mpt_data_reshape: Reshape wide MPT frequency tables to the long format of...

In bmm: Easy and Accessible Bayesian Measurement Models Using 'brms'

Reshape wide MPT frequency tables to the long format of bmm()

Description

MPTinR and TreeBUGS take one row per participant and one column per response category of the EQN file. bmm() takes one row per participant and tree, a column naming the tree, and one count column per shared response category (see mpt()). mpt_data_reshape() reshapes the first layout into the second for a given model.

Usage

mpt_data_reshape(data, model, id = NULL, columns = c("names", "mptinr"))

Arguments

data	A data frame or matrix with one row per participant. The count columns are read as described under columns; with columns = "names", all other columns except id are carried to the long data.
model	An mpt model from mpt(), mpt_from_eqn() or mpt_from_string(). For a model from mpt_from_eqn() the mpt_renaming attribute maps the labels of the EQN file onto the response categories of the model. For any other model, the columns of data are named ⁠<tree>.<category>⁠, e.g. target.old, with the tree names and response categories of the model.
id	Character. The name of the participant column in data, or NULL (the default) to number the rows of data, as id = seq_len(nrow(data)) in a column named id. The participants must be unique.
columns	Character. How the count columns of data are identified: "names" (the default) by their names, as in TreeBUGS data; "mptinr" by their position, as in MPTinR data. See Details.

Details

Named columns (columns = "names"). For a model from mpt_from_eqn(), a column is matched to a category by the EQN label (hit), or, when trees share labels, by the tree name and label (target.hit, with the bmm name of the tree); the qualified name wins where both exist. A label used in several trees must be qualified. Labels that the categories argument of mpt_from_eqn() merged onto one response category are summed. All columns of data that are neither count columns nor id are carried unchanged to every long row, e.g. between-participant covariates.

Positional columns (columns = "mptinr"). MPTinR data for an EQN model have no column names. Every column of data except id is read as a count, in the order of check.mpt() in MPTinR (version 1.14.1), elements eqn.order.trees and eqn.order.categories: trees sorted by their label in the EQN file and, within each tree, the category labels of the file sorted. No other column can be carried; add covariates afterwards, e.g. with merge() on the participant column. The help page of check.mpt() states only that these elements give the order; the sorting rule is that of its internal .get.EQN.model.order(), which sorts the columns that read.table() returns for the file: numerically if all tree labels (all category labels) are numbers, else as character strings in the collation of the R session. mpt_data_reshape() applies the same rule, so run it in the session where MPTinR's order was determined if the labels mix upper and lower case. This mode needs a model from mpt_from_eqn(): MPTinR's own model format orders the columns by the lines of the model text, and mpt() reorders the branches of every tree to the categories of the first, so the line order cannot be recovered from the model. For other models, name the columns ⁠<tree>.<category>⁠ and use columns = "names".

Impossible categories. A response category that a tree cannot produce (impossible in mpt_tree(), mpt_from_string() and mpt_from_eqn()) has no column in data and gets 0 in the long rows of that tree, which bmm() accepts.

Counts. The count columns must be numeric, without missing values, and hold non-negative whole numbers; the long data hold them as integers.

Checks. The function cannot tell a correct reshape from one that swaps columns of equal meaning. Run bmm_data_check() on the result, and compare one participant's long rows with the wide row (see the Examples of mpt_from_eqn()).

Value

A data frame with one row per participant and tree, participants first. Its columns are the participant column (id), the tree column named by the tree_id of the model (absent in a model without it), the carried columns, and one integer count column per response category of the model.

See Also

mpt_from_eqn() and mpt_from_string() for importing the model itself; mpt() for the data layout bmm() expects; bmm_data_check() to check the result before fitting.

Examples

eqn_file <- tempfile(fileext = ".eqn")
writeLines(c(
  "6",
  "target  hit   D_o",
  "target  hit   (1-D_o)*g",
  "target  miss  (1-D_o)*(1-g)",
  "lure    fa    (1-D_n)*g",
  "lure    cr    D_n",
  "lure    cr    (1-D_n)*(1-g)"
), eqn_file)
model <- mpt_from_eqn(
  eqn_file,
  restrictions = "D_n = D_o",
  categories = c(hit = "yes", fa = "yes", miss = "no", cr = "no"),
  tree_id = "item_type"
)

# TreeBUGS data: one named column per category label of the EQN file.
# Columns that are no counts, here a between-participant factor, are
# carried to the long data
wide <- data.frame(
  subject = c("s1", "s2"), group = c("a", "b"),
  hit = c(30, 25), miss = c(10, 15), fa = c(8, 12), cr = c(32, 28)
)
long <- mpt_data_reshape(wide, model, id = "subject")
long
bmm_data_check(bmf(Do ~ 1, g ~ 1), long, model)

# MPTinR data: unnamed columns, sorted by tree, then by label (cr, fa for
# tree lure, then hit, miss for tree target)
wide_mptinr <- rbind(c(32, 8, 30, 10), c(28, 12, 25, 15))
identical(
  mpt_data_reshape(wide_mptinr, model, columns = "mptinr"),
  mpt_data_reshape(wide[c("cr", "fa", "hit", "miss")], model)
)

# trees with response categories that only one tree can produce
model_pc <- mpt(
  list(
    mpt_tree("pairs", list(E1 = "c * r", E2 = "1 - c * r"),
             impossible = "F1"),
    mpt_tree("singles", list(F1 = "u", E2 = "1 - u"),
             impossible = "E1")
  ),
  tree_id = "item_type"
)
wide_pc <- data.frame(
  pairs.E1 = c(20, 22), pairs.E2 = c(20, 18),
  singles.F1 = c(25, 24), singles.E2 = c(15, 16)
)
mpt_data_reshape(wide_pc, model_pc)

bmm documentation built on Oct. 16, 2026, 9:08 a.m.