Package 'apmx'

Title: Automated Population Pharmacokinetic Dataset Assembly
Description: Automated methods to assemble population PK (pharmacokinetic) and PKPD (pharmacodynamic) datasets for analysis in 'NONMEM' (non-linear mixed effects modeling) by Bauer (2019) <doi:10.1002/psp4.12404>. The package includes functions to build datasets from SDTM (study data tabulation module) <https://www.cdisc.org/standards/foundational/sdtm>, ADaM (analysis dataset module) <https://www.cdisc.org/standards/foundational/adam>, or other dataset formats. The package will combine population datasets, add covariates, and create documentation to support regulatory submission and internal communication.
Authors: Stephen Amori [aut, cre, cph], Ethan DellaMaestra [aut], Michael Dick [aut], Daniel Litow [ctb], Jonah Lyon [ctb]
Maintainer: Stephen Amori <[email protected]>
License: GPL (>= 3)
Version: 1.1.1
Built: 2025-03-13 04:47:16 UTC
Source: https://github.com/stephen-amori/apmx

Help Index

Apply covariates to PK(PD) dataset

Description

Add covariates to a dataset built by pk_build() or pk_combine() Can add subject-level covariates (by any ID variable) or time-varying (by any time variable)

Usage

cov_apply(
  df,
  cov,
  id.by = "USUBJID",
  time.by = NA,
  direction = "downup",
  exp = FALSE,
  ebe = FALSE,
  cov.rnd = NA,
  na = -999,
  demo.map = TRUE,
  keep.other = TRUE
)

Arguments

df

PK(PD) dataframe generated by pk_build
cov

dataframe of covariates
id.by

id variable to merge covariates
time.by

time variable to merge covariates
direction

fill direction for time-varying covariates
exp

treats new covariates as exposure metrics when TRUE
ebe

treats new covariates as empirical bayes estimates when TRUE
cov.rnd

covariate rounding parameter
na

value to replace NA numeric covariates
demo.map

toggle pre-set numeric values for SEX, RACE, and ETHNIC demographic variables
keep.other

filter to keep or remove other events, EVID = 2

Value

PK(PD) dataset with additional covariates]

Examples

## Simple ex domain with 1 subject and 1 dose
ex <- data.frame(STUDYID = "ABC101",
                 USUBJID = "ABC101-001",
                 EXSTDTC = "2000-01-01 10:00:00",
                 EXSTDY = 1,
                 EXTPTNUM = 0,
                 EXDOSE = 100,
                 CMT = 1,
                 EXTRT = "ABC",
                 EXDOSU = "mg",
                 VISIT = "Day 1",
                 EXTPT = "Dose",
                 EXDOSFRQ = "Once",
                 EXROUTE = "Oral")

## Simple pc domain with 1 subject and 3 observations
pc <- data.frame(USUBJID = "ABC101-001",
                 PCDTC = c("2000-01-01 09:40:00",
                           "2000-01-01 10:29:00",
                           "2000-01-01 12:05:00"),
                 PCDY = 1,
                 PCTPTNUM = c(0, ##Units of hours
                              0.021,
                              0.083),
                 PCSTRESN = c(NA,
                              469,
                              870),
                 PCLLOQ = 25,
                 CMT = 2,
                 VISIT = "Day 1",
                 PCTPT = c("Pre-dose",
                           "30-min post-dose",
                           "2-hr post-dose"),
                 PCTEST = "ABC",
                 PCSTRESU = "ug/mL")

## Create with pk_build()
df <- pk_build(ex, pc)

## Simple dm domain for the corresponding study
dm <- data.frame(USUBJID = c("ABC101-001",
                             "ABC101-002",
                             "ABC101-003"),
                 AGE = c(45,
                         37,
                         73),
                 AGEU = "years",
                 SEX = c("Male",
                         "Female",
                         "Male"),
                 RACE = c("White",
                          "White",
                          "Black"),
                 ETHNIC = c("Not Hispanic/Latino",
                            "Not Hispanic/Latino",
                            "Not Hispanic/Latino"))

## Add covariates with cov_apply()
df1 <- cov_apply(df, dm)

Find covariates of particular types

Description

Can filter for categorical, continuous, or other covariates. Can filter for numeric or character type.

