multinom_TS_chunk: Fit a multinomial Time Series model chunk

In LDATS: Latent Dirichlet Allocation Coupled with Time Series Analyses

Fit a multinomial Time Series model chunk

Description

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.

Usage

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

Arguments

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.

Value

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

References

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.

Examples

  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) 

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