clean up

NAMESPACE

@@ -10,6 +10,7 @@ S3method(print,model_query)
 S3method(print,nodal_types)
 S3method(print,nodal_types_query)
 S3method(print,nodes)
+S3method(print,parameter_mapping)
 S3method(print,parameter_matrix)
 S3method(print,parameters)
 S3method(print,parameters_df)
@@ -20,7 +21,7 @@ S3method(print,posterior_event_probabilities)
 S3method(print,stan_summary)
 S3method(print,statement)
 S3method(print,summary.causal_model)
-S3method(print,type_posterior)
+S3method(print,type_distribution)
 S3method(print,type_prior)
 S3method(summary,causal_model)
 export(collapse_data)

R/grab.R

@@ -13,7 +13,7 @@
 #'   \item \code{"causal_statement"} a character. Statement describing causal relations using dagitty syntax,
 #'   \item \code{"dag"} A data frame with columns ‘parent’ and ‘children’ indicating how nodes relate to each other,
 #'   \item \code{"nodes"} A list containing the nodes in the model,
-#'   \item \code{"parents"} a table listing nodes, whether they are root nodes or not, and the number and names of parents they have,
+#'   \item \code{"parents_df"} a table listing nodes, whether they are root nodes or not, and the number and names of parents they have,
 #'   \item \code{"parameters_df"} a data frame containing parameter information,
 #'   \item \code{"causal_types"} a data frame listing causal types and the nodal types that produce them,
 #'   \item \code{"causal_types_interpretation"} a key to interpreting types; see \code{"?interpret_type"} for options,
@@ -34,7 +34,7 @@
 #'   \item \code{"stan_fit"} the stanfit object generated by Stan,
 #'   \item \code{"stan_summary"} a summary of the stanfit object generated by Stan,
 #'   \item \code{"type_prior"} a matrix of type probabilities using priors,
-#'   \item \code{"type_posterior"} a matrix of type probabilities using posteriors,
+#'   \item \code{"type_distribution"} a matrix of type probabilities using posteriors,
 #'   }
 #' @param ... Other arguments passed to helper \code{"get_*"} functions.
 #' @return Objects from a \code{causal_model} as specified.
@@ -52,7 +52,7 @@
 #' grab(model, object = "causal_statement")
 #' grab(model, object = "dag")
 #' grab(model, object = "nodes")
-#' grab(model, object = "parents")
+#' grab(model, object = "parents_df")
 #' grab(model, object = "parameters_df")
 #' grab(model, object = "causal_types")
 #' grab(model, object = "causal_types_interpretation")
@@ -73,7 +73,7 @@
 #' grab(model, object = "stan_fit")
 #' grab(model, object = "stan_summary")
 #' grab(model, object = "type_prior")
-#' grab(model, object = "type_posterior")
+#' grab(model, object = "type_distribution")
 #'
 #' # Example of arguments passed on to helpers
 #' grab(model,
@@ -88,7 +88,7 @@ grab <- function(model, object = NULL, ...) {
     causal_statement = model$statement,
     dag = model$dag,
     nodes = model$nodes,
-    parents = model$parents_df,
+    parents_df = model$parents_df,
     parameters_df = model$parameters_df,
     causal_types = get_causal_types(model),
     causal_types_interpretation = interpret_type(model, ...),
@@ -144,7 +144,7 @@ grab <- function(model, object = NULL, ...) {
         model$stan_objects$stan_summary
       },
     type_prior =  get_type_prob_multiple(model, using = "priors") |> t(),
-    type_posterior =
+    type_distribution =
       if (is.null(model$stan_objects$type_distribution)) {
         stop(
           "Model does not contain type_distribution; update model with type_distribution = TRUE"

R/methods.R

@@ -134,7 +134,7 @@ print.statement <- function(x, ...) {
   return(invisible(x))
 }
 
-#' Print a short summary for a causal_model nodes
+#' Print a short summary for causal_model nodes
 #'
 #' print method for class \code{nodes}.
 #'
@@ -335,6 +335,23 @@ print.type_prior <- function(x, ...) {
   return(invisible(x))
 }
 
+#' Print a short summary for paramater mapping matrix
+#'
+#' print method for class \code{parameter_mapping}.
+#'
+#' @param x An object of \code{parameter_mapping} class.
+#' @param ... Further arguments passed to or from other methods.
+#'
+#' @export
+print.parameter_mapping <- function(x,  ...) {
+  cat("\nParameter mapping matrix: \n\n")
+  cat("Maps from parameters to data types, with \n")
+  cat("possibly multiple columns for each data type \n")
+  cat("in cases with confounding. \n\n")
+  print(data.frame(x))
+  cat("\n")
+  return(invisible(x))
+}
 
 
 #' Print a short summary for stan fit
@@ -418,15 +435,15 @@ print.event_probabilities <- function(x, ...) {
 
 #' Print a short summary for causal-type posterior distributions
 #'
-#' print method for class \code{type_posterior}.
+#' print method for class \code{type_distribution}.
 #'
-#' @param x An object of \code{type_posterior} class, which is a sub-object of
+#' @param x An object of \code{type_distribution} class, which is a sub-object of
 #'    an object of the \code{causal_model} class produced using
 #'    \code{get_type_prob_multiple}.
 #' @param ... Further arguments passed to or from other methods.
 #'
 #' @export
-print.type_posterior <- function(x, ...) {
+print.type_distribution <- function(x, ...) {
   cat("Posterior draws of causal types (transformed parameters)")
   cat(paste("\nDimensions:", dim(x)[1], "rows (draws) by", dim(x)[2], "cols (types) \n\n", sep = " "))
   cat("Summary: \n\n")

