| Title: | Exploring Accelerometer Data |
|---|---|
| Description: | Processes accelerometer data from uni-axial and tri-axial devices, and generates data summaries. Also includes functions to plot, analyze, and simulate accelerometer data. |
| Authors: | Jaejoon Song, Matthew G. Cox |
| Maintainer: | Jaejoon Song <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.3.3 |
| Built: | 2024-11-26 06:40:09 UTC |
| Source: | CRAN |
Summarizes accelerometer for multiple types of physical activities
acc(data, tri, axis, spuriousDef, nonwearDef, minWear, patype, pacut, epoch, boutsize, tolerance)acc(data, tri, axis, spuriousDef, nonwearDef, minWear, patype, pacut, epoch, boutsize, tolerance)
data |
Data which consists of two columns [TimeStamp,counts] (i.e. accelerometer counts file read in by function readCounts) |
tri |
Whether the data is from a tri-axial accelerometer. Default is tri=‘FALSE’. If tri=‘TRUE’ then option ‘axis’ should be specified. |
axis |
This option is only used for the tri-axial accelerometer. Options are ‘x’,‘y’,‘z’,‘sum’, or ‘vm’. Options ‘x’, ‘y’, or ‘z’ can be specified to summarize data using only data from a single axis. If the option 'vm' is used, the square root of the squared sum of counts from three axes (i.e. |
spuriousDef |
Definition of spurious observation. Defined as minutes of consecutive zeros. For example, if spuriousDef = 20, this means that an observation point will be determined as a spurious observation if there are consequtive counts of at least 20 zeros before and after the single non-zero observation. Default is spuriousDef = 20. |
nonwearDef |
Definition of non-wear time. Defined as minutes of consecutive zeros. For example, if nonwearDef=60, this means that a period will be defined as non-wear time if there are at least 60 consecutive zeros. Default is nonwearDef=60. To consider all observations as wear time specify nonwearDef=‘Inf’. |
minWear |
Minimum wear time definition. Defined as minutes of wear time. or example, if minWear = 600, this means that a day will be considered valid only if the wear time is at least 600 minutes. Default is minWear = 600. To return summary for all dates in the data, set minWear = 0. |
patype |
Types of physical activity for summary. For example, to summarize sedentary and moderate-vigorous physical activities, user specifies patype=c(‘Sedentary’,‘MVPA’). This labels the summary accordingly. |
pacut |
Cut points to be used for the physical activity type. For example, if the user specified patype=c(‘Sedentary’,‘MVPA’), pacut can be specified as pacut=c(c(0,99),c(1952,Inf)). The options requires to have a lower and a upper limit for each activity type (i.e. c(0,99) for sedentary activity). The specified interval includes its lower and upper endpoints (it is a closed interval). |
epoch |
Epoch size. Default is '1 min'. Other epoch size can be specified using this option (e.g., '1 sec') |
boutsize |
Boutsize to summarize a physical activity. If multiple patype is specified, boutsize should be specified for each one (e.g., if patype=c(‘Sedentary’,‘MVPA’) then one can use boutsize=c(10,10)). |
tolerance |
Whether two observations outside the physical activity should be permitted in summarizing a physical activity. If multiple patype is specified, tolerance should be for each one (e.g. if patype=c(‘Sedentary’,‘MVPA’) then one can use tolerance=c(‘FALSE’,‘TRUE’)). |
summary |
Returns summary for each specified physical activity types (number of bouts and minutes of the activity), for valid dates. |
Jaejoon Song <[email protected]>
Choi, L., Liu, Z., Matthews, C.E. and Buchowski, M.S. (2011). Validation of Accelerometer Wear and Nonwear Time Classification Algorithm. Med Sci Sports Exerc, 43(2):357-64.
Hall, K. S., Howe, C. A., Rana, S. R., Martin, C. L., and Morey, M. C. (2013). METs and Accelerometry of Walking in Older Adults: Standard versus Measured Energy Cost. Medicine and Science in Sports and Medicine, 45(3). 574-82.
Freedson, P., Melanson, E., and Sirard, J. (1998). Calibration of the Computer Sciences and Applications, Inc. accelerometer. Medicine and Science in Sports and Exercercise, 30(5):777-81.
Swartz, A. M., Strath, S. J., Bassett, D. R. Jr., O'Brien, W. L., King, G. A., and Ainsworth, B. E. (2000). Estimation of energy expenditure using CSA accelerometers at hip and wrist sites. Medicine and Science in Sports and Exercercise, 32: S450-456.
Copeland, J. L., and Esliger, D. W. (2009). Accelerometer assessment of physical activity in active, healthy older adults. J Aging Phys Act, 17: 17-30.
## ## Example: Loading accelerometer counts data using readCounts function ## ## Not run: library(acc) infile <- "CountsDataName.dat" counts <- readCounts(infile) ## ## Example: Summarizing accelerometer data for a sedentary individual ## # For this example, data is generated using a Hidden Markov model # First, a sequence of time is generated randomTime <- seq(ISOdate(2015,4,1),ISOdate(2015,4,3),"min") # Load the mhsmm package to generate data using a Hidden Makov model library(mhsmm) # It is assumed that the counts are generated from a Hidden Markov model # with three states being non-wear, sedentary, and moderate-vigorous activity J <- 3; initial <- rep(1/J, J) # Set up a transition matrix for the Hidden Markov model. P <- matrix(c(0.95, 0.04, 0.01, 0.09, 0.9, 0.01, 0.1, 0.2, 0.7), byrow='TRUE',nrow = J) # It is assumed that the counts are realized from a mixture of # two normal distributions (for sedentary activity and mvpa) # and a constant at zero (for non-wear time). b <- list(mu = c(0, 30, 2500), sigma = c(0, 30, 1000)) model <- hmmspec(init = initial, trans = P, parms.emission = b,dens.emission = dnorm.hsmm) # Generate data! train <- simulate.hmmspec(model, nsim = (60*24*2), seed = 1234, rand.emis = rnorm.hsmm) # Now set up a dataset that mimicks the accelerometry data counts <- data.frame(TimeStamp = randomTime[1:length(train$x)], counts = train$x) library(acc) # summarize the data using the acc function. # Sedentary and moderate-vigorous activity is summarized, using Freedson's cut points by default. summary1 <- acc(data=counts, 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')) summary1 ## ## Example: Summarizing accelerometer data for an active individual. ## randomTime <- seq(ISOdate(2015,4,1),ISOdate(2015,4,3),"min") library(mhsmm) J <- 3; initial <- rep(1/J, J) P <- matrix(c(0.95, 0.04, 0.01, 0.09, 0.7, 0.21, 0.1, 0.1, 0.8), byrow='TRUE',nrow = J) b <- list(mu = c(0, 30, 2500), sigma = c(0, 30, 1000)) model <- hmmspec(init = initial, trans = P, parms.emission = b,dens.emission = dnorm.hsmm) train <- simulate.hmmspec(model, nsim = (60*24*2), seed = 1234, rand.emission = rnorm.hsmm) counts <- data.frame(TimeStamp = randomTime[1:length(train$x)], counts = train$x) library(acc) summary2 <- acc(data=counts, 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')) summary2 ## End(Not run)## ## Example: Loading accelerometer counts data using readCounts function ## ## Not run: library(acc) infile <- "CountsDataName.dat" counts <- readCounts(infile) ## ## Example: Summarizing accelerometer data for a sedentary individual ## # For this example, data is generated using a Hidden Markov model # First, a sequence of time is generated randomTime <- seq(ISOdate(2015,4,1),ISOdate(2015,4,3),"min") # Load the mhsmm package to generate data using a Hidden Makov model library(mhsmm) # It is assumed that the counts are generated from a Hidden Markov model # with three states being non-wear, sedentary, and moderate-vigorous activity J <- 3; initial <- rep(1/J, J) # Set up a transition matrix for the Hidden Markov model. P <- matrix(c(0.95, 0.04, 0.01, 0.09, 0.9, 0.01, 0.1, 0.2, 0.7), byrow='TRUE',nrow = J) # It is assumed that the counts are realized from a mixture of # two normal distributions (for sedentary activity and mvpa) # and a constant at zero (for non-wear time). b <- list(mu = c(0, 30, 2500), sigma = c(0, 30, 1000)) model <- hmmspec(init = initial, trans = P, parms.emission = b,dens.emission = dnorm.hsmm) # Generate data! train <- simulate.hmmspec(model, nsim = (60*24*2), seed = 1234, rand.emis = rnorm.hsmm) # Now set up a dataset that mimicks the accelerometry data counts <- data.frame(TimeStamp = randomTime[1:length(train$x)], counts = train$x) library(acc) # summarize the data using the acc function. # Sedentary and moderate-vigorous activity is summarized, using Freedson's cut points by default. summary1 <- acc(data=counts, 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')) summary1 ## ## Example: Summarizing accelerometer data for an active individual. ## randomTime <- seq(ISOdate(2015,4,1),ISOdate(2015,4,3),"min") library(mhsmm) J <- 3; initial <- rep(1/J, J) P <- matrix(c(0.95, 0.04, 0.01, 0.09, 0.7, 0.21, 0.1, 0.1, 0.8), byrow='TRUE',nrow = J) b <- list(mu = c(0, 30, 2500), sigma = c(0, 30, 1000)) model <- hmmspec(init = initial, trans = P, parms.emission = b,dens.emission = dnorm.hsmm) train <- simulate.hmmspec(model, nsim = (60*24*2), seed = 1234, rand.emission = rnorm.hsmm) counts <- data.frame(TimeStamp = randomTime[1:length(train$x)], counts = train$x) library(acc) summary2 <- acc(data=counts, 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')) summary2 ## End(Not run)
Summarizes multiple accelerometer datafiles in a batch mode. Summary can be provided for multiple types of physical activities, by day.
accBatch(path, tri, axis, spuriousDef, nonwearDef, minWear, patype, pacut, epoch, boutsize, tolerance)accBatch(path, tri, axis, spuriousDef, nonwearDef, minWear, patype, pacut, epoch, boutsize, tolerance)
path |
Path to accelerometer data files read in by function readCounts or readCountsBath. Files in this path can have both uni-axial and tri-axial data. If at least one tri-axial data is present in the path, please specify tri='TRUE' and axis. This information will be used to summarize tri-axial data. |
tri |
Is there at least one dataset from a tri-axial accelerometer in the folder? Default is tri=‘TRUE’. If tri=‘TRUE’ then option ‘axis’ should be specified. Default axis is axis='vm'. |
axis |
If the data is from a tri-axial device, this option is applied. Options are ‘x’,‘y’,‘z’,‘sum’, or ‘vm’. Options ‘x’, ‘y’, or ‘z’ can be spefied to summarize data using only data from a single axis. If the option 'vm' is used, the square root of the squared sum of counts from three axes (i.e. |
spuriousDef |
Definition of spurious observation. Defined as minutes of consecutive zeros. For example, if spuriousDef = 20, this means that an observation point will be determined as a spurious observation if there are consequtive counts of at least 20 zeros before and after the single non-zero observation. Default is spuriousDef = 20. |
nonwearDef |
Definition of non-wear time. Defined as minutes of consecutive zeros. For example, if nonwearDef=60, this means that a period will be defined as non-wear time if there are at least 60 consecutive zeros. Default is nonwearDef=60. To consider all observations as wear time specify nonwearDef=‘Inf’ |
minWear |
Minimum wear time definition. Defined as minutes of wear time. or example, if minWear = 600, this means that a day will be considered valid only if the wear time is at least 600 minutes. Default is minWear = 600. To return summary for all dates in the data, set minWear = 0. |
patype |
Types of physical activity for summary. For example, to summarize sedentary and moderate-vigorous physical activities, user specifies patype=c(‘Sedentary’,‘MVPA’). This labels the summary accordingly. |
pacut |
Cut points to be used for the physical activity type. For example, if the user specified patype=c(‘Sedentary’,‘MVPA’), pacut can be specified as pacut=c(c(0,99),c(1952,Inf)). The options requires to have a lower and a upper limit for each activity type (i.e. c(0,99) for sedentary activity). The specified interval includes its lower and upper endpoints (it is a closed inerval). |
boutsize |
Boutsize to summarize a physical activity. If multiple patype is specified, boutsize should be for each one (e.g., if patype=c(‘Sedentary’,‘MVPA’) then one can use boutsize=c(10,10)). |
epoch |
Epoch size. Default is '1 min'. Other epoch size can be specified using this option (e.g., '1 sec') |
tolerance |
Whether two observations outside the physical activity should be permitted in summarizing a physical activity. If multiple patype is specified, tolerance should be for each one (e.g., if patype=c(‘Sedentary’,‘MVPA’) then one can use tolerance=c(‘FALSE’,‘TRUE’)). |
A folder ‘summaryfiles’ is created within the specified path. In the folder, summary files are saved by the same filenames as in the accelerometer data for valid days which consists of columns [Date, SedentaryMinutes, wearTime, numberOfBoutsSed, mvpaMinutes, numberOfBoutsMVPA]
Jaejoon Song <[email protected]>
Choi, L., Liu, Z., Matthews, C.E. and Buchowski, M.S. (2011). Validation of Accelerometer Wear and Nonwear Time Classification Algorithm. Med Sci Sports Exerc, 43(2):357-64.
Hall, K. S., Howe, C. A., Rana, S. R., Martin, C. L., and Morey, M. C. (2013). METs and Accelerometry of Walking in Older Adults: Standard versus Measured Energy Cost. Medicine and Science in Sports and Medicine, 45(3). 574-82.
