1d:T5ed,# Package the output of the chunk-level multinomial models into a multinom_TS_fit list Takes the list of fitted chunk-level models returned from `TS_chunk_memo` (the memoised version of `multinom_TS_chunk` and packages it as a `multinom_TS_fit` object. This involves naming the model fits based on the chunk time windows, combining the log likelihood values across the chunks, and setting the class of the output object. ```r package_chunk_fits(chunks, fits) ``` ## Arguments - `chunks`: Data frame of `start` and `end` times for each chunk (row). - `fits`: List of chunk-level fits returned by `TS_chunk_memo`, the memoised version of `multinom_TS_chunk`. ## Returns Object of class `multinom_TS_fit`, which is a list of [1] chunk-level model fits, [2] the total log likelihood combined across all chunks, and [3] the chunk time data table. ## Examples ```r 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) formula <- gamma ~ 1 changepoints <- c(20,50) timename <- "newmoon" TS_chunk_memo <- memoise_fun(multinom_TS_chunk, TRUE) chunks <- prep_chunks(dct, changepoints, timename) nchunks <- nrow(chunks) fits <- vector("list", length = nchunks) for (i in 1:nchunks){ fits[[i]] <- TS_chunk_memo(dct, formula, chunks[i, ], timename, weights, TS_control()) } package_chunk_fits(chunks, fits) ```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 3.60629 11.0711C4.25298 12.1788 5.30335 11.8588 5.71638 11.6732C5.78225 11.205 5.96962 10.8854 6.17658 10.7043C4.56675 10.5209 2.87415 9.89918 2.87415 7.

package_chunk_fits function