Tidy a(n) varest objectSource:
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, ...)
varestobject produced by a call to
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 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.
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 pass
conf.lvel = 0.9, all computation will proceed using
conf.level = 0.95. Two exceptions here are:
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.
# load libraries for models and data library(vars) # load data data("Canada", package = "vars") # fit models mod <- VAR(Canada, p = 1, type = "both") # summarize model fit with tidiers tidy(mod) #> # A tibble: 24 × 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 #> # ℹ 14 more rows glance(mod) #> # A tibble: 1 × 4 #> lag.order logLik nobs n #> <dbl> <dbl> <dbl> <dbl> #> 1 1 -208. 83 84