gratia (version 0.9.0)

rootogram: Rootograms to assess goodness of model fit

Description

A rootogram is a model diagnostic tool that assesses the goodness of fit of a statistical model. The observed values of the response are compared with those expected from the fitted model. For discrete, count responses, the frequency of each count (0, 1, 2, etc) in the observed data and expected from the conditional distribution of the response implied by the model are compared. For continuous variables, the observed and expected frequencies are obtained by grouping the data into bins. The rootogram is drawn using ggplot2::ggplot() graphics. The design closely follows Kleiber & Zeileis (2016).

Usage

rootogram(object, ...)
# S3 method for gam
rootogram(object, max_count = NULL, breaks = "Sturges", ...)

Arguments

object

an R object

...

arguments passed to other methods

max_count

integer; the largest count to consider

breaks

for continuous responses, how to group the response. Can be anything that is acceptable as the breaks argument of graphics::hist.default()

References

Kleiber, C., Zeileis, A., (2016) Visualizing Count Data Regressions Using Rootograms. Am. Stat. 70, 296–303. tools:::Rd_expr_doi("10.1080/00031305.2016.1173590")

Examples

load_mgcv()
# \dontshow{
op <- options(cli.unicode = FALSE, pillar.sigfig = 6)
# }
df <- data_sim("eg1", n = 1000, dist = "poisson", scale = 0.1, seed = 6)

# A poisson example
m <- gam(y ~ s(x0, bs = "cr") + s(x1, bs = "cr") + s(x2, bs = "cr") +
  s(x3, bs = "cr"), family = poisson(), data = df, method = "REML")
rg <- rootogram(m)
rg
draw(rg) # plot the rootogram

# A Gaussian example
df <- data_sim("eg1", dist = "normal", seed = 2)
m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = "REML")
draw(rootogram(m, breaks = "FD"), type = "suspended")
# \dontshow{
options(op)
# }