This function plots posterior empirical quantiles for a series-specific smooth term
plot_mvgam_smooth(
object,
trend_effects = FALSE,
series = 1,
smooth,
residuals = FALSE,
n_resid_bins = 25,
realisations = FALSE,
n_realisations = 15,
derivatives = FALSE,
newdata
)A base R graphics plot
list object of class mvgam. See mvgam()
logical. If TRUE and a trend_formula was used in
model fitting, terms from the trend (i.e. process) model will be plotted
integer specifying which series in the set is to be
plotted
Either a character or integer specifying which
smooth term to be plotted
logical. If TRUE, posterior quantiles of
partial residuals are added to plots of 1-D smooths as a series of ribbon
rectangles. Partial residuals for a smooth term are the median Dunn-Smyth
residuals that would be obtained by dropping the term concerned from the
model, while leaving all other estimates fixed (i.e. the estimates for the
term plus the original median Dunn-Smyth residuals). Note that because
mvgam works with Dunn-Smyth residuals and not working residuals,
which are used by mgcv, the magnitudes of partial residuals will be
different to what you would expect from plot.gam.
Interpretation is similar though, as these partial residuals should be
evenly scattered around the smooth function if the function is well
estimated
integer specifying the number of bins to group
the covariate into when plotting partial residuals. Setting this argument
too high can make for messy plots that are difficult to interpret, while
setting it too low will likely mask some potentially useful patterns in
the partial residuals. Default is 25
logical. If TRUE, posterior realisations
are shown as a spaghetti plot, making it easier to visualise the diversity
of possible functions. If FALSE, the default, empirical quantiles
of the posterior distribution are shown
integer specifying the number of posterior
realisations to plot, if realisations = TRUE. Ignored otherwise
logical. If TRUE, an additional plot will
be returned to show the estimated 1st derivative for the specified smooth
(Note: this only works for univariate smooths)
Optional dataframe for predicting the smooth,
containing at least 'series' in addition to any other variables included
in the linear predictor of the original model's formula. Note that
this currently is only supported for plotting univariate smooths
Nicholas J Clark
Smooth functions are shown as empirical quantiles (or spaghetti
plots) of posterior partial expectations across a sequence of values
between the variable's min and max, while zeroing out
effects of all other variables. At present, only univariate and bivariate
smooth plots are allowed, though note that bivariate smooths rely on
default behaviour from plot.gam. plot_mvgam_smooth
generates posterior predictions from an object of class mvgam,
calculates posterior empirical quantiles and plots them. If
realisations = FALSE, the returned plot shows 90, 60, 40 and 20 percent
posterior quantiles (as ribbons of increasingly darker shades of red) as
well as the posterior median (as a dark red line). If
realisations = TRUE, a set of n_realisations posterior draws are
shown. For more nuanced visualisation, supply newdata just as you
would when predicting from a gam model or use the more
flexible conditional_effects.mvgam. Alternatively, if you
prefer to use partial effect plots in the style of gratia, and if you
have the gratia package installed, you can use draw.mvgam. See
gratia_mvgam_enhancements for details.
plot.gam,
conditional_effects.mvgam,
gratia_mvgam_enhancements