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.
# S3 method for lmodel2
tidy(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.
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")
.
Other lmodel2 tidiers:
glance.lmodel2()
A tibble::tibble()
with columns:
Upper bound on the confidence interval for the estimate.
Lower bound on the confidence interval for the estimate.
The estimated value of the regression term.
The two-sided p-value associated with the observed statistic.
The name of the regression term.
Either OLS/MA/SMA/RMA
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("lmodel2", quietly = TRUE)) {
# 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))
}