Simulates data and returns parameter values using Wordfish model assumptions: Counts are sampled under the assumption of independent Poisson draws with log expected means linearly related to a lattice of document positions.
sim.wordfish( docs = 10, vocab = 20, doclen = 500, dist = c("spaced", "normal"), scaled = TRUE )
| docs | How many `documents' should be generated |
|---|---|
| vocab | How many `word' types should be generated |
| doclen | A scalar `document' length or vector of lengths |
| dist | the distribution of `document' positions |
| scaled | whether the document positions should be mean 0, unit sd |
A sample word-document matrix
The `document' positions
The `document' lengths
`Word' intercepts
`Word' slopes
This function draws `docs' document positions from a Normal distribution, or regularly spaced between 1/`docs' and 1.
`vocab'/2 word slopes are 1, the rest -1. All word intercepts are 0. `doclen' words are then sampled from a multinomial with these parameters.
Document position (theta) is sorted in increasing size across the documents. If `scaled' is true it is normalized to mean zero, unit standard deviation. This is most helpful when dist=normal.
Will Lowe