Perform SSM analyses on long-format repeated-measures data

A convenience wrapper around the occasions interface of ssm_analyze() for data stored in long format (one row per person per occasion). It reshapes the data into the wide, one-row-per-person layout that ssm_analyze() consumes and then delegates to it; all estimation is performed by ssm_analyze() unchanged. See the occasions argument of ssm_analyze() for the analysis semantics – per-occasion profiles, paired within-person contrasts, and the listwise-only handling of missing waves (a person missing any occasion is dropped from all occasions).

Usage

ssm_analyze_long(
  data,
  scales,
  angles = octants(),
  id,
  occasion,
  grouping = NULL,
  contrast = FALSE,
  boots = 2000,
  interval = 0.95,
  parallel = "no",
  ncpus = 1,
  method = "bootstrap"
)

Arguments

data

Required. A data frame (or matrix) in long format containing an identifier column, an occasion column, and the circumplex scale scores (one set of score columns, repeated across occasions in different rows).

scales

Required. A character vector of column names, or a numeric vector of column indexes, giving the circumplex scale scores in data (the same scales measured at every occasion).

angles

Optional. A numeric vector containing the angular displacement of each circumplex scale included in scales (in degrees) (default = octants()).

id

Required. A single column name or index identifying the person that each row belongs to.

occasion

Required. A single column name or index identifying the occasion (wave) that each row belongs to. Occasion order – which governs the second-minus-first direction of a paired contrast – is taken from the factor levels of this column when it is a factor, and otherwise from the order in which the occasions first appear in data. It is never sorted alphabetically, so a T10/T2 pair keeps its supplied order.

grouping

Optional. A single column name or index giving a time-invariant grouping variable (one value per person; an error is raised if a person's grouping value varies across occasions).

contrast

Optional. A logical value; if TRUE (and the data contain exactly two occasions in a single group), the paired within-person contrast (second occasion minus first) is calculated (default = FALSE).

boots, interval, parallel, ncpus, method

Optional. Passed through to ssm_analyze(); see its documentation.

Value

A list containing the results and description of the analysis, as returned by ssm_analyze() (with an Occasion column). See ssm_analyze().

See also

ssm_analyze() for the wide-format interface and the analysis semantics this wrapper delegates to.

Other ssm functions: plot.circumplex_ci_accuracy(), ssm_analyze(), ssm_ci_accuracy(), ssm_draws(), ssm_parameters(), ssm_parameters_id(), ssm_score(), ssm_sem(), ssm_sem_parameters(), ssm_table(), summary.circumplex_ssm_id()

Other analysis functions: cpm_fit(), cpm_simulate(), ssm_analyze(), ssm_ci_accuracy(), ssm_draws(), ssm_parameters(), ssm_parameters_id(), ssm_score(), ssm_sem(), ssm_sem_parameters(), summary.circumplex_ssm_id()

Examples

# Build a small two-occasion dataset in long format (one row per person per
# occasion). In practice `data` already stores the repeated occasions this
# way; here we stack two copies of jz2017 as an illustration.
data("jz2017")
scales <- c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO")
t1 <- jz2017[, scales]
t1$id <- seq_len(nrow(t1))
t1$occasion <- "T1"
t2 <- t1
t2$occasion <- "T2"
long <- rbind(t1, t2)

# \donttest{
# Per-occasion SSM profiles from long-format data
ssm_analyze_long(long, scales = scales, id = "id", occasion = "occasion")
#> 
#> # Profile [T1]:
#> 
#>                Estimate   Lower CI   Upper CI
#> Elevation         0.917      0.891      0.944
#> X-Value           0.351      0.325      0.377
#> Y-Value          -0.252     -0.281     -0.223
#> Amplitude         0.432      0.404      0.462
#> Displacement    324.292    320.894    327.733
#> Model Fit         0.878                      
#> 
#> 
#> # Profile [T2]:
#> 
#>                Estimate   Lower CI   Upper CI
#> Elevation         0.917      0.891      0.944
#> X-Value           0.351      0.325      0.377
#> Y-Value          -0.252     -0.281     -0.223
#> Amplitude         0.432      0.404      0.462
#> Displacement    324.292    320.894    327.733
#> Model Fit         0.878                      
#> 
# }