# getfe

##### Retrieve the group fixed effects

Compute the group fixed effects, i.e. the dummy parameters, which were swept
out during an estimation with `felm`

.

- Keywords
- models, regression

##### Usage

```
getfe(obj, references = NULL, se = FALSE, method = "kaczmarz",
ef = "ref", bN = 100, robust = FALSE,
cluster = obj[["clustervar"]], lhs = NULL)
```

##### Arguments

- obj
object of class

`"felm"`

, usually, a result of a call to`felm`

- references
a vector of strings. If there are more than two factors and you have prior knowledge of what the reference levels should be like

`references='id.23'`

. Not used with`method='kaczmarz'`

- se
logical. Set to TRUE if standard errors for the group effects are wanted. This is

**very**time-consuming for large problems, so leave it as FALSE unless absolutely needed.- method
character string. Either 'cholesky', 'cg', or the default 'kaczmarz'. The latter is often very fast and consumes little memory, it requires an estimable function to be specified, see

`efactory`

. The 'cholesky' method is no longer maintained as the author sees no use for it.- ef
function. A function of two variables, a vector of group fixed effects and a logical, i.e.

`function(v,addnames)`

. This function should be estimable and is used to transform the raw-coefficients`v`

from the kaczmarz-method. The second variable indicates whether the function must return a named vector (if this is FALSE, one may skip the names, saving memory allocations and time).If a string is specified, it is fed to the

`efactory`

-function. The default function is one which picks one reference in each component.Can be set to

`ef="ln"`

to yield the minimal-norm solution from the kaczmarz-method.It can also be set to

`ef="zm"`

to get zero means (and intercept) in one of the factors, and a reference in the other.- bN
integer. The number of bootstrap runs when standard errors are requested.

- robust
logical. Should heteroskedastic standard errors be estimated?

- cluster
logical or factor. Estimate clustered standard errors.

- lhs
character vector. Specify which left hand side if

`obj`

has multiple lhs.

##### Details

For the case with two factors (the terms in the second part of the formula
supplied to `felm`

), one reference in each connected component
is adequate when interpreting the results.

For three or more factors, no such easy method is known; for the
`"cholesky"`

method- reference levels are found by analyzing the
pivoted Cholesky-decomposition of a slightly perturbed system. The
`"kaczmarz"`

method provides no rank-deficiency analysis, it is assumed
that the factors beyond the two first contribute nothing to the
rank-deficiency, so one reference in each is used.

If there are more than two factors, only the first two will be used to report connected components. In this case, it is not known which graph theoretic concept may be used to analyze the rank-deficiency.

The standard errors returned by the Kaczmarz-method are bootstrapped,
keeping the other coefficients (from `felm`

) constant, i.e. they
are from the variance when resampling the residuals. If `robust=TRUE`

,
heteroskedastic robust standard errors are estimated. If `robust=FALSE`

and `cluster=TRUE`

, clustered standard errors with the cluster
specified to `felm()`

are estimated. If `cluster`

is a factor, it
is used for the cluster definition.

##### Value

The function `getfe`

computes and returns a data frame
containing the group fixed effects. It has the columns
`c('effect','se','obs','comp','fe','idx')`

`effect`

is the estimated effect.`se`

is the standard error.`obs`

is the number of observations of this level.`comp`

is the graph-theoretic component number, useful for interpreting the effects.`fe`

is the name of factor.`idx`

is the level of the factor.

With the Kaczmarz-method it's possible to specify a different estimable function.

##### Examples

```
# NOT RUN {
oldopts <- options(lfe.threads=2)
## create covariates
x <- rnorm(4000)
x2 <- rnorm(length(x))
## create individual and firm
id <- factor(sample(500,length(x),replace=TRUE))
firm <- factor(sample(300,length(x),replace=TRUE))
## effects
id.eff <- rlnorm(nlevels(id))
firm.eff <- rexp(nlevels(firm))
## left hand side
y <- x + 0.25*x2 + id.eff[id] + firm.eff[firm] + rnorm(length(x))
## estimate and print result
est <- felm(y ~ x+x2 | id + firm)
summary(est)
## extract the group effects
alpha <- getfe(est,se=TRUE)
## find some estimable functions, with standard errors, we don't get
## names so we must precompute some numerical indices in ef
idx <- match(c('id.5','id.6','firm.11','firm.12'),rownames(alpha))
alpha[idx,]
ef <- function(v,addnames) {
w <- c(v[idx[[2]]]-v[idx[[1]]],v[idx[[4]]]+v[idx[[1]]],
v[idx[[4]]]-v[idx[[3]]])
if(addnames) names(w) <-c('id6-id5','f12+id5','f12-f11')
w
}
getfe(est,ef=ef,se=TRUE)
options(oldopts)
# }
# NOT RUN {
summary(lm(y ~ x+x2+id+firm-1))
# }
# NOT RUN {
# }
```

*Documentation reproduced from package lfe, version 2.8-2, License: Artistic-2.0*