Freedson, P., Melanson, E., and Sirard, J. (1998). Calibration of the Computer Sciences and Applications, Inc. accelerometer. Medicine and Science in Sports and Exercercise, 30(5):777-81.
Swartz, A. M., Strath, S. J., Bassett, D. R. Jr., O'Brien, W. L., King, G. A., and Ainsworth, B. E. (2000). Estimation of energy expenditure using CSA accelerometers at hip and wrist sites. Medicine and Science in Sports and Exercercise, 32: S450-456.
Copeland, J. L., and Esliger, D. W. (2009). Accelerometer assessment of physical activity in active, healthy older adults. J Aging Phys Act, 17: 17-30.
## ## Example ## ## Not run: mypath <- "C:/Accelerometry files/readfiles" accBatch(path=mypath, tri='TRUE', axis='vm', 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')) ## End(Not run)## ## Example ## ## Not run: mypath <- "C:/Accelerometry files/readfiles" accBatch(path=mypath, tri='TRUE', axis='vm', 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')) ## End(Not run)
Summarizes accelerometer data for a single type of physical activity. Functionality is same as function acc, except that this function provides a summary for a single type of physical activity, with more detailed information.
accSummary(data, tri, axis, spuriousDef, nonwearDef, minWear, patype, pacut, epoch, boutsize, tolerance, returnbout)accSummary(data, tri, axis, spuriousDef, nonwearDef, minWear, patype, pacut, epoch, boutsize, tolerance, returnbout)
data |
Data which consists of two columns [TimeStamp,counts] (i.e. raw accelerometer file read in by function readRaw) |
tri |
Whether the data is from a tri-axial accelerometer. Default is tri=‘FALSE’. If tri=‘TRUE’ then option ‘axis’ should be specified. |
axis |
This option is only used for the tri-axial accelerometer. Options are ‘x’,‘y’,‘z’,‘sum’, or ‘vm’. Options ‘x’, ‘y’, or ‘z’ can be spefied to summarize data using only data from a single axis. If the option 'vm' is used, the square root of the squared sum of counts from three axes (i.e. |
spuriousDef |
Definition of spurious observation. Defined as minutes of consecutive zeros. For example, if spuriousDef = 20, this means that an observation point will be determined as a spurious observation if there are consequtive counts of at least 20 zeros before and after the single non-zero observation. Default is spuriousDef = 20. |
nonwearDef |
Definition of non-wear time. Defined as minutes of consecutive zeros. For example, if nonwearDef=60, this means that a period will be defined as non-wear time if there are at least 60 consecutive zeros. Default is nonwearDef=60. To consider all observations as wear time specify nonwearDef=‘Inf’ |
minWear |
Minimum wear time definition. Defined as minutes of wear time. or example, if minWear = 600, this means that a day will be considered valid only if the wear time is at least 600 minutes. Default is minWear = 600. To return summary for all dates in the data, set minWear = 0. |
patype |
Type of physical activity for summary. For example, to summarize sedentary activity, use option patype=c(‘Sedentary’). To summarize moderate-vigorous physical activities, user specifies patype=c(‘MVPA’). This labels the summary accordingly. |
pacut |
Cut points to be used for the physical activity type. For example, if the user specified patype=c(‘Sedentary’), pacut can be specified as pacut=c(c(0,99)). The options requires to have a lower and a upper limit for each activity type (i.e. c(0,99) for sedentary activity). The specified interval includes its lower and upper endpoints (it is a closed inerval). |
boutsize |
Boutsize to summarize a physical activity. If multiple patype is specified, boutsize should be for each one (e.g., if patype=c(‘Sedentary’) then one can use boutsize=c(10)). |
epoch |
Epoch size. Default is '1 min'. Other epoch size can be specified using this option (e.g., '1 sec') |
tolerance |
Whether two observations outside the physical activity cut point should be permitted in summarizing a physical activity (e.g. if patype=c(‘Sedentary’) then one can use tolerance=c(‘FALSE’)). |
returnbout |
Whether to return data with bout indicators. If returnbout='FALSE' then only a summary of daily physical activity is returned. |
If returnbout='FALSE', then a summary for each specified physical activity types (number of bouts and minutes of the activity), for valid dates. Defaults to 'TRUE'.
If returnbout='TRUE', then a list of summary object is returned with:
totalDates |
Number of unique days available in data. |
validDates |
A summary for each specified physical activity types (number of bouts and minutes of the activity), for valid dates (as defined by minWear). |
PA |
Data supplied by the user is returned with three additional columns [inPA, nonwear, inboutPA], indicating whether the observation was considered to be in the defined cutpoint of the physical activity (inPA), in the defined cutpoint of nonwear time (nonwear), or in bout (inboutPA), respectively. |
Jaejoon Song <[email protected]>
Choi, L., Liu, Z., Matthews, C.E. and Buchowski, M.S. (2011). Validation of Accelerometer Wear and Nonwear Time Classification Algorithm. Med Sci Sports Exerc, 43(2):357-64.
Hall, K. S., Howe, C. A., Rana, S. R., Martin, C. L., and Morey, M. C. (2013). METs and Accelerometry of Walking in Older Adults: Standard versus Measured Energy Cost. Medicine and Science in Sports and Medicine, 45(3). 574-82.
Freedson, P., Melanson, E., and Sirard, J. (1998). Calibration of the Computer Sciences and Applications, Inc. accelerometer. Medicine and Science in Sports and Exercercise, 30(5):777-81.
Swartz, A. M., Strath, S. J., Bassett, D. R. Jr., O'Brien, W. L., King, G. A., and Ainsworth, B. E. (2000). Estimation of energy expenditure using CSA accelerometers at hip and wrist sites. Medicine and Science in Sports and Exercercise, 32: S450-456.
Copeland, J. L., and Esliger, D. W. (2009). Accelerometer assessment of physical activity in active, healthy older adults. J Aging Phys Act, 17: 17-30.
