Score each person's own circumplex profile through the closed-form SSM
transform and return a per-person parameter table. When id is NULL,
every row of data is treated as one person's profile (like
ssm_score(), but returning a fresh table rather than appending columns).
When id names a column, rows sharing an id (e.g., occasions of intensive
longitudinal data) are first averaged within person -- each scale's mean
uses that person's available (non-missing) rows -- and the within-person
mean profile is scored.
ssm_parameters_id(data, scales, angles = octants(), id = NULL)A data frame of class "circumplex_ssm_id" with one row per
person, in order of first appearance: the id column (named after id,
or id when NULL), n_obs (rows contributing to that person),
na_rate (proportion of missing scale cells among those rows), and the
SSM parameters Elev, Xval, Yval, Ampl, Disp (degrees in
[0, 360], with the 0/360 pole reported as 360 per the package's
LM = 360 convention), and Fit. Use summary.circumplex_ssm_id() for
group-level summaries with circular statistics for displacement.
Required. A data frame or matrix containing at least
circumplex scales, with one row per person or (with id) per
person-occasion.
Required. The variable names or column numbers for the
variables in data that contain circumplex scales to be analyzed.
Optional. A numeric vector containing the angular
displacement of each circumplex scale included in scales, in degrees
(default = octants()). The closed-form SSM estimator used here equals
the ordinary-least-squares cosine fit for equally spaced angles --
more generally, for any angle set satisfying first- and second-harmonic
balance; see ssm_parameters().
Optional. A single variable name or column number identifying
persons. If NULL (default), each row is scored as its own person;
otherwise rows sharing an id are averaged within person before scoring.
Missing id values are an error (a person cannot be silently dropped).
Degenerate profiles keep their row and are reported as NA, never
silently dropped: a flat (zero-variance) profile has undefined
displacement and fit, a profile with real variance but zero
first-harmonic amplitude has undefined displacement and a fit of 0, and a
person with a completely missing scale has an undefined profile (all
parameters NA). The na_rate column exposes each person's share of
missing scale cells so missingness is visible alongside its consequences.
Other ssm functions:
plot.circumplex_ci_accuracy(),
ssm_analyze(),
ssm_analyze_long(),
ssm_ci_accuracy(),
ssm_draws(),
ssm_parameters(),
ssm_score(),
ssm_sem(),
ssm_sem_parameters(),
ssm_table(),
summary.circumplex_ssm_id()
Other analysis functions:
cpm_fit(),
cpm_simulate(),
ssm_analyze(),
ssm_analyze_long(),
ssm_ci_accuracy(),
ssm_draws(),
ssm_parameters(),
ssm_score(),
ssm_sem(),
ssm_sem_parameters(),
summary.circumplex_ssm_id()
data("aw2009")
ssm_parameters_id(
aw2009,
scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO")
)
Run the code above in your browser using DataLab