NOTE: If you are viewing this page via CRAN note that the main GGIR documentation has been migrated to the GGIR GitHub pages.
The GGIR shell function takes the input arguments and groups them into parameter objects. The first section below displays all optional GGIR input argument names, the GGIR part (1, 2, 3, 4 and/or 5) they are used in, and the parameter object they are stored in. As you will see, a few parameters are not part of any parameter object because they are direct arguments of the GGIR shell function.
In the second section of this vignette you will find a description and default value for all the arguments.
Input arguments/parameters overview
| Argument (parameter) | Used in GGIR part | Parameter object |
|---|---|---|
| datadir | 1, 2, 4, 5 | not in parameter objects |
| f0 | 1, 2, 3, 4, 5 | not in parameter objects |
| f1 | 1, 2, 3, 4, 5 | not in parameter objects |
| windowsizes | 1, 5 | params_general |
| desiredtz | 1, 2, 3, 4, 5 | params_general |
| overwrite | 1, 2, 3, 4, 5 | params_general |
| do.parallel | 1, 2, 3, 5 | params_general |
| maxNcores | 1, 2, 3, 5 | params_general |
| myfun | 1, 2, 3 | not in parameter objects |
| outputdir | 1 | not in parameter objects |
| studyname | 1 | not in parameter objects |
| chunksize | 1 | params_rawdata |
| do.enmo | 1 | params_metrics |
| do.lfenmo | 1 | params_metrics |
| do.en | 1 | params_metrics |
| do.bfen | 1 | params_metrics |
| do.hfen | 1 | params_metrics |
| do.hfenplus | 1 | params_metrics |
| do.mad | 1 | params_metrics |
| do.anglex | 1 | params_metrics |
| do.angley | 1 | params_metrics |
| do.angle | 1 | params_metrics |
| do.enmoa | 1 | params_metrics |
| do.roll_med_acc_x | 1 | params_metrics |
| do.roll_med_acc_y | 1 | params_metrics |
| do.roll_med_acc_z | 1 | params_metrics |
| do.dev_roll_med_acc_x | 1 | params_metrics |
| do.dev_roll_med_acc_y | 1 | params_metrics |
| do.dev_roll_med_acc_z | 1 | params_metrics |
| do.lfen | 1 | params_metrics |
| do.lfx | 1 | params_metrics |
| do.lfy | 1 | params_metrics |
| do.lfz | 1 | params_metrics |
| do.hfx | 1 | params_metrics |
| do.hfy | 1 | params_metrics |
| do.hfz | 1 | params_metrics |
| do.bfx | 1 | params_metrics |
| do.bfy | 1 | params_metrics |
| do.bfz | 1 | params_metrics |
| do.zcx | 1 | params_metrics |
| do.zcy | 1 | params_metrics |
| do.zcz | 1 | params_metrics |
| do.neishabouricounts | 1 | params_metrics |
| actilife_LFE | 1 | params_metrics |
| lb | 1 | params_metrics |
| hb | 1 | params_metrics |
| n | 1 | params_metrics |
| do.cal | 1 | params_rawdata |
| spherecrit | 1 | params_rawdata |
| minloadcrit | 1 | params_rawdata |
| printsummary | 1 | params_rawdata |
| print.filename | 1 | params_general |
| backup.cal.coef | 1 | params_rawdata |
| rmc.noise | 1 | params_rawdata |
| rmc.dec | 1 | params_rawdata |
| rmc.firstrow.acc | 1 | params_rawdata |
| rmc.firstrow.header | 1 | params_rawdata |
| rmc.col.acc | 1 | params_rawdata |
| rmc.col.temp | 1 | params_rawdata |
| rmc.col.time | 1 | params_rawdata |
| rmc.unit.acc | 1 | params_rawdata |
| rmc.unit.temp | 1 | params_rawdata |
| rmc.origin | 1 | params_rawdata |
| rmc.header.length | 1 | params_rawdata |
| mc.format.time | 1 | params_rawdata |
| rmc.bitrate | 1 | params_rawdata |
| rmc.dynamic_range | 1 | params_rawdata |
| rmc.unsignedbit | 1 | params_rawdata |
| rmc.desiredtz | 1 | params_rawdata |
| rmc.sf | 1 | params_rawdata |
| rmc.headername.sf | 1 | params_rawdata |
| rmc.headername.sn | 1 | params_rawdata |
| rmc.headername.recordingid | 1 | params_rawdata |
| rmc.header.structure | 1 | params_rawdata |
| rmc.check4timegaps | 1 | params_rawdata |
| rmc.col.wear | 1 | params_rawdata |
| rmc.doresample | 1 | params_rawdata |
| imputeTimegaps | 1 | params_rawdata |
| dayborder | 1, 2, 5 | params_general |
| dynrange | 1 | params_rawdata |
| nonwear_range_threshold | 1 | params_rawdata |
| configtz | 1 | params_general |
| minimumFileSizeMB | 1 | params_rawdata |
| interpolationType | 1 | params_rawdata |
| expand_tail_max_hours | deprecated | params_general |
| recordingEndSleepHour | 1 | params_general |
| maxRecordingInterval | 1 | params_general |
| nonwear_approach | 1 | params_general |
| dataFormat | 1 | params_general |
