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 'betareg'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)Arguments
- x
A
betaregobject produced by a call tobetareg::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 passconf.lvel = 0.9, all computation will proceed usingconf.level = 0.95. Two exceptions here are:
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.
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)
mod
#>
#> Call:
#> betareg(formula = yield ~ batch + temp, data = GasolineYield)
#>
#> Coefficients (mean model with logit link):
#> (Intercept) batch1 batch2 batch3 batch4
#> -6.15957 1.72773 1.32260 1.57231 1.05971
#> batch5 batch6 batch7 batch8 batch9
#> 1.13375 1.04016 0.54369 0.49590 0.38579
#> temp
#> 0.01097
#>
#> Phi coefficients (precision model with identity link):
#> (phi)
#> 440.3
#>
# summarize model fit with tidiers
tidy(mod)
#> # A tibble: 12 × 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 × 8
#> component term estimate std.error statistic p.value conf.low
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 mean (Intercept) -6.16 0.182 -33.8 3.44e-250 -6.52
#> 2 mean batch1 1.73 0.101 17.1 2.59e- 65 1.53
#> 3 mean batch2 1.32 0.118 11.2 3.34e- 29 1.09
#> 4 mean batch3 1.57 0.116 13.5 8.81e- 42 1.34
#> 5 mean batch4 1.06 0.102 10.4 4.06e- 25 0.859
#> 6 mean batch5 1.13 0.104 11.0 6.52e- 28 0.931
#> 7 mean batch6 1.04 0.106 9.81 1.03e- 22 0.832
#> 8 mean batch7 0.544 0.109 4.98 6.29e- 7 0.330
#> 9 mean batch8 0.496 0.109 4.55 5.30e- 6 0.282
#> 10 mean batch9 0.386 0.119 3.25 1.14e- 3 0.153
#> 11 mean temp 0.0110 0.000413 26.6 1.26e-155 0.0102
#> 12 precision (phi) 440. 110. 4.00 6.29e- 5 225.
#> # ℹ 1 more variable: conf.high <dbl>
tidy(mod, conf.int = TRUE, conf.level = .99)
#> # A tibble: 12 × 8
#> component term estimate std.error statistic p.value conf.low
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 mean (Intercept) -6.16 0.182 -33.8 3.44e-250 -6.63
#> 2 mean batch1 1.73 0.101 17.1 2.59e- 65 1.47
#> 3 mean batch2 1.32 0.118 11.2 3.34e- 29 1.02
#> 4 mean batch3 1.57 0.116 13.5 8.81e- 42 1.27
#> 5 mean batch4 1.06 0.102 10.4 4.06e- 25 0.796
#> 6 mean batch5 1.13 0.104 11.0 6.52e- 28 0.867
#> 7 mean batch6 1.04 0.106 9.81 1.03e- 22 0.767
#> 8 mean batch7 0.544 0.109 4.98 6.29e- 7 0.263
#> 9 mean batch8 0.496 0.109 4.55 5.30e- 6 0.215
#> 10 mean batch9 0.386 0.119 3.25 1.14e- 3 0.0803
#> 11 mean temp 0.0110 0.000413 26.6 1.26e-155 0.00990
#> 12 precision (phi) 440. 110. 4.00 6.29e- 5 157.
#> # ℹ 1 more variable: conf.high <dbl>
augment(mod)
#> # A tibble: 32 × 6
#> yield batch temp .fitted .resid .cooksd
#> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 0.122 1 205 0.101 1.41 0.0791
#> 2 0.223 1 275 0.195 1.44 0.0917
#> 3 0.347 1 345 0.343 0.170 0.00155
#> 4 0.457 1 407 0.508 -2.14 0.606
#> 5 0.08 2 218 0.0797 0.0712 0.0000168
#> 6 0.131 2 273 0.137 -0.318 0.00731
#> 7 0.266 2 347 0.263 0.169 0.00523
#> 8 0.074 3 212 0.0943 -1.52 0.0805
#> 9 0.182 3 272 0.167 0.831 0.0441
#> 10 0.304 3 340 0.298 0.304 0.0170
#> # ℹ 22 more rows
glance(mod)
#> # A tibble: 1 × 7
#> pseudo.r.squared df.null logLik AIC BIC df.residual nobs
#> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
#> 1 0.962 30 84.8 -146. -128. 20 32