Package 'PAutilities' reference manual

Title:	Streamline Physical Activity Research
Description:	Functions that support a broad range of common tasks in physical activity research, including but not limited to creation of Bland-Altman plots (<doi:10.1136/bmj.313.7049.106>), metabolic calculations such as basal metabolic rate predictions (<https://europepmc.org/article/med/4044297/reloa>), demographic calculations such as age-for-body-mass-index percentile (<https://www.cdc.gov/growthcharts/cdc_charts.htm>), and analysis of bout detection algorithm performance (<https://pubmed.ncbi.nlm.nih.gov/34258524/>).
Authors:	Paul R. Hibbing [aut, cre], Centers for Disease Control and Prevention [ctb]
Maintainer:	Paul R. Hibbing <[email protected]>
License:	GPL-3
Version:	1.3.0
Built:	2026-05-13 09:03:56 UTC
Source:	https://github.com/paulhibbing/pautilities

As("summaryTransition", "data.frame")

Description

As("summaryTransition", "data.frame")

As("summaryTransition", "list")

Perform Bland-Altman analysis on a data frame

Description

Perform Bland-Altman analysis on a data frame

Usage

ba_analysis(df, x_var, y_var, regress_against = c("Y", "XY_mean"), ...)
ba_analysis(df, x_var, y_var, regress_against = c("Y", "XY_mean"), ...)

Arguments

df

the data frame on which to operate

x_var

character. The column name of the X variable

y_var

character. The column name of the Y variable (criterion measure, if applicable)

regress_against

character. One of "Y" (to regress bias against yvar) or "XY_mean" (to regress bias against rowMeans(x_var, y_var)).

...

optional arguments passed to data.frame, e.g. to give the output results a label

Value

A data frame that has various summaries (means, standard deviations, and missing data details) plus mean bias (mean_bias column) and limits of agreement (lower_LOA and upper_LOA columns)

Examples

data(ex_data, package = "PAutilities")
ba_analysis(ex_data, "Axis1", "Vector.Magnitude", "XY_mean")
ba_analysis(
  ex_data, "Axis1", "Vector.Magnitude", "XY_mean",
  an_arbitrary_added_column = "Example of passing an extra column"
)
data(ex_data, package = "PAutilities")
ba_analysis(ex_data, "Axis1", "Vector.Magnitude", "XY_mean")
ba_analysis(
  ex_data, "Axis1", "Vector.Magnitude", "XY_mean",
  an_arbitrary_added_column = "Example of passing an extra column"
)

Create a Bland-Altman plot

Description

Create a Bland-Altman plot

Usage

ba_plot(plotdata, x_var, y_var, x_name, y_name, shape = 16, ...)
ba_plot(plotdata, x_var, y_var, x_name, y_name, shape = 16, ...)

Arguments

plotdata

dataframe from which to build the plot

x_var

character expression to evaluate for the x-axis

y_var

character expression to evaluate for the y-axis

x_name

axis label for the x-axis

y_name

axis label for the y-axis

shape

numeric. The point shape to display.

...

further arguments passed to theme

Value

a Bland-Altman plot

References

Bland, J. M., & Altman, D. G. (1986). Statistical methods for assessing agreement between two methods of clinical measurement. lancet, 1(8476), 307-310.

Examples

data(ex_data, package = "PAutilities")

# Reduce the number of data points (for illustration purposes) by isolating
# the 150 largest cases

illustration_threshold <-
    quantile(ex_data$Axis1, probs = 1 - (150 / nrow(ex_data)))
ex_data <- ex_data[ex_data$Axis1 > illustration_threshold, ]

# Generate the plot
my_ba <- ba_plot(
    ex_data,
    "(Axis1 + Axis3) / 2",
    "Axis1 - Axis3",
    "mean(Axis1, Axis3)",
    "Axis1 - Axis3"
)

my_ba


# You can add to the plot as you would a normal ggplot object
    my_ba +
      ggplot2::geom_text(
      x = 2000, y = 9000, label = "A",
      size = 8, fontface = "bold", colour = "blue"
      )

# With caution, you can change some automatic options (e.g. color of
# regression line) by overwriting in a new layer

    my_ba + ggplot2::geom_smooth(method = "lm", se = FALSE, colour = "blue")


data(ex_data, package = "PAutilities")

# Reduce the number of data points (for illustration purposes) by isolating
# the 150 largest cases

illustration_threshold <-
    quantile(ex_data$Axis1, probs = 1 - (150 / nrow(ex_data)))
ex_data <- ex_data[ex_data$Axis1 > illustration_threshold, ]

# Generate the plot
my_ba <- ba_plot(
    ex_data,
    "(Axis1 + Axis3) / 2",
    "Axis1 - Axis3",
    "mean(Axis1, Axis3)",
    "Axis1 - Axis3"
)

my_ba


# You can add to the plot as you would a normal ggplot object
    my_ba +
      ggplot2::geom_text(
      x = 2000, y = 9000, label = "A",
      size = 8, fontface = "bold", colour = "blue"
      )

# With caution, you can change some automatic options (e.g. color of
# regression line) by overwriting in a new layer

    my_ba + ggplot2::geom_smooth(method = "lm", se = FALSE, colour = "blue")

Classify moderate-to-vigorous physical activity in bouts of a specific minimum length

Description

Classify moderate-to-vigorous physical activity in bouts of a specific minimum length

Usage

bout_mvpa(
  intensity,
  var_type = c("METs", "Intensity"),
  min_duration = 10,
  termination = 3,
  MoreArgs = list(breaks = c(-Inf, 1.51, 3, Inf), labels = c("SB", "LPA", "MVPA"), right
    = FALSE),
  ...,
  timestamps = NULL,
  output_var = c("is_MVPA", "bout_tracker")
)
bout_mvpa(
  intensity,
  var_type = c("METs", "Intensity"),
  min_duration = 10,
  termination = 3,
  MoreArgs = list(breaks = c(-Inf, 1.51, 3, Inf), labels = c("SB", "LPA", "MVPA"), right
    = FALSE),
  ...,
  timestamps = NULL,
  output_var = c("is_MVPA", "bout_tracker")
)

Arguments

intensity

a vector of intensity classifications to be re-classified according to the bout definition

var_type

character scalar indicating whether the intensity variable is a numeric vector of metabolic equivalents, or a factor variable giving activity intensity classification

min_duration

numeric scalar: minimum duration of a qualifying bout, in minutes

termination

numeric scalar: consecutive minutes of non-MVPA required to terminate the bout

MoreArgs

required arguments passed to cut

...

optional arguments passed to cut for converting METs to intensity classification

timestamps

optional vector of POSIX-formatted timestamps. Must have same length as intensity

output_var

the output variable(s) to give

Value

based on the setting of output_var, one or both of is_MVPA and bout_tracker will be returned, the former being a vector of indicators (1 or 0) specifying whether a minute is part of a valid MVPA bout, and the latter being a collapsed data frame giving only the valid bouts of MVPA and the relevant information (i.e., duration of the bout, minutes of MVPA, and percentage of time spent in MVPA within the bout). If both are selected, they are returned in a list.

