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# xpose.xtras <img src="man/figures/logo.png" align="right" height="139" alt="" />

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## Introduction

This package adds some extra functionality and plots to the
[`xpose`](https://uupharmacometrics.github.io/xpose/) framework. This
includes some plots that have been missing in translation from
[`xpose4`](https://uupharmacometrics.github.io/xpose4/), but also some
useful features that truly extend the capabilities of what can be done
with `xpose`.

There are a few bugfixes here and functionality which could easily be
suggested as pull requests to the parent package. Given the size and
broad use of `xpose`, it appears even minor pull requests take some time
to implement. As such, this package implements those features directly
and if at any point in the future these are added (perhaps in a better
state) to the parent package, they will be deprecated if this package is
in active use.

For those wondering, [`conflicted`](https://conflicted.r-lib.org/) is
used to manage bugfix conflicts, so users should be comfortable loading
packages in any order.

## Installation

The package can be installed from CRAN:

``` r
install.packages("xpose.xtras")
```

The typical github installation will also work.

``` r
pak::pak("jprybylski/xpose.xtras")
```

The main github branch is reserved for CRAN release-ready versions. The
dev branch is usually ahead if there is active development.

``` r
pak::pkg_install("jprybylski/xpose.xtras@dev")
```

## Preview

The grandparent package, `xpose4`, used to have a nice collection of
figures and documentation that is referred to as a “bestiarium”. The
documentation site for this package serves as a complete bestiary, but
see the uncommented examples below as a sort of menagerie. There is no
assumption that these examples are self-explanatory, but hopefully users
familiar with `xpose` will recognize the new (and *re*newed) tools made
available by this package.

### EBEs versus covariates

``` r
described <- xpdb_x %>%
  set_var_labels(AGE="Age", MED1 = "Digoxin", .problem = 1) %>%
  set_var_units(AGE="yrs")  %>%
  set_var_levels(SEX=lvl_sex(), MED1 = lvl_bin())
eta_vs_contcov(described,etavar=ETA1, quiet=TRUE)
#> `geom_smooth()` using formula = 'y ~ x'
```

<img src="man/figures/README-eta_vs-1.png" alt="" width="100%" style="display: block; margin: auto;" />

``` r
eta_vs_catcov(described,etavar=ETA1, quiet=TRUE)
```

<img src="man/figures/README-eta_vs-2.png" alt="" width="100%" style="display: block; margin: auto;" />

### Shark plots

``` r
pheno_set %>%
  focus_qapply(backfill_iofv) %>%
  dofv_vs_id(run6, run9, quiet = TRUE)
```

<img src="man/figures/README-shark-1.png" alt="" width="100%" style="display: block; margin: auto;" />

### Categorical DVs

``` r
pkpd_m3 %>%
  set_var_types(catdv=BLQ,dvprobs=LIKE) %>%
  set_dv_probs(1, 1~LIKE, .dv_var = BLQ) %>%
  set_var_levels(1, BLQ = lvl_bin()) %>%
  catdv_vs_dvprobs(quiet=TRUE)
#> `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
```

<img src="man/figures/README-m3-1.png" alt="" width="100%" style="display: block; margin: auto;" />

``` r
nlmixr2_m3 %>%
  set_var_types(catdv=CENS,dvprobs=BLQLIKE) %>%
  set_dv_probs(1, 1~BLQLIKE, .dv_var = CENS) %>%
  set_var_levels(1, CENS = lvl_bin()) %>%
  roc_plot(quiet = TRUE)
```

<img src="man/figures/README-m3_roc-1.png" alt="" width="100%" style="display: block; margin: auto;" />

### Parameter correlation matrix

``` r
cormat(xpdb_x, quiet = TRUE)
```

<img src="man/figures/README-cormat-1.png" alt="" width="100%" style="display: block; margin: auto;" />
