multinom_TS_chunk: Fit a multinomial Time Series model chunk
In weecology/LDATS: Latent Dirichlet Allocation Coupled with Time Series Analyses

Description Usage Arguments Value References Examples

Fit a multinomial regression model (via multinom, Ripley 1996, Venables and Ripley 2002) to a defined chunk of time (a.k.a. segment) [chunk$start, chunk$end] within a time series.

1 2	multinom_TS_chunk(data, formula, chunk, timename = "time", weights = NULL, control = list())

`data`	Class `data.frame` object including the predictor and response variables.
`formula`	Formula as a `formula` or `character` object describing the chunk.
`chunk`	Length-2 vector of times: [1] `start`, the start time for the chunk and [2] `end`, the end time for the chunk.
`timename`	`character` element indicating the time variable used in the time series. Defaults to `"time"`. The variable must be integer-conformable or a `Date`. If the variable named is a `Date`, the input is converted to an integer, resulting in the timestep being 1 day, which is often not desired behavior.
`weights`	Optional class `numeric` vector of weights for each document. Defaults to `NULL`, translating to an equal weight for each document. When using `multinom_TS` in a standard LDATS analysis, it is advisable to weight the documents by their total size, as the result of `LDA` is a matrix of proportions, which does not account for size differences among documents. For most models, a scaling of the weights (so that the average is 1) is most appropriate, and this is accomplished using `document_weights`.
`control`	A `list` of parameters to control the fitting of the Time Series model including the parallel tempering Markov Chain Monte Carlo (ptMCMC) controls. Values not input assume defaults set by `TS_control`.

Fitted model object for the chunk, of classes multinom and nnet.

Ripley, B. D. 1996. Pattern Recognition and Neural Networks. Cambridge.

Venables, W. N. and B. D. Ripley. 2002. Modern Applied Statistics with S. Fourth edition. Springer.

  data(rodents)
  dtt <- rodents$document_term_table
  lda <- LDA_set(dtt, 2, 1, list(quiet = TRUE))
  dct <- rodents$document_covariate_table
  dct$gamma <- lda[[1]]@gamma
  weights <- document_weights(dtt)
  chunk <- c(start = 0, end = 100)
  mtsc <- multinom_TS_chunk(dct, formula = gamma ~ 1, chunk = chunk,
                     timename = "newmoon", weights = weights)