## ## Example 1: Loading the activity counts data using readCounts function ## ## Not run: library(acc) infile <- "DataName.dat" counts <- readCounts(infile) ## ## Example 2: Summarizing accelerometer data for a sedentary individual" ## # For this example, data is generated using a Hidden Markov model # First, a sequence of time is generated randomTime <- seq(ISOdate(2015,4,1),ISOdate(2015,4,3),"min") # Load the mhsmm package to generate data using a Hidden Makov model library(mhsmm) # It is assumed that the counts are generated from a Hidden Markov model # with three states, being non-wear, sedentary, and moderate-vigorous activity J <- 3; initial <- rep(1/J, J) # Set up a transition matrix for the Hidden Markov model. P <- matrix(c(0.95, 0.04, 0.01, 0.09, 0.9, 0.01, 0.1, 0.2, 0.7), byrow='TRUE',nrow = J) # It is assumed that the counts are realized from a mixture of # two normal distributions (for sedentary activity and mvpa) # and a constant at zero (for non-wear time). b <- list(mu = c(0, 30, 2500), sigma = c(0, 30, 1000)) model <- hmmspec(init = initial, trans = P, parms.emission = b,dens.emission = dnorm.hsmm) # Generate data! train <- simulate.hmmspec(model, nsim = (60*24*2), seed = 1234, rand.emis = rnorm.hsmm) # Now set up a dataset that mimicks the accelerometry data counts <- data.frame(TimeStamp = randomTime[1:length(train$x)], counts = train$x) library(acc) # summarize the data using the acc function. # Sedentary and moderate-vigorous activity is summarized, using Freedson's cut points by default. # Option returnbout='TRUE' returns a more detailed information on how the summary was calculated. summary1 <- accSummary(data=counts, tri='FALSE', axis=NULL, spuriousDef=20, nonwearDef=60, minWear=600, patype='MVPA',pacut=c(1952,Inf), boutsize=10, tolerance='TRUE',returnbout='TRUE') summary1$validDates # This returns the same summary as when returnbout='FALSE' # summary1$PA # This returns the activity classification and bout information ## ## Example 3: Summarizing accelerometer data for an active individual. ## randomTime <- seq(ISOdate(2015,4,1),ISOdate(2015,4,3),"min") library(mhsmm) J <- 3; initial <- rep(1/J, J) P <- matrix(c(0.95, 0.04, 0.01, 0.09, 0.7, 0.21, 0.1, 0.1, 0.8), byrow='TRUE',nrow = J) b <- list(mu = c(0, 30, 2500), sigma = c(0, 30, 1000)) model <- hmmspec(init = initial, trans = P, parms.emission = b,dens.emission = dnorm.hsmm) train <- simulate.hmmspec(model, nsim = (60*24*2), seed = 1234, rand.emission = rnorm.hsmm) counts <- data.frame(TimeStamp = randomTime[1:length(train$x)], counts = train$x) library(acc) # Option returnbout='TRUE' returns a more detailed information on how the summary was calculated. summary2 <- accSummary(data=counts, tri='FALSE', axis=NULL, spuriousDef=20, nonwearDef=60, minWear=600, patype='MVPA',pacut=c(1952,Inf), boutsize=10, tolerance='TRUE',returnbout='TRUE') summary2$validDates # This returns the same summary as when returnbout='FALSE' # summary2$PA # This returns the activity classification and bout information ## End(Not run)## ## Example 1: Loading the activity counts data using readCounts function ## ## Not run: library(acc) infile <- "DataName.dat" counts <- readCounts(infile) ## ## Example 2: Summarizing accelerometer data for a sedentary individual" ## # For this example, data is generated using a Hidden Markov model # First, a sequence of time is generated randomTime <- seq(ISOdate(2015,4,1),ISOdate(2015,4,3),"min") # Load the mhsmm package to generate data using a Hidden Makov model library(mhsmm) # It is assumed that the counts are generated from a Hidden Markov model # with three states, being non-wear, sedentary, and moderate-vigorous activity J <- 3; initial <- rep(1/J, J) # Set up a transition matrix for the Hidden Markov model. P <- matrix(c(0.95, 0.04, 0.01, 0.09, 0.9, 0.01, 0.1, 0.2, 0.7), byrow='TRUE',nrow = J) # It is assumed that the counts are realized from a mixture of # two normal distributions (for sedentary activity and mvpa) # and a constant at zero (for non-wear time). b <- list(mu = c(0, 30, 2500), sigma = c(0, 30, 1000)) model <- hmmspec(init = initial, trans = P, parms.emission = b,dens.emission = dnorm.hsmm) # Generate data! train <- simulate.hmmspec(model, nsim = (60*24*2), seed = 1234, rand.emis = rnorm.hsmm) # Now set up a dataset that mimicks the accelerometry data counts <- data.frame(TimeStamp = randomTime[1:length(train$x)], counts = train$x) library(acc) # summarize the data using the acc function. # Sedentary and moderate-vigorous activity is summarized, using Freedson's cut points by default. # Option returnbout='TRUE' returns a more detailed information on how the summary was calculated. summary1 <- accSummary(data=counts, tri='FALSE', axis=NULL, spuriousDef=20, nonwearDef=60, minWear=600, patype='MVPA',pacut=c(1952,Inf), boutsize=10, tolerance='TRUE',returnbout='TRUE') summary1$validDates # This returns the same summary as when returnbout='FALSE' # summary1$PA # This returns the activity classification and bout information ## ## Example 3: Summarizing accelerometer data for an active individual. ## randomTime <- seq(ISOdate(2015,4,1),ISOdate(2015,4,3),"min") library(mhsmm) J <- 3; initial <- rep(1/J, J) P <- matrix(c(0.95, 0.04, 0.01, 0.09, 0.7, 0.21, 0.1, 0.1, 0.8), byrow='TRUE',nrow = J) b <- list(mu = c(0, 30, 2500), sigma = c(0, 30, 1000)) model <- hmmspec(init = initial, trans = P, parms.emission = b,dens.emission = dnorm.hsmm) train <- simulate.hmmspec(model, nsim = (60*24*2), seed = 1234, rand.emission = rnorm.hsmm) counts <- data.frame(TimeStamp = randomTime[1:length(train$x)], counts = train$x) library(acc) # Option returnbout='TRUE' returns a more detailed information on how the summary was calculated. summary2 <- accSummary(data=counts, tri='FALSE', axis=NULL, spuriousDef=20, nonwearDef=60, minWear=600, patype='MVPA',pacut=c(1952,Inf), boutsize=10, tolerance='TRUE',returnbout='TRUE') summary2$validDates # This returns the same summary as when returnbout='FALSE' # summary2$PA # This returns the activity classification and bout information ## End(Not run)
Creates an aee object for semiparametric regression with augmented estimating equation.
aee(ID, time, minutes)aee(ID, time, minutes)
ID |
Individual identifier |
time |
Observation time |
minutes |
Minutes of physical activity |
psDF |
A data frame, part of original input data frame with variable "ID", "time" and "count" |
timeGrid |
Ordered distinct observation times in the set of all observation times |
panelMatrix |
a matrix representation of panel count data, one row per subject, one column per time point in "timeGrid" |
Jaejoon Song <[email protected]>
Wang, X. and Yan, J. (2011). Fitting semiparametric regressions for panel count survival data with an R package spef. Computer Methods and Programs in Biomedicine, 104, 278-285.
Wang, X., Ma, S., and Yan, J. (2013). Augmented estimating equations for semiparametric panel count regression with informative observation times and censoring time. Statistica Sinica, 23, 359-381.
