Formats awareness and influence task data into a standardized structure for
hsdt(). Use usdt_data_tasks() when tasks are stored in separate data
frames, or usdt_data_long() when both tasks are kept in a single data frame
with a column that identifies each task.
Usage
usdt_data_tasks(
awareness,
influence,
subject_col,
condition_col = NULL,
condition_levels = NULL,
response_col = NULL,
response_levels = NULL,
successes_col = NULL,
trials_col = NULL,
successes_type = c("auto", "counts", "proportions"),
sdt_cols = NULL,
dichotomize = c("none", "influence", "awareness", "both"),
ties = c("noise", "random"),
coding = c("deviation", "treatment"),
labels = c(awareness = "Awareness", influence = "Influence")
)
usdt_data_long(
data,
task_col,
task_levels,
subject_col,
condition_col = NULL,
condition_levels = NULL,
response_col = NULL,
response_levels = NULL,
successes_col = NULL,
trials_col = NULL,
successes_type = c("auto", "counts", "proportions"),
sdt_cols = NULL,
dichotomize = c("none", "influence", "awareness", "both"),
ties = c("noise", "random"),
coding = c("deviation", "treatment"),
labels = NULL
)
# S3 method for class 'usdt_data'
print(x, ...)Arguments
- awareness, influence
Data frames for each task (used in
usdt_data_tasks()).- subject_col
Name of the column that identifies participants.
- condition_col
Name of the column for signal and noise conditions. Not needed when using
sdt_cols.- condition_levels
Named vector mapping condition labels, like
c(signal = "old", noise = "new"). Required: which label is the signal and the noise.- response_col
Name of the column with responses. Can be binary choices or continuous values (like response times) to split at the median.
- response_levels
Named vector mapping responses, like
c(signal = 1, noise = 0). For a task named indichotomizeit names the side of the median instead, asc(signal = "faster", noise = "slower")or the reverse. Required in both cases.- successes_col, trials_col
Names of columns with pre-calculated counts or proportions of signal responses and total trials. Use these instead of
response_col.- successes_type
Format of
successes_col:"counts","proportions", or"auto".- sdt_cols
Named vector for SDT table columns, like
c(hit = "H", miss = "M", fa = "FA", cr = "CR").- dichotomize
Which tasks to split at the median using
meyen_split(). Use"none","awareness","influence","both", or a list likelist(awareness = FALSE, influence = TRUE).- ties
How to handle trials that fall exactly on the median. See
meyen_split().- coding
How condition is coded in the model:
"deviation"(-0.5, 0.5) or"treatment"(0, 1). See Details.- labels
Optional names for the tasks in printed output.
- data
A single data frame with both tasks (used in
usdt_data_long()).- task_col
Name of the column that identifies the task in
data.- task_levels
Named vector mapping task labels, like
c(awareness = "A", influence = "I").- x
A
usdt_dataobject.- ...
Ignored.
Value
An object of class usdt_data. The $agg table contains the counts
used by hsdt(), and $meta contains setup details and summaries.
Details
The functions count responses for each subject and condition, check that the same subjects appear in both tasks, and print a summary table so you can verify the column settings before fitting the model.
Settings per task
Arguments for columns and levels take either a single value (used for both tasks) or a list with separate settings for each task:
condition_col = list(awareness = "cond", influence = "cue")
condition_levels = list(awareness = c(signal = "old", noise = "new"),
influence = c(signal = "congruent",
noise = "incongruent"))
dichotomize = list(awareness = FALSE, influence = TRUE)This works for all column and level arguments, so you can combine trial-level data in one task with summary tables in the other.
Condition coding
Under "deviation" coding (-0.5 vs. +0.5), the intercept is \(-c\), the
criterion measured from the point between the two distributions. Under
"treatment" coding (0 vs. 1), the intercept is \(z(\mathrm{FAR})\), the
criterion measured from the noise distribution.
