Eta continuous covariate plots (typical)

Usage

eta_vs_contcov(
  xpdb,
  mapping = NULL,
  etavar = NULL,
  covvar = NULL,
  drop_fixed = TRUE,
  linsm = FALSE,
  type = "ps",
  list = TRUE,
  title = "Eta versus continuous covariates | @run",
  subtitle = "Based on @nind individuals, Eta shrink: @etashk",
  caption = "@dir",
  tag = NULL,
  log = NULL,
  guide = TRUE,
  facets,
  .problem,
  quiet,
  ...
)

Arguments

xpdb: <xp_xtras> or <xpose_data`> object
mapping: ggplot2 style mapping
etavar: tidyselect for eta 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 etavar resolves to more than one eta. If TRUE (default, for backwards compatibility), returns a plain list of one plot per eta. If FALSE, all etas are instead combined onto one shared plot – faceted by eta, 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 etavar resolves to more than one eta and list = TRUE) a plain list of one plot per eta.

Examples

# \donttest{

eta_vs_contcov(xpdb_x)
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, SEX, MED1, MED2, DOSE ... and 23 more variables
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, SEX, MED1, MED2, DOSE ... and 23 more variables
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, SEX, MED1, MED2, DOSE ... and 23 more variables
#> [[1]]
#> `geom_smooth()` using formula = 'y ~ x'

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

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

#> 

# Labels and units are also supported
xpdb_x %>%
  xpose::set_var_labels(AGE="Age", MED1 = "Digoxin") %>%
  xpose::set_var_units(AGE="yrs") %>%
  set_var_levels(SEX=lvl_sex(), MED1 = lvl_bin()) %>%
  eta_vs_contcov()
#> Warning: There was 1 warning in `dplyr::mutate()`.
#> ℹ In argument: `out = purrr::map_if(...)`.
#> Caused by warning:
#> ! In $prob no.2 columns: MED1 not present in the data.
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, SEX, MED1, MED2, DOSE ... and 23 more variables
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, SEX, MED1, MED2, DOSE ... and 23 more variables
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, SEX, MED1, MED2, DOSE ... and 23 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 etas onto one shared, faceted plot instead of a list
eta_vs_contcov(xpdb_x, list = FALSE)
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, SEX, MED1, MED2, DOSE ... and 23 more variables
#> `geom_smooth()` using formula = 'y ~ x'


# Restrict to specific covariates with covvar, just like etavar
eta_vs_contcov(xpdb_x, covvar = AGE)
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, SEX, MED1, MED2, DOSE ... and 25 more variables
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, SEX, MED1, MED2, DOSE ... and 25 more variables
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, SEX, MED1, MED2, DOSE ... and 25 more variables
#> [[1]]
#> `geom_smooth()` using formula = 'y ~ x'

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

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

#> 
# }