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.
Usage
ssm_parameters_id(data, scales, angles = octants(), id = NULL)Arguments
- data
Required. A data frame or matrix containing at least circumplex scales, with one row per person or (with
id) per person-occasion.- scales
Required. The variable names or column numbers for the variables in
datathat contain circumplex scales to be analyzed.- angles
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 spacedangles– more generally, for any angle set satisfying first- and second-harmonic balance; seessm_parameters().- id
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).
Value
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.
Details
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.
See also
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()
Examples
data("aw2009")
ssm_parameters_id(
aw2009,
scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO")
)
#> id n_obs na_rate Elev Xval Yval Ampl Disp Fit
#> 1 1 1 0 0.4325 1.2514177 -1.3091258 1.8110374 313.70892 0.9706769
#> 2 2 1 0 0.2325 1.4193555 0.5073528 1.5073079 19.66958 0.9172710
#> 3 3 1 0 -0.0700 0.7822361 0.4476346 0.9012602 29.78035 0.7137698
#> 4 4 1 0 0.6250 0.8313567 -0.5560749 1.0001866 326.22237 0.8784837
#> 5 5 1 0 0.8950 0.4382412 -0.4121320 0.6015880 316.75861 0.9674101