broom (version 0.4.1)

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

"tidy"(x, conf.int = FALSE, conf.level = 0.95, fe = FALSE, fe.error = fe, ...)
"augment"(x, data = NULL, ...)
"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

Run this code

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)
}

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