| extEpochData_timeformat | 1 | params_general |
| metadatadir | 2, 3, 4, 5 | not in parameter objects |
| minimum_MM_length.part5 | 5 | params_cleaning |
| strategy | 2, 5 | params_cleaning |
| hrs.del.start | 2, 5 | params_cleaning |
| hrs.del.end | 2, 5 | params_cleaning |
| maxdur | 2, 5 | params_cleaning |
| max_calendar_days | 2 | params_cleaning |
| includedaycrit | 2, 5 | params_cleaning |
| nonWearEdgeCorrection | 2 | params_cleaning |
| L5M5window | 2 | params_247 |
| M5L5res | 2, 5 | params_247 |
| winhr | 2, 5 | params_247 |
| qwindow | 2 | params_247 |
| qlevels | 2 | params_247 |
| ilevels | 2 | params_247 |
| mvpathreshold | 2 | params_phyact |
| boutcriter | 2 | params_phyact |
| ndayswindow | 2 | params_cleaning |
| idloc | 2, 4 | params_general |
| do.imp | 2 | params_cleaning |
| storefolderstructure | 2, 4, 5 | params_output |
| epochvalues2csv | 2 | params_output |
| do.part2.pdf | 2 | params_output |
| sep_reports | 2, 4, 5 | params_output |
| dec_reports | 2, 4, 5 | params_output |
| sep_config | 1, 2, 3, 4, 5 | params_output |
| dec_config | 1, 2, 3, 4, 5 | params_output |
| mvpadur | 2 | params_phyact |
| bout.metric | 2, 5 | params_phyact |
| closedbout | 2 | params_phyact |
| IVIS_windowsize_minutes | 2 | params_247 |
| IVIS_epochsize_seconds | 2 | params_247 |
| IVIS.activity.metric | 2 | params_247 |
| iglevels | 2, 5 | params_247 |
| TimeSegments2ZeroFile | 2 | params_cleaning |
| qM5L5 | 2 | params_247 |
| MX.ig.min.dur | 2 | params_247 |
| qwindow_dateformat | 2 | params_247 |
| anglethreshold | 3 | params_sleep |
| timethreshold | 3 | params_sleep |
| ignorenonwear | 3 | params_sleep |
| HDCZA_threshold | 3 | params_sleep |
| acc.metric | 3, 5 | params_general |
| do.part3.pdf | 3 | params_output |
| sensor.location | 3, 4 | params_general |
| HASPT.algo | 3 | params_sleep |
| HASIB.algo | 3 | params_sleep |
| Sadeh_axis | 3 | params_sleep |
| longitudinal_axis | 3 | params_sleep |
| HASPT.ignore.invalid | 3 | params_sleep |
| loglocation | 4, 5 | params_sleep |
| colid | 4 | params_sleep |
| coln1 | 4 | params_sleep |
| possible_nap_window | 5 | params_sleep |
| possible_nap_dur | 5 | params_sleep |
| do.visual | 4 | params_output |
| outliers.only | 4 | params_output |
| excludefirstlast | 4 | params_cleaning |
| criterror | 4 | params_output |
| includenightcrit | 4 | params_cleaning |
| relyonguider | 4 | params_sleep |
| relyonsleeplog | 4 | deprecated |
| sleepefficiency.metric | 4 | params_sleep |
| def.noc.sleep | 4 | params_sleep |
| data_cleaning_file | 4, 5 | params_cleaning |
| excludefirst.part4 | 4 | params_cleaning |
| excludelast.part4 | 4 | params_cleaning |
| sleepwindowType | 4 | params_cleaning |
| excludefirstlast.part5 | 5 | params_cleaning |
| boutcriter.mvpa | 5 | params_phyact |
| boutcriter.in | 5 | params_phyact |
| boutcriter.lig | 5 | params_phyact |
| threshold.lig | 5 | params_phyact |
| threshold.mod | 5 | params_phyact |
| threshold.vig | 5 | params_phyact |
| boutdur.mvpa | 5 | params_phyact |
| boutdur.in | 5 | params_phyact |
| boutdur.lig | 5 | params_phyact |
| save_ms5rawlevels | 5 | params_output |
| part5_agg2_60seconds | 5 | params_general |
| includedaycrit.part5 | 5 | params_cleaning |
| includenight.part5 | 5 | params_cleaning |
| frag.metrics | 5 | params_phyact |
| LUXthresholds | 5 | params_247 |
| LUX_cal_constant | 5 | params_247 |
| LUX_cal_exponent | 5 | params_247 |
| LUX_day_segments | 5 | params_247 |
| timewindow | 5 | params_output |
| save_ms5raw_format | 5 | params_output |
| save_ms5raw_without_invalid | 5 | params_output |
| do.sibreport | 5 | params_output |
| includecrit.part6 | 6 | params_cleaning |
| part6_threshold_combi | 6 | params_phyact |
| part6CR | 6 | params_247 |
| part6HCA | 6 | params_247 |
| part6Window | 6 | params_247 |
| part6DFA | 6 | params_247 |
| require_complete_lastnight_part5 | 5 | params_output |
| visualreport_without_invalid | visualreport | params_output |
| dofirstpage | visualreport | params_output |
| visualreport | visualreport | params_output |
| viewingwindow | visualreport | params_output |
Arguments/parameters description
All information as shown below has been auto-generated and is identical to the information provided in the GGIR package pdf manual.
