R/plot_spaghetti.R
pred_vs_idv.Rd
Plot of observations (DV), individual model predictions (IPRED) and/or population predictions (PRED) plotted against the independent variable (IDV).
dv_vs_idv(
xpdb,
mapping = NULL,
group = "ID",
type = "pls",
title = "@y vs. @x | @run",
subtitle = "Ofv: @ofv",
caption = "@dir",
tag = NULL,
log = NULL,
facets,
.problem,
quiet,
...
)
ipred_vs_idv(
xpdb,
mapping = NULL,
group = "ID",
type = "pls",
facets,
title = "@y vs. @x | @run",
subtitle = "Ofv: @ofv, Eps shrink: @epsshk",
caption = "@dir",
tag = NULL,
log = NULL,
.problem,
quiet,
...
)
pred_vs_idv(
xpdb,
mapping = NULL,
group = "ID",
type = "pls",
facets,
title = "@y vs. @x | @run",
subtitle = "Ofv: @ofv",
caption = "@dir",
tag = NULL,
log = NULL,
.problem,
quiet,
...
)
dv_preds_vs_idv(
xpdb,
mapping = NULL,
group = "ID",
type = "pls",
facets,
title = "Observations, Individual and Population Predictions vs. @x | @run",
subtitle = "Ofv: @ofv, Eps shrink: @epsshk",
caption = "@dir",
tag = NULL,
log = NULL,
.problem,
quiet,
...
)
An xpose database object.
List of aesthetics mappings to be used for the xpose plot
(e.g. point_color
).
Grouping variable to be used for lines.
String setting the type of plot to be used. Can be points 'p', line 'l', smooth 's' and text 't' or any combination of the four.
Plot title. Use NULL
to remove.
Plot subtitle. Use NULL
to remove.
Page caption. Use NULL
to remove.
Plot identification tag. Use NULL
to remove.
String assigning logarithmic scale to axes, can be either '', 'x', y' or 'xy'.
Either a character string to use facet_wrap_paginate
or a formula to use facet_grid_paginate
.
The $problem number to be used. By default returns the last estimation problem.
Logical, if FALSE
messages are printed to the console.
Any additional aesthetics to be passed on xplot_scatter
.
Plots can be customized by mapping arguments to specific layers. The naming convention is layer_option where layer is one of the names defined in the list below and option is any option supported by this layer e.g. point_color = 'blue', smooth_method = 'lm', etc.
point: options to geom_point
line: options to geom_line
guide: options to geom_abline
smooth: options to geom_smooth
text: options to geom_text
xscale: options to scale_x_continuous
or scale_x_log10
yscale: options to scale_y_continuous
or scale_y_log10
Every xpose plot function has built-in faceting functionalities. Faceting arguments
are passed to the functions facet_wrap_paginate
when the facets
argument is a character string (e.g. facets = c('SEX', 'MED1')
) or
facet_grid_paginate
when facets is a formula (e.g. facets = SEX~MED1
).
All xpose plot functions accept all the arguments for the facet_wrap_paginate
and facet_grid_paginate
functions e.g. dv_vs_ipred(xpdb_ex_pk,
facets = SEX~MED1, ncol = 3, nrow = 3, page = 1, margins = TRUE, labeller = 'label_both')
.
Faceting options can either be defined in plot functions (e.g. dv_vs_ipred(xpdb_ex_pk,
facets = 'SEX')
) or assigned globally to an xpdb object via the xp_theme
(e.g. xpdb
<- update_themes(xpdb_ex_pk, xp_theme = list(facets = 'SEX'))
). In the latter example all plots
generate from this xpdb will automatically be stratified by `SEX`.
By default, some plot functions use a custom stratifying variable named `variable`, e.g.
eta_distrib()
. When using the facets
argument, `variable` needs to be added manually
e.g. facets = c('SEX', 'variable')
or facets = c('SEX', 'variable')
, but is optional,
when using the facets
argument in xp_theme
variable is automatically added whenever needed.
Template titles can be used to create highly informative diagnostics plots.
They can be applied to any plot title, subtitle, caption and tag. Template titles
are defined via a single string containing key variables staring with a `@` (e.g. `@ofv`)
which will be replaced by their actual value when rendering the plot.
For example `'@run, @nobs observations in @nind subjects'` would become
`'run001, 1022 observations in 74 subjects'`. The available key variables
are listed under template_titles
.
dv_vs_idv(xpdb_ex_pk)
#> Using data from $prob no.1
#> Filtering data by EVID == 0
#> `geom_smooth()` using formula = 'y ~ x'
#> `geom_smooth()` using formula = 'y ~ x'
ipred_vs_idv(xpdb_ex_pk)
#> Using data from $prob no.1
#> Filtering data by EVID == 0
#> `geom_smooth()` using formula = 'y ~ x'
#> `geom_smooth()` using formula = 'y ~ x'
pred_vs_idv(xpdb_ex_pk)
#> Using data from $prob no.1
#> Filtering data by EVID == 0
#> `geom_smooth()` using formula = 'y ~ x'
#> `geom_smooth()` using formula = 'y ~ x'
dv_preds_vs_idv(xpdb_ex_pk)
#> Using data from $prob no.1
#> Filtering data by EVID == 0
#> Tidying data by ID, SEX, MED1, MED2, DOSE ... and 23 more variables
#> `geom_smooth()` using formula = 'y ~ x'
#> `geom_smooth()` using formula = 'y ~ x'