feat: compute state column when reconstructing indicators

Author: Quentin62

R/reconstructData.R

@@ -2,7 +2,7 @@
 #'
 #' @description
 #' The reconstruction formula is:
-#' \deqn{1^{x}(t) = p^x(t) ( 1 + \sum_{i\geq 1} z_i^{kx}*a_i^x(t)})
+#' \deqn{1^{x}(t) = p^x(t) ( 1 + \sum_{i\geq 1} z_i*a_i^x(t)})
 #'
 #' with \eqn{z_i}, the i-th principal component,
 #' encoding \eqn{a_i^x = \sum_j \alpha_{(x, j)} * \phi_j(t)}
@@ -13,7 +13,9 @@
 #' @param timeValues vector containing time values at which compute the indicators. If NULL, the time values from the data
 #' @param propMinEigenvalues Only if nComp = NULL. Minimal proportion used to estimate the number of non-null eigenvalues
 #'
-#' @return a data.frame with columns: time, id, state1, ..., stateK
+#' @return a data.frame with columns: time, id, state1, ..., stateK, state.
+#' state1 contains the estimated indicator values for the first state.
+#' state contains the state with the maximum values of all indicators
 #'
 #' @examples
 #' set.seed(42)
@@ -37,11 +39,15 @@
 #' plotData(d_JK2[d_JK2$id == iInd, ])
 #'
 #' plotIndicatorsReconstruction(indicators, id = iInd)
+#'
+#' # the column state contains the state associated with the greatest indicator.
+#' # So, the output can be used with plotData function
+#' plotData(remove_duplicated_states(indicators[indicators$id == iInd, ]))
 #' }
 #' @author Quentin Grimonprez
 #' @seealso \code{\link{plotIndicatorsReconstruction}}
 #' @export
-reconstructIndicators <- function(x, nComp = NULL, timeValues = NULL, propMinEigenvalues = 1e-6) {
+reconstructIndicators <- function(x, nComp = NULL, timeValues = NULL, propMinEigenvalues = 1e-4) {
   checkFmca(x)
   if (
     any(is.na(propMinEigenvalues)) || !is.numeric(propMinEigenvalues) || (length(propMinEigenvalues) != 1) || (
@@ -51,6 +57,7 @@ reconstructIndicators <- function(x, nComp = NULL, timeValues = NULL, propMinEig
   }
   if (is.null(nComp)) {
     nComp <- sum(x$eigenvalues / sum(x$eigenvalues) > propMinEigenvalues)
+    print(paste0("Reconstruct data using ", nComp, " components (out of ", length(x$eigenvalues), ")"))
   }
   checkInteger(
     nComp,
@@ -92,6 +99,7 @@ reconstructIndicators <- function(x, nComp = NULL, timeValues = NULL, propMinEig
       df[[paste0("state", x$label$label[i])]] <- yy[, i]
     }
     allDf[[iInd]] <- df
+    allDf[[iInd]]$state <- x$label$label[apply(allDf[[iInd]][paste0("state", x$label$label)], 1, which.max)]
   }
 
   return(do.call(rbind, allDf))
@@ -105,36 +113,15 @@ reconstructIndicators <- function(x, nComp = NULL, timeValues = NULL, propMinEig
 #'
 #' @return ggplot
 #'
-#' @examples
-#' set.seed(42)
-#' # Simulate the Jukes-Cantor model of nucleotide replacement
-#' K <- 3
-#' Tmax <- 1
-#' d_JK <- generate_Markov(n = 100, K = K, Tmax = Tmax)
-#' d_JK2 <- cut_data(d_JK, Tmax)
-#'
-#' # create basis object
-#' m <- 20
-#' b <- create.bspline.basis(c(0, Tmax), nbasis = m, norder = 4)
-#' \donttest{
-#' # compute encoding
-#' encoding <- compute_optimal_encoding(d_JK2, b, computeCI = FALSE, nCores = 1)
-#'
-#' indicators <- reconstructIndicators(encoding)
-#'
-#' # we plot the first path and its reconstructed indicators
-#' iInd <- 3
-#' plotData(d_JK2[d_JK2$id == iInd, ])
-#'
-#' plotIndicatorsReconstruction(indicators, id = iInd)
-#' }
+#' @inherit reconstructIndicators examples
 #'
 #' @author Quentin Grimonprez
 #' @seealso \code{\link{reconstructIndicators}}
 #' @export
 plotIndicatorsReconstruction <- function(reconstruction, id) {
   x <- reconstruction[reconstruction$id == id, ]
   x$id <- NULL
+  x$state <- NULL
 
   p <- ggplot(
     data = pivot_longer(x, cols = -1, values_to = "probability", names_to = "state"),