sim_LDA_TS_data: Simulate LDA_TS data from LDA and TS model structures and...
In weecology/LDATS: Latent Dirichlet Allocation Coupled with Time Series Analyses

Description Usage Arguments Value Examples

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

1	sim_LDA_TS_data(N, Beta, X, Eta, rho, tD, err = 0, seed = NULL)

`N`	A vector of document sizes (total word counts). Must be integer conformable. Is used to infer the total number of documents.
`Beta`	`matrix` of categorical distribution parameters defining terms within topics. Dimension: k x V (number of topics x number of terms). Used to infer both (k) and (V). Must be non-negative and sum to 1 within topics.
`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-term matrix of counts (dim: M x V).

  N <- c(10, 22, 15, 31)
  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))
  Beta <- matrix(c(0.1, 0.1, 0.8, 0.2, 0.6, 0.2), 2, 3, byrow = TRUE)
  err <- 1
  sim_LDA_TS_data(N, Beta, X, Eta, rho, tD, err)