wear.time.plot: Proportion of Wearing over Time

Description

Displays the proportion of wearing over time among the daily profiles.

Usage

wear.time.plot(PA, label, flag, mark.missing = 0)

Arguments

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 (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().

mark.missing

If mark.missing = 0 (default), the nonwearing time is marked by 0 while the wearing time is marked by 1 in flag matrix. If mark.missing = 1, it is the opposite.

Value

Plot with the proportion of wearing in y-axis and the time index in x-axis, also displaying the standard measurement day.

References

[1] Lee JA, Gill J (2016). Missing value imputation for physical activity data measured by accelerometer. Statistical Methods in Medical Research. [2] Catellier, D. J., Hannan, P. J., Murray, D. M., Addy, C. L., Conway, T. L., Yang, S., and Rice, J. C. (2005). Imputation of missing data when measuring physical activity by accelerometry. Medicine and Science in Sports and Exercise, 37(11 Suppl).

Examples

Run this code

# NOT RUN {
data(acceldata) # read data
ls(acceldata)     # list with four data matrix objects, PA, label, flag, and demo
attach(acceldata)

# plot the proportion of wearing over time
wear.time.plot(PA, label, flag)	
# }