| Title: | Missing Value Imputation for Accelerometer Data |
|---|---|
| Description: | Imputation for the missing count values in accelerometer data. The methodology includes both parametric and semi-parametric multiple imputations under the zero-inflated Poisson lognormal model. This package also provides multiple functions to pre-process the accelerometer data previous to the missing data imputation. These includes detecting wearing and non-wearing time, selecting valid days and subjects, and creating plots. |
| Authors: | Jung Ae Lee <[email protected]> |
| Maintainer: | Jung Ae Lee <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.4 |
| Built: | 2024-10-29 03:45:53 UTC |
| Source: | https://github.com/cran/accelmissing |
This packages provides a statistical method to impute the missing values in accelerometer data. The methodology includes both parametric and semi-parametric multiple imputations under the zero-inflated poisson lognormal model. It also provides multiple functions to preprocess the accelerometer data previous to the missing data imputation. These includes detecting wearing and nonwearing time, selecting valid days and subjects, and creating plots.
| Package: | accelmissing |
| Type: | Package |
| Version: | 1.4 |
| Date: | 2018-04-04 |
| License: | GPL (>=2) |
Jung Ae Lee <[email protected]>
Lee JA, Gill J (2016). Missing value imputation for physical activity data measured by accelerometer. Statistical Methods and Medical Research.
mice, pscl
This function imputes the missing count values generated by the accelerometer. The imputation is performed during the user-defined daytime (9am-9pm as a default). At each minute, the function runs the multiple imputation with chained equations under the assumption of the zero-inflated poisson log-normal distribution.
accel.impute(PA, label, flag, demo=NA, method = "zipln", time.range = c("09:00","20:59"), K = 3, D = 5, mark.missing = 0, thresh = 10000, graph.diagnostic = TRUE, seed = 1234, m = 5, maxit = 6)accel.impute(PA, label, flag, demo=NA, method = "zipln", time.range = c("09:00","20:59"), K = 3, D = 5, mark.missing = 0, thresh = 10000, graph.diagnostic = TRUE, seed = 1234, m = 5, maxit = 6)
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 |
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 |
demo |
an n by p dataframe where n is the total number of subject. The first column must include the unique person id, which equals to |
method |
Either "zipln" or "zipln.pmm." The former conducts the parametric imputation assumming the zero-inflated Poisson Log-normal (zipln) distribution. The latter conducts the semiparametric impuation with the predictive mean matching (pmm) under the zipln assumption. |
time.range |
Define the time range for imputation. Default is 9am-9pm, coded by |
K |
The number of the lag and lead variables. |
D |
The number of donors when |
mark.missing |
If |
thresh |
The upper bound of count values. |
graph.diagnostic |
If |
seed |
A seed number for random process. |
m |
The number of imputation datasets. |
maxit |
The number of maximum iteration at a fixed time point. |
listimp |
List with |
seed, m, maxit are the input arguments in mice function.
Jung Ae Lee <[email protected]>
[1] Lee JA, Gill J (2016). Missing value imputation for physical activity data measured by accelerometer. Statistical Methods in Medical Research.
[2] van Buuren S, Groothuis-Oudshoorn K (2011). mice: Multivariate imputations by chained equations in R. Journal of Statistical Software.
[3] Jackman S (2014). pscl: Classes and Methods for R Developed in the Political Science Computational Laboratory. Stanford University. R package version 1.4.6.
