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, ...)
x | A |
---|---|
quick | Logical indiciating if the only the |
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
A tibble::tibble()
with columns:
The estimated value of the regression term.
The two-sided p-value associated with the observed statistic.
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
The standard error of the regression term.
The name of the regression term.
Type of coefficient being estimated: `mu`, `sigma`, `nu`, or `tau`.
library(gamlss)#>#>#>#> #>#>#> #>#>#> #>#>#>#>#>#> #>#>#> #>#>#> #>#> 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 = 4770.994tidy(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