Usage

cov_find(df, cov, type)

Arguments

df

PK(PD) dataset
cov

covariate distribution
type

covariate type

Value

vector of desired column names

Examples

## Simple ex domain with 1 subject and 1 dose
ex <- data.frame(STUDYID = "ABC101",
                 USUBJID = "ABC101-001",
                 EXSTDTC = "2000-01-01 10:00:00",
                 EXSTDY = 1,
                 EXTPTNUM = 0,
                 EXDOSE = 100,
                 CMT = 1,
                 EXTRT = "ABC",
                 EXDOSU = "mg",
                 VISIT = "Day 1",
                 EXTPT = "Dose",
                 EXDOSFRQ = "Once",
                 EXROUTE = "Oral")

## Simple pc domain with 1 subject and 3 observations
pc <- data.frame(USUBJID = "ABC101-001",
                 PCDTC = c("2000-01-01 09:40:00",
                           "2000-01-01 10:29:00",
                           "2000-01-01 12:05:00"),
                 PCDY = 1,
                 PCTPTNUM = c(0, ##Units of hours
                              0.021,
                              0.083),
                 PCSTRESN = c(NA,
                              469,
                              870),
                 PCLLOQ = 25,
                 CMT = 2,
                 VISIT = "Day 1",
                 PCTPT = c("Pre-dose",
                           "30-min post-dose",
                           "2-hr post-dose"),
                 PCTEST = "ABC",
                 PCSTRESU = "ug/mL")

## Create with pk_build()
df <- pk_build(ex, pc)

## Simple dm domain for the corresponding study
dm <- data.frame(USUBJID = c("ABC101-001",
                             "ABC101-002",
                             "ABC101-003"),
                 AGE = c(45,
                         37,
                         73),
                 AGEU = "years",
                 SEX = c("Male",
                         "Female",
                         "Male"),
                 RACE = c("White",
                          "White",
                          "Black"),
                 ETHNIC = c("Not Hispanic/Latino",
                            "Not Hispanic/Latino",
                            "Not Hispanic/Latino"))

## Add covariates with cov_apply()
df1 <- cov_apply(df, dm)

## Find covariates with cov_find()
cov_find(df1, cov="categorical", type="numeric")
cov_find(df1, cov="categorical", type="character")
cov_find(df1, cov="continuous", type="numeric")
cov_find(df1, cov="units", type="character")

