1d:T683,# Fit the chunk-level models to a time series, given a set of proposed change points within the ptMCMC algorithm This function wraps around `TS_memo` (optionally memoised `multinom_TS`) to provide a simpler interface within the ptMCMC algorithm and is implemented within `propose_step`. ```r proposed_step_mods(prop_changepts, inputs) ``` ## Arguments - `prop_changepts`: `matrix` of proposed change points across chains. - `inputs`: Class `ptMCMC_inputs` list, containing the static inputs for use within the ptMCMC algorithm. ## Returns List of models associated with the proposed step, with an element for each chain. ## 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$gamma <- LDA_models@gamma weights <- document_weights(document_term_table) data <- data[order(data[,"newmoon"]), ] saves <- prep_saves(1, TS_control()) inputs <- prep_ptMCMC_inputs(data, gamma ~ 1, 1, "newmoon", weights, TS_control()) cpts <- prep_cpts(data, gamma ~ 1, 1, "newmoon", weights, TS_control()) i <- 1 pdist <- inputs$pdist ntemps <- length(inputs$temps) selection <- cbind(pdist$which_steps[i, ], 1:ntemps) prop_changepts <- cpts$changepts curr_changepts_s <- cpts$changepts[selection] prop_changepts_s <- curr_changepts_s + pdist$steps[i, ] if(all(is.na(prop_changepts_s))){ prop_changepts_s <- NULL } prop_changepts[selection] <- prop_changepts_s mods <- proposed_step_mods(prop_changepts, inputs) ```1e:T49a,M7.49933 0.25C3.49635 0.25 0.25 3.49593 0.25 7.50024C0.25 10.703 2.32715 13.4206 5.2081 14.3797C5.57084 14.446 5.70302 14.2222 5.70302 14.0299C5.70302 13.8576 5.69679 13.4019 5.69323 12.797C3.67661 13.235 3.25112 11.825 3.25112 11.825C2.92132 10.9874 2.44599 10.7644 2.44599 10.7644C1.78773 10.3149 2.49584 10.3238 2.49584 10.3238C3.22353 10.375 3.60629 11.0711 3.6

proposed_step_mods function