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 varest tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to
The confidence level to use for the confidence interval
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
The tibble has one row for each term in the regression. The
component column indicates whether a particular
term was used to model either the
"precision". Here the
precision is the inverse of the variance, often referred to as
At least one term will have been used to model the precision
vars package does not include a
confint method and does not report
confidence intervals for
varest objects. Setting the
conf.int = TRUE will return a warning.
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.
Whether a particular term was used to model the mean or the precision in the regression. See details.
#> # A tibble: 24 x 6 #> group term estimate std.error statistic p.value #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 e e.l1 1.24 0.0863 14.4 1.82e-23 #> 2 e prod.l1 0.195 0.0361 5.39 7.49e- 7 #> 3 e rw.l1 -0.0678 0.0283 -2.40 1.90e- 2 #> 4 e U.l1 0.623 0.169 3.68 4.30e- 4 #> 5 e const -279. 75.2 -3.71 3.92e- 4 #> 6 e trend -0.0407 0.0197 -2.06 4.24e- 2 #> 7 prod e.l1 1.24 0.0863 14.4 1.82e-23 #> 8 prod prod.l1 0.195 0.0361 5.39 7.49e- 7 #> 9 prod rw.l1 -0.0678 0.0283 -2.40 1.90e- 2 #> 10 prod U.l1 0.623 0.169 3.68 4.30e- 4 #> # … with 14 more rowsglance(mod)#> # A tibble: 1 x 4 #> lag.order logLik nobs n #> <dbl> <dbl> <dbl> <dbl> #> 1 1 -208. 83 84