Loads the output from g.part1 and then applies g.impute and g.analyse, after which the output is converted to .RData-format which will be used by g.shell.GGIR to generate reports. The variables in these reports are the same variables as described in g.analyse.
g.part2(datadir=c(), metadatadir=c(), f0=c(), f1=c(),
myfun=c(), params_cleaning = c(), params_247 = c(),
params_phyact = c(), params_output = c(), params_general = c(), ...)Directory where the accelerometer files are stored or list, e.g. "C:/mydata" of accelerometer filenames and directories, e.g. c("C:/mydata/myfile1.bin", "C:/mydata/myfile2.bin").
Directory where the output from g.part1 was stored
File index to start with (default = 1). Index refers to the filenames sorted in increasing order
File index to finish with (defaults to number of files available)
External function object to be applied to raw data. See details applyExtFunction.
See g.part1
See details
See details
See details
See g.part1
To ensure compatibility with R scripts written for older GGIR versions, the user can also provide parameters listed in the params_ objects as direct argument.
The function provides no values, it only ensures that other functions are called and that their output is stored in the folder structure as created with g.part1.
GGIR comes with many processing parameters, which have been thematically grouped in parameter objects (R list). By running print(load_params()) you can see the default values of all the parameter objects. When g.part 2 is used via g.shell.GGIR you have the option to specifiy a configuration file, which will overrule the default parameter values. Further, as user you can set parameter values as input argument to both g.part2 and g.shell.GGIR. Directly specified argument overrule the configuration file and default values.
See the GGIR package vignette for a more elaborate overview of parameter objects and their usage across GGIR.
The parameter objects used by GGIR part 2 (g.part2) that are no already discussed in g.part1 are:
A list of parameters used to specify whether and how GGIR stores its output at various stages of the process.
Boolean. Store folder structure of the accelerometer data.
Boolean. In g.part3: Whether to generate a pdf for part 3 (default is TRUE).
In g.part5: Timewindow over which summary statistics are derived. Value can be "MM" (midnight to midnight), "WW" (waking time to waking time), or both c("MM","WW").
Boolean, whether to save the time series classification (levels) as a csv files.
Character string to specify how data should be stored: either "csv" (default) or "RData". Only used if save_ms5rawlevels=TRUE.
Boolean to indicate whether to remove invalid days from the time series output files. Only used if save_ms5rawlevels=TRUE.
Boolean. If TRUE then epoch values are exported to a CSV spreadsheet. Here, non-wear time is imputed where possible (default = FALSE).
Boolean. Applied in g.part5. Boolean to indicate whether to generate report for the sustained inactivity bouts (sib).
Boolean. If g.part4 is run with do.visual == TRUE then the function will generate a pdf with a visual representation of the overlap between the sleeplog entries and the accelerometer detections. This can be used to visualy verify that the sleeplog entries do not come with obvious mistakes.
Boolean. Relevant for do.visual == TRUE. Outliers.only == FALSE will visualise all available nights in the data. Outliers.only == TRUE will visualise only for nights with a difference in onset or waking time larger than the variable of argument criterror.
Numeric. Relevant for do.visual == TRUE and outliers.only == TRUE. criterror specifies the number of minimum number of hours difference between sleep log and accelerometer estimate for the night to be included in the visualisation.
Boolean. If TRUE then generate visual report based on combined output from part 2 and 4. This is in beta-version at the moment.
Numeric. Centre the day as displayed around noon (value = 1) or around midnight (value = 2).
Boolean, see g.report.part5
Boolean, see g.plot5
Timewindow over which summary statistics are derived. Value can be "MM" (midnight to midnight), "WW" (waking time to waking time), or both c("MM","WW").
A list of parameters releated to physical activity as used in GGIRpart2 and GGIRpart5.
Numeric. In g.part5: Threshold for light physical activity to separate inactivity from light. Value can be one number or an array of multiple numbers, e.g. threshold.lig =c(30,40). If multiple numbers are entered then analysis will be repliced for each combination of threshold values. Threshold is applied to the first metric in the milestone data, so if you have only specified do.ENMO == TRUE then it will be applied to ENMO.
Numeric. In g.part5: Threshold for moderate physical activity to separate light from moderate. Value can be one number or an array of multiple numbers, e.g. threshold.mod =c(100,110). If multiple numbers are entered then analysis will be repliced for each ombination of threshold values. Threshold is applied to the first metric in the milestone data, so if you have only specified do.ENMO == TRUE then it will be applied to ENMO.
Numeric. In g.part5: Threshold for vigorous physical activity to separate moderate from vigorous. Value can be one number or an array of multiple numbers, e.g. threshold.mod =c(400,500). If multiple numbers are entered then analysis will be repliced for each combination of threshold values. Threshold is applied to the first metric in the milestone data, so if you have only specified do.ENMO == TRUE then it will be applied to ENMO.
