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 then 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 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 or red) as well as the posterior
median (as a dark red line). If realisations = FALSE, 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