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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,146 @@ | ||
| #' Average Marginal Predictions for Non-homogenous Hidden Markov Models | ||
| #' | ||
| #' @param model A Hidden Markov Model of class `nhmm` or `mnhmm`. | ||
| #' @param variable Name of the variable of interest. | ||
| #' @param values Vector containing one or two values for `variable`. | ||
| #' @param newdata Optional data frame which is used for marginalization. | ||
| #' @param nsim Non-negative integer defining the number of samples from the | ||
| #' normal approximation of the model parameters used in | ||
| #' computing the approximate quantiles of the estimates. If `0`, only point | ||
| #' estimates are returned. | ||
| #' @param probs Vector defining the quantiles of interest. Default is | ||
| #' `c(0.025, 0.5, 0.975)`. | ||
| #' @export | ||
| average_marginal_prediction <- function( | ||
| model, variable, values, marginalize_B_over = "sequences", newdata = NULL, | ||
| nsim = 0, probs = c(0.025, 0.5, 0.975)) { | ||
| marginalize_over <- match.arg( | ||
| marginalize_over, c("sequences", "states", "clusters"), several.ok = TRUE) | ||
| stopifnot_( | ||
| checkmate::test_count(nsim), | ||
| "Argument {.arg nsim} should be a single non-negative integer." | ||
| ) | ||
| stopifnot_( | ||
| checkmate::test_string(x = variable), | ||
| "Argument {.arg variable} must be a single character string." | ||
| ) | ||
| stopifnot_( | ||
| length(values) != 2, | ||
| "Argument {.arg values} should contain two values for | ||
| variable {.var variable}.") | ||
| if (is.null(newdata)) { | ||
| time <- model$time_variable | ||
| id <- model$id_variable | ||
| stopifnot_( | ||
| is.data.frame(newdata), | ||
| "Argument {.arg newdata} must be a {.cls data.frame} object." | ||
| ) | ||
| stopifnot_( | ||
| !is.null(newdata[[id]]), | ||
| "Can't find grouping variable {.var {id}} in {.arg newdata}." | ||
| ) | ||
| stopifnot_( | ||
| !is.null(newdata[[time]]), | ||
| "Can't find time index variable {.var {time}} in {.arg newdata}." | ||
| ) | ||
| stopifnot_( | ||
| !is.null(newdata[[variable]]), | ||
| "Can't find time variable {.var {variable}} in {.arg newdata}." | ||
| ) | ||
| } else { | ||
| stopifnot( | ||
| !is.null(model$data), | ||
| "Model does not contain original data and argument {.arg newdata} is | ||
| {.var NULL}." | ||
| ) | ||
| newdata <- model$data | ||
| } | ||
| # use same RNG seed so that the same samples of coefficients are drawn | ||
| newdata[[variable]] <- values[1] | ||
| seed <- sample(.Machine$integer.max, 1) | ||
| set.seed(seed) | ||
| pred <- predict(model, newdata, nsim, return_samples = TRUE) | ||
| if (length(values) == 2) { | ||
| newdata[[variable]] <- values[2] | ||
| set.seed(seed) | ||
| pred2 <- predict(model, newdata, nsim, return_samples = TRUE) | ||
| pred <- mapply("-", pred, pred2, SIMPLIFY = FALSE) | ||
| } | ||
| T <- model$length_of_sequences | ||
| N <- model$n_sequences | ||
| S <- model$n_states | ||
| M <- model$n_symbols | ||
| C <- model$n_channels | ||
| D <- model$n_clusters | ||
| if (C == 1) { | ||
| ids <- rownames(model$observations) | ||
| times <- colnames(model$observations) | ||
| symbol_names <- list(model$symbol_names) | ||
| } else { | ||
| ids <- rownames(model$observations[[1]]) | ||
| times <- colnames(model$observations[[1]]) | ||
| symbol_names <- model$symbol_names | ||
| } | ||
| marginalize <- dplyr::recode( | ||
| marginalize_B_over, | ||
| "clusters" = "cluster", | ||
| "states" = "state", | ||
| "sequences" = "id") | ||
| id_var <- model$id_variable | ||
| time_var <- model$time_variable | ||
|
|
||
| pi <- data.frame( | ||
| cluster = rep(model$cluster_names, each = S * N), | ||
| id = rep(ids, each = S), | ||
| state = model$state_names, | ||
| estimate = unlist(pred$pi) | ||
| ) |> | ||
| dplyr::group_by(.data[[marginalize]]) |> | ||
| summarise(estimate = mean(estimate)) | ||
| colnames(pi)[2] <- id_var | ||
|
|
||
| A <- data.frame( | ||
| cluster = rep(model$cluster_names, each = S^2 * T * N), | ||
| id = rep(ids, each = S^2 * T), | ||
| time = rep(times, each = S^2), | ||
| state_from = model$state_names, | ||
| state_to = rep(model$state_names, each = S), | ||
| estimate = unlist(pred$A) | ||
| ) | ||
| colnames(A)[2] <- id_var | ||
| colnames(A)[3] <- time_var | ||
|
|
||
| B <- data.frame( | ||
| cluster = rep(model$cluster_names, each = S * sum(M) * T * N), | ||
| id = unlist(lapply(seq_len(C), function(i) rep(ids, each = S * M[i] * T))), | ||
| time = unlist(lapply(seq_len(C), function(i) rep(times, each = S * M[i]))), | ||
| state = model$state_names, | ||
| channel = unlist(lapply(seq_len(C), function(i) { | ||
| rep(model$channel_names[i], each = S * M[i]* T * N) | ||
| })), | ||
| observation = unlist(lapply(seq_len(C), function(i) { | ||
| rep(symbol_names[[i]], each = S) | ||
| })), | ||
| estimate = unlist(pred$B) | ||
| ) |> | ||
| dplyr::group_by(.data[[marginalize]]) |> | ||
| summarise(estimate = mean(estimate)) | ||
| idx <- which(names(B) == "id") | ||
| if(length(idx) == 1) names(B)[idx] <- id_var | ||
| idx <- which(names(B) == "time") | ||
| if(length(idx) == 1) names(B)[idx] <- time_var | ||
| if (C == 1) emission_probs$channel <- NULL | ||
|
|
||
| if (D > 1) { | ||
| omega <- data.frame( | ||
| cluster = model$cluster_names, | ||
| id = rep(ids, each = D), | ||
| estimate = c(pred$omega) | ||
| ) | ||
| out <- list(omega = omega, pi = pi, A = A, B = B) | ||
| } else { | ||
| out <- list(pi = pi, A = A, B = B) | ||
| } | ||
| class(out) <- "amp" | ||
| out | ||
| } |
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