DM

Description

Randomly generated demographic data domain

Usage

DM

Format

## 'DM' A data frame with 22 rows and 12 variables:

STUDYID

study label

SITEID

site code

SUBJID

subject code

USUBJID

unique subject ID

SCRFL

screen fail flag

ICFDT

informed consent date

ICFDTC

informed consent date character form

DOBDT

date of birth

AGE

subject baseline age

SEX

subject sex

RACE

subject race

ETHNIC

subject ethnicity

EX

Description

Randomly generated exposure domain

Usage

EX

Format

## 'EX' A data frame with 42 rows and 19 variables:

STUDYID

study label

SITEID

site code

USUBJID

unique subject ID

EXCAT

domain category

VISIT

visit label

EXSTDY

numeric study day

VISCRFN

visit numeric code

EXTRT

treatment label

EXDOSE

treatment amount

EXDOSU

treatment unit label

EXROUTE

treatment route label

EXDOSFRQ

treatment frequency

EXDT

treatment administration date

EXDTC

treatment administration date character form

EXTM

treatment administration time

EXTMC

treatment administration time character form

EXSTDTC

treatment administration date and time

EXTPT

treatment timepoint label

EXTPTNUM

treatment numeric timepoint

LB

Description

Randomly generated laboratory data domain

Usage

LB

Format

## 'LB' A data frame with 2159 rows and 16 variables:

STUDYID

study label

SITEID

site code

USUBJID

unique subject ID

LBCAT

domain category

LBCOMPFL

completion flag

LBDT

date of assessment

LBVST

visit label

VISCRFN

visit numeric code

LBTPT

timepoint label

LBTPTN

timepoint numeric code

LBPARAMCD

parameter code

LBPARAM

parameter

LBORRES

original parameter result

LBORRESC

original parameter result, character form

LBORRESU

original parameter unit label

PC

Description

Randomly generated pharmacokinetic observation domain

Usage

PC

Format

## 'PC' A data frame with 420 rows and 19 variables:

STUDYID

study label

SITEID

site code

USUBJID

unique subject ID

PCCAT

domain category

PCTEST

analyte category

VISIT

visit label

PCDY

study numeric day

VISCRFN

visit numeric code

PCTPT

timepoint label

PCTPTN

timepoint numeric code

PCSTAT

completion status

PCDT

observation date

PCTM

observation time

PCTMC

observation time character form

PCDTC

observation date and time

PCORRES

original pharmacokinetic observation

PCORRESU

original pharmacokinetic observation unit label

PCSTRESN

standardized pharmacokinetic numeric observation

PCSTRESC

standardized pharmacokinetic character observation

PCSTRESU

standardized pharmacokinetic observation unit label

PCLLOQ

standardized pharmacokinetic observation lower limit of quantification

Create a NONMEM PK(PD) dataset

Description

Input a pre-processed ex and pc domain for combination into a NONMEM dataset. Additional pd endpoints, subject-level covariates, and time-varying covariates can also be added. Other parameters can customize some calculations and formatting.

Usage

pk_build(
  ex,
  pc = NA,
  pd = NA,
  sl.cov = NA,
  tv.cov = NA,
  time.units = "days",
  cycle.length = NA,
  na = -999,
  time.rnd = NULL,
  amt.rnd = NULL,
  dv.rnd = NULL,
  cov.rnd = NULL,
  impute = NA,
  BDV = FALSE,
  DDV = FALSE,
  PDV = FALSE,
  sparse = 3,
  demo.map = TRUE,
  tv.cov.fill = "downup",
  keep.other = TRUE
)

Arguments

ex

dose event dataframe
pc

pc event dataframe
pd

pd event dataframe
sl.cov

subject-level covariate dataframe
tv.cov

time-varying covariate dataframe
time.units

units for time attributes
cycle.length

cycle length in units of days
na

value for missing numeric items
time.rnd

time attribute rounding parameter
amt.rnd

amount attribute rounding parameter
dv.rnd

dependent variable attribute rounding parameter
cov.rnd

covariate attribute rounding parameter
impute

imputation method
BDV

baseline pd attribute
DDV

change from baseline pd attribute
PDV

percent change from baseline pd attribute
sparse

threshold for sparse sampling
demo.map

toggle pre-set numeric values for SEX, RACE, and ETHNIC demographic variables
tv.cov.fill

time-varying covariate fill direction
keep.other

filter to keep or remove other events, EVID = 2

Value

PK(PD) dataset

Examples

## Simple ex domain with 1 subject and 1 dose
ex <- data.frame(STUDYID = "ABC101",
                 USUBJID = "ABC101-001",
                 EXSTDTC = "2000-01-01 10:00:00",
                 EXSTDY = 1,
                 EXTPTNUM = 0,
                 EXDOSE = 100,
                 CMT = 1,
                 EXTRT = "ABC",
                 EXDOSU = "mg",
                 VISIT = "Day 1",
                 EXTPT = "Dose",
                 EXDOSFRQ = "Once",
                 EXROUTE = "Oral")

## Simple pc domain with 1 subject and 3 observations
pc <- data.frame(USUBJID = "ABC101-001",
                 PCDTC = c("2000-01-01 09:40:00",
                           "2000-01-01 10:29:00",
                           "2000-01-01 12:05:00"),
                 PCDY = 1,
                 PCTPTNUM = c(0, ##Units of hours
                              0.021,
                              0.083),
                 PCSTRESN = c(NA,
                              469,
                              870),
                 PCLLOQ = 25,
                 CMT = 2,
                 VISIT = "Day 1",
                 PCTPT = c("Pre-dose",
                           "30-min post-dose",
                           "2-hr post-dose"),
                 PCTEST = "ABC",
                 PCSTRESU = "ug/mL")

## Create with pk_build()
df <- pk_build(ex, pc)

combine study level datasets to form population dataset

Description

Input two datasets created by pk_build(). Any character descriptions that were numerically mapped will be re-mapped to the whole population.

