix

In HVT: Constructing Hierarchical Voronoi Tessellations and Overlay Heatmaps for Data Analysis

Creating Transition Probability Matrix

Description

This is the main function to create transition probability matrix The transition probability matrix quantifies the likelihood of transitioning from one state to another. States: The table includes the current states and the possible next states. Probabilities: For each current state, it lists the probability of transitioning to each of the next possible states.

Usage

getTransitionProbability(
  df,
  cellid_column,
  time_column,
  type = "with_self_state"
)

Arguments

df	Data frame. The input data frame should contain two columns, cell ID from scoreHVT function and time stamp of that dataset.
cellid_column	Character. Name of the column containing cell IDs.
time_column	Character. Name of the column containing time stamps.
type	Character. A character value indicating the type of transition probability table to create. Accepted entries are "with_self_state" and "without_self_state".

Value

Stores a data frames with transition probabilities.

Author(s)

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

Examples

dataset <- data.frame(t = as.numeric(time(EuStockMarkets)),
                      DAX = EuStockMarkets[, "DAX"],
                      SMI = EuStockMarkets[, "SMI"],
                      CAC = EuStockMarkets[, "CAC"],
                      FTSE = EuStockMarkets[, "FTSE"])
hvt.results<- trainHVT(dataset[-1],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$t
dataset <- data.frame(cell_id, time_stamp)
table <- getTransitionProbability(dataset, cellid_column = "cell_id",time_column = "time_stamp")

HVT documentation built on April 3, 2025, 8:45 p.m.