## Not run: # We illustrate the use of function aeexfit # with the sample data from the National Health and Nutrition Examination Survey (NHANES) # to examine the association between the cardiorespiratory function (i.e., VO2max) # and daily minutes of moderate to vigorous physical activity (MVPA). data(NHANES) formula <- aee(ID, Day, mvpaMinutes) ~ VO2max # Standard errors are obtained using sandwich estimation fitted <- aeexfit(formula = formula, data = NHANES, se = "Sandwich") summary(fitted) ## End(Not run)## Not run: # We illustrate the use of function aeexfit # with the sample data from the National Health and Nutrition Examination Survey (NHANES) # to examine the association between the cardiorespiratory function (i.e., VO2max) # and daily minutes of moderate to vigorous physical activity (MVPA). data(NHANES) formula <- aee(ID, Day, mvpaMinutes) ~ VO2max # Standard errors are obtained using sandwich estimation fitted <- aeexfit(formula = formula, data = NHANES, se = "Sandwich") summary(fitted) ## End(Not run)
Fits semiparametric regression models for irregularly observed physical activity data under conditional independent censoring
aeefit(formula, data, weight, se, control=list(), boot)aeefit(formula, data, weight, se, control=list(), boot)
formula |
A formula object as returned by aee. |
data |
A data frame which includes individuals' ID, observation times, and minutes of physical activity since the last observation time. |
weight |
A vector of sampling weights, for each individual.By default, no sampling weights are applied. |
se |
The method of estimating standard errors can be chosen by the argument se. Two options are available: i) the sandwhich estimation (se = 'Sandwich'), or ii) the bootstrap procedure (se = 'Bootstrap'). |
control |
A list of control parameters. See 'Details'. |
boot |
The number of resamples generated for the bootstrap procedure. |
The control argument is a list athat can supply any of the following components:
betaInit: Initial value for covariate coefficient, default is 0.
interval: Initial search interval for solving beta. Default is (-5,5).
maxIter: Maximum iterations allowed. Default is 150.
absTol: Absolute tolerance. Default is 1e-6.
relTol: Relative tolerance. Default is 1e-6.
a: A tune parameter. Default is .1. In case of gamma fraility, "a" corresponds to the value of both shape and rate parameters.
Jaejoon Song <[email protected]>
Wang, X. and Yan, J. (2011). Fitting semiparametric regressions for panel count survival data with an R package spef. Computer Methods and Programs in Biomedicine, 104, 278-285.
Wang, X., Ma, S., and Yan, J. (2013). Augmented estimating equations for semiparametric panel count regression with informative observation times and censoring time. Statistica Sinica, 23, 359-381.
## Not run: data(NHANES) # Example of analyzing NHANES data # Example 1: Not adjusted for sampling weights nhanesToFit <- NHANES[ which(NHANES$mvpaMinutes!=0), ] formula <- aee(ID, Day, mvpaMinutes) ~ Age+raceBi+VO2max+Gender fitted1 <- aeefit(formula=formula, data=nhanesToFit) summary(fitted1) ## End(Not run)## Not run: data(NHANES) # Example of analyzing NHANES data # Example 1: Not adjusted for sampling weights nhanesToFit <- NHANES[ which(NHANES$mvpaMinutes!=0), ] formula <- aee(ID, Day, mvpaMinutes) ~ Age+raceBi+VO2max+Gender fitted1 <- aeefit(formula=formula, data=nhanesToFit) summary(fitted1) ## End(Not run)
Fits semiparametric regression models robust to informative observation times and censoring
aeexfit(formula, data, weight, se, control=list(), boot)aeexfit(formula, data, weight, se, control=list(), boot)
formula |
A formula object as returned by aee. |
data |
A data frame which includes individuals' ID, observation times, and minutes of physical activity since the last observation time. |
weight |
A vector of sampling weights, for each individual.By default, no sampling weights are applied. |
se |
The method of estimating standard errors can be chosen by the argument se. Two options are available: i) the sandwhich estimation (se = 'Sandwich'), or ii) the bootstrap procedure (se = 'Bootstrap'). |
control |
A list of control parameters. See 'Details'. |
boot |
The number of resamples generated for the bootstrap procedure. |
The control argument is a list athat can supply any of the following components:
betaInit: Initial value for covariate coefficient, default is 0.
interval: Initial search interval for solving beta. Default is (-5,5).
maxIter: Maximum iterations allowed. Default is 150.
absTol: Absolute tolerance. Default is 1e-6.
relTol: Relative tolerance. Default is 1e-6.
a: A tune parameter. Default is .1. In case of gamma fraility, "a" corresponds to the value of both shape and rate parameters.
Jaejoon Song <[email protected]>
Wang, X. and Yan, J. (2011). Fitting semiparametric regressions for panel count survival data with an R package spef. Computer Methods and Programs in Biomedicine, 104, 278-285.
Wang, X., Ma, S., and Yan, J. (2013). Augmented estimating equations for semiparametric panel count regression with informative observation times and censoring time. Statistica Sinica, 23, 359-381.
## Not run: data(NHANES) formula <- aee(ID, Day, mvpaMinutes) ~ VO2max # Standard errors are obtained using sandwich estimation fitted <- aeexfit(formula = formula, data = NHANES, se = "Sandwich") summary(fitted) ## End(Not run)## Not run: data(NHANES) formula <- aee(ID, Day, mvpaMinutes) ~ VO2max # Standard errors are obtained using sandwich estimation fitted <- aeexfit(formula = formula, data = NHANES, se = "Sandwich") summary(fitted) ## End(Not run)
Aggregates multiple accelerometer summary files (e.g. summary files for multiple individuals) by day. This function can be used in sequence with readRawBatch and accBatch.
aggAcc(path)aggAcc(path)
path |
Path to accelerometer summary files created by function acc or accBatch |
A folder ‘aggregate’ is created within the specified path. In the folder, aggregate.Rdata file (object ‘aggregate’) consists of columns [ID, Date, SedentaryMinutes, wearTime, numberOfBoutsSed, mvpaMinutes, numberOfBoutsMVPA], where ID is the filename.
Jaejoon Song <[email protected]>
## ## Example ## ## Not run: mypath <- "C:/Accelerometry files/readfiles/summaryfiles" aggAcc(mypath) ## End(Not run)## ## Example ## ## Not run: mypath <- "C:/Accelerometry files/readfiles/summaryfiles" aggAcc(mypath) ## End(Not run)
NHANES 2003 data
data(NHANES)data(NHANES)
A data frame with 19323 rows and 11 variables. This consists physical activity data for 2763 individuals.
ID: Individual's ID
Day: Order of the seven day measurement perior 1-7.
SedentaryMinutes: Sedentary minutes per day in 10 minute bouts.
numberOfBoutsSed: Number of sedentary bouts per day in 10 minute bouts.
mvpaMinutes: Minutes of moderate to vigorous physical activity per day in 10 minute bouts.
numberOfBoutsMVPA: Number of moderate to vigorous physical activity bouts per day in 10 minute bouts.
wearTime: Wear time per day.
Gender: Participant's gender.
Age: Participant's age.
VO2max: Participant's V02max at baseline.
raceBI: Participant's race (white=1, otherwise=0).
