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accelmissing (version 1.4)

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.

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Version

Install

install.packages('accelmissing')

Monthly Downloads

616

Version

1.4

License

GPL (>= 2)

Maintainer

Jung Ae Lee

Last Published

April 6th, 2018

Functions in accelmissing (1.4)

accelmissing-package

accelmissing
acceldata2

Accelerometer Data Example 2
mice.impute.2l.zipln

Imputation by Bayesian ZIPLN model.
accel.plot.7days

Daily Activity Plot
accel.impute

Missing Value Imputation for Accelerometer Data
create.flag

Create a Missing Flag Matrix
mice.impute.2l.zip.pmm

Imputation by PMM under ZIP model.
acceldata

Accelerometer Data Example
mice.impute.2l.zipln.pmm

Imputation by PMM under ZIPLN model.
accelimp

Accelerometer Data Example with Imputations
valid.subjects

Include or Exclude Subjects by Criteria
valid.days

Select the Valid Days
missing.rate

Computing Missing Rate
wear.time.plot

Proportion of Wearing over Time