acceldata2: Accelerometer Data Example 2

In accelmissing: Missing Value Imputation for Accelerometer Data

Description

Data example from 2003-4 National Health and Nutrition Examination Survey dataset. This data example includes 184 individuals to give 1288 daily profiles. This only includes valid subjects that have at least three complete days, a subset of acceldata as a result of valid.subjects().

Usage

data(acceldata2)

Format

List with four matrix objects:

• acceldata2$PA: matrix

• acceldata2$label: matrix

• acceldata2$flag: matrix

• acceldata2$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=1288, 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=184). 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.

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.

See Also

acceldata, valid.subjects

Examples

data(acceldata2)
ls(acceldata2)

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