Plots accelerometer data. This function receives summary object from function accsummary.
plotAcc(object,markbouts)plotAcc(object,markbouts)
object |
An object returned from either the function accsummary. |
markbouts |
Whether to mark bouts. If markbout='TRUE' a bar along the time axis will indicate whether the epoch was counted as in bout or not. Default is false. |
A plot is returned.
Jaejoon Song <[email protected]>
## Not run: ## ## Example: Simulate a dataset for two days, for an individual with low MVPA level. ## mvpaLowData <- simAcc(timelength=(60*24*2),paLevel='low') summary <- accSummary(data=mvpaLowData) summary$validDates plotAcc(summary,markbouts='FALSE') ## ## Example: Simulate a dataset for two days, for an individual with moderate MVPA level. ## mvpaModData <- simAcc(timelength=(60*24*2),paLevel='moderate') summary <- accSummary(data=mvpaModData, tri='FALSE', axis=NULL, spuriousDef=20, nonwearDef=60, minWear=600, patype='MVPA',pacut=c(1952,Inf), boutsize=10, tolerance='TRUE', returnbout='TRUE') summary$validDates plotAcc(summary,markbouts='FALSE') ## ## Example: Simulate a dataset for two days, for an individual with high MVPA level. ## mvpaHighData <- simAcc(timelength=(60*24*2),paLevel='high') summary <- accSummary(data=mvpaHighData, tri='FALSE', axis=NULL, spuriousDef=20, nonwearDef=60, minWear=600, patype='MVPA',pacut=c(1952,Inf), boutsize=10, tolerance='TRUE', returnbout='TRUE') summary$validDates plotAcc(summary,markbouts='FALSE') ## ## Example: Simulate a tri-axial dataset for five days. ## library(acc) library(mhsmm) seedset=1234 minutes=(60*24*5) randomTime <- seq(ISOdate(2015,1,1),ISOdate(2020,1,1),"min") J <- 3; initial <- rep(1/J, J) P <- matrix(rep(NA,9),byrow='TRUE',nrow=J) P1 <- matrix(c(0.95, 0.04, 0.01, 0.09, 0.9, 0.01, 0.1, 0.2, 0.7), byrow='TRUE',nrow = J) b <- list(mu = c(0, 30, 2500), sigma = c(0, 30, 1000)) model1 <- hmmspec(init = initial, trans = P1, parms.emis = b,dens.emis = dnorm.hsmm) x <- simulate.hmmspec(model1, nsim = (minutes), seed = seedset, rand.emis = rnorm.hsmm) seedset=12345 P2 <- matrix(c(0.95, 0.04, 0.01, 0.09, 0.8, 0.11, 0.1, 0.1, 0.8), byrow='TRUE',nrow = J) model2 <- hmmspec(init = initial, trans = P2, parms.emis = b,dens.emis = dnorm.hsmm) y <- simulate.hmmspec(model2, nsim = (minutes), seed = seedset, rand.emis = rnorm.hsmm) seedset=123456 P3 <- matrix(c(0.95, 0.04, 0.01, 0.09, 0.8, 0.11, 0.1, 0.1, 0.8), byrow='TRUE',nrow = J) model3 <- hmmspec(init = initial, trans = P3, parms.emis = b,dens.emis = dnorm.hsmm) z <- simulate.hmmspec(model3, nsim = (minutes), seed = seedset, rand.emis = rnorm.hsmm) counts <- data.frame(TimeStamp = randomTime[1:minutes], x=x$x, y=y$x, z=z$x) summary <- accSummary(data=counts, tri='TRUE', axis='vm', spuriousDef=20, nonwearDef=60, minWear=600, patype='MVPA',pacut=c(1952,Inf), boutsize=10, tolerance='TRUE', returnbout='TRUE') summary$validDates plotAcc(summary,markbouts='FALSE') ## End(Not run)## Not run: ## ## Example: Simulate a dataset for two days, for an individual with low MVPA level. ## mvpaLowData <- simAcc(timelength=(60*24*2),paLevel='low') summary <- accSummary(data=mvpaLowData) summary$validDates plotAcc(summary,markbouts='FALSE') ## ## Example: Simulate a dataset for two days, for an individual with moderate MVPA level. ## mvpaModData <- simAcc(timelength=(60*24*2),paLevel='moderate') summary <- accSummary(data=mvpaModData, tri='FALSE', axis=NULL, spuriousDef=20, nonwearDef=60, minWear=600, patype='MVPA',pacut=c(1952,Inf), boutsize=10, tolerance='TRUE', returnbout='TRUE') summary$validDates plotAcc(summary,markbouts='FALSE') ## ## Example: Simulate a dataset for two days, for an individual with high MVPA level. ## mvpaHighData <- simAcc(timelength=(60*24*2),paLevel='high') summary <- accSummary(data=mvpaHighData, tri='FALSE', axis=NULL, spuriousDef=20, nonwearDef=60, minWear=600, patype='MVPA',pacut=c(1952,Inf), boutsize=10, tolerance='TRUE', returnbout='TRUE') summary$validDates plotAcc(summary,markbouts='FALSE') ## ## Example: Simulate a tri-axial dataset for five days. ## library(acc) library(mhsmm) seedset=1234 minutes=(60*24*5) randomTime <- seq(ISOdate(2015,1,1),ISOdate(2020,1,1),"min") J <- 3; initial <- rep(1/J, J) P <- matrix(rep(NA,9),byrow='TRUE',nrow=J) P1 <- matrix(c(0.95, 0.04, 0.01, 0.09, 0.9, 0.01, 0.1, 0.2, 0.7), byrow='TRUE',nrow = J) b <- list(mu = c(0, 30, 2500), sigma = c(0, 30, 1000)) model1 <- hmmspec(init = initial, trans = P1, parms.emis = b,dens.emis = dnorm.hsmm) x <- simulate.hmmspec(model1, nsim = (minutes), seed = seedset, rand.emis = rnorm.hsmm) seedset=12345 P2 <- matrix(c(0.95, 0.04, 0.01, 0.09, 0.8, 0.11, 0.1, 0.1, 0.8), byrow='TRUE',nrow = J) model2 <- hmmspec(init = initial, trans = P2, parms.emis = b,dens.emis = dnorm.hsmm) y <- simulate.hmmspec(model2, nsim = (minutes), seed = seedset, rand.emis = rnorm.hsmm) seedset=123456 P3 <- matrix(c(0.95, 0.04, 0.01, 0.09, 0.8, 0.11, 0.1, 0.1, 0.8), byrow='TRUE',nrow = J) model3 <- hmmspec(init = initial, trans = P3, parms.emis = b,dens.emis = dnorm.hsmm) z <- simulate.hmmspec(model3, nsim = (minutes), seed = seedset, rand.emis = rnorm.hsmm) counts <- data.frame(TimeStamp = randomTime[1:minutes], x=x$x, y=y$x, z=z$x) summary <- accSummary(data=counts, tri='TRUE', axis='vm', spuriousDef=20, nonwearDef=60, minWear=600, patype='MVPA',pacut=c(1952,Inf), boutsize=10, tolerance='TRUE', returnbout='TRUE') summary$validDates plotAcc(summary,markbouts='FALSE') ## End(Not run)
Race plots of minutes of activity per day, for several activity types
racePlot(summary,title,cex.title,cex.text,cex.center,color)racePlot(summary,title,cex.title,cex.text,cex.center,color)
summary |
An object returned from function acc. |
title |
Title of the plot |
cex.title |
Font size of the title |
cex.text |
Font size of the race track labels |
cex.center |
Font size of the day indicator in the center of the plot |
color |
Color of the race tracks |
A plot is returned.
