sim_TS_data: Simulate TS data from a TS model structure given parameters
In weecology/LDATS: Latent Dirichlet Allocation Coupled with Time Series Analyses

Description Usage Arguments Value Examples

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.

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

`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`.

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

  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)