Boolean, see g.getbout
Character, see g.fragmentation
Numeric, Acceleration threshold for MVPA estimation in GGIR part2. This can be a single number or an array of numbers, e.g. c(100,120). In the later case the code will estimate MVPA seperately for each threshold. If this variable is left blank c() then MVPA is not estimated
Numeric, The variable boutcriter is a number between 0 and 1 and defines what fraction of a bout needs to be above the mvpathreshold, only used in GGIR part 2
Numeric, default = c(1,5,10). Three bout duration for which MVPA will be calculated. Only used in GGIR part 2
Numeric, Specify a metric for bout detection. A description of these bout metrics can be found in the new function g.getbout
Numeric, see Durations of mvpa bouts in minutes to be extracted. The default values is c(1,5,10) and will start with the identification of 10 minute bouts, followed by 5 minute bouts in the rest of the data, and followed by 1 minute bouts in the rest of the data.
Numeric, see Durations of inactivty bouts in minutes to be extracted. Inactivity bouts are detected in the segments of the data which were not labelled as sleep or MVPA bouts. The default duration values is c(10,20,30), this will start with the identification of 30 minute bouts, followed by 20 minute bouts in the rest of the data, and followed by 10 minute bouts in the rest of the data.
Numeric, see Durations of light activty bouts in minutes to be extracted. Light activity bouts are detected in the segments of the data which were not labelled as sleep, MVPA, or inactivity bouts. The default duration values is c(1,5,10), this will start with the identification of 10 minute bouts, followed by 5 minute bouts in the rest of the data, and followed by 1 minute bouts in the rest of the data.
Numeric, see A number between 0 and 1 and defines what fraction of a bout needs to be below the light threshold
Numeric, see A number between 0 and 1 and defines what fraction of a bout needs to be between the light and moderage threshold
Numeric, see A number between 0 and 1 and defines what fraction of a bout needs to be above the mvpathreshold
A list of parameters releated to description of 24/7 behaviours that do not fall under conventional physical activity or sleep outcomes, these parameters are used in GGIRpart2 and GGIRpart5:
Numeric or character, To specify windows over which all variables are calculated, e.g. acceleration distirbution, number of valid hours, LXMX analysis, MVPA. If value = c(0,24), which is the default, all variables will only be calculated over the full 24 hours in a day, If value =c(8,24) variables will be calculated over the window 0-8, 8-24 and 0-24. All days in the recording will be segmented based on these values. If you want to use a day specific segmentation then you can set qwindow to be the full path to activity diary file. See documentation g.conv.actlog for details.
Numeric, see g.conv.actlog
Numeric, resolution of L5 and M5 analysis in minutes (default: 10 minutes)
Numeric, Vector of window size(s) (unit: hours) of L5 and M5 analysis (dedault = 5 hours)
Numeric, array of percentiles for which value needs to be extracted. These need to be expressed as a fraction of 1, e.g. c(0.1, 0.5, 0.75). There is no limit to the number of percentiles. If left empty then percentiles will not be extracted. Distribution will be derived from short epoch metric data.
Numeric, Levels for acceleration value frequency distribution in mg, e.g. c(0,100,200). There is no limit to the number of levels.
Numeric, Functionality designed for the London Centre of Longidutinal studies. Size in minutes of the summary window
Numeric, Levels for acceleration value frequency distribution in mg used for intensity gradient calculation (according to the method by Rowlands 2018). By default this is argument is empty and the intensity gradient calculation is not done. The user can either provide a single value (any) to make the intensity gradient use the bins c(seq(0,4000,by=25),8000) or the user could specify their own distribution. There is no constriction to the number of levels.
Numeric, see g.IVIS
depricated. Numeric, see g.IVIS
Numeric, see g.IVIS
Numeric, see g.getM5L5
Numeric, see g.getM5L5
Numeric. Vector with numeric sequece corresponding to the thresholds used to calculated time spent in LUX ranges.
Numeric, if both LUX_cal_constant and LUX_cal_exponent are provided LUX LUX values are converted based on formula y = constant * exp(x * exponent)
Numeric, if both LUX_cal_constant and LUX_cal_exponent are provided LUX LUX values are converted based on formula y = constant * exp(x * exponent)
Numeric vector with hours at which the day should be segmented for the LUX analysis.
Argument depricated after version 1.5-24. This argument used to define the start and end time, in 24 hour clock hours, over which L5M5 needs to be calculated. Now this is done with argument qwindow
van Hees VT, Gorzelniak L, Dean Leon EC, Eder M, Pias M, et al. (2013) Separating Movement and Gravity Components in an Acceleration Signal and Implications for the Assessment of Human Daily Physical Activity. PLoS ONE 8(4): e61691. doi:10.1371/journal.pone.0061691
van Hees VT, Fang Z, Langford J, Assah F, Mohammad A, da Silva IC, Trenell MI, White T, Wareham NJ, Brage S. Auto-calibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents. J Appl Physiol (1985). 2014 Aug 7
# NOT RUN {
# }
# NOT RUN {
metadatadir = "C:/myresults/output_mystudy"
g.part2(metadatadir)
# }
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