Make a canonical regression formula, possibly with control variables.

Usage

make_regression_formula(
  Yobs = "Yobs",
  Z = "Z",
  clusterID = "clusterID",
  blockID = "blockID",
  control_formula = NULL,
  interacted = FALSE,
  control_interacted = FALSE,
  FE = FALSE,
  cluster_RE = FALSE,
  data = NULL
)

Arguments

Yobs: Name of outcome variable (assumed to exist in data)
Z: Name of treatment variable (assumed to exist in data)
clusterID: Name of cluster ID variable (assumed to exist in data)
blockID: Name of block ID variable (assumed to exist in data, if this is not null).
control_formula: What variables to control for, in the form of "~ X1 + X2".
interacted: TRUE means include treatment by block interactions. Will override FE flag and set FE to true.
control_interacted: TRUE means include treatment by control interactions in regression
FE: TRUE means include block dummy variables.
cluster_RE: Add a random effect term for clusterID
data: Dataframe holding all variables to be used in formula.

Value

Something like "Yobs ~ 1 + Z" or "Yobs ~ 1 + Z + X1 + X2"