Function reference • PKNCA

All functions

PKNCA-package PKNCA: Compute noncompartmental pharmacokinetics

PKNCA.choose.option(): Choose either the value from an option list or the current set value for an option.

PKNCA.options(): Set default options for PKNCA functions

PKNCA.options.describe(): Describe a PKNCA.options option by name.

PKNCA.set.summary(): Define how NCA parameters are summarized.

PKNCA_impute_fun_list(): Separate out a vector of PKNCA imputation methods into a list of functions

PKNCA_impute_method_start_conc0() PKNCA_impute_method_start_cmin() PKNCA_impute_method_start_predose(): Methods for imputation of data with PKNCA

PKNCAconc(): Create a PKNCAconc object

PKNCAdata(): Create a PKNCAdata object.

PKNCAdose(): Create a PKNCAdose object

PKNCAresults(): Generate a PKNCAresults object

add.interval.col(): Add columns for calculations within PKNCA intervals

addProvenance(): Add a hash and associated information to enable checking object provenance.

adj.r.squared(): Calculate the adjusted r-squared value

any_sparse_dense_in_interval(): Determine if there are any sparse or dense calculations requested within an interval

as.data.frame(<PKNCAresults>): Extract the parameter results from a PKNCAresults and return them as a data.frame.

as_PKNCAconc() as_PKNCAdose() as_PKNCAdata() as_PKNCAresults(): Convert an object into a PKNCAconc object

as_sparse_pk(): Generate a sparse_pk object

assert_PKNCAdata(): Assert that an object is a PKNCAdata object

assert_aucmethod(): Assert that a value is a valid AUC method

assert_conc() assert_time() assert_conc_time(): Verify that concentration measurements are valid

assert_dosetau(): Assert that a value is a dosing interval

assert_intervaltime_single(): Assert that an interval is accurately defined as an interval, and return the interval

assert_lambdaz(): Assert that a lambda.z value is valid

assert_number_between(): Confirm that a value is greater than another value

assert_numeric_between(): Confirm that a value is greater than another value

auc_integrate(): Support function for AUC integration

business.mean() business.sd() business.cv() business.geomean() business.geocv() business.min() business.max() business.median() business.range(): Generate functions to do the named function (e.g. mean) applying the business rules.

check.conversion(): Check that the conversion to a data type does not change the number of NA values

check.interval.deps(): Take in a single row of an interval specification and return that row updated with any additional calculations that must be done to fulfill all dependencies.

check.interval.specification(): Check the formatting of a calculation interval specification data frame.

checkProvenance(): Check the hash of an object to confirm its provenance.

choose.auc.intervals(): Choose intervals to compute AUCs from time and dosing information

choose_interval_method(): Choose how to interpolate, extrapolate, or integrate data in each concentration interval

clean.conc.blq(): Handle BLQ values in the concentration measurements as requested by the user.

clean.conc.na(): Handle NA values in the concentration measurements as requested by the user.

cov_holder(): Calculate the covariance for two time points with sparse sampling

check.conc.time(): The following functions are defunct

exclude(): Exclude data points or results from calculations or summarization.

exclude_nca_span.ratio() exclude_nca_max.aucinf.pext() exclude_nca_min.hl.r.squared(): Exclude NCA parameters based on examining the parameter set.

filter(<PKNCAresults>) filter(<PKNCAconc>) filter(<PKNCAdose>): dplyr filtering for PKNCA

find.tau(): Find the repeating interval within a vector of doses

findOperator(): Find the first occurrence of an operator in a formula and return the left, right, or both sides of the operator.

fit_half_life(): Perform the half-life fit given the data. The function simply fits the data without any validation. No selection of points or any other components are done.

formula(<PKNCAconc>) formula(<PKNCAdose>): Extract the formula from a PKNCAconc object.

geomean() geosd() geocv(): Compute the geometric mean, sd, and CV

get.best.model(): Extract the best model from a list of models using the AIC.

get.first.model(): Get the first model from a list of models

get.interval.cols(): Get the columns that can be used in an interval specification

get.parameter.deps(): Get all columns that depend on a parameter

getAttributeColumn(): Retrieve the value of an attribute column.

getColumnValueOrNot(): Get the value from a column in a data frame if the value is a column there, otherwise, the value should be a scalar or the length of the data.

getDataName(): Get the name of the element containing the data for the current object.

