Model diagnostic plots
Model diagnostic plots
appraise(model, ...)
## S3 method for class 'gam'
appraise(
model,
method = c("uniform", "simulate", "normal", "direct"),
n_uniform = 10,
n_simulate = 50,
type = c("deviance", "pearson", "response"),
n_bins = c("sturges", "scott", "fd"),
ncol = NULL,
nrow = NULL,
guides = "keep",
level = 0.9,
ci_col = "black",
ci_alpha = 0.2,
point_col = "black",
point_alpha = 1,
line_col = "red",
...
)
## S3 method for class 'lm'
appraise(model, ...)model |
a fitted model. Currently only class |
... |
arguments passed to |
method |
character; method used to generate theoretical quantiles. Note
that |
n_uniform |
numeric; number of times to randomize uniform quantiles
in the direct computation method ( |
n_simulate |
numeric; number of data sets to simulate from the estimated
model when using the simulation method ( |
type |
character; type of residuals to use. Only |
n_bins |
character or numeric; either the number of bins or a string indicating how to calculate the number of bins. |
ncol, nrow |
numeric; the numbers of rows and columns over which to spread the plots. |
guides |
character; one of |
level |
numeric; the coverage level for QQ plot reference intervals.
Must be strictly |
ci_alpha, ci_col |
numeric; the level of alpha transparency for the
QQ plot reference interval when |
point_col, point_alpha |
colour and transparency used to draw points in
the plots. See |
line_col |
colour specification for the 1:1 line in the QQ plot and the reference line in the residuals vs linear predictor plot. |
The wording used in mgcv::qq.gam() uses direct in reference to the
simulated residuals method (method = "simulated"). To avoid confusion,
method = "direct" is deprecated in favour of method = "uniform".
The plots are produced by functions qq_plot(),
residuals_linpred_plot(), residuals_hist_plot(),
and observed_fitted_plot().
library(mgcv) ## simulate some data... dat <- gamSim(1, n = 400, dist = "normal", scale = 2) mod <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat) ## run some basic model checks appraise(mod, point_col = "steelblue", point_alpha = 0.4)
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