tidy.lmodel2: Tidy a(n) lmodel2 object

Description

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.

Usage

# S3 method for lmodel2
tidy(x, ...)

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.
term: The name of the regression term.
method: Either OLS/MA/SMA/RMA

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. Two exceptions here are:

tidy() methods will warn when supplied an exponentiate argument if it will be ignored.
augment() methods will warn when supplied a newdata argument if it will be ignored.

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).

The returned p-value is one-tailed and calculated via a permutation test. A permutational test is used because distributional assumptions may not be valid. More information can be found in vignette("mod2user", package = "lmodel2").

Examples

Run this code

if (FALSE) { # rlang::is_installed("lmodel2")

# load libraries for models and data
library(lmodel2)

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

# summarize model fit with tidiers + visualization
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))
}