stepwise | R Documentation |
Functions to perform stepwise estimations.
sw(...)
csw(...)
sw0(...)
csw0(...)
... |
Represents formula variables to be added in a stepwise fashion to an estimation. |
To include multiple independent variables, you need to use the stepwise functions. There are 4 stepwise functions: sw, sw0, csw, csw0. Let's explain that.
Assume you have the following formula: fml = y ~ x1 + sw(x2, x3)
. The stepwise function sw
will estimate the following two models: y ~ x1 + x2
and y ~ x1 + x3
. That is, each element in sw()
is sequentially, and separately, added to the formula. Would have you used sw0
in lieu of sw
, then the model y ~ x1
would also have been estimated. The 0
in the name implies that the model without any stepwise element will also be estimated.
Finally, the prefix c
means cumulative: each stepwise element is added to the next. That is, fml = y ~ x1 + csw(x2, x3)
would lead to the following models y ~ x1 + x2
and y ~ x1 + x2 + x3
. The 0
has the same meaning and would also lead to the model without the stepwise elements to be estimated: in other words, fml = y ~ x1 + csw0(x2, x3)
leads to the following three models: y ~ x1
, y ~ x1 + x2
and y ~ x1 + x2 + x3
.
base = iris
names(base) = c("y", "x1", "x2", "x3", "species")
# Regular stepwise
feols(y ~ sw(x1, x2, x3), base)
# Cumulative stepwise
feols(y ~ csw(x1, x2, x3), base)
# Using the 0
feols(y ~ x1 + x2 + sw0(x3), base)
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