R: Plot mvgam smooth terms

plot_mvgam_smooth {mvgam}

R Documentation

Plot mvgam smooth terms

Description

This function plots posterior empirical quantiles for a series-specific smooth term

Usage

plot_mvgam_smooth(
  object,
  trend_effects = FALSE,
  series = 1,
  smooth,
  residuals = FALSE,
  n_resid_bins = 25,
  realisations = FALSE,
  n_realisations = 15,
  derivatives = FALSE,
  newdata
)

Arguments

`object`	`list` object returned from `mvgam`. See `mvgam()`
`trend_effects`	logical. If `TRUE` and a `trend_formula` was used in model fitting, terms from the trend (i.e. process) model will be plotted
`series`	`integer` specifying which series in the set is to be plotted
`smooth`	either a `character` or `integer` specifying which smooth term to be plotted
`residuals`	`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
`n_resid_bins`	`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`
`realisations`	`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
`n_realisations`	`integer` specifying the number of posterior realisations to plot, if `realisations = TRUE`. Ignored otherwise
`derivatives`	`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)
`newdata`	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

Details

Smooth functions are shown as empirical quantiles (or spaghetti plots) of posterior partial expectations across a sequence of 500 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. For more nuanced visualisation, supply newdata just as you would when predicting from a gam model

Value