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, ...)
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 |
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
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 "mean"
or "precision"
. Here the
precision is the inverse of the variance, often referred to as phi
.
At least one term will have been used to model the precision phi
.
The vars
package does not include a confint
method and does not report
confidence intervals for varest
objects. Setting the tidy
argument
conf.int = TRUE
will return a warning.
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.
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 rows#> # A tibble: 1 x 4 #> lag.order logLik nobs n #> <dbl> <dbl> <dbl> <dbl> #> 1 1 -208. 83 84