wear.time.plot {accelmissing}

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

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 (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.

Note

By looking at the plot, we may decide the standard measurement day, which is the time range that exhibits the sufficiently large portion of wearing (60 or 70 percent).

Author(s)

Jung Ae Lee <[e-mail]>

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).

See Also

missing.rate

Examples

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)