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, ...)

Arguments

x

A gamlss object returned from gamlss::gamlss().

...

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:

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.

parameter

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

Examples

library(gamlss)
#> Loading required package: gamlss.data
#> #> Attaching package: ‘gamlss.data’
#> The following object is masked from ‘package:boot’: #> #> aids
#> The following object is masked from ‘package:mclust’: #> #> acidity
#> The following object is masked from ‘package:datasets’: #> #> sleep
#> 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 5.1-5 **********
#> 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
g <- gamlss( y ~ pb(x), sigma.fo = ~ pb(x), family = BCT, data = abdom, method = mixed(1, 20) )
#> 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
tidy(g)
#> # 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