Type:	Package
Title:	Cardiovascular Applications in Research Data
Version:	0.1.1
Description:	A collection of cardiovascular research datasets and analytical tools, including methods for cardiovascular procedural data, such as electrocardiography, echocardiography, and catheterization data. Additional methods exist for analysis of procedural billing codes.
License:	MIT + file LICENSE
URL:	https://cran.r-project.org/package=card
BugReports:	https://github.com/shah-in-boots/card/issues
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
Depends:	R (≥ 4.1),
Imports:	stats, generics, ggplot2, tidyr, dplyr, purrr, tibble, rlang, parsnip, hardhat,
Suggests:	kableExtra, knitr, rmarkdown, covr, testthat, broom.mixed, ggrepel
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2024-10-29 03:08:29 UTC; asshah4
Author:	Anish S. Shah [aut, cre, cph]
Maintainer:	Anish S. Shah <shah.in.boots@gmail.com>
Repository:	CRAN
Date/Publication:	2024-11-06 07:40:02 UTC

card: Cardiovascular Applications in Research Data

Description

A collection of cardiovascular research datasets and analytical tools, including methods for cardiovascular procedural data, such as electrocardiography, echocardiography, and catheterization data. Additional methods exist for analysis of procedural billing codes.

Author(s)

Maintainer: Anish S. Shah shah.in.boots@gmail.com (ORCID) [copyright holder]

See Also

Useful links:

• https://cran.r-project.org/package=card

• Report bugs at https://github.com/shah-in-boots/card/issues

Augment data with information from a cosinor object

Description

Augment accepts a cosinor model object and adds information about each observation in the dataset. This includes the predicted values in the .fitted column and the residuals in the .resid column. New columns always begin with a . prefix to avoid overwriting columns in original dataset.

Usage

## S3 method for class 'cosinor'
augment(x, ...)

Arguments

Value

a tibble object

See Also

Other cosinor: cosinor(), ggcosinor()

Fit a cosinor

Description

cosinor() fits a regression model of a time variable to a continuous outcome use trigonometric features. This approaches uses the linearization of the parameters to assess their statistics and distribution.

Usage

cosinor(t, ...)

## Default S3 method:
cosinor(t, ...)

## S3 method for class 'data.frame'
cosinor(t, y, tau, population = NULL, ...)

## S3 method for class 'matrix'
cosinor(t, y, tau, population = NULL, ...)

## S3 method for class 'formula'
cosinor(formula, data, tau, population = NULL, ...)

## S3 method for class 'recipe'
cosinor(t, data, tau, population = NULL, ...)

Arguments

Value

A cosinor object.

See Also

Other cosinor: augment.cosinor(), ggcosinor()

Examples

# Data setup
data("twins")

# Formula interface
model <- cosinor(rDYX ~ hour, twins, tau = 24)

Area of Ellipse

Description

Formulas for creating the area of the ellipse to identify confidence intervals, direction, and graphing purposes.

Usage

cosinor_area(object, level = 0.95, ...)

Arguments

Value

Area of potential cosinor for graphical analysis as matrix stored in a list.

Multiple Component Cosinor Features

Description

Extract the special/global features of a multiple component cosinor. In a multiple component model, there are specific parameters that are not within the model itself, but must be extracted from the model fit. When extracted, can be used to improve the plot of a multiple component cosinor. However, this is only possible if the cosinor is harmonic (see details). For single-component models, the orthophase is the same as the acrophase and the global amplitude

• Global Amplitude (Ag) = the overall amplitude is defined as half the difference between the peak and trough values

• Orthophase (Po) = the lag until the peak time

• Bathyphase (Pb) = the lag until the trough time

Usage

cosinor_features(object, population = TRUE, ...)

Arguments

Details

These calculations can only occur if the periods of the cosinor are harmonic - as in, the longest period is a integer multiple of the smallest period (known as the fundamental frequency). Otherwise, these statistics are not accurate or interpretable.

Value

When returning the cosinor features for a single model, will return an object of class list. When returning the cosinor features for every individual in a population cosinor, will return an object of class tibble.

Examples

data(twins)
model <- cosinor(rDYX ~ hour, twins, c(24, 8), "patid")
results <- cosinor_features(model, population = FALSE)
head(results)

Goodness of Fit of Cosinor

Description

Goodness of fit of a cosinor from data that has multiple collections at different timepoints or from multiple cycles. The RSS is partitioned into pure error (SSPE) and lack of fit (SSLOF). An F-test compares the SSPE and SSLOF to detect appropriateness of model.

SSLOF = RSS - SSPE

SSPE = \sum_{i} \sum_{l} ( Y_{il} - \overline{Y}_{i} )^2

The fitted values for each time point are:

\overline{Y}_{i} = \frac{ \sum_{l} Y_{il} }{ n_{i}}

Usage

cosinor_goodness_of_fit(object, level = 0.95, ...)

Arguments

Value

f-statistic as result of goodness of fit

General Interface for Cosinor Regression Models

Description

cosinor_reg() is a parsnip friendly method for specification of cosinor regression model before fitting.

