acceldata: Accelerometer Data Example

In accelmissing: Missing Value Imputation for Accelerometer Data

Description

Data example from 2003-4 National Health and Nutrition Examination Survey dataset. The dataset is available at the website: http://wwwn.cdc.gov/nchs/nhanes/search/nhanes03_04.aspx. This data example only includes 218 individuals, which gives 1526 daily profiles, from 7176 total participants in the physical activity survey.

Usage

data(acceldata)

Format

List with four matrix objects:

• acceldata$PA: matrix

• acceldata$label: matrix

• acceldata$flag: matrix

• acceldata$demo: matrix

Details

PA

an N by T matrix including activity counts, where N is the total number of daily profiles, and T is the total minutes of a day (N=1526, T=1440).

label

an N by 2 matrix including the labels corresponding to PA matrix. The first column, label[,1], includes the person id, and the second column, label[,2], includes the day label of 1 to 7, indicating Sunday to Saturday.

flag

an N by T matrix with the values of either 1 or 0 which indicating wearing or missing. This matrix can be created from create.flag().

demo

an n by p matrix (or dataframe) where n is the total number of subject (n=218). The first column must include the unique person id, which equals to unique(label[,1]). From the second column to p-th column, one may include the demographic variables of intrest, for example, age, sex, body mass index, and race. These variables will be used as covariates in the imputation model.

Note

This data format is strongly recommended for proceeding the missing value imputation from this package.

Source

http://wwwn.cdc.gov/nchs/nhanes/search/nhanes03_04.aspx

References

[1] Lee JA, Gill J (2016). Missing value imputation for physical activity data measured by accelerometer. Statistical Methods in Medical Research.

Examples

data(acceldata)
ls(acceldata)
dim(acceldata$PA)

Example output

Loading required package: mice
Loading required package: lattice

Attaching package: 'mice'

The following objects are masked from 'package:base':

    cbind, rbind

Loading required package: pscl
Classes and Methods for R developed in the
Political Science Computational Laboratory
Department of Political Science
Stanford University
Simon Jackman
hurdle and zeroinfl functions by Achim Zeileis
[1] "PA"    "demo"  "flag"  "label"
[1] 1526 1440

accelmissing documentation built on May 2, 2019, 1:44 p.m.