Tidy a(n) lmodel2 object

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 lmodel2
tidy(x, ...)

Arguments

x

A lmodel2 object returned by lmodel2::lmodel2().

...

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.

Value

A tibble::tibble within eight rows (one for each term estimated with each method) and columns:

method

Either OLS/MA/SMA/RMA

term

Either "Intercept" or "Slope"

estimate

Estimated coefficient

conf.low

Lower bound of 95% confidence interval

conf.high

Upper bound of 95% confidence interval

Details

There are always only two terms in an lmodel2: "Intercept" and "Slope". These are computed by four methods: OLS (ordinary least squares), MA (major axis), SMA (standard major axis), and RMA (ranged major axis).

See also

tidy(), lmodel2::lmodel2()

Other lmodel2 tidiers: glance.lmodel2

Examples

if (require("lmodel2", quietly = TRUE)) {

  library(lmodel2)

  data(mod2ex2)
  Ex2.res <- lmodel2(Prey ~ Predators, data=mod2ex2, "relative", "relative", 99)
  Ex2.res

  tidy(Ex2.res)
  glance(Ex2.res)

  # this allows coefficient plots with ggplot2
  library(ggplot2)
  ggplot(tidy(Ex2.res), aes(estimate, term, color = method)) +
    geom_point() +
    geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)) +
    geom_errorbarh(aes(xmin = conf.low, xmax = conf.high))
}
#> Warning: there is no package called ‘lmodel2’