R: Tidying methods for models with multiple group fixed effects

felm_tidiers {broom} | R Documentation

## Tidying methods for models with multiple group fixed effects

### Description

These methods tidy the coefficients of a linear model with multiple group fixed effects

### Usage

    ## S3 method for class 'felm'
    tidy(x, conf.int = FALSE, conf.level = 0.95, fe = FALSE,
      fe.error = fe, ...)

    ## S3 method for class 'felm'
    augment(x, data = NULL, ...)

    ## S3 method for class 'felm'
    glance(x, ...)

### Arguments

`x` | felm object
`conf.int` | whether to include a confidence interval
`conf.level` | confidence level of the interval, used only if `conf.int=TRUE`
`fe` | whether to include estimates of fixed effects
`fe.error` | whether to include standard error of fixed effects
`...` | extra arguments (not used)
`data` | Original data, defaults to extracting it from the model

### Details

If `conf.int=TRUE`, the confidence interval is computed

### Value

All tidying methods return a data.frame without rownames, whose structure depends on the method chosen.

`tidy.felm` returns one row for each coefficient. If `fe=TRUE`, it also includes rows for for fixed effects estimates. There are five columns:

`term` | The term in the linear model being estimated and tested
`estimate` | The estimated coefficient
`std.error` | The standard error from the linear model
`statistic` | t-statistic
`p.value` | two-sided p-value

If `cont.int=TRUE`, it also includes columns for `conf.low` and `conf.high`, computed with `confint`.

`augment.felm` returns one row for each observation, with multiple columns added to the original data:

`.fitted` | Fitted values of model
`.resid` | Residuals

If fixed effect are present,

`.comp` | Connected component
`.fe_` | Fixed effects (as many columns as factors)

`glance.lm` returns a one-row data.frame with the columns

`r.squared` | The percent of variance explained by the model
`adj.r.squared` | r.squared adjusted based on the degrees of freedom
`sigma` | The square root of the estimated residual variance
`statistic` | F-statistic
`p.value` | p-value from the F test
`df` | Degrees of freedom used by the coefficients
`df.residual` | residual degrees of freedom

### Examples

    if (require("lfe", quietly = TRUE)) {
        N=1e2
        DT <- data.frame(
          id = sample(5, N, TRUE),
          v1 =  sample(5, N, TRUE),
          v2 =  sample(1e6, N, TRUE),
          v3 =  sample(round(runif(100,max=100),4), N, TRUE),
          v4 =  sample(round(runif(100,max=100),4), N, TRUE)
        )

        result_felm <- felm(v2~v3, DT)
        tidy(result_felm)
        augment(result_felm)
        result_felm <- felm(v2~v3|id+v1, DT)
        tidy(result_felm, fe = TRUE)
        augment(result_felm)
        v1<-DT$v1
        v2 <- DT$v2
        v3 <- DT$v3
        id <- DT$id
        result_felm <- felm(v2~v3|id+v1)
        tidy(result_felm)
        augment(result_felm)
        glance(result_felm)
    }

[Package broom version 0.4.2 Index]