# Initialize and update the change point matrix used in the ptMCMC algorithm Each of the chains is initialized by `prep_cpts` using a draw from the available times (i.e. assuming a uniform prior), the best fit (by likelihood) draw is put in the focal chain with each subsequently worse fit placed into the subsequently hotter chain. `update_cpts` updates the change points after every iteration in the ptMCMC algorithm. ```r prep_cpts(data, formula, nchangepoints, timename, weights, control = list()) update_cpts(cpts, swaps) ``` ## 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 relationship between the change points, see `formula`. 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 data for each continuous model and each LDA model. - `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`. - `cpts`: The existing matrix of change points. - `swaps`: Chain configuration after among-temperature swaps. ## Returns `list` of [1] `matrix` of change points (rows) for each temperature (columns) and [2] `vector` of log-likelihood values for each of the chains. ## Examples ```r 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$gamma <- LDA_models@gamma weights <- document_weights(document_term_table) data <- data[order(data[,"newmoon"]), ] saves <- prep_saves(1, TS_control()) inputs <- prep_ptMCMC_inputs(data, gamma ~ 1, 1, "newmoon", weights, TS_control()) cpts <- prep_cpts(data, gamma ~ 1, 1, "newmoon", weights, TS_control()) ids <- prep_ids(TS_control()) for(i in 1:TS_control()$nit){ steps <- step_chains(i, cpts, inputs) swaps <- swap_chains(steps, inputs, ids) saves <- update_saves(i, saves, steps, swaps) cpts <- update_cpts(cpts, swaps) ids <- update_ids(ids, swaps) } ```

prep_cpts function