Jaejoon Song <[email protected]>
## Not run: library(acc) ## ## Example: Simulate a dataset for seven days, for an individual with low MVPA level. ## mvpaModData <- simAcc(timelength=(60*24*7),paLevel='moderate') summary1 <- acc(data=mvpaModData, tri='FALSE', axis='NULL', spuriousDef=20, nonwearDef=60, minWear=600,epoch=c('1 min','1 min'), patype=c('Sedentary','MVPA'),pacut=c(c(0,99),c(1952,Inf)), boutsize=c(10,10), tolerance=c('FALSE','TRUE')) summary1 racePlot(summary1,title="Summary of Physical Activity Per Day", cex.title=1,cex.text=1.2) ## End(Not run)## Not run: library(acc) ## ## Example: Simulate a dataset for seven days, for an individual with low MVPA level. ## mvpaModData <- simAcc(timelength=(60*24*7),paLevel='moderate') summary1 <- acc(data=mvpaModData, tri='FALSE', axis='NULL', spuriousDef=20, nonwearDef=60, minWear=600,epoch=c('1 min','1 min'), patype=c('Sedentary','MVPA'),pacut=c(c(0,99),c(1952,Inf)), boutsize=c(10,10), tolerance=c('FALSE','TRUE')) summary1 racePlot(summary1,title="Summary of Physical Activity Per Day", cex.title=1,cex.text=1.2) ## End(Not run)
Reads counts data in .dat, .agd, or .csv format for Actigraph GT1M and GT3X devices. Device type and epoch is automatically detected and reported in the console.
readCounts(filename)readCounts(filename)
filename |
Speficy full file path and file name. e.g. C:/mydata.dat or C:/mydata.csv |
For uni-axial accelerometer (GT1M), two columns are returned, consisting of: [TimeStamp,Counts] For tri-axial accelerometer (GT3X), four columns are returned, consisting of: [TimeStamp,x,y,z]
Jaejoon Song <[email protected]>
## ## A example to read counts data ## ## Not run: accData1 <- readCounts("C:/mydata.agd") accData1 <- readCounts("C:/mydata.dat") accData2 <- readCounts("C:/mydata.csv") ## End(Not run)## ## A example to read counts data ## ## Not run: accData1 <- readCounts("C:/mydata.agd") accData1 <- readCounts("C:/mydata.dat") accData2 <- readCounts("C:/mydata.csv") ## End(Not run)
Reads multiple accelerometer counts data in a folder. This is a batch mode of the readCounts function.
readCountsBatch(path,filetype)readCountsBatch(path,filetype)
path |
Path to a folder which contains accelerometer counts data in .dat or .csv format. |
filetype |
Specify whether the data to read is in dat or csv format. Options are either 'dat' or 'csv'. For example if filetype = 'csv' is specified, all csv data will be read and all other types in the same folder will be ignored. By defalt, it is assumed that all files in the specified path are either csv or dat files and are intended to be read. |
A folder ‘readfiles’ is created inside the specified 'path'. In the folder, files are saved by the same filenames as in the raw data.
For uni-axial accelerometer (GT1M), two columns are returned, consisting of: [TimeStamp,Counts] For tri-axial accelerometer (GT3X), four columns are returned, consisting of: [TimeStamp,x,y,z]
Jaejoon Song <[email protected]>
## ## Example ## # filepath to locate the activity counts data files ## Not run: mypath <- "C:/Accelerometry files" readCountsBatch(mypath,filetype='csv') ## End(Not run)## ## Example ## # filepath to locate the activity counts data files ## Not run: mypath <- "C:/Accelerometry files" readCountsBatch(mypath,filetype='csv') ## End(Not run)
Reads raw data in .csv format for Actigraph GT3X devices. Device type and epoch is automatically detected and reported in the console. Marks activity intensity based on two existing methods.
readRaw(filepath,type,resting)readRaw(filepath,type,resting)
filepath |
Speficy full file path and file name. (e.g. 'C:/mydata.csv') |
type |
Specify how activity intensity is identified. Options: i) 'mad' for Vaha-Ypya method, ii) 'ai' for Bai et al.'s method, or iii) 'resting' to calculate resting state intensity level required for Bai's method |
resting |
Resting state intensity level, required for Bai et al.'s method. |
If the option 'type' is 'mad' or 'ai', five columns are returned, consisting of first four columns [TimeStamp,x,y,z], and the intensity category/level. If the option 'type' is 'resting' then a vector of resting state intensity level is returned.
Jaejoon Song <[email protected]>
## ## A example to read counts data ## ## Not run: # For Vaha-Ypya et al.'s method accData1 <- readRaw("C:/mydata.csv",type='mad') # For Bai et al.'s method with known resting state intensities accData2 <- readRaw("C:/mydata.csv",type='ai',resting=c(.15,.16,.17)) # Calculating resting state intensity for Bai et al.'s method restingIntensity <- readRaw("C:/mydata.csv",type='resting') accData3 <- readRaw("C:/mydata.csv",type='ai',resting=restingIntensity) ## End(Not run)## ## A example to read counts data ## ## Not run: # For Vaha-Ypya et al.'s method accData1 <- readRaw("C:/mydata.csv",type='mad') # For Bai et al.'s method with known resting state intensities accData2 <- readRaw("C:/mydata.csv",type='ai',resting=c(.15,.16,.17)) # Calculating resting state intensity for Bai et al.'s method restingIntensity <- readRaw("C:/mydata.csv",type='resting') accData3 <- readRaw("C:/mydata.csv",type='ai',resting=restingIntensity) ## End(Not run)
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 mimic 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.
