R: Tidy a(n) nls object

tidy.nls {broom}

R Documentation

Tidy a(n) nls 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 class 'nls'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

Arguments

`x`	An `nls` object returned from `stats::nls()`.
`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`. 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.

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



# fit model
n <- nls(mpg ~ k * e^wt, data = mtcars, start = list(k = 1, e = 2))

# summarize model fit with tidiers + visualization
tidy(n)
augment(n)
glance(n)

library(ggplot2)

ggplot(augment(n), aes(wt, mpg)) +
  geom_point() +
  geom_line(aes(y = .fitted))

newdata <- head(mtcars)
newdata$wt <- newdata$wt + 1

augment(n, newdata = newdata)