# Get History Object `get_history` summarizes the history and action at a given stage from a policy_data object. ```r get_history(object, stage = NULL, full_history = FALSE, event_set = c(0)) ``` ## Arguments - `object`: Object of class policy_data . - `stage`: Stage number. If NULL, the state/Markov-type history across all stages is returned. - `full_history`: Logical. If TRUE, the full history is returned If FALSE, only the state/Markov-type history is returned. - `event_set`: Integer vector. Subset of c(0,1,2). ## Returns Object of class history . The object is a list containing the following elements: - **`H`**: data.table::data.table with keys id and stage and with variables {$B$, $X_k$} (state history) or {$B$, $X_1$, $A_1$, ..., $X_k$} (full history), see details. - **`A`**: data.table::data.table with keys id and stage and variable $A_k$, see details. - **action_name**: Name of the action variable in `A`. - **action_set**: Sorted character vector defining the action set. - **U**: (If `stage` is not NULL) data.table::data.table with keys id and stage and with variables U_bar and U_Aa for every a in the actions set. U_bar is the accumulated rewards up till and including the given stage, i.e., $\sum_{j=1}^k U_j$. U_Aa is the deterministic reward of action a. ## Details Each observation has the sequential form $$ O= {B, U_1, X_1, A_1, ..., U_K, X_K, A_K, U_{K+1}}, $$ for a possibly stochastic number of stages K. * $B$ is a vector of baseline covariates. * $U_k$ is the reward at stage k (not influenced by the action $A_k$). * $X_k$ is a vector of state covariates summarizing the state at stage k. * $A_k$ is the categorical action at stage k. ## Examples ```r library("polle") ### Single stage: d1 <- sim_single_stage(5e2, seed=1) # constructing policy_data object: pd1 <- policy_data(d1, action="A", covariates=list("Z", "B", "L"), utility="U") pd1 # In the single stage case, set stage = NULL h1 <- get_history(pd1) head(h1$H) head(h1$A) ### Two stages: d2 <- sim_two_stage(5e2, seed=1) # constructing policy_data object: pd2 <- policy_data(d2, action = c("A_1", "A_2"), baseline = c("B"), covariates = list(L = c("L_1", "L_2"), C = c("C_1", "C_2")), utility = c("U_1", "U_2", "U_3")) pd2 # getting the state/Markov-type history across all stages: h2 <- get_history(pd2) head(h2$H) head(h2$A) # getting the full history at stage 2: h2 <- get_history(pd2, stage = 2, full_history = TRUE) head(h2$H) head(h2$A) head(h2$U) # getting the state/Markov-type history at stage 2: h2 <- get_history(pd2, stage = 2, full_history = FALSE) head(h2$H) head(h2$A) ### Multiple stages d3 <- sim_multi_stage(5e2, seed = 1) # constructing policy_data object: pd3 <- policy_data(data = d3$stage_data, baseline_data = d3$baseline_data, type = "long", id = "id", stage = "stage", event = "event", action = "A", utility = "U") pd3 # getting the full history at stage 2: h3 <- get_history(pd3, stage = 2, full_history = TRUE) head(h3$H) # note that not all observations have two stages: nrow(h3$H) # number of observations with two stages. get_n(pd3) # number of observations in total. ```

get_history function