Usage

cosinor_reg(mode = "regression", period = NULL)

## S3 method for class 'cosinor_reg'
update(object, period = NULL, fresh = FALSE, ...)

## S3 method for class 'cosinor_reg'
print(x, ...)

Arguments

Examples

library(parsnip)
cosinor_reg(period = c(24, 8)) |>
	parsnip::set_engine("card") |>
	parsnip::set_mode("regression")

Zero Amplitude Test

Description

Zero amplitude test assesses how well the circadian pattern fits the data, essentially detecting the present of a rhythm to the data.

Usage

cosinor_zero_amplitude(object, level = 0.95)

Arguments

Value

Returns a list of test statistics, as well prints out a report of analysis.

GEH parameters in a large clinical cohort

Description

Used in the model-building examples for repeat testing.

Usage

geh

Format

A tibble

ggplot of cosinor model

Description

ggplot of cosinor model that can visualize a variety of cosinor model subtypes, including single-component, multiple-component, individual, and population cosinor models, built using cosinor. For single component cosinor, the following values are plotted:

• M = midline estimating statistic of rhythm

• A = amplitude

• P = phi or acrophase (shift from 0 to peak)

If using a multiple-component cosinor, the terms are different. If the periods or frequencies resonate or are harmonic, then the following are calculated. If the periods are not harmonic, the values are just descriptors of the curve.

• M = midline estimating statistic of rhythm

• Ag = global amplitude, which is the distance between peak and trough (this is the same value as the amplitude from single component)

• Po = orthophase (the equivalent of the acrophase in a single component), the lag time to peak value

• Pb = bathyphase, the lag time to trough value

Usage

ggcosinor(object, labels = TRUE, ...)

Arguments

Value

Object of class ggplot that can be layered

See Also

Other cosinor: augment.cosinor(), cosinor()

Examples

data(triplets)
m1 <- cosinor(rDYX ~ hour, twins, tau = 24)
m2 <- cosinor(rDYX ~ hour, twins, tau = c(24, 12))
ggcosinor(m1, labels = FALSE)
ggcosinor(m2)
ggcosinor(list(single = m1, multiple = m2))

Graphical Assessment of Amplitude and Acrophase

Description

This is a ggplot-styled graphical representation of the ellipse region generated by the cosinor analysis. It requires the same data used by cosinor model to be fit with the model cosinor. This includes the amplitude, acrophase,

Usage

ggellipse(object, level = 0.95, ...)

Arguments

Value

Object of class ggplot to help identify confidence intervals

Examples

data("twins")
m <- cosinor(rDYX ~ hour, twins, tau = 24)
ggellipse(m)

Output from MATLAB HRV Toolbox

Description

Data is a single patient data output from HRV Toolbox. It contains granular data of calculated HRV in 5-second sliding windows.

Usage

hrv

Format

An tibble data frame

Predict from a cosinor

Description

Predict from a cosinor

Usage

## S3 method for class 'cosinor'
predict(object, new_data, type = "numeric", ...)

Arguments

Value

A tibble of predictions. The number of rows in the tibble is guaranteed to be the same as the number of rows in new_data.

Center for Medicare and Medicaid Services (CMS) Procedure Codes

Description

This is a generative function used to call CMS procedure codes. It is used to create a dataset that can be generally used to map procedure codes to their descriptions, allowing for understanding of interventions performed. The currently supported codes are explained in the details.

The following procedure codes are currently supported:

• ICD9 procedure codes, most recently updated on 2014-10-01

• ICD10 procedure codes, most recently updated on 2023-01-11

• HCPCS prcoedure codes, most recently updated on 2023-11-29

• CPT procedure codes, most recently updated on 2023-11-29

Usage

procedure_codes(format, version)

Arguments

Details

CMS will usually release updated version of these codes on an annual basis. Each dataset that is supported below can be identified by the year it was published (not the go-live date, but the publically-available date). The previous versions that are included in the package are as below.

• ICD9: 2014

• ICD10: 2023

• HCPCS: 2023

• CPT: 2023

Value

A tbl_df with two columns: code and description. The code refers to the procedure code, while the description refers to the description of the procedure.

Examples

# Procedure codes from the 2014 version of ICD-9
icd9 <- procedure_codes(format = "icd9", version = 2014)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

generics

augment, tidy

Recurrent event sample data

Description

Data is from a outcomes study on cardiovascular outcomes. It contains the first visit date, the last known date, and times of various events that have happened. They document death at right censoring as well. These events are non-ordered.

Usage

stress

Format

An tibble data frame

Tidy a(n) cosinor object

Description

Tidy summarizes information about the components of a cosinor model.

Usage

## S3 method for class 'cosinor'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

Arguments

Details

cosinor objects do not necessarily have a T-statistic as the standard error is not based on a mean value, but form a joint-confidence interval. The standard error is generated using Taylor series expansion as the object is a subspecies of harmonic regressions.

Value

a tibble object

Hourly time series data with clinical covariates

Description

Clinical data is also available for visualization and comparison. Other HRV measures are used here for comparison and testing out functions.

Usage

triplets

Format

A tbl_df

Hourly time series data with clinical covariates

Description

Data is from an algorithm that generates a summary HRV measure using the Poincare phase-space plot, generated from kurtoses of the x and y axis. Clinical data is also available for visualization and comparison. There are repeat rows for each hour that Dyx was taken.

Usage

twins

Format

An tibble data frame

Zipcodes with Associated Latitude and Longitude

Description

This is a dataset from the archived/orphaned zipcode package.

Usage

zipcode

Format

A data frame with character vector zipcodes and latitude/longitude