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

Arguments

x

A betareg object produced by a call to betareg::betareg().

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.

Details

The tibble has one row for each term in the regression. The component column indicates whether a particular term was used to model either the "mean" or "precision". Here the precision is the inverse of the variance, often referred to as phi. At least one term will have been used to model the precision phi.

See also

tidy(), betareg::betareg()

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.

component

Whether a particular term was used to model the mean or the precision in the regression. See details.

Examples

# load libraries for models and data
library(betareg)

# load dats
data("GasolineYield", package = "betareg")

# fit model
mod <- betareg(yield ~ batch + temp, data = GasolineYield)
#> Warning: NaNs produced
#> Error in while (testhalf & stepFactor < 11) {    fit <- fitfun(par, deriv = 2L)    scores <- gradfun(par, fit = fit)    InfoInv <- try(hessfun(par, fit = fit, inverse = TRUE))    if (failedInv <- inherits(InfoInv, "try-error")) {        warning("failed to invert the information matrix: iteration stopped prematurely")        break    }    bias <- if (type == "BR")         biasfun(par, fit = fit, vcov = InfoInv)$bias    else 0    par <- par + 2^(-stepFactor) * (step <- InfoInv %*% scores -         bias)    stepFactor <- stepFactor + 1    testhalf <- drop(crossprod(stepPrev) < crossprod(step))}: missing value where TRUE/FALSE needed

mod
#> Error: object 'mod' not found

# summarize model fit with tidiers
tidy(mod)
#> Error: object 'mod' not found
tidy(mod, conf.int = TRUE)
#> Error: object 'mod' not found
tidy(mod, conf.int = TRUE, conf.level = .99)
#> Error: object 'mod' not found

augment(mod)
#> Error: object 'mod' not found

glance(mod)
#> Error: object 'mod' not found