appraise | R Documentation |
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()
.
load_mgcv() ## simulate some data... dat <- data_sim("eg1", n = 400, dist = "normal", scale = 2, seed = 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) ## To change the theme for all panels use the & operator, for example to ## change the ggplot theme for all panels library("ggplot2") appraise(mod, point_col = "steelblue", point_alpha = 0.4, line_col = "black") & theme_minimal()
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