Examples

data(ex_data, package = "PAutilities")
ex_data$DateTime <- as.POSIXct(ex_data$DateTime, "UTC")


# Runs with a warning

bout_mvpa(ex_data$METs, "METs")

bout_mvpa(ex_data$METs, "METs", timestamps = ex_data$DateTime)


# Recommended usage
lapply(split(ex_data, strftime(ex_data$DateTime, "%Y-%m-%d", "UTC")),
function(x) {
bout_mvpa(x$METs, "METs", timestamps = x$DateTime)
})

lapply(split(ex_data, strftime(ex_data$DateTime, "%Y-%m-%d", "UTC")),
function(x) {
bout_mvpa(x$METs,
"METs",
timestamps = x$DateTime,
output_var = "is_MVPA")
})

lapply(split(ex_data, strftime(ex_data$DateTime, "%Y-%m-%d", "UTC")),
function(x) {
bout_mvpa(x$METs,
"METs",
timestamps = x$DateTime,
output_var = "bout_tracker")
})

data(ex_data, package = "PAutilities")
ex_data$DateTime <- as.POSIXct(ex_data$DateTime, "UTC")


# Runs with a warning

bout_mvpa(ex_data$METs, "METs")

bout_mvpa(ex_data$METs, "METs", timestamps = ex_data$DateTime)


# Recommended usage
lapply(split(ex_data, strftime(ex_data$DateTime, "%Y-%m-%d", "UTC")),
function(x) {
bout_mvpa(x$METs, "METs", timestamps = x$DateTime)
})

lapply(split(ex_data, strftime(ex_data$DateTime, "%Y-%m-%d", "UTC")),
function(x) {
bout_mvpa(x$METs,
"METs",
timestamps = x$DateTime,
output_var = "is_MVPA")
})

lapply(split(ex_data, strftime(ex_data$DateTime, "%Y-%m-%d", "UTC")),
function(x) {
bout_mvpa(x$METs,
"METs",
timestamps = x$DateTime,
output_var = "bout_tracker")
})

Calculate risk of cardiovascular disease

Description

Calculate risk of cardiovascular disease

Usage

cvd_risk(
  x = NULL,
  method = "D'Agostino_2008",
  sex,
  age,
  total_cholesterol,
  hdl,
  systolic,
  bp_treated,
  diabetes,
  smoker,
  points = TRUE,
  ...
)

## Default S3 method:
cvd_risk(
  x = NULL,
  method = "D'Agostino_2008",
  sex,
  age,
  total_cholesterol,
  hdl,
  systolic,
  bp_treated,
  diabetes,
  smoker,
  points = TRUE,
  ...
)

## S3 method for class 'data.frame'
cvd_risk(
  x = NULL,
  method = "D'Agostino_2008",
  sex,
  age,
  total_cholesterol,
  hdl,
  systolic,
  bp_treated,
  diabetes,
  smoker,
  points = TRUE,
  combine = TRUE,
  ...
)
cvd_risk(
  x = NULL,
  method = "D'Agostino_2008",
  sex,
  age,
  total_cholesterol,
  hdl,
  systolic,
  bp_treated,
  diabetes,
  smoker,
  points = TRUE,
  ...
)

## Default S3 method:
cvd_risk(
  x = NULL,
  method = "D'Agostino_2008",
  sex,
  age,
  total_cholesterol,
  hdl,
  systolic,
  bp_treated,
  diabetes,
  smoker,
  points = TRUE,
  ...
)

## S3 method for class 'data.frame'
cvd_risk(
  x = NULL,
  method = "D'Agostino_2008",
  sex,
  age,
  total_cholesterol,
  hdl,
  systolic,
  bp_treated,
  diabetes,
  smoker,
  points = TRUE,
  combine = TRUE,
  ...
)

Arguments

x

optional data frame. If provided, the other arguments will be taken as column names under the assumption that each row represents a separate person, and each column provides one of the requested pieces of information

method

character. Currently only method = "D'Agostino_2008" is supported.

sex

character scalar indicating either sex for one person (i.e., male or female), or a column name in x containing sex values for multiple people

age

either a numeric scalar indicating age for one person, or a character scalar indicating the name of the column in x that contains age information. Units are years

total_cholesterol

same as age, but for total cholesterol, in mg/dL

hdl

same as age, but for HDL, in mg/dL

systolic

same as age, but for systolic blood pressure, in mmHg

bp_treated

either a logical scalar indicating whether a person is taking blood pressure medication, or a character scalar pointing to the column in x that contains the same information for multiple people

diabetes

same as bp_treated, but for the presence of diabetes

smoker

same asbp_treated, but for smoking status

points

logical. Return as points (default) or risk percentage?

...

arguments passed to other methods

combine

logical. Give results as a list of risk_profile objects, or combine the list into an integer vector (default)?

Value

One or more risk profiles (for default method with points = TRUE, or for data frames with combine = FALSE & points = TRUE). Otherwise numeric risk percentage (for points = FALSE, scalars and data frames) or an integer vector (for data frames with combine = TRUE & points = FALSE)

References

D'Agostino et al. (2008)

Examples


cvd_risk(sex = "Female", age = 111, total_cholesterol = 111, systolic = 111,
hdl = 11, bp_treated = FALSE, diabetes = TRUE, smoker = TRUE)


df <- data.frame(
  sex = sample(c("Male", "Female"), 5, TRUE),
  age = sample(30:100, 5, TRUE),
  tc = sample(150:300, 5, TRUE),
  hdl = sample(30:70, 5, TRUE),
  sbp = sample(100:180, 5, TRUE),
  bpmed = sample(c(TRUE, FALSE), 5, TRUE),
  diabetes = sample(c(TRUE, FALSE), 5, TRUE),
  smoker = sample(c(TRUE, FALSE), 5, TRUE)
)

cvd_risk(
  df, sex = "sex", age = "age",
  total_cholesterol = "tc", hdl = "hdl",
  systolic = "sbp", bp_treated = "bpmed",
  diabetes = "diabetes", smoker = "smoker",
  combine = FALSE
)



cvd_risk(sex = "Female", age = 111, total_cholesterol = 111, systolic = 111,
hdl = 11, bp_treated = FALSE, diabetes = TRUE, smoker = TRUE)


df <- data.frame(
  sex = sample(c("Male", "Female"), 5, TRUE),
  age = sample(30:100, 5, TRUE),
  tc = sample(150:300, 5, TRUE),
  hdl = sample(30:70, 5, TRUE),
  sbp = sample(100:180, 5, TRUE),
  bpmed = sample(c(TRUE, FALSE), 5, TRUE),
  diabetes = sample(c(TRUE, FALSE), 5, TRUE),
  smoker = sample(c(TRUE, FALSE), 5, TRUE)
)

cvd_risk(
  df, sex = "sex", age = "age",
  total_cholesterol = "tc", hdl = "hdl",
  systolic = "sbp", bp_treated = "bpmed",
  diabetes = "diabetes", smoker = "smoker",
  combine = FALSE
)