getDepVar(): Get the dependent variable (left hand side of the formula) from a PKNCA object.

getGroups(<PKNCAconc>) getGroups(<PKNCAdose>) getGroups(<PKNCAresults>): Get the groups (right hand side after the | from a PKNCA object).

getIndepVar(): Get the independent variable (right hand side of the formula) from a PKNCA object.

get_impute_method(): Get the impute function from either the intervals column or from the method

group_by(<PKNCAresults>) group_by(<PKNCAconc>) group_by(<PKNCAdose>) ungroup(<PKNCAresults>) ungroup(<PKNCAconc>) ungroup(<PKNCAdose>): dplyr grouping for PKNCA

group_vars(<PKNCAconc>) group_vars(<PKNCAdose>): Get grouping variables for a PKNCA object

inner_join(<PKNCAresults>) left_join(<PKNCAresults>) right_join(<PKNCAresults>) full_join(<PKNCAresults>) inner_join(<PKNCAconc>) left_join(<PKNCAconc>) right_join(<PKNCAconc>) full_join(<PKNCAconc>) inner_join(<PKNCAdose>) left_join(<PKNCAdose>) right_join(<PKNCAdose>) full_join(<PKNCAdose>): dplyr joins for PKNCA

interp.extrap.conc() interpolate.conc() extrapolate.conc() interp.extrap.conc.dose(): Interpolate concentrations between measurements or extrapolate concentrations after the last measurement.

interpolate_conc_linear() interpolate_conc_log() extrapolate_conc_lambdaz(): Interpolate or extrapolate concentrations using the provided method

is_sparse_pk(): Is a PKNCA object used for sparse PK?

model.frame(<PKNCAconc>) model.frame(<PKNCAdose>): Extract the columns used in the formula (in order) from a PKNCAconc or PKNCAdose object.

mutate(<PKNCAresults>) mutate(<PKNCAconc>) mutate(<PKNCAdose>): dplyr mutate-based modification for PKNCA

normalize_exclude(): Normalize the exclude column by setting blanks to NA

parse_formula_to_cols(): Convert a formula representation to the columns for input data

pk.business(): Run any function with a maximum missing fraction of X and 0s possibly counting as missing. The maximum fraction missing comes from PKNCA.options("max.missing").

pk.calc.ae(): Calculate amount excreted (typically in urine or feces)

pk.calc.aucabove(): Calculate the AUC above a given concentration

pk.calc.aucint() pk.calc.aucint.last() pk.calc.aucint.all() pk.calc.aucint.inf.obs() pk.calc.aucint.inf.pred(): Calculate the AUC over an interval with interpolation and/or extrapolation of concentrations for the beginning and end of the interval.

pk.calc.auciv() pk.calc.auciv_pbext(): Calculate AUC for intravenous dosing

pk.calc.aucpext(): Calculate the AUC percent extrapolated

pk.calc.auxc() pk.calc.auc() pk.calc.auc.last() pk.calc.auc.inf() pk.calc.auc.inf.obs() pk.calc.auc.inf.pred() pk.calc.auc.all() pk.calc.aumc() pk.calc.aumc.last() pk.calc.aumc.inf() pk.calc.aumc.inf.obs() pk.calc.aumc.inf.pred() pk.calc.aumc.all(): A compute the Area Under the (Moment) Curve

pk.calc.c0() pk.calc.c0.method.logslope() pk.calc.c0.method.c0() pk.calc.c0.method.c1() pk.calc.c0.method.set0() pk.calc.c0.method.cmin(): Estimate the concentration at dosing time for an IV bolus dose.

pk.calc.cav(): Calculate the average concentration during an interval.

pk.calc.ceoi(): Determine the concentration at the end of infusion

pk.calc.cl(): Calculate the (observed oral) clearance

pk.calc.clast.obs(): Determine the last observed concentration above the limit of quantification (LOQ).

pk.calc.clr(): Calculate renal clearance

pk.calc.cmax() pk.calc.cmin(): Determine maximum observed PK concentration

pk.calc.count_conc(): Count the number of concentration measurements in an interval

pk.calc.cstart(): Determine the concentration at the beginning of the interval

pk.calc.ctrough(): Determine the trough (end of interval) concentration

pk.calc.deg.fluc(): Determine the degree of fluctuation

pk.calc.dn(): Determine dose normalized NCA parameter

pk.calc.f(): Calculate the absolute (or relative) bioavailability

pk.calc.fe(): Calculate fraction excreted (typically in urine or feces)

pk.calc.half.life(): Compute the half-life and associated parameters

pk.calc.kel(): Calculate the elimination rate (Kel)

pk.calc.mrt() pk.calc.mrt.iv(): Calculate the mean residence time (MRT) for single-dose data or linear multiple-dose data.

