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:

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
#> 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
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