Compute descriptive statistics for a variable in the metabolic data set

Description

Compute descriptive statistics for a variable in the metabolic data set

Usage

descriptives(dataset, variable, group = NULL)
descriptives(dataset, variable, group = NULL)

Arguments

dataset

the dataset to analyze

variable

character scalar giving the variable name to summarize

group

character scalar giving an optional grouping variable for the summary

Value

a data frame of formatted summary statistics

Examples

data(ex_data, package = "PAutilities")
ex_data$group_var <- rep(
 c("One", "Two", "Three"),
 each = ceiling(nrow(ex_data)/3)
)[seq(nrow(ex_data))]
descriptives(ex_data, "Axis1", "group_var")

data(ex_data, package = "PAutilities")
ex_data$group_var <- rep(
 c("One", "Two", "Three"),
 each = ceiling(nrow(ex_data)/3)
)[seq(nrow(ex_data))]
descriptives(ex_data, "Axis1", "group_var")

Check if a dataframe is continuous

Description

Check if a dataframe is continuous

Usage

df_continuous(df, time_var = "Timestamp", digits = 6, ...)
df_continuous(df, time_var = "Timestamp", digits = 6, ...)

Arguments

df

the input data frame

time_var

character scalar giving the column name of the variable containing timestamp information (either character or POSIXt format)

digits

see epoch_length_sec

...

arguments passed to as.POSIXct, for use if time_var is a character rather than POSIXt variable

Value

a logical scalar indicating whether the dataframe reflects a continuous time series

Examples

data(ex_data, package = "PAutilities")
df_continuous(ex_data, "DateTime", tz = "UTC")
df_continuous(ex_data[-c(300:500), ], "DateTime", tz = "UTC")
data(ex_data, package = "PAutilities")
df_continuous(ex_data, "DateTime", tz = "UTC")
df_continuous(ex_data[-c(300:500), ], "DateTime", tz = "UTC")

Reorder the columns of a data frame

Description

Reorder the columns of a data frame

Usage

df_reorder(df, columns, after)
df_reorder(df, columns, after)

Arguments

df

the data frame

columns

the column(s) to move (either as character names or numeric indices)

after

the column after which to insert columns (must be a scalar, either a character name or a numeric index)

Value

The reordered data frame

Examples

df <- data.frame(a = 1:10, b = 11:20, c = 21:30, d = 31:40)
df_reorder(df, 2:3, "d")
df_reorder(df, c("c", "d"), "a")
df <- data.frame(a = 1:10, b = 11:20, c = 21:30, d = 31:40)
df_reorder(df, 2:3, "d")
df_reorder(df, c("c", "d"), "a")

Determine epoch length in seconds

Description

Determine epoch length in seconds

Usage

epoch_length_sec(timestamps, digits = 6)
epoch_length_sec(timestamps, digits = 6)

Arguments

timestamps

POSIX-formatted input

digits

for rounding. See details

Details

The function is designed to work even when the epoch length is less than one second (e.g., for raw accelerometry data). Thus, it is not possible to base the code on convenient difftime methods. Instead, numeric operations are performed after running unclass on the input. This sometimes results in minuscule fluctuations of the calculated epoch length (e.g., +/- 0.0000002). Thus, the code rounds everything to the precision indicated by digits. For most applications, the default value (digits = 6) should be well past the range of meaningful fluctuations and lead to a favorable outcome. But the digits argument can also be adjusted if greater assurance is needed.

After rounding, the code checks for the existence of multiple epoch lengths. If they are detected (e.g., due to a discontinuity in the file), a warning is issued and the most prevalent epoch length is returned. The warning will specify all the different epoch lengths that were detected, which may be useful information for troubleshooting.

Value

The epoch length of the data, in seconds

Examples

epoch_length_sec(Sys.time() + 0:5)
epoch_length_sec(Sys.time() + seq(0, 25, 5))
epoch_length_sec(Sys.time() + 0:5)
epoch_length_sec(Sys.time() + seq(0, 25, 5))

Example data for calculating bouts of moderate-to-vigorous physical activity

Description

A dataset containing accelerometer data and predicted energy expenditure in metabolic equivalents (METs) that can be used to classify moderate-to-vigorous physical activity in continuous bouts.

Usage

ex_data
ex_data

Format

A data frame with 10080 rows and 12 variables:

FileID: character. Name of the file originating the data
Date: character giving the date ("%m/%d/%Y")
Time: character giving the time ("%H:%M:%S")
DateTime: full timestamp (%Y-%m-%d %H:%M:%S) given as character
dayofyear: numeric day of the year (i.e., julian date)
minofday: numeric minute of the day (i.e., 0 for midnight and 1439 for 11:59)
Axis1: activity counts for the device's first axis
Axis2: activity counts for the device's second axis
Axis3: activity counts for the device's third axis
Steps: number of steps taken
Vector.Magnitude: vector magnitude (Euclidian norm) of the activity counts from the three axes
METs: predicted energy expenditure, in metabolic equivalents

Drop incomplete days from a dataset

Description

Drop incomplete days from a dataset

Usage

full_days(
  df,
  time_var = "Timestamp",
  drop = c("all", "leading", "trailing", "label"),
  epoch_length_sec = NULL,
  label_name = "is_full_day",
  digits = 6,
  check_continuous = TRUE,
  discontinuous_action = c("stop", "warn"),
  ...
)
full_days(
  df,
  time_var = "Timestamp",
  drop = c("all", "leading", "trailing", "label"),
  epoch_length_sec = NULL,
  label_name = "is_full_day",
  digits = 6,
  check_continuous = TRUE,
  discontinuous_action = c("stop", "warn"),
  ...
)

Arguments

df

the input data frame

time_var

character scalar giving the column name of the variable containing timestamp information (either character or POSIXt format)

drop

character scalar indicating which incomplete days to drop. Can be all (default), leading (only day/s at the start of the file), trailing (only day/s at the end of the file), or label. If the latter is selected, the full dataset is returned with an additional column indicating whether each row of data corresponds with a complete day (useful for troubleshooting, among other things)

epoch_length_sec

optional. The epoch length of the data. If no value is passed, epoch_length_sec is invoked on the time_var column

label_name

character scalar. Name to give the indicator column when drop == "label"

digits

see epoch_length_sec

check_continuous

logical. Check the dataframe after dropping to see if it is continuous?

discontinuous_action

character scalar telling what to do if a discontinuity is expected when check_continuous = TRUE. Can be either warn (the default) or stop

...

arguments passed to as.POSIXct, for use if time_var is a character rather than POSIXt variable

Value

an updated copy of df, in which incomplete days are addressed according to the selected value of drop.

