Shrinkage contribution versus continuous covariates — shk_vs

Mirrors eta_vs_contcov(), but for the per-individual shrinkage contribution diagnostic (shk type columns, see derive_shk()/backfill_shk()) instead of the etas themselves.

Usage

shk_vs_contcov(
  xpdb,
  mapping = NULL,
  shkvar = NULL,
  covvar = NULL,
  drop_fixed = TRUE,
  linsm = FALSE,
  type = "ps",
  list = TRUE,
  title = "Shrinkage contribution versus continuous covariates | @run",
  subtitle = "Based on @nind individuals",
  caption = "@dir",
  tag = NULL,
  log = NULL,
  guide = TRUE,
  facets,
  .problem,
  quiet,
  ...
)

Arguments

xpdb: <xp_xtras> or <xpose_data> object
mapping: ggplot2 style mapping
shkvar: tidyselect for shk variables
covvar: tidyselect for continuous covariate variables; NULL (default) selects every continuous covariate in the xpdb data index.
drop_fixed: As in xpose
linsm: If type contains "s" should the smooth method by lm?
type: Passed to xplot_scatter
list: <logical> Only relevant when shkvar resolves to more than one shk column. If TRUE (default, for backwards compatibility), returns a plain list of one plot per shk column. If FALSE, they are instead combined onto one shared plot – faceted by shk column, in addition to the existing per-covariate facet – automatically paginating (at most 9 panels per page, i.e. ncol/nrow of 3) via xpose's own facet_wrap_paginate mechanism. Printing the returned plot renders every page; pass page to print() to select a specific one.
title: Plot title
subtitle: Plot subtitle
caption: Plot caption
tag: Plot tag
log: Log scale covariate value?
guide: Add guide line?
facets: Additional facets
.problem: Problem number
quiet: Silence output
...: Any additional aesthetics.

Value

The desired plot, or (when shkvar resolves to more than one shk column and list = TRUE) a plain list of one plot per column.

Examples

# \donttest{

xpdb_x %>%
  backfill_shk() %>%
  shk_vs_contcov()
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, SEX, MED1, MED2, DOSE ... and 26 more variables
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, SEX, MED1, MED2, DOSE ... and 26 more variables
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, SEX, MED1, MED2, DOSE ... and 26 more variables
#> [[1]]
#> `geom_smooth()` using formula = 'y ~ x'

#> 
#> [[2]]
#> `geom_smooth()` using formula = 'y ~ x'

#> 
#> [[3]]
#> `geom_smooth()` using formula = 'y ~ x'

#> 

# Combine all shk columns onto one shared, faceted plot instead of a list
xpdb_x %>%
  backfill_shk() %>%
  shk_vs_contcov(list = FALSE)
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, SEX, MED1, MED2, DOSE ... and 26 more variables
#> `geom_smooth()` using formula = 'y ~ x'

# }