accelmissing (version 1.4)

acceldata: Accelerometer Data Example

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.

% item

References

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

Examples

# NOT RUN {
data(acceldata)
ls(acceldata)
dim(acceldata$PA)
# }