Standardize values of an scdf file

This function is superseded by the much more versatile transform.scdf function (see example below). This function scales the measured values of an scdf file. It allows for mean centering and standardization based on each single-case data set or a scaling across all cases included in an scdf.

Usage

standardize(
  data,
  var,
  center = TRUE,
  scale = FALSE,
  m = 0,
  sd = 1,
  grand = TRUE
)

Arguments

data

A single-case data frame. See scdf() to learn about this format.

var

A character string or a vector of character strings with variable names that should be scaled.

center

If set TRUE, data are mean centered.

scale

If set TRUE, the standard deviation is set.

m

The target mean for centering.

sd

The target standard deviation for scaling

grand

If set TRUE, scaling is based on the mean and standard deviation of all values across all single-cases within the scdf.

Value

An scdf with the scaled values.

See also

Other data manipulation functions: add_l2(), as.data.frame.scdf(), as_scdf(), fill_missing(), moving_median(), outlier(), ranks(), rescale(), scdf(), select_cases(), set_vars(), shift(), smooth_cases(), truncate_phase()

Author

Juergen Wilbert

Examples

## Standardize a multiple case scdf and compute an hplm
exampleAB_50 |>
  standardize("values", center = TRUE, scale = TRUE) |>
  hplm()
#> Hierarchical Piecewise Linear Regression
#> 
#> Estimation method ML 
#> Contrast model: W / level: first, slope: first
#> 50 Cases
#> 
#> ICC = 0.287; L = 339.0; p = 0.000
#> 
#> Fixed effects (values ~ 1 + mt + phaseB + interB)
#> 
#>                    B    SE   df       t p
#> Intercept     -1.251 0.075 1328 -16.716 0
#> Trend mt       0.029 0.006 1328   5.006 0
#> Level phase B  0.708 0.033 1328  21.436 0
#> Slope phase B  0.046 0.006 1328   7.588 0
#> 
#> Random effects (~1 | case)
#> 
#>           EstimateSD
#> Intercept      0.503
#> Residual       0.266

## The more versatile transform function supersedes standardize:
exampleAB_50 |>
  transform(values = (values - mean(all(values))) / sd(all(values))) |>
  hplm()
#> Hierarchical Piecewise Linear Regression
#> 
#> Estimation method ML 
#> Contrast model: W / level: first, slope: first
#> 50 Cases
#> 
#> ICC = 0.287; L = 339.0; p = 0.000
#> 
#> Fixed effects (values ~ 1 + mt + phaseB + interB)
#> 
#>                    B    SE   df       t p
#> Intercept     -1.251 0.075 1328 -16.716 0
#> Trend mt       0.029 0.006 1328   5.006 0
#> Level phase B  0.708 0.033 1328  21.436 0
#> Slope phase B  0.046 0.006 1328   7.588 0
#> 
#> Random effects (~1 | case)
#> 
#>           EstimateSD
#> Intercept      0.503
#> Residual       0.266