R: Summary for fixest_multi objects

summary.fixest_multi {fixest} | R Documentation

## Summary for fixest_multi objects

### Description

Summary information for fixest_multi objects. In particular, this is used to specify the type of standard-errors to be computed.

### Usage

    ## S3 method for class 'fixest_multi'
    summary(
      object,
      type = "short",
      vcov = NULL,
      se = NULL,
      cluster = NULL,
      ssc = NULL,
      .vcov = NULL,
      stage = 2,
      lean = FALSE,
      n = 1000,
      ...
    )

### Arguments

`object` | A `fixest_multi` object, obtained from a `fixest` estimation leading to multiple results.
`type` | A character either equal to `"short"`, `"long"`, `"compact"`, `"se_compact"` or `"se_long"`. If `short`, only the table of coefficients is displayed for each estimation. If `long`, then the full results are displayed for each estimation. If `compact`, a `data.frame` is returned with one line per model and the formatted coefficients + standard-errors in the columns. If `se_compact`, a `data.frame` is returned with one line per model, one numeric column for each coefficient and one numeric column for each standard-error. If `"se_long"`, same as `"se_compact"` but the data is in a long format instead of wide.
`vcov` | Versatile argument to specify the VCOV. In general, it is either a character scalar equal to a VCOV type, either a formula of the form: `vcov_type ~ variables`. The VCOV types implemented are: "iid", "hetero" (or "HC1"), "cluster", "twoway", "NW" (or "newey_west"), "DK" (or "driscoll_kraay"), and "conley". It also accepts object from `vcov_cluster`, `vcov_NW`, `NW`, `vcov_DK`, `DK`, `vcov_conley` and `conley`. It also accepts covariance matrices computed externally. Finally it accepts functions to compute the covariances. See the 'vcov' documentation in the vignette.
`se` | Character scalar. Which kind of standard error should be computed: “standard”, “hetero”, “cluster”, “twoway”, “threeway” or “fourway”? By default if there are clusters in the estimation: `se = "cluster"`, otherwise `se = "iid"`. Note that this argument is deprecated, you should use `vcov` instead.
`cluster` | Tells how to cluster the standard-errors (if clustering is requested). Can be either a list of vectors, a character vector of variable names, a formula or an integer vector. Assume we want to perform 2-way clustering over `var1` and `var2` contained in the data.frame `base` used for the estimation. All the following `cluster` arguments are valid and do the same thing: `cluster = base[, c("var1", "var2")]`, `cluster = c("var1", "var2")`, `cluster = ~var1+var2`. If the two variables were used as fixed-effects in the estimation, you can leave it blank with `vcov = "twoway"` (assuming `var1` [resp. `var2`] was the 1st [res. 2nd] fixed-effect). You can interact two variables using `^` with the following syntax: `cluster = ~var1^var2` or `cluster = "var1^var2"`.
`ssc` | An object of class `ssc.type` obtained with the function `ssc`. Represents how the degree of freedom correction should be done.You must use the function `ssc` for this argument. The arguments and defaults of the function `ssc` are: `adj = TRUE`, `fixef.K="nested"`, `cluster.adj = TRUE`, `cluster.df = "conventional"`, `t.df = "conventional"`, `fixef.force_exact=FALSE)`. See the help of the function `ssc` for details.
`.vcov` | A user provided covariance matrix or a function computing this matrix. If a matrix, it must be a square matrix of the same number of rows as the number of variables estimated. If a function, it must return the previously mentioned matrix.
`stage` | Can be equal to `2` (default), `1`, `1:2` or `2:1`. Only used if the object is an IV estimation: defines the stage to which `summary` should be applied. If `stage = 1` and there are multiple endogenous regressors or if `stage` is of length 2, then an object of class `fixest_multi` is returned.
`lean` | Logical, default is `FALSE`. Used to reduce the (memory) size of the summary object. If `TRUE`, then all objects of length N (the number of observations) are removed from the result. Note that some `fixest` methods may consequently not work when applied to the summary.
`n` | Integer, default is 1000. Number of coefficients to display when the print method is used.
`...` | Not currently used.

### Value

It returns either an object of class `fixest_multi` (if `type` equals `short` or `long`), either a `data.frame` (if type equals `compact` or `se_compact`).

### See Also

The main fixest estimation functions: `feols`, `fepois`, `fenegbin`, `feglm`, `feNmlm`. Tools for mutliple fixest estimations: `summary.fixest_multi`, `print.fixest_multi`, `as.list.fixest_multi`, `sub-sub-.fixest_multi`, `sub-.fixest_multi`, `cash-.fixest_multi`.

### Examples

    base = iris
    names(base) = c("y", "x1", "x2", "x3", "species")

    # Multiple estimation
    res = feols(y ~ csw(x1, x2, x3), base, split = ~species)

    # By default, the type is "short"
    # You can still use the arguments from summary.fixest
    summary(res, se = "hetero")

    summary(res, type = "long")

    summary(res, type = "compact")

    summary(res, type = "se_compact")

    summary(res, type = "se_long")

[Package fixest version 0.10.1 Index]