broom (version 0.4.4)

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 felm
tidy(x, conf.int = FALSE, conf.level = 0.95, fe = FALSE,
  fe.error = fe, ...)
# S3 method for felm
augment(x, data = NULL, ...)
# S3 method for 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

# NOT RUN {
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)
}
# }