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mrgsim.sa is a package that allows you to do various types of ad-hoc or local sensitivity analysis for models written in mrgsolve. This vignette will help you get started.
By “ad-hoc” sensitivity analysis, I mean selecting some model parameters of interest, varying them at certain discrete, systematic way, simulating from those varied parameters and visualizing the outputs. For example, “what happens when you double the value of a parameter or cut it in half; lets plots outputs for parameters within those two extremes, filling in with 3 or 4 values in between.”
So first, we need a model. This can be any model you write
using the mrgsolve package. Here, we’ll use the example
house
model provided by mrgsolve
This model has parameters
.
. Model parameters (N=14):
. name value . name value
. CL 1 | SEX 0
. D1 2 | SEXCL 0.7
. F1 1 | SEXVC 0.85
. IC50 10 | VC 20
. KA 1.2 | WT 70
. KIN 100 | WTCL 0.75
. KOUT 2 | WTVC 1
and outputs
. $cmt
. [1] "RESP"
.
. $capture
. [1] "CP"
Let’s vary CL
and VC
and look at the
RESP
output.
As suggested in the example above, lets double and halve those
parameters, and look a total of 5 values between those extremes. Use the
parseq_fct()
function after selecting the parameters you
want to vary
The “base” value for each parameter is whatever is currently in the model; in this case it is
. $CL
. [1] 1
.
. $VC
. [1] 20
Passing the .factor
argument as 2 means multiply those
base values by 2 for the upper extreme and 1/2 for the lower extreme.
The .n
argument says to fill in 5 parameter values between
those two extremes.
The sens_each()
function call above tells you to vary
CL
and VC
one at a time.
The output is a tibble in long format, with class
sens_each
. # A tibble: 9,680 × 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 RESP 50 50
. 2 1 0 CL 0.5 RESP 50 50
. 3 1 0 CL 0.5 CP 0 0
. 4 1 0 CL 0.5 CP 0 0
. 5 1 0 CL 0.5 RESP 50 50
. 6 1 0 CL 0.5 RESP 50 50
. 7 1 0 CL 0.5 CP 0 0
. 8 1 0 CL 0.5 CP 0 0
. 9 1 0.25 CL 0.5 RESP 48.7 48.7
. 10 1 0.25 CL 0.5 CP 1.29 1.29
. # ℹ 9,670 more rows
. [1] "sens_each" "tbl_df" "tbl" "data.frame"
Taking inventory of this output
. # A tibble: 4 × 3
. p_name dv_name n
. <chr> <chr> <int>
. 1 CL CP 2420
. 2 CL RESP 2420
. 3 VC CP 2420
. 4 VC RESP 2420
We pass this output object to sens_plot()
and name the
variable we want to plot
There are other ways to vary parameters in the model
parseq_fct()
- increase and decrease by a certain
factorparseq_cv()
- increase and decrease by a certain
coefficient of variationparseq_range()
- manually specify the range
for varying parametersparseq_manual()
- manually specify all values for the
parametersFor example, to vary CL
by 60% coefficient of variation,
plotting 5 values between -2 and 2 sd and looking at CP
output
Or we can look at how VC
influences approach to steady
state
out <-
mod %>%
ev(amt = 100, ii = 24, addl = 10) %>%
update(end = 240) %>%
parseq_manual(VC = seq(10,100,20)) %>%
sens_each()
sens_plot(out, "CP")
We can also look at multiple outputs on the same plot
The facet_wrap
layout puts parameters in rows and
outputs in columns
The facet_grid
layout puts outputs in rows and
parameters in columns
You can also plot this in “grid” format, where the actual parameter values are shown in the legend
Or look at multiple outputs
out %>%
select_sens(dv_name = "RESP,CP") %>%
sens_plot(grid = TRUE) %>%
patchwork::wrap_plots(ncol = 1)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.