rootogram | R Documentation |
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).
rootogram(object, ...)
## S3 method for class 'gam'
rootogram(object, max_count = NULL, breaks = "Sturges", ...)
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 |
Kleiber, C., Zeileis, A., (2016) Visualizing Count Data Regressions Using Rootograms. Am. Stat. 70, 296–303. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00031305.2016.1173590")}
load_mgcv()
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")
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