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 cross 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 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. 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.

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

Value

A tibble::tibble() with columns:

conf.high

The upper end of a confidence interval for the term under consideration. Included only if `conf.int = TRUE`.

conf.low

The lower end of a confidence interval for the term under consideration. Included only if `conf.int = TRUE`.

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

library(betareg) data("GasolineYield", package = "betareg") mod <- betareg(yield ~ batch + temp, data = GasolineYield) mod
#> #> Call: #> betareg(formula = yield ~ batch + temp, data = GasolineYield) #> #> Coefficients (mean model with logit link): #> (Intercept) batch1 batch2 batch3 batch4 batch5 #> -6.15957 1.72773 1.32260 1.57231 1.05971 1.13375 #> batch6 batch7 batch8 batch9 temp #> 1.04016 0.54369 0.49590 0.38579 0.01097 #> #> Phi coefficients (precision model with identity link): #> (phi) #> 440.3 #>
tidy(mod)
#> # A tibble: 12 x 6 #> component term estimate std.error statistic p.value #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 mean (Intercept) -6.16 0.182 -33.8 3.44e-250 #> 2 mean batch1 1.73 0.101 17.1 2.59e- 65 #> 3 mean batch2 1.32 0.118 11.2 3.34e- 29 #> 4 mean batch3 1.57 0.116 13.5 8.81e- 42 #> 5 mean batch4 1.06 0.102 10.4 4.06e- 25 #> 6 mean batch5 1.13 0.104 11.0 6.52e- 28 #> 7 mean batch6 1.04 0.106 9.81 1.03e- 22 #> 8 mean batch7 0.544 0.109 4.98 6.29e- 7 #> 9 mean batch8 0.496 0.109 4.55 5.30e- 6 #> 10 mean batch9 0.386 0.119 3.25 1.14e- 3 #> 11 mean temp 0.0110 0.000413 26.6 1.26e-155 #> 12 precision (phi) 440. 110. 4.00 6.29e- 5
tidy(mod, conf.int = TRUE)
#> # A tibble: 12 x 8 #> component term estimate std.error statistic p.value conf.low conf.high #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 mean (Inter… -6.16 1.82e-1 -33.8 3.44e-250 -6.52e+0 -5.80 #> 2 mean batch1 1.73 1.01e-1 17.1 2.59e- 65 1.53e+0 1.93 #> 3 mean batch2 1.32 1.18e-1 11.2 3.34e- 29 1.09e+0 1.55 #> 4 mean batch3 1.57 1.16e-1 13.5 8.81e- 42 1.34e+0 1.80 #> 5 mean batch4 1.06 1.02e-1 10.4 4.06e- 25 8.59e-1 1.26 #> 6 mean batch5 1.13 1.04e-1 11.0 6.52e- 28 9.31e-1 1.34 #> 7 mean batch6 1.04 1.06e-1 9.81 1.03e- 22 8.32e-1 1.25 #> 8 mean batch7 0.544 1.09e-1 4.98 6.29e- 7 3.30e-1 0.758 #> 9 mean batch8 0.496 1.09e-1 4.55 5.30e- 6 2.82e-1 0.709 #> 10 mean batch9 0.386 1.19e-1 3.25 1.14e- 3 1.53e-1 0.618 #> 11 mean temp 0.0110 4.13e-4 26.6 1.26e-155 1.02e-2 0.0118 #> 12 precision (phi) 440. 1.10e+2 4.00 6.29e- 5 2.25e+2 656.
tidy(mod, conf.int = TRUE, conf.level = .99)
#> # A tibble: 12 x 8 #> component term estimate std.error statistic p.value conf.low conf.high #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 mean (Inter… -6.16 1.82e-1 -33.8 3.44e-250 -6.63e+0 -5.69 #> 2 mean batch1 1.73 1.01e-1 17.1 2.59e- 65 1.47e+0 1.99 #> 3 mean batch2 1.32 1.18e-1 11.2 3.34e- 29 1.02e+0 1.63 #> 4 mean batch3 1.57 1.16e-1 13.5 8.81e- 42 1.27e+0 1.87 #> 5 mean batch4 1.06 1.02e-1 10.4 4.06e- 25 7.96e-1 1.32 #> 6 mean batch5 1.13 1.04e-1 11.0 6.52e- 28 8.67e-1 1.40 #> 7 mean batch6 1.04 1.06e-1 9.81 1.03e- 22 7.67e-1 1.31 #> 8 mean batch7 0.544 1.09e-1 4.98 6.29e- 7 2.63e-1 0.825 #> 9 mean batch8 0.496 1.09e-1 4.55 5.30e- 6 2.15e-1 0.776 #> 10 mean batch9 0.386 1.19e-1 3.25 1.14e- 3 8.03e-2 0.691 #> 11 mean temp 0.0110 4.13e-4 26.6 1.26e-155 9.90e-3 0.0120 #> 12 precision (phi) 440. 1.10e+2 4.00 6.29e- 5 1.57e+2 724.
augment(mod)
#> # A tibble: 32 x 6 #> yield batch temp .fitted .resid .cooksd #> * <dbl> <fct> <dbl> <dbl> <dbl> <dbl> #> 1 0.122 1 205 0.101 1.59 0.0791 #> 2 0.223 1 275 0.195 1.66 0.0917 #> 3 0.347 1 345 0.343 0.211 0.00155 #> 4 0.457 1 407 0.508 -2.88 0.606 #> 5 0.08 2 218 0.0797 0.109 0.0000168 #> 6 0.131 2 273 0.137 -0.365 0.00731 #> 7 0.266 2 347 0.263 0.260 0.00523 #> 8 0.074 3 212 0.0943 -1.77 0.0805 #> 9 0.182 3 272 0.167 1.02 0.0441 #> 10 0.304 3 340 0.298 0.446 0.0170 #> # ... with 22 more rows
glance(mod)
#> # A tibble: 1 x 6 #> pseudo.r.squared df.null logLik AIC BIC df.residual #> <dbl> <dbl> <dbl> <dbl> <dbl> <int> #> 1 0.962 30 84.8 -146. -128. 20