Usage

pk_combine(df1, df2, demo.map = TRUE, na = -999)

Arguments

df1

original PK(PD) dataset
df2

additional PK(PD) dataset
demo.map

toggle pre-set numeric values for SEX, RACE, and ETHNIC demographic variables
na

value for missing numeric items

Value

population PK(PD) dataset

Examples

## Simple ex domain with 1 subject and 1 dose, study 101
ex101 <- data.frame(STUDYID = "ABC101",
                    USUBJID = "ABC101-001",
                    EXSTDTC = "2000-01-01 10:00:00",
                    EXSTDY = 1,
                    EXTPTNUM = 0,
                    EXDOSE = 100,
                    CMT = 1,
                    EXTRT = "ABC",
                    EXDOSU = "mg",
                    VISIT = "Day 1",
                    EXTPT = "Dose",
                    EXDOSFRQ = "Once",
                    EXROUTE = "Oral")

## Simple ex domain with 1 subject and 1 dose, study 102
ex102 <- data.frame(STUDYID = "ABC102",
                    USUBJID = "ABC102-001",
                    EXSTDTC = "2001-01-01 08:09:00",
                    EXSTDY = 1,
                    EXTPTNUM = 0,
                    EXDOSE = 200,
                    CMT = 1,
                    EXTRT = "ABC",
                    EXDOSU = "mg",
                    VISIT = "Day 1",
                    EXTPT = "Dose",
                    EXDOSFRQ = "QW",
                    EXROUTE = "Oral")

## Simple pc domain with 1 subject and 3 observations, study 101
pc101 <- data.frame(USUBJID = "ABC101-001",
                    PCDTC = c("2000-01-01 09:40:00",
                              "2000-01-01 10:29:00",
                              "2000-01-01 12:05:00"),
                    PCDY = 1,
                    PCTPTNUM = c(0, ##Units of hours
                                 0.021,
                                 0.083),
                    PCSTRESN = c(NA,
                                 469,
                                 870),
                    PCLLOQ = 25,
                    CMT = 2,
                    VISIT = "Day 1",
                    PCTPT = c("Pre-dose",
                              "30-min post-dose",
                              "2-hr post-dose"),
                    PCTEST = "ABC",
                    PCSTRESU = "ug/mL")

## Simple pc domain with 1 subject and 3 observations, study 102
pc102 <- data.frame(USUBJID = "ABC102-001",
                    PCDTC = c("2001-01-01 08:05:00",
                              "2001-01-01 11:38:00",
                              "2001-01-02 08:11:00"),
                    PCDY = 1,
                    PCTPTNUM = c(0, ##Units of hours
                                 0.125,
                                 1),
                    PCSTRESN = c(NA,
                                 1150,
                                 591),
                    PCLLOQ = 25,
                    CMT = 2,
                    VISIT = "Day 1",
                    PCTPT = c("Pre-dose",
                              "2-4 hr post-dose",
                              "24 hr post-dose"),
                    PCTEST = "ABC",
                    PCSTRESU = "ug/mL")

## Create with pk_build()
df101 <- pk_build(ex101, pc101)
df102 <- pk_build(ex102, pc102)

## Combine with pk_combine()
df_combine <- pk_combine(df101, df102)

Create definition file from published dataset

Description

Definition file table can be read into a template word document (.docx) or blank document if desired. Definitions are sourced from a variable list stored separately on your server. Please refer to apmx::variable_list_export() for a standard copy of the variable list.

Usage

pk_define(
  df,
  file = NULL,
  project,
  data,
  variable.list,
  template = NULL,
  font = "Times New Roman",
  size = 9,
  na = -999
)

Arguments

df

apmx analysis dataset
file

optional filepath for defintion file (.docx file)
project

project name
data

dataset name
variable.list

reference dataframe for variable definitions
template

optional filepath for definition file template (.docx file)
font

font for table contents
size

font size for table contents
na

value used for missing or na numeric covariates

Value

dataset definition file

Examples

## Simple ex domain with 1 subject and 1 dose
ex <- data.frame(STUDYID = "ABC101",
                 USUBJID = "ABC101-001",
                 EXSTDTC = "2000-01-01 10:00:00",
                 EXSTDY = 1,
                 EXTPTNUM = 0,
                 EXDOSE = 100,
                 CMT = 1,
                 EXTRT = "ABC",
                 EXDOSU = "mg",
                 VISIT = "Day 1",
                 EXTPT = "Dose",
                 EXDOSFRQ = "Once",
                 EXROUTE = "Oral")

