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.

# S3 method for felm tidy( x, conf.int = FALSE, conf.level = 0.95, fe = FALSE, se.type = c("default", "iid", "robust", "cluster"), ... )

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

conf.int | Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |

conf.level | The confidence level to use for the confidence interval
if |

fe | Logical indicating whether or not to include estimates of
fixed effects. Defaults to |

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. |

Other felm tidiers:
`augment.felm()`

A `tibble::tibble()`

with columns:

Upper bound on the confidence interval for the estimate.

Lower bound on the confidence interval for the estimate.

The estimated value of the regression term.

The two-sided p-value associated with the observed statistic.

The value of a T-statistic to use in a hypothesis that the regression term is non-zero.

The standard error of the regression term.

The name of the regression term.

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