Get a list of possible parameters — get

When specifying prior distributions, it is often useful to see which parameters are included in a given model. Using the Q-matrix and type of diagnostic model to estimated, we can create a list of all included parameters for which a prior can be specified.

Usage

get_parameters(
  qmatrix,
  item_id = NULL,
  rename_att = FALSE,
  rename_item = FALSE,
  type = c("lcdm", "dina", "dino", "crum"),
  attribute_structure = c("unconstrained", "independent")
)

Arguments

qmatrix: The Q-matrix. A data frame with 1 row per item and 1 column per attribute. All cells should be either 0 (item does not measure the attribute) or 1 (item does measure the attribute).
item_id: Optional. Variable name of a column in qmatrix that contains item identifiers. NULL (the default) indicates that no identifiers are present in the Q-matrix.
rename_att: Should attribute names from the qmatrix be replaced with generic, but consistent names (e.g., "att1", "att2", "att3").
rename_item: Should item names from the qmatrix be replaced with generic, but consistent names (e.g., 1, 2, 3).
type: Type of DCM to estimate. Must be one of lcdm, dina, dino, or crum.
attribute_structure: Structural model specification. Must be one of unconstrained, or independent. unconstrained makes no assumptions about the relationships between attributes, whereas independent assumes that proficiency statuses on attributes are independent of each other.

Value

A tibble with one row per parameter.

Examples

get_parameters(ecpe_qmatrix, item_id = "item_id", type = "lcdm")
#> # A tibble: 75 × 4
#>    item_id class       attributes                coef  
#>    <fct>   <chr>       <chr>                     <glue>
#>  1 E1      intercept   NA                        l1_0  
#>  2 E1      maineffect  morphosyntactic           l1_11 
#>  3 E1      maineffect  cohesive                  l1_12 
#>  4 E1      interaction morphosyntactic__cohesive l1_212
#>  5 E2      intercept   NA                        l2_0  
#>  6 E2      maineffect  cohesive                  l2_12 
#>  7 E3      intercept   NA                        l3_0  
#>  8 E3      maineffect  morphosyntactic           l3_11 
#>  9 E3      maineffect  lexical                   l3_13 
#> 10 E3      interaction morphosyntactic__lexical  l3_213
#> # ℹ 65 more rows

get_parameters(ecpe_qmatrix, item_id = "item_id", type = "lcdm",
               rename_att = TRUE)
#> # A tibble: 75 × 4
#>    item_id class       attributes coef  
#>    <fct>   <chr>       <chr>      <glue>
#>  1 E1      intercept   NA         l1_0  
#>  2 E1      maineffect  att1       l1_11 
#>  3 E1      maineffect  att2       l1_12 
#>  4 E1      interaction att1__att2 l1_212
#>  5 E2      intercept   NA         l2_0  
#>  6 E2      maineffect  att2       l2_12 
#>  7 E3      intercept   NA         l3_0  
#>  8 E3      maineffect  att1       l3_11 
#>  9 E3      maineffect  att3       l3_13 
#> 10 E3      interaction att1__att3 l3_213
#> # ℹ 65 more rows