mtscr_model {mtscr}

Create MTS model

Description

Create MTS model for creativity analysis.

Usage

mtscr_model(
  df,
  id_column,
  item_column = NULL,
  score_column,
  top = 1,
  prepared = FALSE,
  ties_method = c("random", "average"),
  normalise = TRUE,
  self_ranking = NULL
)

Arguments

df

Data frame in long format.

id_column

Name of the column containing participants' id.

item_column

Optional, name of the column containing distinct trials (e.g. names of items in AUT).

score_column

Name of the column containing divergent thinking scores (e.g. semantic distance).

top

Integer or vector of integers (see examples), number of top answers to include in the model. Default is 1, i.e. only the top answer.

prepared

Logical, is the data already prepared with mtscr_prepare()?

ties_method

Character string specifying how ties are treated when ordering. Can be "average" (better for continuous scores like semantic distance) or "random" (default, better for ratings). See rank() for details.

normalise

Logical, should the creativity score be normalised? Default is TRUE and it's recommended to leave it as such.

self_ranking

Name of the column containing answers' self-ranking. Provide if model should be based on top answers self-chosen by the participant. Every item should have its own ranks. The top answers should have a value of 1, and the other answers should have a value of 0. In that case, the top argument doesn't change anything and should be left as top = 1. ties_method is not used if self_ranking was provided. See mtscr_self_rank for example.

Value

The return value depends on length of the top argument. If top is a single integer, a glmmTMB model is returned. If top is a vector of integers, a list of glmmTMB models is returned, with names corresponding to the top values, e.g. top1, top2, etc.

Examples

data("mtscr_creativity", package = "mtscr")

mtscr_creativity <- mtscr_creativity |>
  dplyr::slice_sample(n = 300) # for performance, ignore

mtscr_model(mtscr_creativity, id, item, SemDis_MEAN) |>
  summary()

# three models for top 1, 2, and 3 answers
mtscr_model(mtscr_creativity, id, item, SemDis_MEAN, top = 1:3) |>
  mtscr_model_summary()

# you can prepare data first
data <- mtscr_prepare(mtscr_creativity, id, item, SemDis_MEAN)
mtscr_model(data, id, item, SemDis_MEAN, prepared = TRUE)

# extract effects for creativity score by hand
model <- mtscr_model(mtscr_creativity, id, item, SemDis_MEAN, top = 1)
creativity_score <- glmmTMB::ranef(model)$cond$id[, 1]