library(mrgsim.sa)

mod<- mread("pk1", modlib(),end=48,delta=0.1)

. Building pk1 ... done.

param(mod)

. .  Model parameters (N=3):.  name value . name value.  CL   1     | V    20   .  KA   1     | .    .

PK model sensitivity analysis by factor

out<-mod %>%   ev(amt=100) %>%   select_par(CL,V) %>%   parseq_fct(.n=8) %>%   sens_each() sens_plot(out,"CP")

out

. # A tibble: 23,232 × 7.    case  time p_name p_value dv_name dv_value ref_value. * <int> <dbl> <chr>    <dbl> <chr>      <dbl>     <dbl>. 1     1     0 CL         0.5 EV             0         0. 2     1     0 CL         0.5 EV             0       100. 3     1     0 CL         0.5 CENT           0         0. 4     1     0 CL         0.5 CENT           0         0. 5     1     0 CL         0.5 CP             0         0. # ℹ 23,227 more rows

sens_plot(out,"CP",grid=TRUE)

HIV viral dynamic model

mod<- mread("hiv","inst/example")

. Building hiv ... done.

mod %>%   update(end=365*10) %>%  parseq_range(N= c(900,1500),.n=10) %>%  sens_each(tscale=1/365) %>%   sens_plot("L",grid=TRUE)

Sensitivity analysis on custom sequences

mod<- mread("inst/example/rifampicin.cpp") %>% update(delta=0.1)

. Building rifampicin_cpp ... done.

mod %>%   ev(amt=600) %>%   parseq_manual(SFKp= seq_fct(.$SFKp,n=20),Kp_muscle= seq_even(0.001,0.1,n=6)  ) %>%   sens_each() %>%   sens_plot("Ccentral")

Simulate a grid

out<-mod %>%   update(outvars="Ccentral") %>%  ev(amt=600) %>%   parseq_cv(fBCLint_all_kg,.n=7) %>%   parseq_cv(SFKp,Kp_muscle,.n=3) %>%   sens_grid(recsort=3)out

. # A tibble: 15,372 × 8.    case fBCLint_all_kg  SFKp Kp_muscle  time dv_name  dv_value ref_value. * <int>          <dbl> <dbl>     <dbl> <dbl> <chr>       <dbl>     <dbl>. 1     1          0.138  3.65    0.0520   0   Ccentral     0         0   . 2     1          0.138  3.65    0.0520   0   Ccentral     0         0   . 3     1          0.138  3.65    0.0520   0   Ccentral     0         0   . 4     1          0.138  3.65    0.0520   0   Ccentral     0         0   . 5     1          0.138  3.65    0.0520   0.1 Ccentral     3.66      3.14. # ℹ 15,367 more rows

out %>% sens_plot("Ccentral")

Local sensitivity analysis

mod<- modlib("pk2",delta=0.1,end=72)

. Building pk2 ... done.

doses<- ev(amt=100)out<- lsa(mod,var="CP",par="CL,V2,Q",events=doses)out

. # A tibble: 2,166 × 5.    time dv_name dv_value p_name     sens.   <dbl> <chr>      <dbl> <chr>     <dbl>. 1   0   CP         0     CL      0      . 2   0   CP         0     CL      0      . 3   0.1 CP         0.472 CL     -0.00254. 4   0.2 CP         0.893 CL     -0.00514. 5   0.3 CP         1.27  CL     -0.00782. # ℹ 2,161 more rows

lsa_plot(out,pal=NULL)