Glance accepts a model object and returns a `tibble::tibble()`

with exactly one row of model summaries. The summaries are typically
goodness of fit measures, p-values for hypothesis tests on residuals,
or model convergence information.

Glance never returns information from the original call to the modeling function. This includes the name of the modeling function or any arguments passed to the modeling function.

Glance does not calculate summary measures. Rather, it farms out these
computations to appropriate methods and gathers the results together.
Sometimes a goodness of fit measure will be undefined. In these cases
the measure will be reported as `NA`

.

Glance returns the same number of columns regardless of whether the
model matrix is rank-deficient or not. If so, entries in columns
that no longer have a well-defined value are filled in with an `NA`

of the appropriate type.

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

x | A |
---|---|

... | Additional arguments. Not used. Needed to match generic
signature only. |

A `tibble::tibble()`

with exactly one row and columns:

Adjusted R squared statistic, which is like the R squared statistic except taking degrees of freedom into account.

Degrees of freedom used by the model.

Residual degrees of freedom.

Number of observations used.

P-value corresponding to the test statistic.

R squared statistic, or the percent of variation explained by the model. Also known as the coefficient of determination.

Estimated standard error of the residuals.

Test statistic.

if (require(lfe)) { library(lfe) # Use built-in "airquality" dataset head(airquality) # No FEs; same as lm() est0 <- felm(Ozone ~ Temp + Wind + Solar.R, airquality) tidy(est0) augment(est0) # Add month fixed effects est1 <- felm(Ozone ~ Temp + Wind + Solar.R | Month, airquality) tidy(est1) tidy(est1, fe = TRUE) augment(est1) glance(est1) # 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") tidy(est1, se.type = "robust") # Add clustered SEs (also by month) est2 <- felm(Ozone ~ Temp + Wind + Solar.R | Month | 0 | Month, airquality) tidy(est2, conf.int = TRUE) tidy(est2, conf.int = TRUE, se.type = "cluster") tidy(est2, conf.int = TRUE, se.type = "robust") 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