GGIR function input arguments
mode
Numeric (default = 1:5). Specify which of the five parts need to be run, e.g., mode = 1 makes that g.part1 is run; or mode = 1:5 makes that the whole GGIR pipeline is run, from g.part1 to g.part5. Optionally mode can also include the number 6 to tell GGIR to run g.part6 which is currently under development.
datadir
Character (default = c()). Directory where the accelerometer files are stored, e.g., “C:/mydata”, or list of accelerometer filenames and directories, e.g. c(“C:/mydata/myfile1.bin”, “C:/mydata/myfile2.bin”).
outputdir
Character (default = c()). Directory where the output needs to be stored. Note that this function will attempt to create folders in this directory and uses those folder to keep output.
studyname
Character (default = c()). If the datadir is a folder, then the study will be given the name of the data directory. If datadir is a list of filenames then the studyname as specified by this input argument will be used as name for the study.
f0
Numeric (default = 1). File index to start with (default = 1). Index refers to the filenames sorted in alphabetical order.
do.report
Numeric (default = c(2, 4, 5, 6)). For which parts to generate a summary spreadsheet: 2, 4, 5, and/or 6. Default is c(2, 4, 5, 6). A report will be generated based on the available milestone data. When creating milestone data with multiple machines it is advisable to turn the report generation off when generating the milestone data, value = c(), and then to merge the milestone data and turn report generation back on while setting overwrite to FALSE.
configfile
Character (default = c()). Configuration file previously generated by function GGIR. See details.
myfun
List (default = c()). External function object to be applied to raw data. See package vignette for detailed tutorial with examples on how to use the function embedding: https://cran.r-project.org/package=GGIR/vignettes/ExternalFunction.html
General Parameters
overwrite
Boolean (default = FALSE). Do you want to overwrite analysis for which milestone data exists? If overwrite = FALSE, then milestone data from a previous analysis will be used if available and visual reports will not be created again.
acc.metric
Character (default = “ENMO”). Which one of the acceleration metrics do you want to use for all acceleration magnitude analyses in GGIR part 5 and the visual report? For example: “ENMO”, “LFENMO”, “MAD”, “NeishabouriCount_y”, or “NeishabouriCount_vm”. Only one acceleration metric can be specified and the selected metric needs to have been calculated in part 1 (see g.part1) via arguments such as do.enmo = TRUE or do.mad = TRUE.
maxNcores
Numeric (default = NULL). Maximum number of cores to use when argument do.parallel is set to true. GGIR by default uses either the maximum number of available cores or the number of files to process (whichever is lower), but this argument allows you to set a lower maximum.
print.filename
Boolean (default = FALSE). Whether to print the filename before analysing it (in case do.parallel = FALSE). Printing the filename can be useful to investigate problems (e.g., to verify that which file is being read).
do.parallel
Boolean (default = TRUE). Whether to use multi-core processing (only works if at least 4 CPU cores are available).
windowsizes
Numeric vector, three values (default = c(5, 900, 3600)). To indicate the lengths of the windows as in c(window1, window2, window3): window1 is the short epoch length in seconds, by default 5, and this is the time window over which acceleration and angle metrics are calculated; window2 is the long epoch length in seconds for which non-wear and signal clipping are defined, default 900 (expected to be a multitude of 60 seconds); window3 is the window length of data used for non-wear detection and by default 3600 seconds. So, when window3 is larger than window2 we use overlapping windows, while if window2 equals window3 non-wear periods are assessed by non-overlapping windows.
desiredtz
Character (default = ““, i.e., system timezone). Timezone in which device was configured and experiments took place. If experiments took place in a different timezone, then use this argument for the timezone in which the experiments took place and argument configtz to specify where the device was configured. Use the”TZ identifier” as specified at ://en.wikipedia.org/wiki/Zone.tabhttps://en.wikipedia.org/wiki/Zone.tab to set desiredtz, e.g., “Europe/London”.
configtz
Character (default = ““, i.e., system timezone). At the moment only functional for GENEActiv .bin, AX3 cwa, ActiGraph .gt3x, and ad-hoc csv file format. Timezone in which the accelerometer was configured. Only use this argument if the timezone of configuration and timezone in which recording took place are different. Use the”TZ identifier” as specified at ://en.wikipedia.org/wiki/Zone.tabhttps://en.wikipedia.org/wiki/Zone.tab to set configtz, e.g., “Europe/London”.
idloc
Numeric (default = 1). If idloc = 1 the code assumes that ID number is stored in the obvious header field. Note that for ActiGraph data the ID is never stored in the file header. For value set to 2, 5, 6, and 7, GGIR looks at the filename and extracts the character string preceding the first occurance of a “_” (idloc = 2), ” ” (space, idloc = 5), “.” (dot, idloc = 6), and “-” (idloc = 7), respectively. You may have noticed that idloc 3 and 4 are skipped, they were used for one study in 2012, and not actively maintained anymore, but because it is legacy code not omitted.
part5_agg2_60seconds
Boolean (default = FALSE). Whether to use aggregate epochs to 60 seconds as part of the GGIR g.part5 analysis. Aggregation is doen by averaging. Note that when working with count metrics such as Neishabouri counts this means that the threshold can stay the same as in part 2, because again the threshold is expressed relative to the original epoch size, even if averaged per minute. For example if we want to use a cut-point 100 count per minute then we specify mvpathreshold = 100 * (5/60) as well as `threshold.mod = 100 * (5/60) regardless of whether we set part5_agg2_60seconds to TRUE or FALSE.
sensor.location
Character (default = “wrist”). To indicate sensor location, default is wrist. If it is hip, the HDCZA algorithm for sleep detection also requires longitudinal axis of sensor to be between -45 and +45 degrees.
expand_tail_max_hours
Numeric (default = NULL). This parameter has been replaced by recordingEndSleepHour.
recordingEndSleepHour
Numeric (default = NULL). Time (in hours) at which the recording should end (or later) to expand the g.part1 output with synthetic data to trigger sleep detection for last night. Using argument recordingEndSleepHour implies the assumption that the participant fell asleep at or before the end of the recording if the recording ended at or after recordingEndSleepHour hour of the last day. This assumption may not always hold true and should be used with caution. The synthetic data for metashort entails: timestamps continuing regularly, zeros for acceleration metrics other than EN, one for EN. Angle columns are created in a way that it triggers the sleep detection using the equation: round(sin((1:length_expansion) / (900/epochsize))) * 15. To keep track of the tail expansion g.part1 stores the length of the expansion in the RData files, which is then passed via g.part2, g.part3, and g.part4 to g.part5. In g.part5 the tail expansion size is included as an additional variable in the csv-reports. In the g.part4 csv-report the last night is omitted, because we know that sleep estimates from the last night will not be trustworthy. Similarly, in the g.part5 output columns related to the sleep assessment will be omitted for the last window to avoid biasing the averages. Further, the synthetic data are also ignored in the visualizations and time series output to avoid biased output.
