This method wraps tidy.lm().

# S3 method for speedlm
tidy(x, conf.int = FALSE, conf.level = 0.95,
  exponentiate = FALSE, quick = FALSE, ...)

Arguments

x

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

...

Arguments passed on to tidy.lm

x

An lm object created by stats::lm().

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.

quick

Logical indiciating if the only the term and estimate columns should be returned. Often useful to avoid time consuming covariance and standard error calculations. Defaults to FALSE.

exponentiate

Logical indicating whether or not to exponentiate the the coefficient estimates. This is typical for logistic and multinomial regressions, but a bad idea if there is no log or logit link. Defaults to FALSE.

See also

Examples

mod <- speedglm::speedlm(mpg ~ wt + qsec, data = mtcars) tidy(mod)
#> # 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 10 #> r.squared adj.r.squared statistic p.value df logLik AIC BIC #> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> #> 1 0.826 0.814 69.0 9.39e-12 3 -74.4 157. 163. #> # ... with 2 more variables: df.residual <int>, deviance <dbl>
augment(mod)
#> Error in data.frame(..., check.names = FALSE): arguments imply differing number of rows: 32, 0