With balanced signal/noise trials and an exact half split, the classical
criterion is zero under deviation coding. Ties, odd counts or imbalance can
change this. hsdt() fixes a dichotomized task's criterion only when its
mean absolute descriptive subject criterion is below 0.02.
Examples
# 1. Tasks in separate data frames
# Awareness task: binary choices (old/new)
# Influence task: response times (split at the median)
d_separate <- usdt_data_tasks(
awareness = vadillo_awareness,
influence = vadillo_cuing,
subject_col = "subj",
condition_col = "condition",
condition_levels = c(signal = "old", noise = "new"),
response_col = list(awareness = "judged.old", influence = "rt"),
response_levels = list(awareness = c(signal = 1, noise = 0),
influence = c(signal = "faster",
noise = "slower")),
dichotomize = list(awareness = FALSE, influence = TRUE)
)
d_separate
#> ── Data summary ────────────────────────────────────────────────────────────────
#>
#> Input: 2 data frames (usdt_data_tasks)
#> Subjects: 104 (104 in both tasks, 0 in one only)
#> Trials: 46,592 -> 416 aggregated rows (4 per subject)
#> Coding: deviation (condition coded -0.5 / +0.5; intercept estimates -c)
#> Parameters: 7 (3 fixed effects, 4 (co)variance components)
#>
#> ── Variable mapping ────────────────────────────────────────────────────────────
#>
#> Variable Task Column Signal Noise
#> subject Awareness subj - -
#> Influence subj - -
#> condition Awareness condition old new
#> Influence condition old new
#> response Awareness judged.old 1 0
#> Influence rt faster slower [Meyen split]
#>
#> ── Descriptives: median [min, max] across subjects ─────────────────────────────
#>
#> Task Trials/cell HR FAR d' (method-of-moments)
#> Awareness 32 .56 [ .28, .88] .47 [ .12, .78] 0.24 [-0.58, 1.38]
#> Influence 192 .53 [ .44, .60] .47 [ .40, .56] 0.13 [-0.31, 0.53]
#>
#> No cells at floor or ceiling.
# 2. Tasks combined in a single long data frame
long <- rbind(
data.frame(task = "A",
subj = vadillo_awareness$subj,
condition = vadillo_awareness$condition,
response = vadillo_awareness$judged.old),
data.frame(task = "I",
subj = vadillo_cuing$subj,
condition = vadillo_cuing$condition,
response = vadillo_cuing$rt)
)
d_long <- usdt_data_long(
long,
task_col = "task",
task_levels = c(awareness = "A", influence = "I"),
subject_col = "subj",
condition_col = "condition",
condition_levels = c(signal = "old", noise = "new"),
response_col = "response",
response_levels = list(awareness = c(signal = 1, noise = 0),
influence = c(signal = "faster",
noise = "slower")),
dichotomize = list(awareness = FALSE, influence = TRUE)
)
d_long
#> ── Data summary ────────────────────────────────────────────────────────────────
#>
#> Input: 1 long data frame (usdt_data_long)
#> Subjects: 104 (104 in both tasks, 0 in one only)
#> Trials: 46,592 -> 416 aggregated rows (4 per subject)
#> Coding: deviation (condition coded -0.5 / +0.5; intercept estimates -c)
#> Parameters: 7 (3 fixed effects, 4 (co)variance components)
#>
#> ── Variable mapping ────────────────────────────────────────────────────────────
#>
#> Variable Task Column Signal Noise
#> subject Awareness subj - -
#> Influence subj - -
#> condition Awareness condition old new
#> Influence condition old new
#> response Awareness response 1 0
#> Influence response faster slower [Meyen split]
#>
#> ── Descriptives: median [min, max] across subjects ─────────────────────────────
#>
#> Task Trials/cell HR FAR d' (method-of-moments)
#> Awareness 32 .56 [ .28, .88] .47 [ .12, .78] 0.24 [-0.58, 1.38]
#> Influence 192 .53 [ .44, .60] .47 [ .40, .56] 0.13 [-0.31, 0.53]
#>
#> No cells at floor or ceiling.