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, measures_labels = NULL )
| .data | Required. A data frame containing at least circumplex scales. |
|---|---|
| scales | Required. The variable names or column numbers for the
variables in |
| angles | Optional. A numeric vector containing the angular displacement
of each circumplex scale included in |
| measures | Optional. The variable names or column numbers for one or
more variables in |
| grouping | Optional. The variable name or column number for the variable
in |
| 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). |
| measures_labels | Optional. A character vector providing a label for
each measure provided in |
A list containing the results and description of the analysis.
A tibble with the SSM parameter estimates
A list with the number of bootstrap resamples (boots), the confidence interval percentage level (interval), and the angular displacement of scales (angles)
A language object containing the function call that created this object
A tibble containing the mean scale scores
A string indicating what type of SSM analysis was done
Other ssm functions:
ssm_append(),
ssm_parameters(),
ssm_plot(),
ssm_score(),
ssm_table()
Other analysis functions:
ssm_parameters(),
ssm_score()
# 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 #>ssm_analyze(jz2017, scales = PA:NO, angles = octants(), measures = c(NARPD, ASPD), measures_labels = c("Narcissistic", "Antisocial") )#> Call: #> ssm_analyze(.data = jz2017, scales = PA:NO, angles = octants(), #> measures = c(NARPD, ASPD), measures_labels = c("Narcissistic", #> "Antisocial")) #> #> Profile [Narcissistic]: #> Estimate Lower CI Upper CI #> Elevation 0.202 0.169 0.236 #> X-Value -0.062 -0.094 -0.028 #> Y-Value 0.179 0.146 0.214 #> Amplitude 0.189 0.156 0.226 #> Displacement 108.967 99.182 118.263 #> Model Fit 0.957 #> #> Profile [Antisocial]: #> Estimate Lower CI Upper CI #> Elevation 0.124 0.090 0.159 #> X-Value -0.099 -0.133 -0.066 #> Y-Value 0.203 0.168 0.237 #> Amplitude 0.226 0.191 0.263 #> Displacement 115.927 107.805 124.314 #> Model Fit 0.964 #># 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 0.127 0.215 #> X-Value -0.080 -0.127 -0.034 #> Y-Value 0.202 0.152 0.250 #> Amplitude 0.217 0.167 0.269 #> Displacement 111.669 99.967 123.191 #> Model Fit 0.972 #> #> Profile [Male_NARPD]: #> Estimate Lower CI Upper CI #> Elevation 0.244 0.192 0.295 #> X-Value -0.029 -0.072 0.014 #> Y-Value 0.146 0.098 0.191 #> Amplitude 0.149 0.105 0.195 #> Displacement 101.248 84.228 119.052 #> 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 0.004 0.141 #> X-Value 0.051 -0.017 0.116 #> Y-Value -0.056 -0.119 0.010 #> Amplitude -0.068 -0.133 0.002 #> Displacement -10.421 -32.060 12.016 #> Model Fit -0.071 #># }