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.

ssm_analyze(
  .data,
  scales,
  angles = octants(),
  measures = NULL,
  grouping = NULL,
  contrast = c("none", "test", "model"),
  boots = 2000,
  interval = 0.95,
  listwise = TRUE
)

Arguments

.data

Required. A data frame containing at least circumplex scales.

scales

Required. The variable names or column numbers for the variables in .data that 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()).

measures

Optional. The variable names or column numbers for one or more variables in .data to be correlated with the circumplex scales and analyzed using correlation-based SSM analyses. To analyze the circumplex scales using mean-based analyses, simply omit this argument or set it to NULL (default = NULL).

grouping

Optional. The variable name or column number for the variable in .data that indicates the group membership of each observation. To analyze all observations in a single group, simply omit this argument or set it to NULL (default = NULL).

contrast

Optional. A string indicating what type of contrast to run. Current options are "none" for no contrast, "model" to find SSM parameters for the difference scores, or "test" to find the difference between the SSM parameters. Note that only two groups or measures can be contrasted at a time (default = "none").

boots

Optional. A single positive integer 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).

Value

A list containing the results and description of the analysis.

results

A tibble 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 tibble containing the mean scale scores

type

A string indicating what type of SSM analysis was done

See also

Other ssm functions: ssm_append(), ssm_parameters(), ssm_plot(), 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 = PA:NO, angles = octants())
#> Call: #> ssm_analyze(.data = jz2017, scales = PA:NO, angles = octants()) #> #> Profile [All]: #> Estimate Lower CI Upper CI #> Elevation 0.917 0.888 0.946 #> X-Value 0.351 0.324 0.378 #> Y-Value -0.252 -0.282 -0.222 #> Amplitude 0.432 0.402 0.462 #> Displacement 324.292 320.907 327.921 #> Model Fit 0.878 #>
# Single-group correlation-based SSM ssm_analyze(jz2017, scales = PA:NO, angles = octants(), measures = c(NARPD, ASPD) )
#> Call: #> ssm_analyze(.data = jz2017, scales = PA:NO, angles = octants(), #> measures = c(NARPD, ASPD)) #> #> Profile [NARPD]: #> Estimate Lower CI Upper CI #> Elevation 0.202 0.169 0.236 #> X-Value -0.062 -0.094 -0.030 #> Y-Value 0.179 0.145 0.214 #> Amplitude 0.189 0.153 0.226 #> Displacement 108.967 99.334 118.620 #> Model Fit 0.957 #> #> Profile [ASPD]: #> Estimate Lower CI Upper CI #> Elevation 0.124 0.089 0.159 #> X-Value -0.099 -0.133 -0.063 #> Y-Value 0.203 0.167 0.237 #> Amplitude 0.226 0.190 0.263 #> Displacement 115.927 107.451 124.435 #> Model Fit 0.964 #>
# \donttest{ # Multiple-group mean-based SSM ssm_analyze(jz2017, scales = PA:NO, angles = octants(), grouping = Gender)
#> Call: #> ssm_analyze(.data = jz2017, scales = PA:NO, angles = octants(), #> grouping = Gender) #> #> Profile [Female]: #> Estimate Lower CI Upper CI #> Elevation 0.946 0.907 0.983 #> X-Value 0.459 0.420 0.499 #> Y-Value -0.310 -0.355 -0.268 #> Amplitude 0.554 0.509 0.599 #> Displacement 325.963 322.240 329.833 #> Model Fit 0.889 #> #> Profile [Male]: #> Estimate Lower CI Upper CI #> Elevation 0.884 0.842 0.925 #> X-Value 0.227 0.192 0.262 #> Y-Value -0.186 -0.225 -0.148 #> Amplitude 0.294 0.258 0.332 #> Displacement 320.685 313.267 327.988 #> Model Fit 0.824 #>
# Multiple-group mean-based SSM with contrast ssm_analyze(jz2017, scales = PA:NO, angles = octants(), grouping = Gender, contrast = "model" )
#> Call: #> ssm_analyze(.data = jz2017, scales = PA:NO, angles = octants(), #> grouping = Gender, contrast = "model") #> #> Contrast [Male - Female]: #> Estimate Lower CI Upper CI #> Elevation -0.062 -0.118 -0.004 #> X-Value -0.232 -0.286 -0.181 #> Y-Value 0.124 0.069 0.179 #> Amplitude 0.263 0.208 0.321 #> Displacement 151.858 140.348 163.727 #> Model Fit 0.855 #>
# Single-group correlation-based SSM with contrast ssm_analyze(jz2017, scales = PA:NO, angles = octants(), measures = c(NARPD, ASPD), contrast = "test" )
#> Call: #> ssm_analyze(.data = jz2017, scales = PA:NO, angles = octants(), #> measures = c(NARPD, ASPD), contrast = "test") #> #> Contrast [ASPD - NARPD]: #> Estimate Lower CI Upper CI #> Elevation -0.079 -0.116 -0.041 #> X-Value -0.037 -0.077 0.000 #> Y-Value 0.024 -0.014 0.060 #> Amplitude 0.037 -0.001 0.075 #> Displacement 6.960 -3.385 18.057 #> Model Fit 0.007 #>
# Multiple-group correlation-based SSM ssm_analyze(jz2017, scales = PA:NO, angles = octants(), measures = NARPD, grouping = Gender )
#> Call: #> ssm_analyze(.data = jz2017, scales = PA:NO, angles = octants(), #> measures = NARPD, grouping = Gender) #> #> Profile [Female_NARPD]: #> Estimate Lower CI Upper CI #> Elevation 0.172 0.128 0.218 #> X-Value -0.080 -0.125 -0.033 #> Y-Value 0.202 0.153 0.250 #> Amplitude 0.217 0.168 0.268 #> Displacement 111.669 99.626 122.752 #> Model Fit 0.972 #> #> Profile [Male_NARPD]: #> Estimate Lower CI Upper CI #> Elevation 0.244 0.194 0.295 #> X-Value -0.029 -0.074 0.017 #> Y-Value 0.146 0.097 0.192 #> Amplitude 0.149 0.102 0.195 #> Displacement 101.248 83.457 119.191 #> Model Fit 0.902 #>
# Multiple-group correlation-based SSM with contrast ssm_analyze(jz2017, scales = PA:NO, angles = octants(), measures = NARPD, grouping = Gender, contrast = "test" )
#> Call: #> ssm_analyze(.data = jz2017, scales = PA:NO, angles = octants(), #> measures = NARPD, grouping = Gender, contrast = "test") #> #> Contrast [NARPD: Male - Female]: #> Estimate Lower CI Upper CI #> Elevation 0.072 0.005 0.139 #> X-Value 0.051 -0.012 0.113 #> Y-Value -0.056 -0.124 0.010 #> Amplitude -0.068 -0.138 -0.004 #> Displacement -10.421 -30.538 10.798 #> Model Fit -0.071 #>
# }