#################################################### # A full example from data filtering to imputation #################################################### data(acceldata) # read data ls(acceldata) # This is a list with four matrix objects, PA, label, flag, and demo d = acceldata ## missing rate missing.rate(label=d$label, flag=d$flag)$total # 32 percent # create missing flag with 60 min criterion flag60 = create.flag(PA=d$PA, window=60) ## missing rate with flag60 mr = missing.rate(label=d$label, flag=flag60) mr$total #28.1 percent ## missing proportion by days mean(mr$table < 0.1) # 45.8 percent # wearing proportion over time wear.time.plot(PA=d$PA, label=d$label, flag=flag60) # data filtering for valid days valid.days.out = valid.days(PA=d$PA, label=d$label, flag=flag60, wear.hr=8) ls(valid.days.out) # list with three matrix objects # data filtering for valid subjects x1 = list(PA=d$PA, label=d$label, flag=flag60) # original x2 = valid.days.out # output of valid.days() valid.sub.out = valid.subjects(data1=x1, data2=x2, valid.days=3) length(unique(valid.sub.out$label[,1])) # 184 persons ls(valid.sub.out) ## missing rate with the filtered data missing.rate(valid.sub.out$label, valid.sub.out$flag)$total # 20.1 percent # demographic data for the filtered data idv= unique(valid.sub.out$label[,1]) matchid = match(idv, d$demo[,1]) demo1 = d$demo[matchid, ] # save the data before imputation acceldata2 = list(PA=valid.sub.out$PA, label=valid.sub.out$label, flag=valid.sub.out$flag, demo=demo1) save(acceldata2, file="acceldata2.RData") ################################ # prepare the imputation library(mice); library(pscl) data(acceldata2) # load prepared data in this package, or # load("acceldata2.RData") # to use the data you saved in previous step. data = acceldata2 # imputation: test only 10 minutes with semiparametic method # accelimp = accel.impute(PA=data$PA, label=data$label, flag=data$flag, # demo=data$demo, time.range=c("10:51","11:00"), method="zipln.pmm", D=5) # imputation: test only 10 minutes with parametic method # accelimp = accel.impute(PA=data$PA, label=data$label, flag=data$flag, # demo=data$demo, time.range=c("10:51","11:00"), method="zipln") # plot 7 days before imputation accel.plot.7days(PA=data$PA[1:7, ], label=data$label[1:7, ], flag=data$flag[1:7, ], time.range=c("09:00", "20:59"), save.plot=FALSE) # plot 7 days after imputation data(accelimp) # load prepared data in this package, or use the data you created above. accel.plot.7days(PA=accelimp[[1]][1:7, ], label=data$label[1:7, ], flag=data$flag[1:7, ], time.range=c("09:00", "20:59"), save.plot=FALSE)#################################################### # A full example from data filtering to imputation #################################################### data(acceldata) # read data ls(acceldata) # This is a list with four matrix objects, PA, label, flag, and demo d = acceldata ## missing rate missing.rate(label=d$label, flag=d$flag)$total # 32 percent # create missing flag with 60 min criterion flag60 = create.flag(PA=d$PA, window=60) ## missing rate with flag60 mr = missing.rate(label=d$label, flag=flag60) mr$total #28.1 percent ## missing proportion by days mean(mr$table < 0.1) # 45.8 percent # wearing proportion over time wear.time.plot(PA=d$PA, label=d$label, flag=flag60) # data filtering for valid days valid.days.out = valid.days(PA=d$PA, label=d$label, flag=flag60, wear.hr=8) ls(valid.days.out) # list with three matrix objects # data filtering for valid subjects x1 = list(PA=d$PA, label=d$label, flag=flag60) # original x2 = valid.days.out # output of valid.days() valid.sub.out = valid.subjects(data1=x1, data2=x2, valid.days=3) length(unique(valid.sub.out$label[,1])) # 184 persons ls(valid.sub.out) ## missing rate with the filtered data missing.rate(valid.sub.out$label, valid.sub.out$flag)$total # 20.1 percent # demographic data for the filtered data idv= unique(valid.sub.out$label[,1]) matchid = match(idv, d$demo[,1]) demo1 = d$demo[matchid, ] # save the data before imputation acceldata2 = list(PA=valid.sub.out$PA, label=valid.sub.out$label, flag=valid.sub.out$flag, demo=demo1) save(acceldata2, file="acceldata2.RData") ################################ # prepare the imputation library(mice); library(pscl) data(acceldata2) # load prepared data in this package, or # load("acceldata2.RData") # to use the data you saved in previous step. data = acceldata2 # imputation: test only 10 minutes with semiparametic method # accelimp = accel.impute(PA=data$PA, label=data$label, flag=data$flag, # demo=data$demo, time.range=c("10:51","11:00"), method="zipln.pmm", D=5) # imputation: test only 10 minutes with parametic method # accelimp = accel.impute(PA=data$PA, label=data$label, flag=data$flag, # demo=data$demo, time.range=c("10:51","11:00"), method="zipln") # plot 7 days before imputation accel.plot.7days(PA=data$PA[1:7, ], label=data$label[1:7, ], flag=data$flag[1:7, ], time.range=c("09:00", "20:59"), save.plot=FALSE) # plot 7 days after imputation data(accelimp) # load prepared data in this package, or use the data you created above. accel.plot.7days(PA=accelimp[[1]][1:7, ], label=data$label[1:7, ], flag=data$flag[1:7, ], time.range=c("09:00", "20:59"), save.plot=FALSE)
Displays an individual's physical activity pattern of a day during one week.
