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
)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
datathat 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
NULLor a character vector of column names fromdatathat contains one or more variables to be correlated with the circumplex scales and analyzed using correlation-based SSM analyses.- grouping
Optional. Either
NULLor a string that contains the column name fromdataof 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
NULLor a character vector providing a label for each measure provided inmeasures(in the same order) to appear in the results as well as tables and plots derived from the results.
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
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")
)
#>
#> # 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 = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"),
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 = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"),
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 = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"),
grouping = "Gender",
contrast = TRUE
)
#>
#> # Profile [Female]:
#>
#> Estimate Lower CI Upper CI
#> Elevation 0.946 0.908 0.986
#> X-Value 0.459 0.420 0.496
#> Y-Value -0.310 -0.355 -0.267
#> Amplitude 0.554 0.512 0.598
#> Displacement 325.963 321.951 329.924
#> Model Fit 0.889
#>
#>
#> # Profile [Male]:
#>
#> Estimate Lower CI Upper CI
#> Elevation 0.884 0.842 0.929
#> X-Value 0.227 0.192 0.263
#> Y-Value -0.186 -0.224 -0.148
#> Amplitude 0.294 0.258 0.333
#> Displacement 320.685 313.849 328.047
#> Model Fit 0.824
#>
#>
#> # Contrast [Male - Female]:
#>
#> Estimate Lower CI Upper CI
#> Δ Elevation -0.062 -0.118 -0.004
#> Δ X-Value -0.232 -0.286 -0.181
#> Δ Y-Value 0.124 0.069 0.179
#> Δ Amplitude -0.261 -0.317 -0.204
#> Δ Displacement -5.278 -13.507 2.770
#> Δ Model Fit -0.066
#>
# 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
)
#>
#> # Profile [NARPD]:
#>
#> Estimate Lower CI Upper CI
#> Elevation 0.202 0.168 0.236
#> X-Value -0.062 -0.095 -0.029
#> Y-Value 0.179 0.146 0.212
#> Amplitude 0.189 0.154 0.224
#> Displacement 108.967 99.381 118.797
#> Model Fit 0.957
#>
#>
#> # Profile [ASPD]:
#>
#> Estimate Lower CI Upper CI
#> Elevation 0.124 0.089 0.158
#> X-Value -0.099 -0.134 -0.066
#> Y-Value 0.203 0.168 0.236
#> Amplitude 0.226 0.189 0.262
#> Displacement 115.927 107.782 124.564
#> Model Fit 0.964
#>
#>
#> # Contrast [ASPD - NARPD]:
#>
#> Estimate Lower CI Upper CI
#> Δ Elevation -0.079 -0.116 -0.041
#> Δ X-Value -0.037 -0.077 0.000
#> Δ Y-Value 0.024 -0.014 0.060
#> Δ Amplitude 0.037 -0.001 0.075
#> Δ Displacement 6.960 -3.385 18.057
#> Δ Model Fit 0.007
#>
# Multiple-group correlation-based SSM
ssm_analyze(
jz2017,
scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"),
measures = "NARPD",
grouping = "Gender"
)
#>
#> # Profile [NARPD: Female]:
#>
#> 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 [NARPD: Male]:
#>
#> 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 = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"),
measures = "NARPD",
grouping = "Gender",
contrast = TRUE
)
#>
#> # Profile [NARPD: Female]:
#>
#> 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 99.844 123.191
#> Model Fit 0.972
#>
#>
#> # Profile [NARPD: Male]:
#>
#> 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 84.228 119.107
#> Model Fit 0.902
#>
#>
#> # Contrast [NARPD: Male - Female]:
#>
#> Estimate Lower CI Upper CI
#> Δ Elevation 0.072 0.005 0.139
#> Δ X-Value 0.051 -0.012 0.113
#> Δ Y-Value -0.056 -0.124 0.010
#> Δ Amplitude -0.068 -0.138 -0.004
#> Δ Displacement -10.421 -30.538 10.798
#> Δ Model Fit -0.071
#>
# }