simAcc(timelength,paLevel,epoch,startDate,endDate, mu,sigma,seedset,tpm)simAcc(timelength,paLevel,epoch,startDate,endDate, mu,sigma,seedset,tpm)
timelength |
Number of observations to be generated. |
paLevel |
Pre-specified levels of physical activity for convenience. User can specify all parameter as preferred, by stating the option as paLevel=NULL. Default is ‘moderate’. Options: 1) ‘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, respectively. Moderate specifies a hidden markov model with transition probabilities 0.95, 0.04, 0.01, 0.09, 0.8, 0.11, 0.1, 0.1, 0.8 respectively for P11, P12, P13, P21, P22, P23, P31, P32, P33, respectively. High specifies a hidden markov model with transition probabilities 0.95, 0.04, 0.01, 0.09, 0.7, 0.21, 0.1, 0.1, 0.8, respectively for P11, P12, P13, P21, P22, P23, P31, P32, P33, respectively. For all levels, it is assumed that the activity intensities are realized from a mixture of two normal distributions (for sedentary activity and mvpa) and a constant at zero (for non-wear time), with means mu = c(0, 30, 2500) and variance sigma = c(0, 30, 1000). |
epoch |
Epoch size. User can specify desired epoch size in units of time larger than seconds. Defaults to 1 minute epoch. |
startDate |
Start date in ISOdate format. For example ISOdate(2017,1,1,hour=0,min=0,sec=0,tz="GMT"). |
endDate |
End date in ISOdate format. For example ISOdate(2017,1,1,hour=0,min=0,sec=0,tz="GMT"). |
mu |
Mean levels for each activity type. |
sigma |
Standard deviations for each activity type. |
seedset |
Sets seed for random data generation. Defaults to 1234. |
tpm |
Transition probability matrix that specify probability of change from one activity state to another. |
A simulated dataset is returned with two columns: [TimeStamp, counts]
Jaejoon Song <[email protected]>
## Not run: ## ## Example: Simulate a dataset for two days, for an individual with low activity level. ## mvpaLowData <- simAcc(timelength=(60*24*2),paLevel='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 activity level. ## mvpaLowData <- simAcc(timelength=(60*24*2),paLevel='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 activity level. ## mvpaLowData <- simAcc(timelength=(60*24*2),paLevel='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 ## End(Not run)## Not run: ## ## Example: Simulate a dataset for two days, for an individual with low activity level. ## mvpaLowData <- simAcc(timelength=(60*24*2),paLevel='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 activity level. ## mvpaLowData <- simAcc(timelength=(60*24*2),paLevel='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 activity level. ## mvpaLowData <- simAcc(timelength=(60*24*2),paLevel='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 ## End(Not run)
Simulates minutes of physical activity per day with realistic missing data patterns
simPA(n, type, beta, minday, maxday)simPA(n, type, beta, minday, maxday)
n |
Number of individuals in the simulated data. |
type |
Whether to simulate data from informative/non-informative observation/censoring patterns. Options: i) 'inf', ii) 'noninf'. |
beta |
True coefficient for the binary covariate. |
minday |
Minimun number of observation days. |
maxday |
Maximum number of observation days. |
A simulated dataset is returned with four columns: [ID, day, min, x1, z].
Jaejoon Song <[email protected]>
## ## Simulauting data for a single individual ## with noninformative observation patterns ## simdata <- simPA(n=1, beta=1.5,type='noninf', minday=6, maxday=7)## ## Simulauting data for a single individual ## with noninformative observation patterns ## simdata <- simPA(n=1, beta=1.5,type='noninf', minday=6, maxday=7)
Prints estimated parameters for aeefit object
## S3 method for class 'aeefit' summary(object, digits = 3, dig.tst = 2, ...)## S3 method for class 'aeefit' summary(object, digits = 3, dig.tst = 2, ...)
object |
A |
digits |
Minimum number of significant digits to be used for most numbers. |
dig.tst |
Minimum number of significant digits for the test statistics |
... |
... |
Estimated parameters for aeefit objects
Jaejoon Song <[email protected]>
##Will put an example here##Will put an example here
Prints estimated parameters for aeexfit object
## S3 method for class 'aeexfit' summary(object, digits = 3, dig.tst = 2, ...)## S3 method for class 'aeexfit' summary(object, digits = 3, dig.tst = 2, ...)
object |
A |
digits |
Minimum number of significant digits to be used for most numbers. |
dig.tst |
Minimum number of significant digits for the test statistics |
... |
... |
Estimated parameters for aeefit objects
Jaejoon Song <[email protected]>
##Will put an example here##Will put an example here
Group level plots of minutes of activity per day
visGroup(data,ID,activity,type,title,yaxis,xaxis,time,legendTitle, groupBy,groupFun,levels,heatcol)visGroup(data,ID,activity,type,title,yaxis,xaxis,time,legendTitle, groupBy,groupFun,levels,heatcol)
data |
Summary data by day |
ID |
Name of column that identifies groups or individuals |
activity |
Name of column for activity to be plotted |
type |
Type of plot. Options 'boxplot' or 'heatmap' |
title |
Title of the plot |
yaxis |
Axis title for vertical axis |
xaxis |
Whether to pring the x axis label. Options are TRUE or FALSE |
time |
Name of column that contains time index |
legendTitle |
Title of legend for 'type=heatmap' |
groupBy |
Option to plot by group, for 'type=heatmap' |
groupFun |
If groupBy is specified, how are two groups defined? Provide a function to specify a statistic that divides the groups. For example, specify 'median' to divide the two groups by the overall median of the data. |
levels |
Specifies thresholds for heatmap. For example, user can specify 'levels = c(0,300,600,1440)' to plot a heatmap of four levels. |
heatcol |
Specifies colors for the heatmap. |
A plot is returned.
Jaejoon Song <[email protected]>
## ## Example: A box plot ## ## Not run: library(acc) data(NHANES) NHANES_subset <- NHANES[1:300,] visGroup( data = NHANES_subset, activity = 'wearTime', ID = 'ID', type = 'boxplot', title = "Wear time at baseline", yaxis = "Wear time (minutes per day)",xaxis=TRUE) ## ## Example: A heatmap ## library(acc) data(NHANES) NHANES_subset <- NHANES[1:1000,] visGroup( data = NHANES_subset, ID = 'ID',activity = 'wearTime', type = 'heatmap',title = "Wear time at baseline", yaxis = "ID",time = 'Day', legendTitle = "Wear time", groupBy = 'VO2max',groupFun = 'median', levels = c(0,300,600,1440), heatcol = c("white","mistyrose","lightpink","violetred1")) ## End(Not run)## ## Example: A box plot ## ## Not run: library(acc) data(NHANES) NHANES_subset <- NHANES[1:300,] visGroup( data = NHANES_subset, activity = 'wearTime', ID = 'ID', type = 'boxplot', title = "Wear time at baseline", yaxis = "Wear time (minutes per day)",xaxis=TRUE) ## ## Example: A heatmap ## library(acc) data(NHANES) NHANES_subset <- NHANES[1:1000,] visGroup( data = NHANES_subset, ID = 'ID',activity = 'wearTime', type = 'heatmap',title = "Wear time at baseline", yaxis = "ID",time = 'Day', legendTitle = "Wear time", groupBy = 'VO2max',groupFun = 'median', levels = c(0,300,600,1440), heatcol = c("white","mistyrose","lightpink","violetred1")) ## End(Not run)