dataFormat
Character (default = “raw”). To indicate what the format is of the data in datadir. Alternatives: ukbiobank_csv, actiwatch_csv, actiwatch_awd, actigraph_csv, and sensewear_xls, which correspond to epoch level data files from, respecitively, UK Biobank in csv format, Actiwatch in csv format, Actiwatch in awd format, ActiGraph csv format, and Sensewear in xls format (also works with xlsx). Here, the assumed epoch size for UK Biobank csvdata is 5 seconds. The epoch size for the other non-raw data formats is flexible, but make sure that you set first value of argument windowsizes accordingly. Also when working with non-raw data formats specify argument extEpochData_timeformat as documented below. For ukbiobank_csv nonwear is a column in the data itself, for actiwatch_csv, actiwatch_awd, actigraph_csv, and sensewear_xls non-wear is detected as 60 minute rolling zeros. The length of this window can be modified with the third value of argument windowsizes expressed in seconds.
maxRecordingInterval
Numeric (default = NULL). To indicate the maximum gap in hours between repeated measurements with the same ID for the recordings to be appended. So, the assumption is that the ID can be matched, make sure argument idloc is set correctly. If argument maxRecordingInterval is set to NULL (default) recordings are not appended. If recordings overlap then GGIR will use the data from the latest recording. If recordings are separated then the timegap between the recordings is filled with data points that resemble monitor not worn. The maximum value of maxFile gap is 504 (21 days). Only recordings from the same accelerometer brand are appended. The part 2 csv report will show number of appended recordings, sampling rate for each, time overlap or gap and the names of the filenames of the respective recording.
extEpochData_timeformat
Character (default = “%d-%m-%Y %H:%M:%S”). To specify the time format used in the external epoch level data when argument dataFormat is set to “actiwatch_csv”, “actiwatch_awd”, “actigraph_csv” or “sensewear_xls”. For example “%Y-%m-%d %I:%M:%S %p” for “2023-07-11 01:24:01 PM” or “%m/%d/%Y %H:%M:%S” “2023-07-11 13:24:01”
Raw Data Parameters
chunksize
Numeric (default = 1). Value to specify the size of chunks to be loaded as a fraction of an approximately 12 hour period for auto-calibration procedure and as fraction of 24 hour period for the metric calculation, e.g., 0.5 equals 6 and 12 hour chunks, respectively. For machines with less than 4Gb of RAM memory or with < 2GB memory per process when using do.parallel = TRUE a value below 1 is recommended. The value is constrained by GGIR to not be lower than 0.05. Please note that setting 0.05 will not produce output when 3rd value of parameter windowsizes is 3600.
spherecrit
Numeric (default = 0.3). The minimum required acceleration value (in g) on both sides of 0 g for each axis. Used to judge whether the sphere is sufficiently populated
minloadcrit
Numeric (default = 168). The minimum number of hours the code needs to read for the autocalibration procedure to be effective (only sensitive to multitudes of 12 hrs, other values will be ceiled). After loading these hours only extra data is loaded if calibration error has not been reduced to under 0.01 g.
printsummary
Boolean (default = FALSE). If TRUE will print a summary of the calibration procedure in the console when done.
do.cal
Boolean (default = TRUE). Whether to apply auto-calibration or not by g.calibrate. Recommended setting is TRUE.
backup.cal.coef
Character (default = “retrieve”). Option to use backed-up calibration coefficient instead of deriving the calibration coefficients when analysing the same file twice. Argument backup.cal.coef has two usecase. Use case 1: If the auto-calibration fails then the user has the option to provide back-up calibration coefficients via this argument. The value of the argument needs to be the name and directory of a csv-spreadsheet with the following column names and subsequent values: “filename” with the names of accelerometer files on which the calibration coefficients need to be applied in case auto-calibration fails; “scale.x”, “scale.y”, and “scale.z” with the scaling coefficients; “offset.x”, “offset.y”, and “offset.z” with the offset coefficients, and; “temperature.offset.x”, “temperature.offset.y”, and “temperature.offset.z” with the temperature offset coefficients. This can be useful for analysing short lasting laboratory experiments with insufficient sphere data to perform the auto-calibration, but for which calibration coefficients can be derived in an alternative way. It is the users responsibility to compile the csv-spreadsheet. Instead of building this file the user can also Use case 2: The user wants to avoid performing the auto-calibration repeatedly on the same file. If backup.cal.coef value is set to “retrieve” (default) then GGIR will look out for the “data_quality_report.csv” file in the outputfolder QC, which holds the previously generated calibration coefficients. If you do not want this happen, then deleted the data_quality_report.csv from the QC folder or set it to value “redo”.
minimumFileSizeMB
Numeric (default = 2). Minimum File size in MB required to enter processing. This argument can help to avoid having short uninformative files to enter the analyses. Given that a typical accelerometer collects several MBs per hour, the default setting should only skip the very tiny files.
rmc.dec
Character (default = “.”). Decimal used for numbers, same as dec argument in [utils]read.csv and in [data.table]fread.
rmc.firstrow.header
Numeric (default = NULL). First row (number) of the header. Leave blank if the file does not have a header.