accel.plot.7days(PA, label, flag, time.range = c("00:00", "23:59"), mark.missing = 0, axis.time = TRUE, save.plot = FALSE, directory.plot = getwd() )accel.plot.7days(PA, label, flag, time.range = c("00:00", "23:59"), mark.missing = 0, axis.time = TRUE, save.plot = FALSE, directory.plot = getwd() )
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 |
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 |
time.range |
Define the time range for display. Default is midnight to midnight, which is coded by |
save.plot |
If |
mark.missing |
If |
axis.time |
If TRUE, the x-axis displays the clock times, 8:00, 8:01, 8:02, etc. If FALSE, displays the time index by minute, 481, 482, 483, etc. |
directory.plot |
Directory to save the plots when save.plot=TRUE. If no input, plots are saved to your current directory. |
Plot of activity counts with smoothing curve and missing flag.
Jung Ae Lee <[email protected]>
[1] Lee JA, Gill J (2016). Missing value imputation for physical activity data measured by accelerometer. Statistical Methods in Medical Research.
[2] Ramsay, J. O., Wickham, H., Graves, S., and Hooker, G. (2014). fda: Functional Data Analysis. R package version 2.4.3.
data(acceldata2) ; data=acceldata2 # read data before imputation data(accelimp) # read data after imputation # plot 7 days before imputation accel.plot.7days(PA=data$PA[1:7, ], label=data$label[1:7, ], flag=data$flag[1:7, ], time.range=c("09:00", "20:59"), save.plot=FALSE) # plot 7 days after imputation accel.plot.7days(PA=accelimp[[1]][1:7, ], label=data$label[1:7, ], flag=data$flag[1:7, ], time.range=c("09:00", "20:59"), save.plot=FALSE) # save the plot # setwd("yourfolder") #--- set the directory to save plot when save.plot=TRUE # accel.plot.7days(PA=accelimp[[1]], label=data$label, flag=data$flag, # time.range=c("09:00", "20:59"), save.plot=TRUE)data(acceldata2) ; data=acceldata2 # read data before imputation data(accelimp) # read data after imputation # plot 7 days before imputation accel.plot.7days(PA=data$PA[1:7, ], label=data$label[1:7, ], flag=data$flag[1:7, ], time.range=c("09:00", "20:59"), save.plot=FALSE) # plot 7 days after imputation accel.plot.7days(PA=accelimp[[1]][1:7, ], label=data$label[1:7, ], flag=data$flag[1:7, ], time.range=c("09:00", "20:59"), save.plot=FALSE) # save the plot # setwd("yourfolder") #--- set the directory to save plot when save.plot=TRUE # accel.plot.7days(PA=accelimp[[1]], label=data$label, flag=data$flag, # time.range=c("09:00", "20:59"), save.plot=TRUE)
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.
data(acceldata)data(acceldata)
List with four matrix objects:
acceldata$PA: matrix
acceldata$label: matrix
acceldata$flag: matrix
acceldata$demo: matrix
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).
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.
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().
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.
