circumplex (version 1.0.1)

ssm_analyze: Perform analyses using the Structural Summary Method

Description

Calculate SSM parameters with bootstrapped confidence intervals for a variety of different analysis types. Depending on what arguments are supplied, either mean-based or correlation-based analyses will be performed, one or more groups will be used to stratify the data, and contrasts between groups or measures will be calculated.

Usage

ssm_analyze(
  data,
  scales,
  angles = octants(),
  measures = NULL,
  grouping = NULL,
  contrast = FALSE,
  boots = 2000,
  interval = 0.95,
  listwise = TRUE,
  measures_labels = NULL
)

Value

A list containing the results and description of the analysis.

results

A data frame with the SSM parameter estimates

details

A list with the number of bootstrap resamples (boots), the confidence interval percentage level (interval), and the angular displacement of scales (angles)

call

A language object containing the function call that created this object

scores

A data frame containing the mean scale scores

type

A string indicating what type of SSM analysis was done

Arguments

data

Required. A data frame containing at least circumplex scales.

scales

Required. A character vector of column names, or a numeric vector of column indexes, from data that contains the circumplex scale scores to be analyzed.

angles

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

measures

Optional. Either NULL or a character vector of column names from data that contains one or more variables to be correlated with the circumplex scales and analyzed using correlation-based SSM analyses.

grouping

Optional. Either NULL or a string that contains the column name from data of the variable that indicates the group membership of each observation.

contrast

Optional. A logical indicating whether to output the difference between two measures' or two groups' SSM parameters. Can only be set to TRUE when there are exactly two measures and one group, one measure and two groups, or no measures and two groups (default = FALSE).

boots

Optional. A single positive whole number indicating how many bootstrap resamples to use when estimating the confidence intervals (default = 2000).

interval

Optional. A single positive number between 0 and 1 (exclusive) that indicates what confidence level to use when estimating the confidence intervals (default = 0.95).

listwise

Optional. A logical indicating whether missing values should be handled by listwise deletion (TRUE) or pairwise deletion (FALSE). Note that pairwise deletion may result in different missing data patterns in each bootstrap resample and is slower to compute (default = TRUE).

measures_labels

Optional. Either NULL or a character vector providing a label for each measure provided in measures (in the same order) to appear in the results as well as tables and plots derived from the results.

See Also

Other ssm functions: ssm_parameters(), ssm_score(), ssm_table()

Other analysis functions: ssm_parameters(), ssm_score()

Examples

# Load example data
data("jz2017")

# Single-group mean-based SSM
ssm_analyze(
  jz2017,
  scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO")
)

# Single-group correlation-based SSM
ssm_analyze(
  jz2017,
  scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"),
  measures = c("NARPD", "ASPD")
)
# \donttest{
# Multiple-group mean-based SSM
ssm_analyze(
  jz2017,
  scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"),
  grouping = "Gender"
)

# Multiple-group mean-based SSM with contrast
ssm_analyze(
  jz2017,
  scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"),
  grouping = "Gender",
  contrast = TRUE
)

# Single-group correlation-based SSM with contrast
ssm_analyze(
  jz2017,
  scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"),
  measures = c("NARPD", "ASPD"),
  contrast = TRUE
)

# Multiple-group correlation-based SSM
ssm_analyze(
  jz2017,
  scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"),
  measures = "NARPD",
  grouping = "Gender"
)

# Multiple-group correlation-based SSM with contrast
ssm_analyze(
  jz2017,
  scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"),
  measures = "NARPD",
  grouping = "Gender",
  contrast = TRUE
)
# }