sim_TS_data: Simulate TS data from a TS model structure given parameters

In LDATS: Latent Dirichlet Allocation Coupled with Time Series Analyses

Simulate TS data from a TS model structure given parameters

Description

For a given set of covariates X; parameters Eta, rho, and err; and document-specific time stamps tD, simulate a document-by-topic matrix. Additional structuring variables (numbers of topics (k), documents (M), segments (S), and covariates per segment (C)) are inferred from input objects.

Usage

sim_TS_data(X, Eta, rho, tD, err = 0, seed = NULL)

Arguments

X	matrix of covariates, dimension M (number of documents) x C (number of covariates, including the intercept) (a.k.a. the design matrix).
Eta	matrix of regression parameters across the segments, dimension: SC (number of segments x number of covariates, including the intercept) x k (number of topics).
rho	Vector of integer-conformable time locations of changepoints or NULL if no changepoints. Used to determine the number of segments. Must exist within the bounds of the times of the documents, tD.
tD	Vector of integer-conformable times of the documents. Must be of length M (as determined by X).
err	Additive error on the link-scale. Must be a non-negative numeric value. Default value of 0 indicates no error.
seed	Input to set.seed.

Value

A document-by-topic matrix of probabilities (dim: M x k).

Examples

  tD <- c(1, 3, 4, 6)
  rho <- 3
  X <- cbind(rep(1, 4), 1:4)
  Eta <- cbind(c(0.5, 0.3, 0.9, 0.5), c(1.2, 1.1, 0.1, 0.5))
  sim_TS_data(X, Eta, rho, tD, err = 1)
  

LDATS documentation built on Sept. 19, 2023, 5:08 p.m.