Examples

data(ex_data, package = "PAutilities")
ex_data <- full_days(
  ex_data, "DateTime", "label", 60,
  "full_day_indicator", tz = "UTC"
)
head(ex_data)
data(ex_data, package = "PAutilities")
ex_data <- full_days(
  ex_data, "DateTime", "label", 60,
  "full_day_indicator", tz = "UTC"
)
head(ex_data)

Calculate age

Description

Takes two Date objects and calculates age based on difftime (in days) divided by 365.2425 days per year (for age in years) or 30.4375 days per month (for age in months).

Usage

get_age(birthdate, current_date, units = c("years", "months"))
get_age(birthdate, current_date, units = c("years", "months"))

Arguments

birthdate

Date object giving the date of birth

current_date

Date object giving the date from which age is to be calculated

units

The units in which age should be reported

Value

Numeric value giving age in the specified units.

Examples

get_age(as.Date("2000-01-01"), Sys.Date(), "years")
get_age(as.Date("2000-01-01"), Sys.Date(), "years")

Retrieve estimated basal metabolic rate for an individual

Description

Retrieve estimated basal metabolic rate for an individual

Usage

get_bmr(
  Sex = c("M", "F"),
  Ht = NULL,
  Wt,
  Age,
  verbose = FALSE,
  RER = NULL,
  equation = c("ht_wt", "wt", "both"),
  kcal_table = c("Lusk", "Peronnet", "both"),
  method = c("Schofield", "FAO", "both"),
  MJ_conversion = c("thermochemical", "dry", "convenience", "all"),
  kcal_conversion = 5
)
get_bmr(
  Sex = c("M", "F"),
  Ht = NULL,
  Wt,
  Age,
  verbose = FALSE,
  RER = NULL,
  equation = c("ht_wt", "wt", "both"),
  kcal_table = c("Lusk", "Peronnet", "both"),
  method = c("Schofield", "FAO", "both"),
  MJ_conversion = c("thermochemical", "dry", "convenience", "all"),
  kcal_conversion = 5
)

Arguments

Sex

The individual's sex

Ht

The individual's height, in meters

Wt

The individual's weight, in kilograms

Age

The individual's age, in years

verbose

Logical. Should processing updates be printed?

RER

numeric. The respiratory exchange ratio

equation

The equation to apply

kcal_table

The table to reference for converting kilocalories to oxygen consumption. See get_kcal_vo2_conversion

method

The calculation method to use

MJ_conversion

The value to use for converting megajoules to kilocalories. Defaults to thermochemical.

kcal_conversion

numeric. If RER is NULL (default), the factor to use for converting kilocalories to oxygen consumption

Value

a data frame containing predictions of basal metabolic rate in one column, along with additional columns that indicate how the predictions were obtained (e.g., which sources and conversions were applied)

References

Schofield, W. N. (1985). Predicting basal metabolic rate, new standards and review of previous work. Human nutrition. Clinical nutrition, 39, 5-41.

Examples


# Get BMR for an imaginary 900-year-old person (Age is only
# used to determine which equations to use, in this case the
# equations for someone older than 60)

get_bmr(
  Sex = "M", Ht = 1.5, Wt = 80, Age = 900, equation = "both",
  method = "both", RER = 0.865, kcal_table = "both",
  MJ_conversion = c("all")
)

get_bmr(
  Sex = "M", Ht = 1.5, Wt = 80, Age = 900, MJ_conversion = "all",
  kcal_conversion = 4.86
)

get_bmr(
  Sex = "M", Ht = 1.5, Wt = 80, Age = 900, method = "FAO",
  kcal_conversion = 4.86
)

# Get BMR for an imaginary 900-year-old person (Age is only
# used to determine which equations to use, in this case the
# equations for someone older than 60)

get_bmr(
  Sex = "M", Ht = 1.5, Wt = 80, Age = 900, equation = "both",
  method = "both", RER = 0.865, kcal_table = "both",
  MJ_conversion = c("all")
)

get_bmr(
  Sex = "M", Ht = 1.5, Wt = 80, Age = 900, MJ_conversion = "all",
  kcal_conversion = 4.86
)

get_bmr(
  Sex = "M", Ht = 1.5, Wt = 80, Age = 900, method = "FAO",
  kcal_conversion = 4.86
)

Retrieve indices for a rolling window analysis

Description

Retrieve indices for a rolling window analysis

Usage

get_indices(y_var, window_size = 15L)
get_indices(y_var, window_size = 15L)

Arguments

y_var

NumericVector. Input on which to define the indices for each roll of the window

window_size

int. The size of the window

Value

a list in which each element contains window_size consecutive integers that indicate which elements of y_var would be extracted for that roll of the window

Note

For this function, the output elements contain positions (i.e., indices) from y_var, whereas for rolling_groups the output elements contain the raw values found at each index

Examples

result <- get_indices(1:100, 10)
head(result)
tail(result)
result <- get_indices(1:100, 10)
head(result)
tail(result)

Classify activity intensity

Description

Supports intensity classification via energy expenditure with or without additional posture requirements (i.e., for sedentary behavior to be in lying/seated posture)

Usage

get_intensity(mets, posture = NULL, ...)
get_intensity(mets, posture = NULL, ...)

Arguments

mets

numeric vector of metabolic equivalents to classify

posture

character vector of postures

...

further arguments passed to cut

Details

If breaks and labels arguments are not provided, default values are <= 1.5 METs for sedentary behavior, 1.51-2.99 METs for light physical activity, and >= 3.0 METs for moderate-to-vigorous physical activity.

It is expected for the elements of posture to be one of c("lie", "sit", "stand", "other"). The function will run (with a warning) if that requirement is not met, but the output will likely be incorrect.

