[ ] update get_vcov() to return the Bayesian covariance matrix; shouldn't impact marginaleffects functionality
[ ] predict(object, type = 'terms')
[ ] predict(object, type = 'lpmatrix')
[ ] residuals(object, type = 'working')
[ ] weights(object)
[ ] smoothCon() and PredictMat() functions for gp() and dynamic() terms
... actually will be easier to
use expand.grid() to get pred values for terms of interest. Accept an argument that allows multidimensional effects to be drawn either as heatmaps or in plot_predictions() style with faceting
fix all other vars to representative row idxs by replicating a single entry in original data (only filling in the vars of interest). This will work with df or list types and the values for non-focal vars won't matter as we will use 'terms' predictions
use predict(type = 'terms') to get partial contribution from effect of interest
plot by sending to gratia::draw_smooth_estimates() and with modified multidimensional plot functions, i.e. plot_smooth.bivariate_smooth_facet() perhaps
Would likely require methods for:
vcov()
with same arguments asvcov.gam()
get_vcov()
to return the Bayesian covariance matrix; shouldn't impactmarginaleffects
functionalitypredict(object, type = 'terms')
predict(object, type = 'lpmatrix')
residuals(object, type = 'working')
weights(object)
smoothCon()
andPredictMat()
functions forgp()
anddynamic()
terms... actually will be easier to
expand.grid()
to get pred values for terms of interest. Accept an argument that allows multidimensional effects to be drawn either as heatmaps or inplot_predictions()
style with facetingdf
orlist
types and the values for non-focal vars won't matter as we will use'terms'
predictionspredict(type = 'terms')
to get partial contribution from effect of interestgratia::draw_smooth_estimates()
and with modified multidimensional plot functions, i.e.plot_smooth.bivariate_smooth_facet()
perhaps