These mirror eta_grid()/eta_vs_cov_grid(), but for the
per-individual shrinkage contribution diagnostic (shk type columns,
see derive_shk()/backfill_shk()) instead of the etas
themselves.
shk_grid(
xpdb,
mapping = NULL,
shkvar = NULL,
drop_fixed = TRUE,
title = "Shrinkage contribution correlations | @run",
subtitle = "Based on @nind individuals",
caption = "@dir",
tag = NULL,
pairs_opts,
.problem,
quiet,
...
)shk_vs_cov_grid(
xpdb,
mapping = NULL,
shkvar = NULL,
cols = NULL,
covvar = NULL,
covtypes = c("cont", "cat"),
show_n = TRUE,
drop_fixed = TRUE,
title = "Shrinkage contribution covariate correlations | @run",
subtitle = "Based on @nind individuals",
caption = "@dir",
tag = NULL,
shkcov = TRUE,
pairs_opts,
.problem,
quiet,
...
)
xp_tras_plot object
<xp_xtras> or <xpose_data> object
ggplot2 style mapping
tidyselect for shk variables
As in xpose
Plot title
Plot subtitle
Plot caption
Plot tag
List of arguments to pass to _opts. See <xplot_pairs>
Problem number
Silence extra debugging output
Passed to xplot_pairs
tidyselect for covariates variables
For shk_vs_cov_grid only: an alias for cols (matching
the covvar argument of shk_vs_contcov()/shk_vs_catcov()). If
supplied (non-NULL), takes precedence over cols.
Subset to specific covariate type?
Count the number of IDs in each category
For shk_vs_cov_grid, shk are sorted after covariates
to give an x orientation to covariate relationships.
# \donttest{
xpdb_shk <- backfill_shk(xpdb_x)
shk_grid(xpdb_shk)
shk_vs_cov_grid(xpdb_shk)
# }
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