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 speedlm
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

Arguments

x

A speedlm object returned from speedglm::speedlm().

conf.int

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

conf.level

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. 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:

conf.high

Upper bound on the confidence interval for the estimate.

conf.low

Lower bound on the confidence interval for the estimate.

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

mod <- speedglm::speedlm(mpg ~ wt + qsec, data = mtcars, fitted = TRUE) tidy(mod)
#> Joining, by = c("term", "estimate")
#> # A tibble: 3 x 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 19.7 5.25 3.76 7.65e- 4 #> 2 wt -5.05 0.484 -10.4 2.52e-11 #> 3 qsec 0.929 0.265 3.51 1.50e- 3
glance(mod)
#> # A tibble: 1 x 11 #> r.squared adj.r.squared statistic p.value df logLik AIC BIC deviance #> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> #> 1 0.826 0.814 69.0 9.39e-12 3 -74.4 157. 163. 195. #> # … with 2 more variables: df.residual <int>, nobs <int>
augment(mod)
#> # A tibble: 32 x 6 #> .rownames mpg wt qsec .fitted .resid #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Mazda RX4 21 2.62 16.5 21.8 0.815 #> 2 Mazda RX4 Wag 21 2.88 17.0 21.0 0.0482 #> 3 Datsun 710 22.8 2.32 18.6 25.3 2.53 #> 4 Hornet 4 Drive 21.4 3.22 19.4 21.6 0.181 #> 5 Hornet Sportabout 18.7 3.44 17.0 18.2 -0.504 #> 6 Valiant 18.1 3.46 20.2 21.1 2.97 #> 7 Duster 360 14.3 3.57 15.8 16.4 2.14 #> 8 Merc 240D 24.4 3.19 20 22.2 -2.17 #> 9 Merc 230 22.8 3.15 22.9 25.1 2.32 #> 10 Merc 280 19.2 3.44 18.3 19.4 0.185 #> # … with 22 more rows