Description Usage Arguments Value Examples

This function sets globally the default arguments of fixest estimations.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
setFixest_estimation(
fixef.rm = "perfect",
fixef.tol = 1e-06,
fixef.iter = 10000,
collin.tol = 1e-10,
lean = FALSE,
verbose = 0,
warn = TRUE,
combine.quick = NULL,
demeaned = FALSE,
mem.clean = FALSE,
glm.iter = 25,
glm.tol = 1e-08,
panel.id = NULL,
reset = FALSE
)
getFixest_estimation()
``` |

`fixef.rm` |
Can be equal to "perfect" (default), "singleton", "both" or "none". Controls which observations are to be removed. If "perfect", then observations having a fixed-effect with perfect fit (e.g. only 0 outcomes in Poisson estimations) will be removed. If "singleton", all observations for which a fixed-effect appears only once will be removed. The meaning of "both" and "none" is direct. |

`fixef.tol` |
Precision used to obtain the fixed-effects. Defaults to |

`fixef.iter` |
Maximum number of iterations in fixed-effects algorithm (only in use for 2+ fixed-effects). Default is 10000. |

`collin.tol` |
Numeric scalar, default is |

`lean` |
Logical, default is |

`verbose` |
Integer. Higher values give more information. In particular, it can detail the number of iterations in the demeaning algorithm (the first number is the left-hand-side, the other numbers are the right-hand-side variables). |

`warn` |
Logical, default is |

`combine.quick` |
Logical. When you combine different variables to transform them into a single fixed-effects you can do e.g. |

`demeaned` |
Logical, default is |

`mem.clean` |
Logical, default is |

`glm.iter` |
Number of iterations of the glm algorithm. Default is 25. |

`glm.tol` |
Tolerance level for the glm algorithm. Default is |

`panel.id` |
The panel identifiers. Can either be: i) a one sided formula (e.g. |

`reset` |
Logical, default to |

The function `getFixest_estimation`

returns the currently set global defaults.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
#
# Example: removing singletons is FALSE by default
#
# => changing this default
# Let's create data with singletons
base = iris
names(base) = c("y", "x1", "x2", "x3", "species")
base$fe_singletons = as.character(base$species)
base$fe_singletons[1:5] = letters[1:5]
res = feols(y ~ x1 + x2 | fe_singletons, base)
res_noSingle = feols(y ~ x1 + x2 | fe_singletons, base, fixef.rm = "single")
# New defaults
setFixest_estimation(fixef.rm = "single")
res_newDefault = feols(y ~ x1 + x2 | fe_singletons, base)
etable(res, res_noSingle, res_newDefault)
# Resetting the defaults
setFixest_estimation(reset = TRUE)
``` |

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