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 cross 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 orcutt
tidy(x, ...)

Arguments

x

An orcutt object returned from orcutt::cochrane.orcutt().

...

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. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

See also

Value

A tibble::tibble() with columns:

estimate

The estimated value of the regression term.

p.value

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

statistic

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

std.error

The standard error of the regression term.

term

The name of the regression term.

Examples

library(orcutt) reg <- lm(mpg ~ wt + qsec + disp, mtcars) tidy(reg)
#> # A tibble: 4 x 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 19.8 5.94 3.33 0.00244 #> 2 wt -5.03 1.22 -4.11 0.000310 #> 3 qsec 0.927 0.342 2.71 0.0114 #> 4 disp -0.000128 0.0106 -0.0121 0.990
co <- cochrane.orcutt(reg) co
#> Cochrane-orcutt estimation for first order autocorrelation #> #> Call: #> lm(formula = mpg ~ wt + qsec + disp, data = mtcars) #> #> number of interaction: 7 #> rho 0.26819 #> #> Durbin-Watson statistic #> (original): 1.49575 , p-value: 4.063e-02 #> (transformed): 2.05696 , p-value: 5.21e-01 #> #> coefficients: #> (Intercept) wt qsec disp #> 21.814858 -4.852590 0.797029 -0.001359
tidy(co)
#> # A tibble: 4 x 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 21.8 6.63 3.29 0.00279 #> 2 wt -4.85 1.33 -3.65 0.00112 #> 3 qsec 0.797 0.370 2.15 0.0402 #> 4 disp -0.00136 0.0110 -0.123 0.903
glance(co)
#> # A tibble: 1 x 9 #> r.squared adj.r.squared rho number.interact… dw.original p.value.original #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 0.799 0.777 0.268 7 1.50 0.0406 #> # … with 3 more variables: dw.transformed <dbl>, p.value.transformed <dbl>, #> # nobs <int>