library(xpose) knitr::opts_chunk$set(fig.dpi = 96, fig.align = 'center', fig.height = 4, fig.width = 6, out.width = '75%', comment = '', message = FALSE)
The Visual predictive checks (VPC) shown below are only intended to demonstrate the various options available in xpose and should not be used as reference for modeling practice. Furthermore, the plots are only based on 20 simulations to minimize the computing time of the examples, the size of the
xpdb_ex_pk object and of the xpose package in general.
VPC can be created either by:
The VPC computing and plotting parts have been separated into two distinct functions:
vpc() respectively. This allows to:
xpdb_ex_pk %>% vpc_data() %>% # Compute the vpc data vpc() # Plot the vpc
The generated VPC data is stored in the xpdb under specials datasets and can be used later on.
xpdb_w_vpc <- vpc_data(xpdb_ex_pk) # Compute and store VPC data xpdb_w_vpc # The vpc data is now listed under the xpdb "special" data vpc(xpdb_w_vpc) # Plot the vpc from the stored data
Multiple VPC data can be stored in an xpdb, but only one of each
xpdb_w_multi_vpc <- xpdb_ex_pk %>% vpc_data(vpc_type = 'continuous', opt = vpc_opt(n_bins = 6, lloq = 0.1)) %>% vpc_data(vpc_type = 'censored', opt = vpc_opt(n_bins = 6, lloq = 0.1)) vpc(xpdb_w_multi_vpc, vpc_type = 'continuous') vpc(xpdb_w_multi_vpc, vpc_type = 'censored')
vpc_typeallows to specify the type of VPC to be computed: "continuous" (default), "categorical", "censored", "time-to-event".
stratifyoptions defines up to two stratifying variable to be used when computing the VPC data. The
stratifyvariables can either be provided as a character vector (
stratify = c('SEX', 'MED1')) or a formula (
stratify = SEX~MED1) . The former will result in the use of
ggforce::facet_wrap_paginate()and the latter of
ggforce::facet_grid_paginate()when creating the plot. With "categorical" VPC the "group" variable will also be added by default.
optargument expects the output from the
xpdb_ex_pk %>% vpc_data(vpc_type = 'censored', stratify = 'SEX', opt = vpc_opt(bins = 'jenks', n_bins = 7, lloq = 0.5)) %>% vpc()
vpc_typeworks similarly to
vpc_data()and is only required if several VPC data are associated with the xpdb.
smooth = TRUE/FALSEallows to switch between smooth and squared shaded areas.
line_linetypeeach require three values for the low, median and high percentiles respectively.
To create VPC using the xpdb data, at least one simulation and one estimation problem need to present. Hence in the case of NONMEM the run used to generate the xpdb should contain several
vpc_data() the problem number can be specified for the observation (
obs_problem) and the simulation (
sim_problem). By default xpose picks the last one of each to generate the VPC.
# View the xpdb content and data problems xpdb_ex_pk # Generate the vpc xpdb_ex_pk %>% vpc_data(vpc_type = 'continuous', obs_problem = 1, sim_problem = 2) %>% vpc()
vpc_data() contains an argument
psn_folder which can be used to point to a PsN generated VPC folder. As in most xpose function
template_titles keywords can be used to automatize the process e.g.
psn_folder = '@dir/@run_vpc' where
@run will be automatically translated to initial (i.e. when the xpdb was generated) run directory and run number
In this case, the data will be read from the
/m1 sub-folder (or
m1.zip if compressed). Note that PsN drops unused columns to reduce the simtab file size. Thus, in order to allow for more flexibility in R, it is recommended to use multiple stratifying variables (
-stratify_on=VAR1,VAR2) and the prediction corrected (
-predcorr adds the PRED column to the output) options in PsN to avoid having to rerun PsN to add these variables later on.
-stratify_on PsN options are automatically applied to xpose VPC.
The PsN generated binning can also applied to xpose VPC with the
psn_bins = TRUE (disabled by default). However PsN and the vpc package work slightly differently so the results may not be optimal and the output should be evaluated carefully.
xpdb_ex_pk %>% vpc_data(psn_folder = '@dir/run001_vpc', psn_bins = TRUE) %>% vpc()
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