acc (version 1.1.7)

simAcc: Simulates accelerometer data based on a hidden Markov model

Description

Simulates accelerometer data. The simulation function is based on a hidden Markov model, as described in the example for function acc. This function is provided for convenience to generate data from a pre-specified transition probabilities to mimich activity levels of low, moderate and high. To generate data from a specific transition probabilities and distributions, please refer to the example for function acc.

Usage

simAcc(minutes,mvpaLevel,seedset)

Arguments

minutes

Number of observations to be generated.

mvpaLevel

Level of moderate-vigorous activity. Options: `low', `moderate', or `high'. Low specifies a hidden markov model with transition probabilities 0.95, 0.04, 0.01, 0.09, 0.9, 0.01, 0.1, 0.2, 0.7, respectively for P11, P12, P13, P21, P22, P23, P31, P32, P33, r

seedset

Sets seed for random data generation. Defaults to 1234.

Value

• A simulated dataset is returned with two columns: [TimeStamp, counts]

Examples

##
## Example: Simulate a dataset for two days, for an individual with low MVPA level.
##
mvpaLowData <- simAcc(minutes=(60*24*2),mvpaLevel='low')
summary <- acc(data=mvpaLowData, tri='FALSE', axis=NULL,
                     spuriousDef=20, nonwearDef=60, minWear=600, 
                     patype=c('Sedentary','MVPA'),pacut=c(c(0,99),c(1952,Inf)), 
                     boutsize=c(10,10), tolerance=c('FALSE','TRUE'))
summary

##
## Example: Simulate a dataset for two days, for an individual with moderate MVPA level.
##
mvpaLowData <- simAcc(minutes=(60*24*2),mvpaLevel='moderate')
summary <- acc(data=mvpaLowData, tri='FALSE', axis=NULL,
                     spuriousDef=20, nonwearDef=60, minWear=600, 
                     patype=c('Sedentary','MVPA'),pacut=c(c(0,99),c(1952,Inf)), 
                     boutsize=c(10,10), tolerance=c('FALSE','TRUE'))
summary

##
## Example: Simulate a dataset for two days, for an individual with high MVPA level.
##
mvpaLowData <- simAcc(minutes=(60*24*2),mvpaLevel='high')
summary <- acc(data=mvpaLowData, tri='FALSE', axis=NULL,
                     spuriousDef=20, nonwearDef=60, minWear=600, 
                     patype=c('Sedentary','MVPA'),pacut=c(c(0,99),c(1952,Inf)), 
                     boutsize=c(10,10), tolerance=c('FALSE','TRUE'))
summary