Skip to contents

Check for potential parameterization issues

Source: R/diag_constants.R
diagnose_constants.Rd

This function can help diagnose potential flip-flop or other issues related to the parameterization of the model.

Usage

diagnose_constants(
  xpdb,
  df = NULL,
  micro_pattern = "^K(\\d+|EL?)$",
  vol_pattern = "^V(C|D|1|2|)$",
  fo_abs = "KA",
  fo_rates = c("alpha_beta", "lambda", "custom"),
  checks = list(flip_flop = NULL, neg_microvol = NULL, units_match = NULL),
  df_units = NULL,
  .problem,
  quiet = xpdb$options$quiet
)

Arguments

xpdb

<xpose_data> object

df

Optional <data.frame> of parameter values.

micro_pattern

Regex. Pattern for microconstants

vol_pattern

Regex. Pattern for volume parameter (should only match 1)

fo_abs

First-order absorption parameter (singular, fixed, not regex).

fo_rates

Derived ("macro") exponential rate constants (fixed). See Details

checks

See Details

df_units

Named list of units. If NULL, either ignore (df) or pull from xpdb object.

.problem

Used in fetching parameters.

quiet

Should parameter fetching produce output?

Value

Nothing

Details

The function prints output directly, not as an object.

A finding from these checks does not necessarily prove the parameterization is erroneous (indeed, flip-flop PK can exist), but coupled with other findings would help in diagnosing issues.

For fo_rates, "alpha_beta" and "lambda" are convenience placeholders meaning literally c("ALPHA","BETA","GAMMA") and paste0("LAMBA",1:3), respectively. If capitalization or competing names will be an issue, specify a custom set of names (provide a character vector of names, do not pass "custom" to the argument). If only a subset of alpha_beta or lambda are available, but these are the parameterizations used (eg, only ALPHA) these options can still be used. If LAMBDA is used alone, it will not match the "lambda" default. If naming conventions are incompatible, it is suggested xpdb or df be subject to mutation or renaming to use this function.

The available checks at this time are:

  • flip_flop Checks if fo_abs are slower than the derived fo_rates.

  • neg_microvol Checks if any microconstant or volume is negative. Note this check applies to parameterization of microconstants, so only a single volume (parameterizations with multiple volumes do not use microconstants) should match vol_pattern.

  • units_match For any checks, verifies units are consistent. This check requires units are defined by set_var_units() or df_units for parameters applicable to a requested check.

Checks must be requested as a named list of these elements, either TRUE or FALSE (truth determines if the test is done). If the default NULL is used, test will be run if the required parameters are present.

See also

backfill_derived()

Examples


if (!rlang::is_installed("rxode2") ||
   !exists("rxDerived", envir = rlang::ns_env("rxode2"))) {
nlmixr2_m3 %>%
  backfill_derived() %>%
  diagnose_constants(vol_pattern = "^V$")

nlmixr2_m3 %>%
  backfill_derived() %>%
  diagnose_constants(
    vol_pattern = "^V$",
    df_units = list(KA = "1/hr", ALPHA = "1/hr"),
    checks = list(neg_microvol = FALSE)
  )

# Using df form
derive_prm(nlmixr2_m3) %>%
  diagnose_constants(df = ., vol_pattern = "^V$")

}