R/parmap.R

@@ -22,6 +22,7 @@ make_parmap <- function(model, A = NULL, P = NULL) {
     map <- diag(ncol(type_matrix))
     rownames(map) <- colnames(map) <- colnames(A)
     attr(type_matrix, "map") <- map
+    class(type_matrix) <- c("parameter_mapping", class(type_matrix))
     return(type_matrix)
   }
 
@@ -49,8 +50,8 @@ make_parmap <- function(model, A = NULL, P = NULL) {
 
   colnames(type_matrix) <- .type_matrix$d
 
-  # type_matrix <- type_matrix[,match(colnames(type_matrix), colnames(A))]
   attr(type_matrix, "map") <- data_to_data(type_matrix, A)
+  class(type_matrix) <- c("parameter_mapping", class(type_matrix))
   type_matrix
 }
 

R/set_parameter_matrix.R

@@ -82,14 +82,21 @@ set_parameter_matrix <- function(model, P = NULL) {
 print.parameter_matrix <- function(x, ...) {
   cat(paste0("\nRows are parameters, grouped in parameter sets"))
   cat(paste0("\n\nColumns are causal types"))
-  cat(paste0("\n\nCell entries indicate whether a parameter probability is",
-             "used\nin the calculation of causal type probability\n\n"))
+  cat(
+    paste0(
+      "\n\nCell entries indicate whether a parameter probability is",
+      "used\nin the calculation of causal type probability\n\n"
+    )
+  )
 
-  param_set <- attr(x, "param_set")
   class(x) <- "data.frame"
   print(x)
-  cat("\n \n param_set  (P)\n ")
-  cat(paste0(param_set, collapse = "  "))
+  cat("\n")
+  if (!is.null(attr(x, "param_set"))) {
+    param_set <- attr(x, "param_set")
+    cat("\n param_set  (P)\n ")
+    cat(paste0(param_set, collapse = "  "))
+  }
   return(invisible(x))
 }
 

R/update_model.R

@@ -172,7 +172,7 @@ update_model <- function(model,
     model$stan_objects$type_distribution <- extract(newfit, pars = "types")$types
 
     colnames(model$stan_objects$type_distribution) <- colnames(stan_data$P)
-    class(model$stan_objects$type_distribution) <- c("type_posterior", "matrix", "array")
+    class(model$stan_objects$type_distribution) <- c("type_distribution", "matrix", "array")
   }
 
   # Retain event (pre-censoring) probabilities

tests/testthat/test_grab.R

@@ -12,7 +12,7 @@ testthat::test_that(
 	    "causal_statement",
 	    "dag",
 	    "nodes",
-	    "parents",
+	    "parents_df",
 	    "parameters_df",
 	    "causal_types",
 	    "causal_types_interpretation",
@@ -33,7 +33,7 @@ testthat::test_that(
 	    "stan_fit",
 	    "stan_summary",
 	    "type_prior",
-	    "type_posterior"
+	    "type_distribution"
 	  )
 
 	  classes <- c(
@@ -50,7 +50,7 @@ testthat::test_that(
 	    "matrix",
 	    "parameters",
 	    "character",
-	    "matrix",
+	    "parameter_mapping",
 	    "parameter_matrix",
 	    "numeric",
 	    "parameters_prior",
@@ -61,7 +61,7 @@ testthat::test_that(
 	    "stanfit",
 	    "stan_summary",
 	    "type_prior",
-	    "type_posterior"
+	    "type_distribution"
 	  )
 
 
@@ -79,7 +79,7 @@ testthat::test_that(
 
 	  # Proper dimensions
 	  expect_equal(grab(model, "type_prior") |> dim(), c(4000, 8))
-	  expect_equal(grab(model, "type_posterior") |> dim(), c(4000, 8))
+	  expect_equal(grab(model, "type_distribution") |> dim(), c(4000, 8))
 	  expect_equal(grab(model, "posterior_distribution") |> dim(), c(4000, 6))
 	  expect_equal(grab(model, "prior_distribution") |> dim(), c(4000, 6))
 
@@ -98,7 +98,7 @@ testthat::test_that(
 	  # Print methods
 	  out <- capture.output(print(grab(model, object = "nodes")))
 	  expect_true(any(grepl("Nodes:", out)))
-	  out <- capture.output(print(grab(model, object = "parents")))
+	  out <- capture.output(print(grab(model, object = "parents_df")))
 	  expect_true(any(grepl("parents", out)))
 	  out <- capture.output(print(grab(model, object = "parameters_df")))
 	  expect_false(any(grepl("first 10 rows:", out)))

tests/testthat/test_methods.R

@@ -13,7 +13,7 @@ testthat::test_that(
     out <- capture.output(print(grab(model, object = "nodes")))
     expect_true(any(grepl("Nodes:", out)))
 
-    out <- capture.output(print(grab(model, object = "parents")))
+    out <- capture.output(print(grab(model, object = "parents_df")))
     expect_true(any(grepl("parents", out)))
 
     out <- capture.output(print(grab(model, object = "parameters_df")))
@@ -65,7 +65,7 @@ testthat::test_that(
     out <- capture.output(print(grab(model, object = "event_probabilities")))
     expect_true(any(grepl("event_probs", out)))
 
-    out <- capture.output(print(grab(model, object = "type_posterior")))
+    out <- capture.output(print(grab(model, object = "type_distribution")))
     expect_true(any(grepl("Posterior draws", out)))
 
     out <- capture.output(print(   query_model(model, "Y[X=1] - Y[X = 0]", using = "parameters")) )