minor doc change

R/getFittedValues.R

@@ -10,14 +10,14 @@
 #' @param test.dyn.res The output from \code{\link{testDynamic}}. Defaults to NULL.
 #' @param genes A character vector of genes to generate fitted values for. Defaults to NULL.
 #' @param pt A data.frame of pseudotime values for each cell. Defaults to NULL.
-#' @param expr.mat Either a \code{SingleCellExperiment} or \code{Seurat} object from which counts can be extracted, or a matrix of integer-valued counts with genes as rows & cells as columns. Defaults to NULL.
+#' @param expr.mat Either a \code{SingleCellExperiment}, \code{Seurat}, or \code{cell_data_set} object from which counts can be extracted, or a matrix of integer-valued counts with genes as rows & cells as columns. Defaults to NULL.
 #' @param size.factor.offset (Optional) An offset to be used to rescale the fitted values. Can be generated easily with \code{\link{createCellOffset}}. No need to provide if the GEE backend was used. Defaults to NULL.
 #' @param log1p.norm (Optional) Should log1p-normalized versions of expression & model predictions be returned as well? Defaults to TRUE.
 #' @param cell.meta.data (Optional) A data.frame of metadata values for each cell (celltype label, subject characteristics, tissue type, etc.) that will be included in the result table. Defaults to NULL.
 #' @param is.gee Was the GEE mode used to fit the models? Defaults to FALSE. 
 #' @param id.vec (Optional) A vector of subject IDs used in fitting GEE or GLMM models. Defaults to NULL.
 #' @param ci.alpha (Optional) The pre-specified Type I Error rate used in generating (\eqn{1 - \alpha})\% CIs. Defaults to good old 0.05.
-#' @param filter.lineage (Optional) A character vector of lineages to filter out before generating the final plot. Should be letters, i.e. lineage "A" or "B". Defaults to NULL.
+#' @param filter.lineage (Optional) A character vector of lineages to filter out. Should be letters, i.e. lineage "A" or "B". Defaults to NULL.
 #' @return A data.frame containing depth- and log1p-normalized expression, model predictions, and cell-level metadata.
 #' @export
 #' @examples