This data format is strongly recommended for proceeding the missing value imputation from this package.
http://wwwn.cdc.gov/nchs/nhanes/search/nhanes03_04.aspx
[1] Lee JA, Gill J (2016). Missing value imputation for physical activity data measured by accelerometer. Statistical Methods in Medical Research.
data(acceldata) ls(acceldata) dim(acceldata$PA)data(acceldata) ls(acceldata) dim(acceldata$PA)
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().
data(acceldata2)data(acceldata2)
List with four matrix objects:
acceldata2$PA: matrix
acceldata2$label: matrix
acceldata2$flag: matrix
acceldata2$demo: matrix
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).
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.
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().
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.
http://wwwn.cdc.gov/nchs/nhanes/search/nhanes03_04.aspx
[1] Lee JA, Gill J (2016). Missing value imputation for physical activity data measured by accelerometer. Statistical Methods in Medical Research.
acceldata, valid.subjects
data(acceldata2) ls(acceldata2)data(acceldata2) ls(acceldata2)
Imputed Data example from 2003-4 National Health and Nutrition Examination Survey dataset. This data example includes 184 individuals to give 1288 daily profiles, as a result of accel.impute().
data(accelimp)data(accelimp)
List with multiple matrix objects. accelimp includes a single dataset a result of accel.impute(..., m=1,...). You may produce multiple datasets by setting m=5 (default).
accelimp$imp1: matrix
[]...
accelimp$imp5: matrix
[1] Lee JA, Gill J (2016). Missing value imputation for physical activity data measured by accelerometer. Statistical Methods in Medical Research.
data(accelimp) ls(accelimp)data(accelimp) ls(accelimp)
Defines the missing interval by detecting consecutive zeros for a while (20 minutes as a default), and create a flag matrix with the binary indicator for wearing vs. nonwearing time.
create.flag(PA, window = 20, mark.missing = 0)create.flag(PA, window = 20, mark.missing = 0)
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). |
window |
Minimum minutes of missing interval. The default is 20, which means that we define the missing interval when the exact zeros continues more than 20 minutes. 30 or 60 minutes are also commonly used. |
mark.missing |
If |
an N by T matrix with the elements of 0 or 1.
Jung Ae Lee <[email protected]>
[1] Lee JA, Gill J (2016). Missing value imputation for physical activity data measured by accelerometer. Statistical Methods and Medical Research.
[2] Catellier DJ, Hannan PJ, Murray DM, Addy CL, Conway TL, Yang S, Rice JC (2005). Imputation of missing data when measuring physical activity by accelerometry. Medicine and Science in Sports and Exercise; 37 (11 Suppl).
data(acceldata) # read data PA = acceldata$PA # create a missing flag matrix with 60 minutes criterion flag60 = create.flag(PA, window=60, mark.missing=0) # create a missing flag matrix with 30 minutes criterion flag30 = create.flag(PA, window=30, mark.missing=0)data(acceldata) # read data PA = acceldata$PA # create a missing flag matrix with 60 minutes criterion flag60 = create.flag(PA, window=60, mark.missing=0) # create a missing flag matrix with 30 minutes criterion flag30 = create.flag(PA, window=30, mark.missing=0)
Imputes univariate missing data using the predictive mean matching (PMM) under the zero-inflated Poisson (ZIP) model.
mice.impute.2l.zip.pmm(y, ry, x, wy=NULL, type, K, D)mice.impute.2l.zip.pmm(y, ry, x, wy=NULL, type, K, D)
y |
Incomplete data vector of length n |
ry |
Vector of missing data pattern ( |
x |
Matrix (n by p) of complete covariates |
wy |
defalut wy=NULL |
type |
If |
K |
The number of the lag and lead variables. |
D |
The number of donors to be drawn by predictive mean matching. |
A vector of length nmis with imputations
This function runs by the argument in mice(..., method="2l.zip.pmm",...)
Jung Ae Lee <[email protected]>
[1] Lee JA, Gill J (2016). Missing value imputation for physical activity data measured by accelerometer. Statistical Methods in Medical Research.
[2] van Buuren S, Groothuis-Oudshoorn K (2011). mice: Multivariate imputations by chained equations in R. Journal of Statistical Software.
