dh.quartileSplit: Splits a continuous variables into four quartiles

In lifecycle-project/ds-cs-functions: Helper Functions for Use with 'DataSHIELD'

Splits a continuous variables into four quartiles

Description

For some sensitivity analyses you may want to examine potential non-linear associations between an exposure and outcome. This function splits a continuous variable into four continuous variables representing each quartile of the original variable. Participants with no measurement within a quartile will be assigned NA.

Usage

dh.quartileSplit(
  df = NULL,
  var = NULL,
  new_obj = NULL,
  band_action = NULL,
  type = NULL,
  var_suffix = "_q_",
  conns = NULL
)

Arguments

df	Character specifying a server-side data frame.
var	Character specifying continuous variable to transform into quartiles.
new_obj	Character specifying name for created serverside object.
band_action	Character specifying how the quartiles are separated: "g_l" = greater than the lowest band and less than the highest band "ge_le" = greater or equal to the lowest band and less than or equal to the highest band "g_le" = greater than the lowest band and less than or equal to the highest band "ge_l" = greater than or equal to the lowest band and less than the highest band
type	Character specifying whether to derive quartiles from combined data or within each cohort. Use "combine" to use combined quartiles, and "split" to use cohort-specific quartiles.
var_suffix	Character specifying the suffix to give the created variable. Default is "q"
conns	DataSHIELD connections object.

Value

Servside dataframe in containing a maximum of four additional variables representing the quantiles of the original variable. If a cohort has insufficient observations within that quartile (less than the filter threshold) the variable will not be created an a warning will be returned.

See Also

Other data manipulation functions: dh.makeAgePolys(), dh.makeIQR(), dh.zByGroup()

lifecycle-project/ds-cs-functions documentation built on Nov. 18, 2024, 3:36 p.m.