Tidy a(n) nls 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 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 class 'nls' tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
x |
An |
conf.int |
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |
conf.level |
The confidence level to use for the confidence interval
if |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
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. |
Other nls tidiers:
augment.nls(),
glance.nls()
n <- nls(mpg ~ k * e^wt, data = mtcars, start = list(k = 1, e = 2)) 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)
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