tm {rmdl}

Create vectorized terms

Description

Usage

tm(x = unspecified(), ...)

## S3 method for class 'character'
tm(
  x,
  role = character(),
  side = character(),
  label = character(),
  group = integer(),
  type = character(),
  distribution = character(),
  description = character(),
  transformation = character(),
  ...
)

## S3 method for class 'formula'
tm(
  x,
  role = formula(),
  label = formula(),
  group = formula(),
  type = formula(),
  distribution = formula(),
  description = formula(),
  transformation = formula(),
  ...
)

## S3 method for class 'fmls'
tm(x, ...)

## S3 method for class 'tm'
tm(x, ...)

## Default S3 method:
tm(x = unspecified(), ...)

is_tm(x)

Arguments

x

An object that can be coerced to a tm object.

...

Arguments to be passed to or from other methods

role

Specific roles the variable plays within the formula. These are of particular importance, as they serve as special terms that can effect how a formula is interpreted. Please see the Roles section below for further details. The options for roles are as below:

• outcome

• exposure

• predictor

• confounder

• mediator

• interaction

• strata

• group

• unknown

side

Which side of a formula should the term be on. Options are c("left", "right", "meta", "unknown"). The meta option refers to a term that may apply globally to other terms.

label

Display-quality label describing the variable

group

Grouping variable name for modeling or placing terms together. An integer value is given to identify which group the term will be in. The hierarchy will be 1 to n incrementally.

type

Type of variable, either categorical (qualitative) or continuous (quantitative)

distribution

How the variable itself is more specifically subcategorized, e.g. ordinal, continuous, dichotomous, etc

description

Option for further descriptions or definitions needed for the tm, potentially part of a data dictionary

transformation

Modification of the term to be applied when combining with data

Details

A vectorized term object that allows for additional information to be carried with the variable name.

This is not meant to replace traditional stats::terms(), but to supplement it using additional information that is more informative for causal modeling.

Value

A tm object, which is a series of individual terms with corresponding attributes, including the role, formula side, label, grouping, and other related features.

Roles

Specific roles the variable plays within the formula. These are of particular importance, as they serve as special terms that can effect how a formula is interpreted.

Role	Shortcut	Description
outcome	.o(...)	outcome ~ exposure
exposure	.x(...)	outcome ~ exposure
predictor	.p(...)	outcome ~ exposure + predictor
confounder	.c(...)	outcome + exposure ~ confounder
mediator	.m(...)	outcome mediator exposure
interaction	.i(...)	outcome ~ exposure * interaction
strata	.s(...)	outcome ~ exposure / strata
group	.g(...)	outcome ~ exposure + group
unknown	-	not yet assigned

Formulas can be condensed by applying their specific role to individual runes as a function/wrapper. For example, y ~ .x(x1) + x2 + x3. This would signify that x1 has the specific role of an exposure.

Grouped variables are slightly different in that they are placed together in a hierarchy or tier. To indicate the group and the tier, the shortcut can have an integer following the .g. If no number is given, then it is assumed they are all on the same tier. Ex: y ~ x1 + .g1(x2) + .g1(x3)

Warning: Only a single shortcut can be applied to a variable within a formula directly.

Pluralized Labeling Arguments

For a single argument, e.g. for the tm.formula() method, such as to identify variable X as an exposure, a formula should be given with the term of interest on the LHS, and the description or instruction on the RHS. This would look like role = "exposure" ~ X.

For the arguments that would be dispatched for objects that are plural, e.g. containing multiple terms, each formula() should be placed within a list(). For example, the role argument would be written:

role = list(X ~ "exposure", M ~ "mediator", C ~ "confounder")

Further implementation details can be seen in the implementation of labeled_formulas_to_named_list().