rmc.header.length
Numeric (default = NULL). If file has header, specify header length (number of rows).
rmc.col.acc
Numeric, three values (default = c(1, 2, 3)). Vector with three column (numbers) in which the acceleration signals are stored.
rmc.col.temp
Numeric (default = NULL). Scalar with column (number) in which the temperature is stored. Leave in default setting if no temperature is available. The temperature will be used by g.calibrate.
rmc.col.time
Numeric (default = NULL). Scalar with column (number) in which the timestamps are stored. Leave in default setting if timestamps are not stored.
rmc.unit.acc
Character (default = “g”). Character with unit of acceleration values: “g”, “mg”, or “bit”.
rmc.unit.temp
Character (default = “C”). Character with unit of temperature values: (K)elvin, (C)elsius, or (F)ahrenheit.
rmc.unit.time
Character (default = “POSIX”). Character with unit of timestamps: “POSIX”, “UNIXsec” (seconds since origin, see argument rmc.origin), “UNIXmsec” (same as UNIXsec but in milliseconds), “character”, or “ActivPAL” (exotic timestamp format only used in the ActivPAL activity monitor).
rmc.format.time
Character (default = “%Y-%m-%d %H:%M:%OS”). Character giving a date-time format as used by [base]strptime. Only used for rmc.unit.time: character and POSIX.
rmc.bitrate
Numeric (default = NULL). If unit of acceleration is a bit then provide bit rate, e.g., 12 bit.
rmc.dynamic_range
Numeric or character (default = NULL). If unit of acceleration is a bit then provide dynamic range deviation in g from zero, e.g., +/-6g would mean this argument needs to be 6. If you give this argument a character value the code will search the file header for elements with a name equal to the character value and use the corresponding numeric value next to it as dynamic range.
rmc.unsignedbit
Boolean (default = TRUE). If unsignedbit = TRUE means that bits are only positive numbers. if unsignedbit = FALSE then bits are both positive and negative.
rmc.origin
Character (default = “1970-01-01”). Origin of time when unit of time is UNIXsec, e.g., 1970-1-1.
rmc.desiredtz
Character (default = NULL). Timezone in which experiments took place. This argument is scheduled to be deprecated and is now used to overwrite desiredtz if not provided.
rmc.configtz
Character (default = NULL). Timezone in which device was configured. This argument is scheduled to be deprecated and is now used to overwrite configtz if not provided.
rmc.sf
Numeric (default = NULL). Sample rate in Hertz, if this is stored in the file header then that will be used instead (see argument rmc.headername.sf).
rmc.headername.sf
Character (default = NULL). If file has a header: Row name under which the sample frequency can be found.
rmc.headername.sn
Character (default = NULL). If file has a header: Row name under which the serial number can be found.
rmc.headername.recordingid
Character (default = NULL). If file has a header: Row name under which the recording ID can be found.
rmc.header.structure
Character (default = NULL). Used to split the header name from the header value, e.g., “:” or ” “.
rmc.check4timegaps
Boolean (default = FALSE). To indicate whether gaps in time should be imputed with zeros. Some sensing equipment provides accelerometer with gaps in time. The rest of GGIR is not designed for this, by setting this argument to TRUE the gaps in time will be filled with zeros.
rmc.noise
Numeric (default = 13). Noise level of acceleration signal in m-units, used when working ad-hoc .csv data formats using read.myacc.csv. The read.myacc.csv does not take rmc.noise as argument, but when interacting with GGIR or g.part1 rmc.noise is used.
nonwear_range_threshold
Numeric (default 150) used to define maximum value range per axis for non-wear detection, used in combination with brand specific standard deviation per axis.
rmc.col.wear
Numeric (default = NULL). If external wear detection outcome is stored as part of the data then this can be used by GGIR. This argument specifies the column in which the wear detection (Boolean) is stored.
rmc.doresample
Boolean (default = FALSE). To indicate whether to resample the data based on the available timestamps and extracted sample rate from the file header.
interpolationType
Integer (default = 1). To indicate type of interpolation to be used when resampling time series (mainly relevant for Axivity sensors), 1=linear, 2=nearest neighbour.
imputeTimegaps
Boolean (default = TRUE). To indicate whether timegaps larger than 1 sample should be imputed. Currently only used for .gt3x data and ActiGraph .csv format, where timegaps can be expected as a result of Actigraph’s idle sleep.mode configuration.
frequency_tol
Number (default = 0.1) as passed on to readAxivity from the GGIRread package. Represents the frequency tolerance as fraction between 0 and 1. When the relative bias per data block is larger than this fraction then the data block will be imputed by lack of movement with gravitational oriationed guessed from most recent valid data block. Only applicable to Axivity .cwa data.
Metrics Parameters
do.anglex
Boolean (default = FALSE). If TRUE, calculates the angle of the X axis relative to the horizontal: = (^-1_rollmedian(x)(acc_rollmedian(y))^2 + (acc_rollmedian(z))^2) * 180/
do.angley
Boolean (default = FALSE). If TRUE, calculates the angle of the Y axis relative to the horizontal: = (^-1_rollmedian(y)(acc_rollmedian(x))^2 + (acc_rollmedian(z))^2) * 180/
do.anglez
Boolean (default = TRUE). If TRUE, calculates the angle of the Z axis relative to the horizontal: = (^-1_rollmedian(z)(acc_rollmedian(x))^2 + (acc_rollmedian(y))^2) * 180/
do.zcx
Boolean (default = FALSE). If TRUE, calculates metric zero-crossing count for x-axis. For computation specifics see source code of function g.applymetrics
do.zcy
Boolean (default = FALSE). If TRUE, calculates metric zero-crossing count for y-axis. For computation specifics see source code of function g.applymetrics
do.zcz
Boolean (default = FALSE). If TRUE, calculates metric zero-crossing count for z-axis. For computation specifics see source code of function g.applymetrics
do.enmo
Boolean (default = TRUE). If TRUE, calculates the metric: = _x^2 + acc_y^2 + acc_z^2 - 1 (if ENMO < 0, then ENMO = 0).