[3] Kleinke K, Reinecke J (2013). Multiple imputation of incomplete zero-infated count data. Statistica Neerlandica.
mice, mice.impute.2l.zipln.pmm
Imputes univariate missing data using Bayesian model under the zero-inflated Poisson Log-normal (ZIPLN) distribution.
mice.impute.2l.zipln(y, ry, x, wy=NULL, type, K, zs = zs)mice.impute.2l.zipln(y, ry, x, wy=NULL, type, K, zs = zs)
y |
Incomplete data vector of length n |
ry |
Vector of missing data pattern ( |
x |
Matrix (n by p) of complete covariates |
wy |
defalut wy=NULL |
type |
If |
K |
The number of the lag and lead variables. |
zs |
Matrix (N by 2K+1) with the elements of log(yhat)-log(lambda) (See Lee and Gill, 2016) |
A vector of length nmis with imputations
This function runs by the argument in mice(..., method="2l.zipln",...).
Jung Ae Lee <[email protected]>
[1] Lee JA, Gill J (2016). Missing value imputation for physical activity data measured by accelerometer. Statistical Methods in Medical Research.
[2] van Buuren S, Groothuis-Oudshoorn K (2011). mice: Multivariate imputations by chained equations in R. Journal of Statistical Software.
[3] Kleinke K, Reinecke J (2013). Multiple imputation of incomplete zero-infated count data. Statistica Neerlandica.
Imputes univariate missing data using the predictive mean matching (PMM) under the zero-inflated Poisson Log-normal (ZIPLN) model.
mice.impute.2l.zipln.pmm(y, ry, x, wy=NULL, type, K, zs = zs, D)mice.impute.2l.zipln.pmm(y, ry, x, wy=NULL, type, K, zs = zs, D)
y |
Incomplete data vector of length n |
ry |
Vector of missing data pattern ( |
x |
Matrix (n by p) of complete covariates |
wy |
defalut wy=NULL |
type |
If |
K |
The number of the lag and lead variables. |
zs |
Matrix (N by 2K+1) with the elements of log(yhat)-log(lambda) (See Lee and Gill, 2016) |
D |
The number of donors to be drawn by predictive mean matching. |
A vector of length nmis with imputations
This function runs by the argument in mice(..., method="2l.zipln.pmm",...)
Jung Ae Lee <[email protected]>
[1] Lee JA, Gill J (2016). Missing value imputation for physical activity data measured by accelerometer. Statistical Methods in Medical Research.
[2] van Buuren S, Groothuis-Oudshoorn K (2011). mice: Multivariate imputations by chained equations in R. Journal of Statistical Software.
[3] Kleinke K, Reinecke J (2013). Multiple imputation of incomplete zero-infated count data. Statistica Neerlandica.
mice, mice.impute.2l.zip.pmm, mice.impute.2l.zipln
Computes the missing rate from acceleromater data.
missing.rate(label, flag, mark.missing = 0, time.range = c("09:00", "20:59"))missing.rate(label, flag, mark.missing = 0, time.range = c("09:00", "20:59"))
label |
an N by 2 matrix including the labels corresponding to |
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 |
mark.missing |
If |
time.range |
Define the time range during which the missing rate is computed. Default is 9am-9pm, coded by |
Numeric value of a missing rate between 0 to 1. The output is a list of
total |
total missing rate during the time range |
table |
missing rate on days by subject |
table.wh |
wearing hours on days by subject |
label |
wearing hours by subject id and day, same information as table.wh but different data frame |
Jung Ae Lee <[email protected]>
[1] Lee JA, Gill J (2016). Missing value imputation for physical activity data measured by accelerometer. Statistical Methods in Medical Research.