Value

a factor giving intensity classifications for each element of mets

Examples

mets <- seq(1, 8, 0.2)
posture <- rep(
c("lie", "sit", "stand", "other"), 9
)

intensity_no_posture <- get_intensity(mets)
intensity_posture <- get_intensity(mets, posture)
head(intensity_no_posture)
head(intensity_posture)

mets <- seq(1, 8, 0.2)
posture <- rep(
c("lie", "sit", "stand", "other"), 9
)

intensity_no_posture <- get_intensity(mets)
intensity_posture <- get_intensity(mets, posture)
head(intensity_no_posture)
head(intensity_posture)

Retrieve conversion factors from kilocalories to oxygen consumption

Description

Retrieve conversion factors from kilocalories to oxygen consumption

Usage

get_kcal_vo2_conversion(RER, kcal_table = c("Lusk", "Peronnet", "both"))
get_kcal_vo2_conversion(RER, kcal_table = c("Lusk", "Peronnet", "both"))

Arguments

RER

numeric. The respiratory exchange ratio

kcal_table

The table to reference for converting kilocalories to oxygen consumption. See get_kcal_vo2_conversion

Details

RER values are matched to the table entries based on the minimum absolute difference. If there is a tie, the lower RER is taken.

Value

numeric vector giving the conversion factor from the specified table(s)

References

Peronnet, F., & Massicotte, D. (1991). Table of nonprotein respiratory quotient: an update. Can J Sport Sci, 16(1), 23-29.

Lusk, G. (1924). Analysis of the oxidation of mixtures of carbohydrate and fat: a correction. Journal of Biological Chemistry, 59, 41-42.

Examples

get_kcal_vo2_conversion(0.85, "both")

get_kcal_vo2_conversion(0.85, "both")

Calculate resting energy expenditure

Description

Calculate resting energy expenditure

Usage

get_ree(
  method = c("harris_benedict", "schofield_wt", "schofield_wt_ht", "fao", "muller_wt_ht",
    "muller_ffm"),
  sex,
  age_yr = NA,
  ...,
  output = c("default", "mj_day", "kcal_day", "vo2_ml_min"),
  calorie = c("thermochemical", "convenience", "dry"),
  RER = 0.86,
  kcal_table = c("Lusk", "Peronnet", "both"),
  df = NULL
)
get_ree(
  method = c("harris_benedict", "schofield_wt", "schofield_wt_ht", "fao", "muller_wt_ht",
    "muller_ffm"),
  sex,
  age_yr = NA,
  ...,
  output = c("default", "mj_day", "kcal_day", "vo2_ml_min"),
  calorie = c("thermochemical", "convenience", "dry"),
  RER = 0.86,
  kcal_table = c("Lusk", "Peronnet", "both"),
  df = NULL
)

Arguments

method

character. The equation(s) to use, chosen from "harris_benedict", "schofield_wt", "schofield_wt_ht", "fao", "muller_wt_ht", or "muller_ffm"

sex

character. The participant/patient sex, one of "female" or "male"

age_yr

numeric. The participant/patient age in years. Not used for method = "muller_ffm", but a value must still be given if a data frame is passed. (The value does not need to correspond with age, it is simply a placeholder to satisfy internal checks that are applied to all equations when making computations on a data frame.)

...

arguments (e.g. wt_kg or ht_cm) for calculations. An error message will clarify which variables need to be passed if they are missing

output

character. The desired output unit(s), chosen from "default", "mj_day", "kcal_day", or "vo2_ml_min"

calorie

character. The desired conversion factor(s) for calculating MJ from kcal, chosen from "thermochemical", "convenience", or "dry"

RER

numeric. The respiratory exchange ratio

kcal_table

character. The desired conversion table(s) to use for converting kcal to oxygen consumption, chosen from "Lusk", "Peronnet", or "both"

df

optional data frame. If passed, all prior arguments should be character scalars pointing to a column in df that contains the corresponding information is stored

Value

Calculated resting energy expenditure

Examples

get_ree("schofield_wt_ht", "female", 57.8, wt_kg = 80, ht_m = 1.50)
get_ree("schofield_wt_ht", "female", 57.8, wt_kg = 80, ht_m = 1.50)

Run length encoding with indices

Description

Run length encoding with indices

Usage

index_runs(x, zero_index = FALSE)
index_runs(x, zero_index = FALSE)

Arguments

x

vector of values on which to perform run length encoding

zero_index

logical. Should indices be indexed from zero (useful for Rcpp)?

Value

A data frame with information about the runs and start/stop indices

Examples


x <- c(
  FALSE, TRUE, FALSE, FALSE, FALSE, TRUE,
  FALSE, TRUE, TRUE, FALSE, TRUE, FALSE,
  FALSE, FALSE, FALSE, FALSE, TRUE, TRUE,
  FALSE, TRUE
)
head(index_runs(x))
x <- c(
  FALSE, TRUE, FALSE, FALSE, FALSE, TRUE,
  FALSE, TRUE, TRUE, FALSE, TRUE, FALSE,
  FALSE, FALSE, FALSE, FALSE, TRUE, TRUE,
  FALSE, TRUE
)
head(index_runs(x))

Printing and timing utility for managing processes

Description

Printing and timing utility for managing processes

Usage

manage_procedure(part = c("Start", "End"), ..., timer = NULL, verbose = TRUE)

get_duration(timer)
manage_procedure(part = c("Start", "End"), ..., timer = NULL, verbose = TRUE)

get_duration(timer)

Arguments

part

character scalar, either Start or End.

...

character strings to print. Default messages will print if no arguments are provided.

timer

a proc_time object. Required for manage_procedure only if using the default message for part = "End" default message.

verbose

logical. Print to console?

Value

For part = "Start", a proc_time object (i.e., a timer passable to an eventual part = "End" command); for part = "End", invisible

Examples


manage_procedure("Start", "String will be printed\n")
timer <- manage_procedure(
"Start", "Printing a string is optional", verbose = FALSE
)

## Default starting message
manage_procedure("Start")

## Default ending message
manage_procedure("End", timer = timer)

## Other examples
get_duration(timer)
manage_procedure("End", "Custom ending message")

manage_procedure("Start", "String will be printed\n")
timer <- manage_procedure(
"Start", "Printing a string is optional", verbose = FALSE
)

## Default starting message
manage_procedure("Start")

## Default ending message
manage_procedure("End", timer = timer)

## Other examples
get_duration(timer)
manage_procedure("End", "Custom ending message")

Compute the mean and standard deviation of a vector, returning a formatted string containing the values as 'M +/- SD'

Description

Compute the mean and standard deviation of a vector, returning a formatted string containing the values as 'M +/- SD'

Usage

mean_sd(
  x = NULL,
  MoreArgs = NULL,
  give_df = TRUE,
  ...,
  mean_x = NULL,
  sd_x = NULL
)

## Default S3 method:
mean_sd(
  x = NULL,
  MoreArgs = NULL,
  give_df = TRUE,
  ...,
  mean_x = NULL,
  sd_x = NULL
)

## S3 method for class 'data.frame'
mean_sd(
  x = NULL,
  MoreArgs = NULL,
  give_df = TRUE,
  ...,
  mean_x = NULL,
  sd_x = NULL
)
mean_sd(
  x = NULL,
  MoreArgs = NULL,
  give_df = TRUE,
  ...,
  mean_x = NULL,
  sd_x = NULL
)

## Default S3 method:
mean_sd(
  x = NULL,
  MoreArgs = NULL,
  give_df = TRUE,
  ...,
  mean_x = NULL,
  sd_x = NULL
)

## S3 method for class 'data.frame'
mean_sd(
  x = NULL,
  MoreArgs = NULL,
  give_df = TRUE,
  ...,
  mean_x = NULL,
  sd_x = NULL
)

Arguments

x

numeric vector of values to summarize

MoreArgs

named list of arguments to pass to mean and sd

give_df

logical. Should mean, sd, and summary string be returned in a data frame?