## Simple pc domain with 1 subject and 3 observations
pc <- data.frame(USUBJID = "ABC101-001",
                 PCDTC = c("2000-01-01 09:40:00",
                           "2000-01-01 10:29:00",
                           "2000-01-01 12:05:00"),
                 PCDY = 1,
                 PCTPTNUM = c(0, ##Units of hours
                              0.021,
                              0.083),
                 PCSTRESN = c(NA,
                              469,
                              870),
                 PCLLOQ = 25,
                 CMT = 2,
                 VISIT = "Day 1",
                 PCTPT = c("Pre-dose",
                           "30-min post-dose",
                           "2-hr post-dose"),
                 PCTEST = "ABC",
                 PCSTRESU = "ug/mL")

## Create apmx dataset with pk_build()
df <- pk_build(ex, pc)

## Create variable definitions with variable_list_create()
vl <- variable_list_create()

## Create definition file
pk_define(df, variable.list = vl)

Produce summary tables for a PK(PD) dataset

Description

Summarize BLQ distributions, categorical covariates, and continuous covariates in three tables. Outputs are default .csv files, but can also be .docx and/or .pptx Tables are default stratified by study, but can be stratified by any variable requested by the user.

Usage

pk_summarize(
  df,
  dir = NA,
  strat.by = "NSTUDYC",
  ignore.c = TRUE,
  na = -999,
  docx = FALSE,
  pptx = FALSE,
  docx.font = "Times New Roman",
  docx.size = 9,
  docx.template = NULL,
  pptx.template = NULL,
  pptx.font = "Times New Roman",
  pptx.size = 12,
  docx.orientation = "portrait",
  ignore.request = c()
)

Arguments

df

dataset produced by pk_build().
dir

filepath for output directory.
strat.by

vector of variables names to stratify the summary tables.
ignore.c

ignores records flagged in the C column when TRUE.
na

numeric value to be interpreted as NA or missing.
docx

creates summary tables as a Word document when TRUE.
pptx

creates summary tables as a PowerPoint document when TRUE.
docx.font

font for the summary tables in the Word document.
docx.size

font size for the summary tables in the Word document.
docx.template

filepath for template .docx file. When NULL, the summary tables print to a blank document.
pptx.template

filepath for template .pptx file. When NULL, the summary tables print to a blank slide.
pptx.font

font for the summary tables in the PowerPoint document.
pptx.size

font size for the summary tables in the PowerPoint document.
docx.orientation

orientation of .docx files.
ignore.request

vector of additional logical expressions to filter the datase prior to summary.

Value

summary tables as .csv, .docx, and .pptx files

Examples

## Simple ex domain with 1 subject and 1 dose
ex <- data.frame(STUDYID = "ABC101",
                 USUBJID = "ABC101-001",
                 EXSTDTC = "2000-01-01 10:00:00",
                 EXSTDY = 1,
                 EXTPTNUM = 0,
                 EXDOSE = 100,
                 CMT = 1,
                 EXTRT = "ABC",
                 EXDOSU = "mg",
                 VISIT = "Day 1",
                 EXTPT = "Dose",
                 EXDOSFRQ = "Once",
                 EXROUTE = "Oral")

## Simple pc domain with 1 subject and 3 observations
pc <- data.frame(USUBJID = "ABC101-001",
                 PCDTC = c("2000-01-01 09:40:00",
                           "2000-01-01 10:29:00",
                           "2000-01-01 12:05:00"),
                 PCDY = 1,
                 PCTPTNUM = c(0, ##Units of hours
                              0.021,
                              0.083),
                 PCSTRESN = c(NA,
                              469,
                              870),
                 PCLLOQ = 25,
                 CMT = 2,
                 VISIT = "Day 1",
                 PCTPT = c("Pre-dose",
                           "30-min post-dose",
                           "2-hr post-dose"),
                 PCTEST = "ABC",
                 PCSTRESU = "ug/mL")

## Create with pk_build()
df <- pk_build(ex, pc)


## Generate summary statistics with pk_summarize()
pk_summarize(df)

Write PK(PD) dataset to specified location

Description

Dataset created by pk_build() or pk_combine() will be outputted as a .csv file with NONMEM-standard formatting.