do.lfenmo
Boolean (default = FALSE). If TRUE, calculates the metric ENMO over the low-pass filtered accelerations (for computation specifics see source code of function g.applymetrics). The filter bound is defined by the parameter hb.
do.en
Boolean (default = FALSE). If TRUE, calculates the Euclidean Norm of the raw accelerations: = _x^2 + acc_y^2 + acc_z^2
do.enmoa
Boolean (default = FALSE). If TRUE, calculates the metric: = _x^2 + acc_y^2 + acc_z^2 - 1 (if ENMOa < 0, then ENMOa = |ENMOa|).
do.roll_med_acc_x
Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.
do.roll_med_acc_y
Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.
do.roll_med_acc_z
Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.
do.dev_roll_med_acc_x
Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.
do.dev_roll_med_acc_y
Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.
do.dev_roll_med_acc_z
Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.
do.bfen
Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.
do.hfen
Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.
do.hfenplus
Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.
do.lfen
Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.
do.lfx
Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.
do.lfy
Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.
do.lfz
Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.
do.hfx
Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.
do.hfy
Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.
do.hfz
Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.
do.bfx
Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.
do.bfy
Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.
do.bfz
Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.
do.brondcounts
Boolean (default = FALSE). this option has been deprecated (October 2022) due to issues with the activityCounts package that we used as a dependency. If TRUE, calculated the metric via R package activityCounts. We called them BrondCounts because there are large number of activity counts in the physical activity and sleep research field. By calling them brondcounts we clarify that these are the counts proposed by Jan Brønd and implemented in R by Ruben Brondeel. The brondcounts are intended to be an imitation of the counts produced by one of the closed source ActiLife software by ActiGraph.
do.neishabouricounts
Boolean (default = FALSE). If TRUE, calculates the metric via R package actilifecounts, which is an implementation of the algorithm used in the closed-source software ActiLife by ActiGraph (methods published in doi: 10.1038/s41598-022-16003-x). We use the name of the first author (instead of ActiLifeCounts) of the paper and call them NeishabouriCount under the uncertainty that ActiLife will implement this same algorithm over time. To use the Neishabouri counts for the physical activity intensity classification in part 5 (i.e., metric over the threshold.lig, threshold.mod, and threshold.vig would be applied), the acc.metric argument needs to be set as one of the following: “NeishabouriCount_x”, “NeishabouriCount_y”, “NeishabouriCount_z”, “NeishabouriCount_vm” to use the counts in the x-, y-, z-axis or vector magnitude, respectively.
hb
Numeric (default = 15). Higher boundary of the frequency filter (in Hertz) as used in the filter-based metrics.
lb
Numeric (default = 0.2). Lower boundary of the frequency filter (in Hertz) as used in the filter-based metrics.
zc.lb
Numeric (default = 0.25). Used for zero-crossing counts only. Lower boundary of cut-off frequency filter.
zc.hb
Numeric (default = 3). Used for zero-crossing counts only. Higher boundary of cut-off frequencies in filter.
zc.sb
Numeric (default = 0.01). Stop band used for calculation of zero crossing counts. Value is the acceleration threshold in g units below which acceleration will be rounded to zero.
Cleaning Parameters
includedaycrit
Numeric (default = 16). Minimum required number of valid hours in calendar day specific to analysis in part 2. If you specify two values as in c(16, 16) then the first value will be used in part 2 and the second value will be used in part 5 and applied as a criterion on the full part 5 window. Note that this is then applied in addition to parameter includedaycrit.part5 which only looks at valid data during waking hours.
ndayswindow
Numeric (default = 7). If data_masking_strategy is set to 3 or 5, then this is the size of the window as a number of days. For data_masking_strategy 3 value can be fractional, e.g. 7.5, while for data_masking_strategy 5 it needs to be an integer.
strategy
Deprecated and replaced by data_masking_strategy. If strategy is specified then its value is passed on and used for data_masking_strategy.
data_masking_strategy
Numeric (default = 1). How to deal with knowledge about study protocol. data_masking_strategy = 1 means select data based on hrs.del.start and hrs.del.end. data_masking_strategy = 2 makes that only the data between the first midnight and the last midnight is used. data_masking_strategy = 3 selects the most active X days in the file where X is specified by argument ndayswindow, where the days are a series of 24-h blocks starting any time in the day (X hours at the beginning and end of this period can be deleted with arguments hrs.del.start and hrs.del.end) data_masking_strategy = 4 to only use the data after the first midnight. data_masking_strategy = 5 is similar to data_masking_strategy = 3, but it selects X complete calendar days where X is specified by argument ndayswindow (X hours at the beginning and end of this period can be deleted with arguments hrs.del.start and hrs.del.end).
maxdur
Numeric (default = 0). How many DAYS after start of experiment did experiment definitely stop? (set to zero if unknown).
hrs.del.start
Numeric (default = 0). How many HOURS after start of experiment did wearing of monitor start? Used in GGIR g.part2 when data_masking_strategy = 1.
hrs.del.end
Numeric (default = 0). How many HOURS before the end of the experiment did wearing of monitor definitely end? Used in GGIR g.part2 when data_masking_strategy = 1.
includedaycrit.part5
Numeric (default = 2/3). Inclusion criteria used in part 5 for number of valid hours during the waking hours of a day, when value is smaller than or equal to 1 used as fraction of waking hours, when value above 1 used as absolute number of valid hours required. Do not confuse this argument with argument includedaycrit which is only used in GGIR part 2 and applies to the entire day.
excludefirstlast.part5
Boolean (default = FALSE). If TRUE then the first and last window (waking-waking, midnight-midnight, or sleep onset-onset) are ignored in g.part5.
