mt_standardize function

Standardize mouse-tracking measures per level of other variables.

Standardize mouse-tracking measures per level of other variables.

Standardize selected mouse-tracking measures across all trials or per level of one or more other variable, and store them in new variables. This function is a thin wrapper around scale_within , focussed on mouse-tracking data stored in a mousetrap data object.

mt_standardize( data, use = "measures", use_variables = NULL, within = NULL, prefix = "z_", center = TRUE, scale = TRUE )

Arguments

  • data: a mousetrap data object created using one of the mt_import functions (see mt_example for details).
  • use: a character string specifying which data should be used. By default points to the measures data.frame created using mt_measures .
  • use_variables: a vector specifying which variables should be standardized. If unspecified, all variables will be standardized.
  • within: an optional character string specifying one or more variables in data[["data"]]. If specified, all measures will be standardized separately for each level of the variable (or for each combination of levels, if more than one variable is specified).
  • prefix: a character string that is inserted before each standardized variable. If an empty string is specified, the original variables are replaced.
  • center: argument passed on to scale .
  • scale: argument passed on to scale .

Returns

A mousetrap data object (see mt_example ) including the standardized measures.

Examples

mt_example <- mt_measures(mt_example) # Standardize MAD and AD per subject mt_example <- mt_standardize(mt_example, use_variables=c("MAD", "AD"), within="subject_nr", prefix="z_") # Standardize MAD and AD per subject and Condition mt_example <- mt_standardize(mt_example, use_variables=c("MAD", "AD"), within=c("subject_nr", "Condition"), prefix="z_")

See Also

mt_scale_trajectories for standardizing variables in mouse trajectory arrays.

scale_within which is called by mt_standardize.

scale for the R base scale function.

Author(s)

Pascal J. Kieslich

Felix Henninger