Usage

pk_write(df, file)

Arguments

df

PK(PD) dataframe
file

filepath

Value

writes dataset to specified location

Examples

## Simple ex domain with 1 subject and 1 dose
ex <- data.frame(STUDYID = "ABC101",
                 USUBJID = "ABC101-001",
                 EXSTDTC = "2000-01-01 10:00:00",
                 EXSTDY = 1,
                 EXTPTNUM = 0,
                 EXDOSE = 100,
                 CMT = 1,
                 EXTRT = "ABC",
                 EXDOSU = "mg",
                 VISIT = "Day 1",
                 EXTPT = "Dose",
                 EXDOSFRQ = "Once",
                 EXROUTE = "Oral")

## Simple pc domain with 1 subject and 3 observations
pc <- data.frame(USUBJID = "ABC101-001",
                 PCDTC = c("2000-01-01 09:40:00",
                           "2000-01-01 10:29:00",
                           "2000-01-01 12:05:00"),
                 PCDY = 1,
                 PCTPTNUM = c(0, ##Units of hours
                              0.021,
                              0.083),
                 PCSTRESN = c(NA,
                              469,
                              870),
                 PCLLOQ = 25,
                 CMT = 2,
                 VISIT = "Day 1",
                 PCTPT = c("Pre-dose",
                           "30-min post-dose",
                           "2-hr post-dose"),
                 PCTEST = "ABC",
                 PCSTRESU = "ug/mL")

## Create with pk_build()
df <- pk_build(ex, pc)

## Write with pk_write()
name <- "dataset.csv"
pk_write(df, file.path(tempdir(), name))

Create a dataframe with standard variable names and definitions

Description

Variable list should be used as an input to the apmx::pk_define() function. The user should add additional definitions to the file for custom columns with apmx::variable_list_add().

Usage

variable_list_create(
  variable = NULL,
  categorization = NULL,
  description = NULL,
  comment = NA
)

Arguments

variable

vector of variable names
categorization

vector of category names
description

vector of variable descriptions
comment

vector of variable comments (can be left NA)

Value

dataframe of standard variable definitions

Examples

vl <- variable_list_create(variable = c("WEIGHT", "HEIGHT"),
                           categorization = rep("Covariate", 2),
                           description = c("weight", "height"))

Create and maintain a dataset version log

Description

Version log is outputted as a .docx file. Document tracks changes in subject count, record count, new variables, and changing variables. User comments in the word document are preserved between versions.

Usage

version_log(
  df,
  name,
  file = NULL,
  prevdata = NULL,
  template = NULL,
  comp_var,
  src_data = "",
  font = "Times New Roman",
  size = 9,
  orient = "landscape"
)

Arguments

df

filepath of new dataset
name

name of the dataset (filename with .csv suffix)
file

filepath for version log file (.docx)
prevdata

comparison dataset filepath
template

template docx filepath
comp_var

grouping variables for comparison
src_data

string to describe source data
font

font style
size

font size
orient

document orientation

Value

version log as a .docx file

Examples

## Simple ex domain with 1 subject and 1 dose
ex <- data.frame(STUDYID = "ABC101",
                 USUBJID = "ABC101-001",
                 EXSTDTC = "2000-01-01 10:00:00",
                 EXSTDY = 1,
                 EXTPTNUM = 0,
                 EXDOSE = 100,
                 CMT = 1,
                 EXTRT = "ABC",
                 EXDOSU = "mg",
                 VISIT = "Day 1",
                 EXTPT = "Dose",
                 EXDOSFRQ = "Once",
                 EXROUTE = "Oral")

## Simple pc domain with 1 subject and 3 observations
pc <- data.frame(USUBJID = "ABC101-001",
                 PCDTC = c("2000-01-01 09:40:00",
                           "2000-01-01 10:29:00",
                           "2000-01-01 12:05:00"),
                 PCDY = 1,
                 PCTPTNUM = c(0, ##Units of hours
                              0.021,
                              0.083),
                 PCSTRESN = c(NA,
                              469,
                              870),
                 PCLLOQ = 25,
                 CMT = 2,
                 VISIT = "Day 1",
                 PCTPT = c("Pre-dose",
                           "30-min post-dose",
                           "2-hr post-dose"),
                 PCTEST = "ABC",
                 PCSTRESU = "ug/mL")

## Create with pk_build()
df <- pk_build(ex, pc)

## Document with version_log()
vlog <- version_log(df, name = "PK_DATA_V01.csv")

VL

Description

Variable list with apmx variables and definitions

Usage

VL

Format

## 'VL' A data frame with 66 rows and 4 variables:

Variable

Column or variable name

Categorization

Column or variable category

Description

Column or variable description

Comment

NA by default