pk.calc.mrt.md(): Calculate the mean residence time (MRT) for multiple-dose data with nonlinear kinetics.

pk.calc.ptr(): Determine the peak-to-trough ratio

pk.calc.sparse_auc() pk.calc.sparse_auclast(): Calculate AUC and related parameters using sparse NCA methods

pk.calc.swing(): Determine the PK swing

pk.calc.thalf.eff(): Calculate the effective half-life

pk.calc.time_above(): Determine time at or above a set value

pk.calc.tlag(): Determine the observed lag time (time before the first concentration above the limit of quantification or above the first concentration in the interval)

pk.calc.tlast() pk.calc.tfirst(): Determine time of last observed concentration above the limit of quantification.

pk.calc.tmax(): Determine time of maximum observed PK concentration

pk.calc.totdose(): Extract the dose used for calculations

pk.calc.vss(): Calculate the steady-state volume of distribution (Vss)

pk.calc.vz(): Calculate the terminal volume of distribution (Vz)

pk.nca(): Compute NCA parameters for each interval for each subject.

pk.nca.interval(): Compute all PK parameters for a single concentration-time data set

pk.nca.intervals(): Compute NCA for multiple intervals

pk.tss(): Compute the time to steady-state (tss)

pk.tss.data.prep(): Clean up the time to steady-state parameters and return a data frame for use by the tss calculators.

pk.tss.monoexponential(): Compute the time to steady state using nonlinear, mixed-effects modeling of trough concentrations.

pk.tss.monoexponential.individual(): A helper function to estimate individual and single outputs for monoexponential time to steady-state.

pk.tss.monoexponential.population(): A helper function to estimate population and popind outputs for monoexponential time to steady-state.

pk.tss.stepwise.linear(): Compute the time to steady state using stepwise test of linear trend

pk_nca_result_to_df(): Convert the grouping info and list of results for each group into a results data.frame

pknca_find_units_param(): Find NCA parameters with a given unit type

pknca_unit_conversion(): Perform unit conversion (if possible) on PKNCA results

pknca_units_add_paren(): Add parentheses to a unit value, if needed

pknca_units_table(): Create a unit assignment and conversion table

print(<PKNCAconc>) summary(<PKNCAconc>) print(<PKNCAdose>) summary(<PKNCAdose>): Print and/or summarize a PKNCAconc or PKNCAdose object.

print(<PKNCAdata>): Print a PKNCAdata object

print(<provenance>): Print the summary of a provenance object

print(<summary_PKNCAresults>): Print the results summary

roundString(): Round a value to a defined number of digits printing out trailing zeros, if applicable.

roundingSummarize(): During the summarization of PKNCAresults, do the rounding of values based on the instructions given.

setAttributeColumn(): Add an attribute to an object where the attribute is added as a name to the names of the object.

setDuration(): Set the duration of dosing or measurement

setExcludeColumn(): Set the exclude parameter on an object

setRoute(): Set the dosing route

signifString(): Round a value to a defined number of significant digits printing out trailing zeros, if applicable.

sort(<interval.cols>): Sort the interval columns by dependencies.

sparse_auc_weight_linear(): Calculate the weight for sparse AUC calculation with the linear-trapezoidal rule

sparse_mean(): Calculate the mean concentration at all time points for use in sparse NCA calculations

sparse_pk_attribute(): Set or get a sparse_pk object attribute

sparse_to_dense_pk(): Extract the mean concentration-time profile as a data.frame

summary(<PKNCAdata>): Summarize a PKNCAdata object showing important details about the concentration, dosing, and interval information.

summary(<PKNCAresults>): Summarize PKNCA results

superposition(): Compute noncompartmental superposition for repeated dosing

time_calc(): Times relative to an event (typically dosing)

tss.monoexponential.generate.formula(): A helper function to generate the formula and starting values for the parameters in monoexponential models.

var_sparse_auc(): Calculate the variance for the AUC of sparsely sampled PK