Take in a data frame containing circumplex scales, angle definitions for each scale, and normative data (from the package or custom) and return that same data frame with each specified circumplex scale transformed into standard scores (i.e., z-scores) based on comparison to the normative data.

standardize(.data, scales, angles, instrument, sample = 1, prefix = "",
  suffix = "_z")

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 standardized.

angles

Required. A numeric vector containing the angular displacement of each circumplex scale included in scales (in degrees).

instrument

Required. An instrument object from the package. To see the available circumplex instruments, see instruments().

sample

Required. An integer corresponding to the normative sample to use in standardizing the scale scores (default = 1). See ?norms to see the normative samples available for an instrument.

prefix

Optional. A string to include at the beginning of the newly calculated scale variables' names, before the scale name and suffix (default = "").

suffix

Optional. A string to include at the end of the newly calculated scale variables' names, after the scale name and prefix (default = "_z").

Value

A data frame that matches .data except that new variables are appended that contain standardized versions of scales. These new variables will have the same name as scales but with a "_z" suffix.

See also

Other tidying functions: ipsatize, score

Examples

data("jz2017") instrument("iipsc") standardize(jz2017, PA:NO, octants(), instrument = iipsc, sample = 1)
#> # A tibble: 1,166 x 27 #> Gender PA BC DE FG HI JK LM NO PARPD SCZPD SZTPD #> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> <int> #> 1 Female 1.5 1.5 1.25 1 2 2.5 2.25 2.5 4 3 7 #> 2 Female 0 0.25 0 0.25 1.25 1.75 2.25 2.25 1 0 2 #> 3 Female 0 0 0 0 0 0 0 0 0 1 0 #> 4 Male 2 1.75 1.75 2.5 2 1.75 2 2.5 1 0 0 #> 5 Female 0.25 0.5 0.25 0 0 0 0 0 0 0 0 #> 6 Male 1.5 1.75 2.25 1.75 2 1.25 2.25 2.5 5 5 7 #> 7 Male 2 1.75 1.75 2 1.5 1.25 1.25 1.75 3 6 7 #> 8 Female 0 0.25 0.25 0.25 1 1.25 1.25 1 0 0 1 #> 9 Female 0 0 0 0 0.25 0.25 1 0 0 1 0 #> 10 Male 0.75 2 1.75 2.5 2 1.5 2 2.25 4 5 5 #> # ... with 1,156 more rows, and 15 more variables: ASPD <int>, BORPD <int>, #> # HISPD <int>, NARPD <int>, AVPD <int>, DPNPD <int>, OCPD <int>, PA_z <dbl>, #> # BC_z <dbl>, DE_z <dbl>, FG_z <dbl>, HI_z <dbl>, JK_z <dbl>, LM_z <dbl>, #> # NO_z <dbl>