Observations (DV), individual predictions (IPRED) and population predictions (PRED) plotted against the independent variable for every individual

  mapping = NULL,
  group = "variable",
  type = "lp",
  title = "Individual plots | @run",
  subtitle = "Ofv: @ofv, Eps shrink: @epsshk",
  caption = "@dir | Page @page of @lastpage",
  tag = NULL,
  log = NULL,
  color = c("grey60", "deepskyblue4", "deepskyblue3"),
  point_alpha = c(0.8, 0, 0),
  line_linetype = c("blank", "solid", "55"),



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.


Changes the **lines, points and text** color. Should be a vector of 3 values (i.e. DV, IPRED, PRED). This color argument is a special case in xpose as it applies to three different layers (geom_line, geom_point and geom_text). This special case is due to the fact that in ggplot2 it is not possible to have two different color scales for different layers.


Points alpha, should be a vector of 3 values (i.e. DV, IPRED, PRED).


Lines linetype, should be a vector of 3 values (i.e. DV, IPRED, PRED).


Any additional aesthetics to be passed on xplot_scatter.

Layers mapping

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

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.

See also


# Basic example ind_plots(xpdb_ex_pk, page = 1, ncol = 2, nrow = 2)
#> Using data from $prob no.1
#> Filtering data by EVID == 0
#> Tidying data by ID, SEX, MED1, MED2, DOSE ... and 23 more variables