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 gamlss
tidy(x, quick = FALSE, ...)

## Arguments

x |
A `gamlss` object returned from `gamlss::gamlss()` . |

quick |
Logical indiciating if the only the `term` and `estimate`
columns should be returned. Often useful to avoid time consuming
covariance and standard error calculations. Defaults to `FALSE` . |

... |
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. |

## Value

A `tibble::tibble()`

with columns:

estimateThe estimated value of the regression term.

p.valueThe two-sided p-value associated with the observed statistic.

statisticThe value of a T-statistic to use in a hypothesis that the regression term is non-zero.

std.errorThe standard error of the regression term.

termThe name of the regression term.

parameterType of coefficient being estimated: `mu`, `sigma`,
`nu`, or `tau`.

## Examples

library(gamlss)

#> Loading required package: gamlss.data

#> Loading required package: gamlss.dist

#> Loading required package: nlme

#>
#> Attaching package: ‘nlme’

#> The following objects are masked from ‘package:ordinal’:
#>
#> ranef, VarCorr

#> The following object is masked from ‘package:dplyr’:
#>
#> collapse

#> Loading required package: parallel

#> ********** GAMLSS Version 5.1-0 **********

#> For more on GAMLSS look at http://www.gamlss.org/

#> Type gamlssNews() to see new features/changes/bug fixes.

#>
#> Attaching package: ‘gamlss’

#> The following object is masked from ‘package:caret’:
#>
#> calibration

#> The following objects are masked from ‘package:gam’:
#>
#> lo, random

#> GAMLSS-RS iteration 1: Global Deviance = 4771.925
#> GAMLSS-CG iteration 1: Global Deviance = 4771.013
#> GAMLSS-CG iteration 2: Global Deviance = 4770.994
#> GAMLSS-CG iteration 3: Global Deviance = 4770.994

#> # A tibble: 6 x 6
#> parameter term estimate std.error statistic p.value
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 mu (Intercept) -64.4 1.33 -48.5 1.89e-210
#> 2 mu pb(x) 10.7 0.0578 185. 0.
#> 3 sigma (Intercept) -2.65 0.108 -24.5 8.09e- 93
#> 4 sigma pb(x) -0.0100 0.00378 -2.65 8.29e- 3
#> 5 nu (Intercept) -0.107 0.557 -0.192 8.48e- 1
#> 6 tau (Intercept) 2.49 0.301 8.28 7.77e- 16