...

additional arguments passed to format

mean_x

an already-calculated mean value for x

sd_x

an already-calculated sd value for x

Value

either a formatted character scalar (if give_df == FALSE), or else a data frame containing columns for the mean value, standard deviation, and formatted character string combining the two.

Examples

mean_sd(rnorm(100, 50))

mean_sd(rnorm(100, 50))

Perform equivalence testing on paired samples

Description

Perform equivalence testing on paired samples

Usage

## S3 method for class 'data.frame'
paired_equivalence_test(
  x,
  y,
  y_type = c("both", "criterion", "comparison"),
  alpha = 0.05,
  na.rm = TRUE,
  scale = c("relative", "absolute"),
  absolute_region_width = NULL,
  relative_region_width = NULL,
  ...
)

## Default S3 method:
paired_equivalence_test(
  x,
  y,
  y_type = c("both", "criterion", "comparison"),
  alpha = 0.05,
  na.rm = TRUE,
  scale = c("relative", "absolute"),
  absolute_region_width = NULL,
  relative_region_width = NULL,
  ...
)

paired_equivalence_test(
  x,
  y,
  y_type = c("both", "criterion", "comparison"),
  alpha = 0.05,
  na.rm = TRUE,
  scale = c("relative", "absolute"),
  absolute_region_width = NULL,
  relative_region_width = NULL,
  ...
)
## S3 method for class 'data.frame'
paired_equivalence_test(
  x,
  y,
  y_type = c("both", "criterion", "comparison"),
  alpha = 0.05,
  na.rm = TRUE,
  scale = c("relative", "absolute"),
  absolute_region_width = NULL,
  relative_region_width = NULL,
  ...
)

## Default S3 method:
paired_equivalence_test(
  x,
  y,
  y_type = c("both", "criterion", "comparison"),
  alpha = 0.05,
  na.rm = TRUE,
  scale = c("relative", "absolute"),
  absolute_region_width = NULL,
  relative_region_width = NULL,
  ...
)

paired_equivalence_test(
  x,
  y,
  y_type = c("both", "criterion", "comparison"),
  alpha = 0.05,
  na.rm = TRUE,
  scale = c("relative", "absolute"),
  absolute_region_width = NULL,
  relative_region_width = NULL,
  ...
)

Arguments

x

numeric vector representing the (possibly surrogate) sample

y

numeric vector representing the (possibly criterion) sample. Index paired with x

y_type

classification of y for the purpose of analysis. Can be "criterion", "comparison", or "both".

alpha

the alpha level for the test

na.rm

logical. Omit mean values for mean calculations?

scale

character specifying whether the test should occur on an absolute or relative scale. Must be one of "relative" (default) or "absolute".

absolute_region_width

the region width for use when scale = "absolute"

relative_region_width

the region width for use when scale = "relative"

...

further arguments passed to methods. Currently unused.

Value

a 'paired_equivalence' object summarizing the test input and results

Note

If a value is not specified for the region width that corresponds with scale, a default value will be assigned with a warning.

References

Dixon et al.

Examples

set.seed(1544)
x <- data.frame(
  var1 = rnorm(500, 15, 4),
  var2 = rnorm(500, 23, 7.3)
)
y <- rnorm(500, 17.4, 9)

test_result <- paired_equivalence_test(
  x, y, relative_region_width = 0.25
)

lapply(test_result, head)

set.seed(1544)
x <- data.frame(
  var1 = rnorm(500, 15, 4),
  var2 = rnorm(500, 23, 7.3)
)
y <- rnorm(500, 17.4, 9)

test_result <- paired_equivalence_test(
  x, y, relative_region_width = 0.25
)

lapply(test_result, head)

PAutilities: Streamline Physical Activity Research

Description

A collection of utilities that are useful for a broad range of tasks that are common in physical activity research. The main features (with associated functions in parentheses) are:

Details

* Bland-Altman plots (ba_plot) * Bout analysis for moderate-to-vigorous physical activity (bout_mvpa) * Formatted descriptive statistics descriptives * Demographic calculations (get_age and get_BMI_percentile) * Metabolic calculations (get_bmr, weir_equation, and get_kcal_vo2_conversion) * Analysis of bout detection algorithm performance (get_transition_info and associated methods, e.g. summary and plot)

Author(s)

Maintainer: Paul R. Hibbing [email protected]

Other contributors:

Centers for Disease Control and Prevention [contributor]

Plot the outcome of a paired equivalence test

Description

Plot the outcome of a paired equivalence test

Usage

## S3 method for class 'paired_equivalence'
plot(x, shade = "auto", ...)

shaded_equivalence_plot(results, ...)

unshaded_equivalence_plot(results, ...)
## S3 method for class 'paired_equivalence'
plot(x, shade = "auto", ...)

shaded_equivalence_plot(results, ...)

unshaded_equivalence_plot(results, ...)

Arguments

x

the object to be plotted

shade

logical. Should the results be plotted using a shaded equivalence region?

...

arguments passed to ggplot2::theme.

results

data frame. The results component of a paired_equivalence object

Details

shaded_equivalence_plot plots the results of an equivalence test in which a single equivalence region applies to all variables. In that case, the equivalence region is displayed as a shaded region. unshaded_equivalence_plot plots the results of an equivalence test in which variables have unique equivalence regions. In that case, the equivalence regions are displayed as dodged "confidence intervals".

