creditmodel (version 1.3.1)

derived_ts_vars: Derivation of Behavioral Variables

Description

This function is used for derivating behavioral variables and is not intended to be used by end user.

Usage

derived_ts_vars(
  dat,
  grx = NULL,
  td = NULL,
  ID = NULL,
  ex_cols = NULL,
  x_list = NULL,
  der = c("cvs", "sums", "means", "maxs", "max_mins", "time_intervals",
    "cnt_intervals", "total_pcts", "cum_pcts", "partial_acfs"),
  parallel = TRUE,
  note = TRUE
)
derived_ts(
  dat,
  grx_x = NULL,
  x_list = NULL,
  td = NULL,
  ID = NULL,
  ex_cols = NULL,
  der = c("cvs", "sums", "means", "maxs", "max_mins", "time_intervals",
    "cnt_intervals", "total_pcts", "cum_pcts", "partial_acfs")
)

Arguments

dat

A data.frame contained only predict variables.

grx

Regular expressions used to match variable names.

td

Number of variables to derivate.

ID

The name of ID of observations or key variable of data. Default is NULL.

ex_cols

A list of excluded variables. Regular expressions can also be used to match variable names. Default is NULL.

x_list

Names of independent variables.

der

Variables to derivate

parallel

Logical, parallel computing. Default is FALSE.

note

Logical, outputs info. Default is TRUE.

grx_x

Regular expression used to match a group of variable names.

Details

The key to creating a good model is not the power of a specific modelling technique, but the breadth and depth of derived variables that represent a higher level of knowledge about the phenomena under examination.