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   Plots the timecourse of `simulate_rules()` output.

Usage

plot_sr_ev(
  sro = NULL,
  fpage = 1,
  fcol = "id",
  xcol = "time",
  error_msgs = NULL,
  ylog = TRUE,
  ylab_str = "Amount",
  xlab_str = "Time",
  post_proc = "fig  = fig + ggplot2::theme_light()",
  evplot = c(1, 4),
  fncol = 4,
  fnrow = 2
)

Arguments

sro

Output of 'simulate_rules()'.

fpage

If facets are selected and multiple pages are generated then this indcates the page to return.

fcol

Name of column to facet by or NULL to disable faceting ("id").

xcol

Name of column to take x-data from ("time").

error_msgs

Named list with error messages to overwrite (NULL

ylog

Boolean to enable log10 scaling of the y-axis (TRUE

ylab_str

Label for the y-axis ("Output"

xlab_str

Label for the x-axis ("Output"

post_proc

Character object with post processing post-processing code for the figure object named fig internall ("fig = fig + theme_light()")

evplot

Evids to plot can be 1 or 4

fncol

Number of columns in faceted output.

fnrow

Number of rows in faceted output.

Value

List with the followin1g elements:

  • isgood: Return status of the function.

  • msgs: Error or warning messages if any issues were encountered.

  • npages: Total number of pages using the current configuration.

  • error_msgs: List of error messages used.

  • dsp: Intermediate dataset generated from sro to plot in ggplot.

  • fig: Figure generated.

Examples

library(formods)
library(ggplot2)

# For more information see the Clinical Trial Simulation vignette:
# https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html

# None of this will work if rxode2 isn't installed:
if(is_installed("rxode2")){
library(rxode2)
set.seed(8675309)
rxSetSeed(8675309)

my_model = function () 
{
    description <- "One compartment PK model with linear clearance using differential equations"
    ini({
        lka <- 0.45
        label("Absorption rate (Ka)")
        lcl <- 1
        label("Clearance (CL)")
        lvc <- 3.45
        label("Central volume of distribution (V)")
        propSd <- c(0, 0.5)
        label("Proportional residual error (fraction)")
        etalcl ~ 0.1
    })
    model({
        ka <- exp(lka)
        cl <- exp(lcl + etalcl)
        vc <- exp(lvc)
        kel <- cl/vc
        d/dt(depot) <- -ka * depot
        d/dt(central) <- ka * depot - kel * central
        Cc <- central/vc
        Cc ~ prop(propSd)
    })
}

# This creates an rxode2 object
object  = rxode(my_model)

# If you want details about the parameters, states, etc
# in the model you can use this:
rxdetails = fetch_rxinfo(object)

rxdetails$elements

# Next we will create subjects. To do that we need to 
# specify information about covariates:
nsub = 2
covs = list(
  WT         = list(type     = "continuous",
                    sampling = "log-normal",
                    values   = c(70, .15))
)

subs = mk_subjects(object = object,
                   nsub   = nsub,
                   covs   = covs)

head(subs$subjects)

rules = list(
  dose = list(
    condition = "TRUE",
    action    = list(
      type  = "dose",
      state     = "central", 
      values    = "c(1)",
      times     = "c(0)",
      durations = "c(0)")
    )
)

# We evaulate the rules for dosing at time 0
eval_times =  0

# Stop 2 months after the last dose
output_times = seq(0, 56, 1)

# This runs the rule-based simulations
simres = 
  simulate_rules(
    object        = object,
    subjects      = subs[["subjects"]],
    eval_times    = eval_times,
    output_times  = output_times, 
    rules         = rules)

# First subject data:
sub_1 = simres$simall[simres$simall$id == 1, ]

# First subjects events
evall = as.data.frame(simres$evall)
ev_sub_1 = evall[evall$id ==1, ]

# All of the simulation data
simall = simres$simall
simall$id = as.factor(simall$id)

# Timecourse
psim = 
  plot_sr_tc(
    sro    = simres,
    dvcols = "Cc")
psim$fig

# Events
pev = 
  plot_sr_ev(
    sro    = simres,
    ylog   = FALSE)
pev$fig

}
#>  
#>  
#>  parameter labels from comments are typically ignored in non-interactive mode
#>  Need to run with the source intact to parse comments
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#> Warning: multi-subject simulation without without 'omega'
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#> Warning: multi-subject simulation without without 'omega'
#> `geom_line()`: Each group consists of only one observation.
#>  Do you need to adjust the group aesthetic?
#> `geom_line()`: Each group consists of only one observation.
#>  Do you need to adjust the group aesthetic?