Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
Arguments
- x
A
felmobject returned fromlfe::felm().- conf.int
Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to
FALSE.- conf.level
The confidence level to use for the confidence interval if
conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.- fe
Logical indicating whether or not to include estimates of fixed effects. Defaults to
FALSE.- se.type
Character indicating the type of standard errors. Defaults to using those of the underlying felm() model object, e.g. clustered errors for models that were provided a cluster specification. Users can override these defaults by specifying an appropriate alternative: "iid" (for homoskedastic errors), "robust" (for Eicker-Huber-White robust errors), or "cluster" (for clustered standard errors; if the model object supports it).
- ...
Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in
..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you passconf.lvel = 0.9, all computation will proceed usingconf.level = 0.95. Two exceptions here are:
See also
Other felm tidiers:
augment.felm()
Value
A tibble::tibble() with columns:
- conf.high
Upper bound on the confidence interval for the estimate.
- conf.low
Lower bound on the confidence interval for the estimate.
- estimate
The estimated value of the regression term.
- p.value
The two-sided p-value associated with the observed statistic.
- statistic
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
- std.error
The standard error of the regression term.
- term
The name of the regression term.
Examples
# load libraries for models and data
library(lfe)
# use built-in `airquality` dataset
head(airquality)
#> Ozone Solar.R Wind Temp Month Day
#> 1 41 190 7.4 67 5 1
#> 2 36 118 8.0 72 5 2
#> 3 12 149 12.6 74 5 3
#> 4 18 313 11.5 62 5 4
#> 5 NA NA 14.3 56 5 5
#> 6 28 NA 14.9 66 5 6
# no FEs; same as lm()
est0 <- felm(Ozone ~ Temp + Wind + Solar.R, airquality)
# summarize model fit with tidiers
tidy(est0)
#> # A tibble: 4 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) -64.3 23.1 -2.79 0.00623
#> 2 Temp 1.65 0.254 6.52 0.00000000242
#> 3 Wind -3.33 0.654 -5.09 0.00000152
#> 4 Solar.R 0.0598 0.0232 2.58 0.0112
augment(est0)
#> # A tibble: 111 × 7
#> .rownames Ozone Temp Wind Solar.R .fitted .resid
#> <chr> <int> <int> <dbl> <int> <dbl> <dbl>
#> 1 1 41 67 7.4 190 33.0 7.95
#> 2 2 36 72 8 118 35.0 1.00
#> 3 3 12 74 12.6 149 24.8 -12.8
#> 4 4 18 62 11.5 313 18.5 -0.475
#> 5 7 23 65 8.6 299 32.3 -9.26
#> 6 8 19 59 13.8 99 -6.95 25.9
#> 7 9 8 61 20.1 19 -29.4 37.4
#> 8 12 16 69 9.7 256 32.6 -16.6
#> 9 13 11 66 9.2 290 31.4 -20.4
#> 10 14 14 68 10.9 274 28.1 -14.1
#> # ℹ 101 more rows
# add month fixed effects
est1 <- felm(Ozone ~ Temp + Wind + Solar.R | Month, airquality)
# summarize model fit with tidiers
tidy(est1)
#> # A tibble: 3 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Temp 1.88 0.341 5.50 0.000000274
#> 2 Wind -3.11 0.660 -4.71 0.00000778
#> 3 Solar.R 0.0522 0.0237 2.21 0.0296
tidy(est1, fe = TRUE)
#> # A tibble: 8 × 7
#> term estimate std.error statistic p.value N comp
#> <chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
#> 1 Temp 1.88 0.341 5.50 0.000000274 NA NA
#> 2 Wind -3.11 0.660 -4.71 0.00000778 NA NA
#> 3 Solar.R 0.0522 0.0237 2.21 0.0296 NA NA
#> 4 Month.5 -74.2 4.23 -17.5 2.00 24 1
#> 5 Month.6 -89.0 6.91 -12.9 2.00 9 1
#> 6 Month.7 -83.0 4.06 -20.4 2 26 1
#> 7 Month.8 -78.4 4.32 -18.2 2.00 23 1
#> 8 Month.9 -90.2 3.85 -23.4 2 29 1
augment(est1)
#> # A tibble: 111 × 8
#> .rownames Ozone Temp Wind Solar.R Month .fitted .resid
#> <chr> <int> <int> <dbl> <int> <int> <dbl> <dbl>
#> 1 1 41 67 7.4 190 5 38.3 2.69
#> 2 2 36 72 8 118 5 42.1 -6.07
#> 3 3 12 74 12.6 149 5 33.1 -21.1
#> 4 4 18 62 11.5 313 5 22.6 -4.62
#> 5 7 23 65 8.6 299 5 36.5 -13.5
#> 6 8 19 59 13.8 99 5 -1.33 20.3
#> 7 9 8 61 20.1 19 5 -21.3 29.3
#> 8 12 16 69 9.7 256 5 38.4 -22.4
#> 9 13 11 66 9.2 290 5 36.1 -25.1
#> 10 14 14 68 10.9 274 5 33.7 -19.7
#> # ℹ 101 more rows
glance(est1)
#> # A tibble: 1 × 8
#> r.squared adj.r.squared sigma statistic p.value df df.residual nobs
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 0.637 0.612 20.7 25.8 4.57e-20 103 103 111
# the "se.type" argument can be used to switch out different standard errors
# types on the fly. In turn, this can be useful exploring the effect of
# different error structures on model inference.
tidy(est1, se.type = "iid")
#> # A tibble: 3 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Temp 1.88 0.341 5.50 0.000000274
#> 2 Wind -3.11 0.660 -4.71 0.00000778
#> 3 Solar.R 0.0522 0.0237 2.21 0.0296
tidy(est1, se.type = "robust")
#> # A tibble: 3 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Temp 1.88 0.344 5.45 0.000000344
#> 2 Wind -3.11 0.903 -3.44 0.000834
#> 3 Solar.R 0.0522 0.0226 2.31 0.0227
# add clustered SEs (also by month)
est2 <- felm(Ozone ~ Temp + Wind + Solar.R | Month | 0 | Month, airquality)
# summarize model fit with tidiers
tidy(est2, conf.int = TRUE)
#> # A tibble: 3 × 7
#> term estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Temp 1.88 0.182 10.3 0.000497 1.37 2.38
#> 2 Wind -3.11 1.31 -2.38 0.0760 -6.74 0.518
#> 3 Solar.R 0.0522 0.0408 1.28 0.270 -0.0611 0.166
tidy(est2, conf.int = TRUE, se.type = "cluster")
#> # A tibble: 3 × 7
#> term estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Temp 1.88 0.182 10.3 0.000497 1.37 2.38
#> 2 Wind -3.11 1.31 -2.38 0.0760 -6.74 0.518
#> 3 Solar.R 0.0522 0.0408 1.28 0.270 -0.0611 0.166
tidy(est2, conf.int = TRUE, se.type = "robust")
#> # A tibble: 3 × 7
#> term estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Temp 1.88 0.344 5.45 0.00550 0.920 2.83
#> 2 Wind -3.11 0.903 -3.44 0.0262 -5.62 -0.602
#> 3 Solar.R 0.0522 0.0226 2.31 0.0817 -0.0104 0.115
tidy(est2, conf.int = TRUE, se.type = "iid")
#> # A tibble: 3 × 7
#> term estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Temp 1.88 0.341 5.50 0.00532 0.929 2.82
#> 2 Wind -3.11 0.660 -4.71 0.00924 -4.94 -1.28
#> 3 Solar.R 0.0522 0.0237 2.21 0.0920 -0.0135 0.118