Value

A plot of the equivalence test

Examples

set.seed(1544)
y <- rnorm(500, 17.4, 9)
z <- data.frame(
  var1 = rnorm(500, 15, 4),
  var2 = rnorm(500, 23, 7.3)
)

# Optionally create artificial missing values to trigger unshaded plot
missing_indices <- sample(seq(nrow(z)), 250)
z$var1[missing_indices] <- NA

x <- paired_equivalence_test(
  z, y, "criterion", scale = "relative",
  relative_region_width = 0.25
)

plot(x)
set.seed(1544)
y <- rnorm(500, 17.4, 9)
z <- data.frame(
  var1 = rnorm(500, 15, 4),
  var2 = rnorm(500, 23, 7.3)
)

# Optionally create artificial missing values to trigger unshaded plot
missing_indices <- sample(seq(nrow(z)), 250)
z$var1[missing_indices] <- NA

x <- paired_equivalence_test(
  z, y, "criterion", scale = "relative",
  relative_region_width = 0.25
)

plot(x)

Plot a spurious curve

Description

Plot a spurious curve

Usage

## S3 method for class 'spurious_curve'
plot(x, ...)
## S3 method for class 'spurious_curve'
plot(x, ...)

Arguments

x

a spurious_curve object

...

further arguments (currently unused)

Value

a plot of the object

Examples

set.seed(100)
predictions <- (sample(1:100)%%2)
references  <- (sample(1:100)%%2)

trans <- get_transition_info(
  predictions, references, 7
)
result <- spurious_curve(trans)
plot(result)

set.seed(100)
predictions <- (sample(1:100)%%2)
references  <- (sample(1:100)%%2)

trans <- get_transition_info(
  predictions, references, 7
)
result <- spurious_curve(trans)
plot(result)

Plot the transitions and matchings from a `transition` object

Description

Plot the transitions and matchings from a transition object

Usage

## S3 method for class 'transition'
plot(x, ...)
## S3 method for class 'transition'
plot(x, ...)

Arguments

x

the object to plot

...

further methods passed to or from methods, currently unused

Value

A plot of the predicted and actual transitions in a transition object, as well as the matchings between them

Examples

predictions <- (sample(1:100)%%2)
references  <- (sample(1:100)%%2)
window_size <- 7
if (isTRUE(requireNamespace("matchingMarkets", quietly = TRUE))){
  transitions <- get_transition_info(
    predictions, references, window_size
  )
  plot(transitions)
}
predictions <- (sample(1:100)%%2)
references  <- (sample(1:100)%%2)
window_size <- 7
if (isTRUE(requireNamespace("matchingMarkets", quietly = TRUE))){
  transitions <- get_transition_info(
    predictions, references, window_size
  )
  plot(transitions)
}

Reintegrate accelerometer data

Description

Reintegrate accelerometer data

Usage

reintegrate(df, target_sec, time_var = "Timestamp")
reintegrate(df, target_sec, time_var = "Timestamp")

Arguments

df

a data frame to reintegrate

target_sec

the desired epoch length of the output. Starting epoch length will be determined automatically

time_var

The name of the column containing POSIX-formatted timestamp information

Value

A data frame of reintegrated data. Numeric variables will be summed, and others will be labeled with the first value.

Examples


##Example data in 60-second epochs
data(ex_data, package = "PAutilities")
ex_data$Timestamp <- as.POSIXct(
  paste(ex_data$Date, ex_data$Time),
  tz = "UTC",
  format = "%m/%d/%Y %H:%M:%S"
)

##Reintegrate to 120-second epochs
result <- reintegrate(ex_data, 120)

utils::head(result)

##Example data in 60-second epochs
data(ex_data, package = "PAutilities")
ex_data$Timestamp <- as.POSIXct(
  paste(ex_data$Date, ex_data$Time),
  tz = "UTC",
  format = "%m/%d/%Y %H:%M:%S"
)

##Reintegrate to 120-second epochs
result <- reintegrate(ex_data, 120)

utils::head(result)

Perform residual adjustment on an epidemiologic variable

Description

Perform residual adjustment on an epidemiologic variable

Usage

residual_adjust(d, variable, confounder, label, verbose = FALSE)
residual_adjust(d, variable, confounder, label, verbose = FALSE)

Arguments

d

the input data frame on which to perform the adjustment

variable

character. Name of variable needing adjustment

confounder

character. Name of the confounder to adjust for

label

character. Name to give the adjusted variable

verbose

logical. Print updates to console?

Value

The original d object, with an extra column reflecting residual adjustments on the selected variable.

Examples

d <- data.frame(
  VARIABLE = rnorm(100, 10, 2),
  CONFOUNDER = rnorm(100, 3, 7)
)
result <- residual_adjust(d, "VARIABLE", "CONFOUNDER", "ADJUSTED")

head(d)
head(result)
d <- data.frame(
  VARIABLE = rnorm(100, 10, 2),
  CONFOUNDER = rnorm(100, 3, 7)
)
result <- residual_adjust(d, "VARIABLE", "CONFOUNDER", "ADJUSTED")

head(d)
head(result)

Calculate resting metabolic rate using a sliding window method

Description

Calculate resting metabolic rate using a sliding window method

Usage

rmr_sliding(
  vo2_values,
  vo2_timestamps,
  start_time,
  stop_time,
  window_size_minutes = 5
)
rmr_sliding(
  vo2_values,
  vo2_timestamps,
  start_time,
  stop_time,
  window_size_minutes = 5
)

Arguments

vo2_values

numeric vector of oxygen consumption values

vo2_timestamps

timestamps corresponding to each element of vo2_values

start_time

the beginning time of the assessment period

stop_time

the ending time of the assessment period

window_size_minutes

the size of the sliding window, in minutes

Value

A data frame giving the oxygen consumption from the lowest window, as well as the time difference from first to last breath in the same window.

Examples

set.seed(144)
fake_start_time <- Sys.time()
fake_stop_time <- fake_start_time + 1800
fake_timestamps <- fake_start_time + cumsum(sample(1:3, 500, TRUE))
fake_timestamps <- fake_timestamps[fake_timestamps <= fake_stop_time]
fake_breaths <- rnorm(length(fake_timestamps), 450, 0.5)
window_size <- 5

rmr_sliding(
  fake_breaths, fake_timestamps,
  fake_start_time, fake_stop_time,
  window_size
)
set.seed(144)
fake_start_time <- Sys.time()
fake_stop_time <- fake_start_time + 1800
fake_timestamps <- fake_start_time + cumsum(sample(1:3, 500, TRUE))
fake_timestamps <- fake_timestamps[fake_timestamps <= fake_stop_time]
fake_breaths <- rnorm(length(fake_timestamps), 450, 0.5)
window_size <- 5

rmr_sliding(
  fake_breaths, fake_timestamps,
  fake_start_time, fake_stop_time,
  window_size
)

Loop along a vector, returning n elements at a time in a list

Description

Loop along a vector, returning n elements at a time in a list

Usage

rolling_groups(values, n = 2L)
rolling_groups(values, n = 2L)

Arguments

values

IntegerVector. The vector to loop along

n

int. The number of elements to return in each element of the resulting list

Value

a list in which each element contains n elements from values

Note

For this function, the output elements contain raw values from values, whereas for get_indices the output elements contain the positions (i.e., indices) rather than the raw values

