broom (version 0.3.4)

felm_tidiers: 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

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

Details

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

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)
}