# Play with compartment properties

### Prerequisite

For this vignette, please load the `campsismod` package and load the minimalist model that we have created in the first vignette.

    library(campsismod)
    model <- read.campsis("resources/minimalist_model/")

### Create new compartment properties

Let’s invent a very basic scenario: we would like to infuse `1000` into the central compartment with a fixed rate of `100` and a fixed lag time of `2`.
First, we’re going to delete the initial condition that we had in the minimalist model. This is done as follows:

    model_ <- model %>% delete(InitialCondition(compartment=1))
    model_

    ## [MAIN]
    ## K=THETA_K*exp(ETA_K) # Elimination constant
    ##
    ## [ODE]
    ## d/dt(A_CENTRAL)=-K*A_CENTRAL
    ##
    ##
    ## THETA's:
    ##   name index value   fix
    ## 1    K     1  0.06 FALSE
    ## OMEGA's:
    ##   name index index2 value   fix type
    ## 1    K     1      1    15 FALSE  cv%
    ## SIGMA's:
    ## # A tibble: 0 × 0
    ## No variance-covariance matrix
    ##
    ## Compartments:
    ## A_CENTRAL (CMT=1)

This is strictly equal as doing (if you prefer working with compartment names):

    model <- model %>% delete(InitialCondition(compartment= model %>% get_compartment_index("CENTRAL")))

We can now add a fixed rate for all infusions that go into the central compartment:

    model <- model %>% add(InfusionRate(compartment=1, "100"))

Finally, let’s now add a constant lag time:

    model <- model %>% add(LagTime(compartment=1, "2"))

OK, this is how our model looks like now:

    model

    ## [MAIN]
    ## K=THETA_K*exp(ETA_K) # Elimination constant
    ##
    ## [ODE]
    ## d/dt(A_CENTRAL)=-K*A_CENTRAL
    ##
    ## [LAG]
    ## A_CENTRAL=2
    ##
    ## [RATE]
    ## A_CENTRAL=100
    ##
    ##
    ## THETA's:
    ##   name index value   fix
    ## 1    K     1  0.06 FALSE
    ## OMEGA's:
    ##   name index index2 value   fix type
    ## 1    K     1      1    15 FALSE  cv%
    ## SIGMA's:
    ## # A tibble: 0 × 0
    ## No variance-covariance matrix
    ##
    ## Compartments:
    ## A_CENTRAL (CMT=1)

### Simulate our model

Let’s now simulate a few individuals and show `A_CENTRAL`, i.e., the amount of drug in the central compartment.

First, we need to define an infusion of `1000` in a Campsis dataset, as well as the observations times.

    library(campsis)
    dataset <- Dataset(5) %>%
      add(Infusion(time=0, amount=1000)) %>%
      add(Observations(seq(0,36,by=0.5)))

Then, we can run the simulation.

    results <- model %>% simulate(dataset=dataset, seed=1)
    spaghettiPlot(results, "A_CENTRAL")

### A couple of useful functions in action

As previously, let’s demonstrate the use of a couple of interesting functions:

Check the existence of a compartment:

    model %>% contains(Compartment(1))

    ## [1] TRUE

    # Or equivalenty:
    model %>% contains(Compartment(model %>% get_compartment_index("CENTRAL")))

    ## [1] TRUE

Check the existence of a property:

    model %>% contains(InfusionRate(1))

    ## [1] TRUE

    model %>% contains(InfusionDuration(1))

    ## [1] FALSE

Find a compartment:

    model %>% find(Compartment(1))

    ## A_CENTRAL (CMT=1)

Find a compartment property:

    model %>% find(InfusionRate(1))

    ## RATE (CMT=1): 100

Replace a compartment property:

    model %>% replace(InfusionRate(1, "200")) # Previous value of 100 is overridden

    ## [MAIN]
    ## K=THETA_K*exp(ETA_K) # Elimination constant
    ##
    ## [ODE]
    ## d/dt(A_CENTRAL)=-K*A_CENTRAL
    ##
    ## [LAG]
    ## A_CENTRAL=2
    ##
    ## [RATE]
    ## A_CENTRAL=200
    ##
    ##
    ## THETA's:
    ##   name index value   fix
    ## 1    K     1  0.06 FALSE
    ## OMEGA's:
    ##   name index index2 value   fix type
    ## 1    K     1      1    15 FALSE  cv%
    ## SIGMA's:
    ## # A tibble: 0 × 0
    ## No variance-covariance matrix
    ##
    ## Compartments:
    ## A_CENTRAL (CMT=1)

Interestingly, the name of a compartment can be replaced as follows:

    model %>% replace(Compartment(1, name="CENT")) %>%
      delete(Ode("A_CENTRAL")) %>%
      add(Ode("A_CENT", "-K*A_CENT"))

    ## [MAIN]
    ## K=THETA_K*exp(ETA_K) # Elimination constant
    ##
    ## [ODE]
    ## d/dt(A_CENT)=-K*A_CENT
    ##
    ## [LAG]
    ## A_CENT=2
    ##
    ## [RATE]
    ## A_CENT=100
    ##
    ##
    ## THETA's:
    ##   name index value   fix
    ## 1    K     1  0.06 FALSE
    ## OMEGA's:
    ##   name index index2 value   fix type
    ## 1    K     1      1    15 FALSE  cv%
    ## SIGMA's:
    ## # A tibble: 0 × 0
    ## No variance-covariance matrix
    ##
    ## Compartments:
    ## A_CENT (CMT=1)