Calculate the SSM parameters for each row of a data frame and add the results as additional columns. This can be useful when the SSM is being used for the description or visualization of individual data points rather than for statistical inference on groups of data points.
ssm_score(data, scales, angles = octants(), append = TRUE, ...)A data frame containing .data plus six additional columns
containing the SSM parameters (calculated rowwise).
Required. A data frame or matrix containing at least circumplex scales.
Required. The variable names or column numbers for the
variables in .data that contain circumplex scales to be analyzed.
Required. A numeric vector containing the angular displacement
of each circumplex scale included in scales (in degrees). The
closed-form SSM estimator used here equals the ordinary-least-squares
cosine fit for equally spaced angles (e.g., octants at 45-degree
intervals) -- more generally, for any angle set satisfying first- and
second-harmonic balance. For angle sets violating that balance (generic
unequally spaced sets), it is the conventional Gurtman estimator, not a
least-squares fit, and the
reported fit is then no longer a bounded R-squared in [0, 1] (it can
fall below 0).
Optional. A logical indicating whether to append the output to
data or simply return the output (default = "TRUE").
Optional. Additional named arguments passed to
ssm_parameters(), such as prefix and suffix;
each must be a single string. Unnamed or non-scalar arguments raise an
error.
Other ssm functions:
plot.circumplex_ci_accuracy(),
ssm_analyze(),
ssm_analyze_long(),
ssm_ci_accuracy(),
ssm_draws(),
ssm_parameters(),
ssm_parameters_id(),
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_parameters_id(),
ssm_sem(),
ssm_sem_parameters(),
summary.circumplex_ssm_id()
data("aw2009")
ssm_score(
aw2009,
scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO")
)
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