multinom_TS: Fit a multinomial change point Time Series model
In weecology/LDATS: Latent Dirichlet Allocation Coupled with Time Series Analyses

Description Usage Arguments Value References Examples

Fit a set of multinomial regression models (via multinom, Venables and Ripley 2002) to a time series of data divided into multiple segments (a.k.a. chunks) based on given locations for a set of change points.

check_multinom_TS_inputs checks that the inputs to multinom_TS are of proper classes for an analysis.

multinom_TS(data, formula, changepoints = NULL, timename = "time",
  weights = NULL, control = list())

check_multinom_TS_inputs(data, formula = gamma ~ 1,
  changepoints = NULL, timename = "time", weights = NULL,
  control = list())

`data`	`data.frame` including [1] the time variable (indicated in `timename`), [2] the predictor variables (required by `formula`) and [3], the multinomial response variable (indicated in `formula`) as verified by `check_timename` and `check_formula`. Note that the response variables should be formatted as a `data.frame` object named as indicated by the `response` entry in the `control` list, such as `gamma` for a standard TS analysis on LDA output. See `Examples`.
`formula`	`formula` defining the regression between relationship the change points. Any predictor variable included must also be a column in `data` and any (multinomial) response variable must be a set of columns in `data`, as verified by `check_formula`.
`changepoints`	Numeric vector indicating locations of the change points. Must be conformable to `integer` values. Validity checked by `check_changepoints` and `verify_changepoint_locations`.
`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`.

multinom_TS: Object of class multinom_TS_fit, which is a list of [1] chunk-level model fits ("chunk models"), [2] the total log likelihood combined across all chunks ("logLik"), and [3] a data.frame of chunk beginning and ending times ("logLik" with columns "start" and "end").

check_multinom_TS_inputs: an error message is thrown if any input is improper, otherwise NULL.

Venables, W. N. and B. D. Ripley. 2002. Modern and Applied Statistics with S. Fourth Edition. Springer, New York, NY, USA.

  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)
  check_multinom_TS_inputs(dct, timename = "newmoon")
  mts <- multinom_TS(dct, formula = gamma ~ 1, changepoints = c(20,50),
                     timename = "newmoon", weights = weights)