## missing rate calculation: uncomment and run the code below # data(acceldata); attach(acceldata) # missing.rate(label, flag, mark.missing=0, time.range=c("09:00", "20:59"))$total ## create missing flag by 60 min criterion # flag60 = create.flag(PA, window=60, mark.missing=0) # mr = missing.rate(label, flag60, mark.missing=0, time.range=c("09:00", "20:59")) # mr$total #28.1 percent ## missing proportion by days # mean(mr$table < 0.1) # 45.8 percent## missing rate calculation: uncomment and run the code below # data(acceldata); attach(acceldata) # missing.rate(label, flag, mark.missing=0, time.range=c("09:00", "20:59"))$total ## create missing flag by 60 min criterion # flag60 = create.flag(PA, window=60, mark.missing=0) # mr = missing.rate(label, flag60, mark.missing=0, time.range=c("09:00", "20:59")) # mr$total #28.1 percent ## missing proportion by days # mean(mr$table < 0.1) # 45.8 percent
Selects the complete (valid) days that include sufficient wearing time.
valid.days(PA, label, flag, wear.hr = 10, time.range = c("09:00", "20:59"), mark.missing = 0)valid.days(PA, label, flag, wear.hr = 10, time.range = c("09:00", "20:59"), mark.missing = 0)
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 |
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 |
wear.hr |
Minimum wearing hours during the time range. If |
time.range |
Define the time range for the standard measurment day. Default is |
mark.missing |
If |
List with the updated PA, label and flag matrix objects.
Jung Ae Lee <[email protected]>
[1] Lee JA, Gill J (2016). Missing value imputation for physical activity data measured by accelerometer. Statistical Methods in Medical Research.
data(acceldata); attach(acceldata) # read data # filtering data for valid days valid.days.out = valid.days(PA, label, flag, wear.hr=8, time.range=c("09:00","20:59")) ls(valid.days.out)data(acceldata); attach(acceldata) # read data # filtering data for valid days valid.days.out = valid.days(PA, label, flag, wear.hr=8, time.range=c("09:00","20:59")) ls(valid.days.out)
Select the subjects that have at least 3 complete days (or other criteria). By such criteria, some complete days are dropped if one has only one or two completed days, although some incomplete days are included if the subject has already three or more complete days.
valid.subjects(data1, data2, valid.days = 3, valid.week.days = NA, valid.weekend.days = NA, mark.missing = 0, keep.7days=TRUE)valid.subjects(data1, data2, valid.days = 3, valid.week.days = NA, valid.weekend.days = NA, mark.missing = 0, keep.7days=TRUE)
data1 |
A list with three data matrix objects, |
data2 |
A list with three data matrix objects, |
valid.days |
Minimum number of complete days that the subject should have. |
valid.week.days |
Minimum number of complete weekdays that the subject should have. |
valid.weekend.days |
Minimum number of complete weekend days that the subject should have. |
mark.missing |
If |
keep.7days |
If |
List with the updated PA, label and flag matrix objects.
Jung Ae Lee <[email protected]>
[1] Lee JA, Gill J (2016). Missing value imputation for physical activity data measured by accelerometer. Statistical Methods in Medical Research.
data(acceldata); attach(acceldata) # read original data # filtering data for valid days valid.days.out = valid.days(PA, label, flag, wear.hr=8, time.range=c("09:00","20:59")) ls(valid.days.out) # filtering data for valid subjects x1 = list(PA=PA, label=label, flag=flag) # original data x2 = valid.days.out # output of valid.days() valid.sub.out = valid.subjects(data1=x1, data2=x2, valid.days=3) ls(valid.sub.out)data(acceldata); attach(acceldata) # read original data # filtering data for valid days valid.days.out = valid.days(PA, label, flag, wear.hr=8, time.range=c("09:00","20:59")) ls(valid.days.out) # filtering data for valid subjects x1 = list(PA=PA, label=label, flag=flag) # original data x2 = valid.days.out # output of valid.days() valid.sub.out = valid.subjects(data1=x1, data2=x2, valid.days=3) ls(valid.sub.out)
Displays the proportion of wearing over time among the daily profiles.
wear.time.plot(PA, label, flag, mark.missing = 0)wear.time.plot(PA, label, flag, mark.missing = 0)
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 |
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 |
mark.missing |
If |
Plot with the proportion of wearing in y-axis and the time index in x-axis, also displaying the standard measurement day.
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).
Jung Ae Lee <[email protected]>
[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).
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)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)