accel.impute
(Package: accelmissing) :
Missing Value Imputation for Accelerometer Data
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.
● Data Source:
CranContrib
● Keywords: Poisson Log-normal, accelerometer, missing count data, multiple imputation, physical activity, zero-inflated model
● Alias: accel.imp, accel.imputation, accel.impute
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Imputes univariate missing data using the predictive mean matching (PMM) under the zero-inflated Poisson Log-normal (ZIPLN) model.
● Data Source:
CranContrib
● Keywords: mice, predictive mean matching
● Alias: mice.impute.2l.zipln.pmm
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Selects the complete (valid) days that include sufficient wearing time.
● Data Source:
CranContrib
● Keywords: accelerometer, accelmissing
● Alias: valid.days
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Computes the missing rate from acceleromater data.
● Data Source:
CranContrib
● Keywords: accelerometer, accelmissing
● Alias: missing.rate
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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.
● Data Source:
CranContrib
● Keywords: accelerometer, accelmissing
● Alias: create.flag
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Imputes univariate missing data using the predictive mean matching (PMM) under the zero-inflated Poisson (ZIP) model.
● Data Source:
CranContrib
● Keywords: mice, predictive mean matching
● Alias: mice.impute.2l.zip.pmm
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Imputes univariate missing data using Bayesian model under the zero-inflated Poisson Log-normal (ZIPLN) distribution.
● Data Source:
CranContrib
● Keywords: mice, zero-inflated poisson lognormal
● Alias: mice.impute.2l.zipln
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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.
● Data Source:
CranContrib
● Keywords: accelerometer, missing count data, physical activity
● Alias: accelmissing, accelmissing-package
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Displays the proportion of wearing over time among the daily profiles.
● Data Source:
CranContrib
● Keywords: accelerometer, accelmissing
● Alias: wear.time.plot
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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.
● Data Source:
CranContrib
● Keywords: accelerometer, accelmissing
● Alias: valid.subjects
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providing 
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