TimeSegments2ZeroFile
Character (default = NULL). Takes path to a csv file that has columns “windowstart” and “windowend” to refer to the start and end time of a time windows in format “2024-10-12 20:00:00”, and “filename” of the GGIR milestone data file without the “meta_” segment of the name. GGIR part 2 uses this to set all acceleration values to zero and the non-wear classification to zero (meaning sensor worn). Motivation: When the accelerometer is not worn during the night GGIR automatically labels them as invalid, while the user may like to treat them as zero movement. Disclaimer: This functionality was developed in 2019. With hindsight it is not generic enough and in need for revision. Please contact GGIR maintainers if you would like us to invest time in improving this functionality.
do.imp
Boolean (default = TRUE). Whether to impute missing values (e.g., suspected of monitor non-wear or clippling) or not by g.impute in GGIR g.part2. Recommended setting is TRUE.
data_cleaning_file
Character (default = NULL). Optional path to a csv file you create that holds four columns: ID, day_part5, relyonguider_part4, and night_part4. ID should hold the participant ID. Columns day_part5 and night_part4 allow you to specify which day(s) and night(s) need to be excluded from g.part5 and g.part4, respectively. When including multiple day(s)/night(s) create a new line for each day/night. So, this will be done regardless of whether the rest of GGIR thinks those day(s)/night(s) are valid. Column relyonguider_part4 allows you to specify for which nights g.part4 should fully rely on the guider. See also package vignette.
minimum_MM_length.part5
Numeric (default = 23). Minimum length in hours of a MM day to be included in the cleaned g.part5 results.
excludefirstlast
Boolean (default = FALSE). If TRUE then the first and last night of the measurement are ignored for the sleep assessment in g.part4.
includenightcrit
Numeric (default = 16). Minimum number of valid hours per night (24 hour window between noon and noon), used for sleep assessment in g.part4.
excludefirst.part4
Boolean (default = FALSE). If TRUE then the first night of the measurement are ignored for the sleep assessment in g.part4.
excludelast.part4
Boolean (default = FALSE). If TRUE then the last night of the measurement are ignored for the sleep assessment in g.part4.
max_calendar_days
Numeric (default = 0). The maximum number of calendar days to include (set to zero if unknown).
nonWearEdgeCorrection
Boolean (default = TRUE). If TRUE then the non-wear detection around the edges of the recording (first and last 3 hours) are corrected following description in vanHees2013 as has been the default since then. This functionality is advisable when working with sleep clinic or exercise lab data typically lasting less than a day.
nonwear_approach
Character (default = “2023”). Whether to use the traditional version of the non-wear detection algorithm (nonwear_approach = “2013”) or the new version (nonwear_approach = “2023”). The 2013 version would use the longsize window (windowsizes[3], one hour as default) to check the conditions for nonwear identification and would flag as nonwear the mediumsize window (windowsizes[2], 15 min as default) in the middle. The 2023 version differs in which it would flag as nonwear the full longsize window. For the 2013 method the longsize window is centered in the centre of the mediumsize window, while in the 2023 method the longsizewindow is aligned with its left edge to the left edge of the mediumsize window.
segmentWEARcrit.part5
Numeric (default = 0.5). Fraction of qwindow segment expected to be valid in part 5, where 0.3 indicates that at least 30 percent of the time should be valid.
segmentDAYSPTcrit.part5
Numeric vector or length 2 (default = c(0.9, 0)). Inclusion criteria for the proportion of the segment that should be classified as day (awake) and spt (sleep period time) to be considered valid. If you are interested in comparing time spent in behaviour then it is better to set one of the two numbers to 0, and the other defines the proportion of the segment that should be classified as day or spt, respectively. The default setting would focus on waking hour segments and includes all segments that overlap for at least 90 percent with waking hours. In order to shift focus to the SPT you could use c(0, 0.9) which ensures that all segments that overlap for at least 90 percent with the SPT are included. Setting both to zero would be problematic when comparing time spent in behaviours between days or individuals: A complete segment would be averaged with an incomplete segments (someone going to bed or waking up in the middle of a segment) by which it is no longer clear whether the person is less active or sleeps more during that segment. Similarly it is not clear whether the person has more wakefulness during SPT for a segment or woke up or went to bed during the segment.
study_dates_file
Character (default = c()). Full path to csv file containing the first and last date of the expected wear period for every study participant (dates are provided per individual). Expected format of the activity diary is: First column headers followed by one row per recording. There should be three columns: first column is recording ID, which needs to match with the ID GGIR extracts from the accelerometer file; second column should contain the first date of the study; and third column the last date of the study. Date columns should be by default in format “23-04-2017”, or in the date format specified by argument study_dates_dateformat (below). If not specified (default), then GGIR would use the first and last day of the recording as beginning and end of the study. Note that these dates are used on top of the data_masking_strategy selected.
study_dates_dateformat
Character (default = “%d-%m-%Y”). To specify the date format used in the study_dates_file as used by [base]strptime.
includecrit.part6
Numeric (default = c(2/3, 2/3)) Vector of two with the minimum fraction of valid data required for day and spt time, respectively. This criteria is only used for circadian rhythm analysis.
includenightcrit.part5
Numeric (default = 0). Inclusion criteria used in part 5 for number of valid hours during the sleep period hours of a day (the night), when value is smaller than or equal to 1 used as fraction of sleep period hours, when value above 1 used as absolute number of valid hours required. Do not confuse this argument with argument includenightcrit which is only used in GGIR part 4 and applies to the entire 24 hour window from noon to noon or 6pm to 6pm.
