mtscr {mtscr} | R Documentation |
Create MTS model
Description
Create MTS model for creativity analysis. Use with summary.mtscr()
and predict.mtscr()
.
Usage
mtscr(
df,
id_column,
score_column,
item_column = NULL,
top = 1,
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. |
score_column |
Name of the column containing divergent thinking scores (e.g. semantic distance). |
item_column |
Optional, name of the column containing distinct trials (e.g. names of items in AUT). |
top |
Integer or vector of integers (see examples), number of top answers to prepare indicators for. Default is 1, i.e. only the top answer. |
ties_method |
Character string specifying how ties are treated when
ordering. Can be |
normalise |
Logical, should the creativity score be normalised? Default is |
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 |
Value
The return value depends on length of the top
argument. If top
is a single
integer, a mtscr
model is returned. If top
is a vector of integers, a mtscr_list
object
is returned, with names corresponding to the top
values, e.g. top1
, top2
, etc.
See Also
-
summary.mtscr()
for the fit measures of the model. -
predict.mtscr()
for getting the scores.
Examples
data("mtscr_creativity", package = "mtscr")
mtscr_creativity <- mtscr_creativity |>
dplyr::slice_sample(n = 500) # for performance, ignore
# single model for top 1 answer
mtscr(mtscr_creativity, id, SemDis_MEAN, item) |>
summary()
# three models for top 1, 2, and 3 answers
fit3 <- mtscr(
mtscr_creativity,
id,
SemDis_MEAN,
item,
top = 1:3,
ties_method = "average"
)
# add the scores to the database
predict(fit3)
# get the socres only
predict(fit3, minimal = TRUE)