This function executes ptMCMC-based estimation of the
change point location distributions for multinomial Time Series analyses.
est_changepoints(data, formula, nchangepoints, timename, weights,
control = list())
Arguments
| 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.
|
| 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.
|
| nchangepoints |
integer corresponding to the number of
change points to include in the model. 0 is a valid input (corresponding
to no change points, so a singular time series model), and the current
implementation can reasonably include up to 6 change points. The
number of change points is used to dictate the segmentation of the
time series into chunks fit with separate models dictated by
formula.
|
| timename |
character element indicating the time variable
used in the time series.
|
| 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
List of saved data objects from the ptMCMC estimation of
change point locations (unless nchangepoints is 0, then
NULL is returned).
Examples
# \donttest{
data(rodents)
document_term_table <- rodents$document_term_table
document_covariate_table <- rodents$document_covariate_table
LDA_models <- LDA_set(document_term_table, topics = 2)[[1]]
data <- document_covariate_table
data$gamma <- LDA_models@gamma
weights <- document_weights(document_term_table)
formula <- gamma ~ 1
nchangepoints <- 1
control <- TS_control()
data <- data[order(data[,"newmoon"]), ]
rho_dist <- est_changepoints(data, formula, nchangepoints, "newmoon",
weights, control)
# }