Examples

groups <- rolling_groups(0:50, 3)
head(groups)
tail(groups)
groups <- rolling_groups(0:50, 3)
head(groups)
tail(groups)

Perform a spurious curve analysis

Description

Assess performance using the Transition Pairing Method when the spurious pairing threshold is varied

Usage

spurious_curve(trans, predictions, references, thresholds = 1:20)
spurious_curve(trans, predictions, references, thresholds = 1:20)

Arguments

trans

a transition object

predictions

vector of predictions indicating transition (1) or non-transition (2)

references

vector of criteria indicating transition (1) or non-transition (2)

thresholds

the threshold settings to test

Value

an object with class spurious_curve

Examples

set.seed(100)
predictions <- (sample(1:100)%%2)
references  <- (sample(1:100)%%2)

trans <- get_transition_info(
  predictions, references, 7
)
head(spurious_curve(trans))

set.seed(100)
predictions <- (sample(1:100)%%2)
references  <- (sample(1:100)%%2)

trans <- get_transition_info(
  predictions, references, 7
)
head(spurious_curve(trans))

An S4 class containing summary information about a `transition` object

Description

An S4 class containing summary information about a transition object

Slots

result: a data frame with the summary information

Compare numeric variables in a data frame based on root-squared differences

Description

Compare numeric variables in a data frame based on root-squared differences

Usage

test_errors(
  reference,
  target,
  vars,
  tolerance = 0.001005,
  return_logical = TRUE
)
test_errors(
  reference,
  target,
  vars,
  tolerance = 0.001005,
  return_logical = TRUE
)

Arguments

reference

a data frame giving reference data

target

a data frame giving target data

vars

character vector of variable names to compare in each data frame

tolerance

allowable difference between numeric values

return_logical

logical. Should result be given as a logical vector (indicating TRUE/FALSE equality within tolerance) or a data frame of error summary values?

Value

If return_logical = TRUE, a named logical vector with one element per variable compared, indicating whether the maximum and root-mean-squared differences fall within the tolerance. If return_logical = FALSE, a data frame indicating the variables compared and the maximum and root-mean-squared differences.

Note

It is assumed that reference and target have equal numbers of rows.

Examples

reference <- data.frame(
a = 1:100, b = 75:174
)

target <- data.frame(
  a = 0.001 + (1:100),
  b = 76:175
)

test_errors(reference, target, c("a", "b"))
test_errors(reference, target, c("a", "b"), return_logical = FALSE)
reference <- data.frame(
a = 1:100, b = 75:174
)

target <- data.frame(
  a = 0.001 + (1:100),
  b = 76:175
)

test_errors(reference, target, c("a", "b"))
test_errors(reference, target, c("a", "b"), return_logical = FALSE)

Determine weight status from body mass index

Description

Allows users to determine weight status from body mass index (BMI). The function is designed to classify adult weight status, with default settings yielding weight classes defined by the Centers for Disease Control and Prevention (see reference below). Alternatively, the function can be used as a wrapper for get_BMI_percentile to obtain classifications for youth.

Usage

weight_status(BMI = NULL, breaks = c(-Inf, 18.5, 25, 30, 35, 40, Inf),
  labels = c("Underweight", "Healthy Weight", "Overweight", "Class 1 Obese",
  "Class 2 Obese", "Class 3 Obese"), right = FALSE, youth = FALSE, ...)

#get_BMI_percentile(weight_kg, height_cm, age_yrs = NULL, age_mos = NULL,
  #sex = c("Male", "Female"), BMI = NULL, df = NULL,
  #output = c("percentile", "classification", "both", "summary"))
weight_status(BMI = NULL, breaks = c(-Inf, 18.5, 25, 30, 35, 40, Inf),
  labels = c("Underweight", "Healthy Weight", "Overweight", "Class 1 Obese",
  "Class 2 Obese", "Class 3 Obese"), right = FALSE, youth = FALSE, ...)

#get_BMI_percentile(weight_kg, height_cm, age_yrs = NULL, age_mos = NULL,
  #sex = c("Male", "Female"), BMI = NULL, df = NULL,
  #output = c("percentile", "classification", "both", "summary"))

Arguments

BMI

numeric. The participant body mass index

breaks

numeric vector. The boundaries for each weight class; passed to base::cut, with warnings if -Inf and Inf are not included in the vector.

labels

character vector. The labels for each weight class; passed to base::cut, and should have a length one less than the length of breaks

right

logical. See ?base::cut

youth

logical. Use function as a wrapper for get_BMI_percentile?

...

Arguments passed to get_BMI_percentile

Value

a factor reflecting weight status

References

https://www.cdc.gov/bmi/adult-calculator/bmi-categories.html

Examples

weight_status(17:42)
weight_status(17:42)

Calculate energy expenditure using the Weir equation

Description

Calculate energy expenditure using the Weir equation

Usage

weir_equation(VO2, VCO2, epochSecs)
weir_equation(VO2, VCO2, epochSecs)

Arguments

VO2

Oxygen consumption

VCO2

Carbon dioxide production

epochSecs

The averaging window of the metabolic data, in seconds

Value

numeric scalar indicating predicted energy expenditure from the Weir equation, based on the inputs

References

De V Weir, J. B. (1949). New methods for calculating metabolic rate with special reference to protein metabolism. The Journal of physiology, 109(1-2), 1.

Examples

weir_equation(3.5, 3.1, 60)

weir_equation(3.5, 3.1, 60)

Package 'PAutilities'

Help Index

As("summaryTransition", "data.frame")

Description

Perform Bland-Altman analysis on a data frame

Description

Usage

Arguments

Value

Examples

Create a Bland-Altman plot

Description

Usage

Arguments

Value

References

See Also

Examples

Classify moderate-to-vigorous physical activity in bouts of a specific minimum length

Description

Usage

Arguments

Value

Examples

Calculate risk of cardiovascular disease

Description

Usage

Arguments

Value

References

Examples

Compute descriptive statistics for a variable in the metabolic data set

Description

Usage

Arguments

Value

Examples

Check if a dataframe is continuous

Description

Usage

Arguments

Value

Examples

Reorder the columns of a data frame

Description

Usage

Arguments

Value

Examples

Determine epoch length in seconds

Description

Usage

Arguments

Details

Value

Examples

Example data for calculating bouts of moderate-to-vigorous physical activity

Description

Usage

Format

Drop incomplete days from a dataset

Description

Usage

Arguments

Value

See Also

Examples

Calculate age

Description

Usage

Arguments

Value

Examples

Retrieve estimated basal metabolic rate for an individual

Description

Usage

Arguments

Value

References

Examples