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

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.

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.

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()).

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"
)
#> Names that differ from the EQN file (stored in attr(model, 'mpt_renaming')):
#>   parameter  D_o -> Do
#>   parameter  D_n -> Dn
#>   categories in tree target: hit -> yes, miss -> no
#>   categories in tree lure: fa -> yes, cr -> no

# 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
#>   subject item_type group yes no
#> 1      s1    target     a  30 10
#> 2      s1      lure     a   8 32
#> 3      s2    target     b  25 15
#> 4      s2      lure     b  12 28
bmm_data_check(bmf(Do ~ 1, g ~ 1), long, model)
#> Pre-fit data check
#> Model: mpt_from_eqn(file = eqn_file, restrictions = "D_n = D_o", categories = c(hit = "yes", fa = "yes", miss = "no", cr = "no"), tree_id = "item_type") 
#> Data:  4 observations ('long') 
#> 
#> Response variables:
#>   yes  numeric with 4 unique values: 8, 12, 25, 30 (expected: counts per
#>        response category)
#>   no   numeric with 4 unique values: 10, 15, 28, 32 (expected: counts per
#>        response category)
#> 
#> Findings: No issues detected.
#> 
#> Hard checks: passed

# 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)
)
#> [1] TRUE

# 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)
#>   id item_type E1 E2 F1
#> 1  1     pairs 20 20  0
#> 2  1   singles  0 15 25
#> 3  2     pairs 22 18  0
#> 4  2   singles  0 16 24

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

Usage

Arguments

Value

Details

See also

Examples