stepwise | R Documentation |
Functions to perform stepwise estimations in fixest
models.
sw(...)
csw(...)
sw0(...)
csw0(...)
mvsw(...)
... |
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 5 stepwise functions: sw
, sw0
, csw
, csw0
and mvsw
. 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
.
The last stepwise function, mvsw
, refers to 'multiverse' stepwise. It will estimate
as many models as there are unique combinations of stepwise variables. For example
fml = y ~ x1 + mvsw(x2, x3)
will estimate y ~ x1
, y ~ x1 + x2
, y ~ x1 + x3
,
y ~ x1 + x2 + x3
. Beware that the number of estimations grows pretty fast (2^n
,
with n
the number of stewise variables)!
base = setNames(iris, 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)
# Multiverse stepwise
feols(y ~ x1 + mvsw(x2, x3), base)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.