Provides an overview of a model and the terms in that model

Usage

overview(model, ...)

# S3 method for gam
overview(
  model,
  parametric = TRUE,
  random_effects = TRUE,
  dispersion = NULL,
  frequentist = FALSE,
  accuracy = 0.001,
  stars = FALSE,
  ...
)

Arguments

model: a fitted model object to overview.
...: arguments passed to other methods.
parametric: logical; include the model parametric terms in the overview?
random_effects: tests of fully penalized smooth terms (those with a zero-dimensional null space, e.g. random effects) are computationally expensive and for large data sets producing these p values can take a very long time. If random_effects = FALSE, the tests of the expensive terms will be skipped.
dispersion: numeric; a known value for the dispersion parameter. The default NULL implies that the estimated value or the default value (1 for the Poisson distribution for example) where this is specified is used instead.
frequentist: logical; by default the Bayesian estimated covariance matrix of the parameter estimates is used to calculate p values for parametric terms. If frequentist = FALSE, the frequentist covariance matrix of the parameter estimates is used.
accuracy: numeric; accuracy with which to report p values, with p values below this value displayed as "< accuracy".
stars: logical; should significance stars be added to the output?

Examples


load_mgcv()
# \dontshow{
op <- options(pillar.sigfig = 3, cli.unicode = FALSE)
# }
df <- data_sim(n = 400, seed = 2)
m <- gam(y ~ x3 + s(x0) + s(x1, bs = "bs") + s(x2, bs = "ts"),
  data = df, method = "REML"
)
overview(m)
#> 
#> Generalized Additive Model with 4 terms
#> 
#>   term  type              k   edf statistic p.value
#>   <chr> <chr>         <dbl> <dbl>     <dbl> <chr>  
#> 1 x3    parametric       NA  1         4.28 0.03926
#> 2 s(x0) TPRS              9  3.02      6.25 < 0.001
#> 3 s(x1) B spline          9  2.81     71.0  < 0.001
#> 4 s(x2) TPRS (shrink)     9  7.91     83.8  < 0.001
# \dontshow{
options(op)
# }