Sleep Parameters
anglethreshold
Numeric (default = 5). Angle threshold (degrees) for sustained inactivity periods detection. The algorithm will look for periods of time (timethreshold) in which the angle variability is lower than anglethreshold. This can be specified as multiple thresholds, each of which will be implemented, e.g., anglethreshold = c(5,10).
timethreshold
Numeric (default = 5). Time threshold (minutes) for sustained inactivity periods detection. The algorithm will look for periods of time (timethreshold) in which the angle variability is lower than anglethreshold. This can be specified as multiple thresholds, each of which will be implemented, e.g., timethreshold = c(5,10).
ignorenonwear
Boolean (default = TRUE). If TRUE then ignore detected monitor non-wear periods to avoid confusion between monitor non-wear time and sustained inactivity.
HASPT.algo
Character (default = “HDCZA”). To indicate what algorithm should be used for the sleep period time detection. Default “HDCZA” is Heuristic algorithm looking at Distribution of Change in Z-Angle as described in van Hees et al. 2018. Other options included: “HorAngle”, which is based on HDCZA but replaces non-movement detection of the HDCZA algorithm by looking for time segments where the angle of the longitudinal sensor axis has an angle relative to the horizontal plane between -45 and +45 degrees. And “NotWorn” which is also the same as HDCZA but looks for time segments when a rolling average of acceleration magnitude is below 5 per cent of its standard deviation, see Cookbook vignette in the Annexes of https://wadpac.github.io/GGIR/ for more detailed guidance on how to use “NotWorn”.
HASIB.algo
Character (default = “vanHees2015”). To indicate which algorithm should be used to define the sustained inactivity bouts (i.e., likely sleep). Options: “vanHees2015”, “Sadeh1994”, “Galland2012”.
Sadeh_axis
Character (default = “Y”). To indicate which axis to use for the Sadeh1994 algorithm, and other algortihms that relied on count-based Actigraphy such as Galland2012.
longitudinal_axis
Integer (default = NULL). To indicate which axis is the longitudinal axis. If not provided, the function will estimate longitudinal axis as the axis with the highest 24 hour lagged autocorrelation. Only used when sensor.location = “hip” or HASPT.algo = “HorAngle”.
HASPT.ignore.invalid
Boolean (default = FALSE). To indicate whether invalid time segments should be ignored in the heuristic guiders. If FALSE (default), the imputed angle or activity metric during the invalid time segments are used. If TRUE, invalid time segments are ignored (i.e., they cannot contribute to the guider). If NA, then invalid time segments are considered to be no movement segments and can contribute to the guider. When HASPT.algo is “NotWorn”, HASPT.ignore.invalid is automatically set to NA.
loglocation
Character (default = NULL). Path to csv file with sleep log information. See package vignette for how to format this file.
colid
Numeric (default = 1). Column number in the sleep log spreadsheet in which the participant ID code is stored.
coln1
Numeric (default = 2). Column number in the sleep log spreadsheet where the onset of the first night starts.
relyonguider
Boolean (default = FALSE). Sustained inactivity bouts (sib) that overlap with the guider are labelled as sleep. If relyonguider = FALSE and the sib overlaps only partially with the guider then it is the sib that defines the edge of the SPT window and not the guider. If relyonguider = TRUE and the sib overlaps only partially with the guider then it is the guider that defines the edge of the SPT window and not the sib. If participants were instructed NOT to wear the accelerometer during waking hours and ignorenonware=FALSE then set to relyonguider=TRUE, in all other scenarios set to FALSE.
def.noc.sleep
Numeric (default = 1). The time window during which sustained inactivity will be assumed to represent sleep, e.g., def.noc.sleep = c(21, 9). This is only used if no sleep log entry is available. If left blank def.noc.sleep = c() then the 12 hour window centred at the least active 5 hours of the 24 hour period will be used instead. Here, L5 is hardcoded and will not change by changing argument winhr in function g.part2. If def.noc.sleep is filled with a single integer, e.g., def.noc.sleep=c(1) then the window will be detected with based on built in algorithms. See argument HASPT.algo from HASPT for specifying which of the algorithms to use.
sleepwindowType
Character (default = “SPT”). To indicate type of information in the sleeplog, “SPT” for sleep period time. Set to “TimeInBed” if sleep log recorded time in bed to enable calculation of sleep latency and sleep efficiency.
possible_nap_window
Numeric (default = c(9, 18)). Numeric vector of length two with range in clock hours during which naps are assumed to take place, e.g., possible_nap_window = c(9, 18). Currently used in the context of an explorative nap classification algortihm that was trained in 3.5 year olds.
possible_nap_dur
Numeric (default = c(15, 240)). Numeric vector of length two with range in duration (minutes) of a nap, e.g., possible_nap_dur = c(15, 240). Currently used in the context of an explorative nap classification algortihm that was trained in 3.5 year olds.
nap_model
Character (default = NULL). To specify classification model. Currently the only option is “hip3yr”, which corresponds to a model trained with hip data in 3-3.5 olds trained with parent diary data.
sleepefficiency.metric
Numeric (default = 1). If 1 (default), sleep efficiency is calculated as detected sleep time during the SPT window divided by log-derived time in bed. If 2, sleep efficiency is calculated as detected sleep time during the SPT window divided by detected duration in sleep period time plus sleep latency (where sleep latency refers to the difference between time in bed and sleep onset). sleepefficiency.metric is only considered when argument sleepwindowType = “TimeInBed”
possible_nap_edge_acc
Numeric (default = Inf). Maximum acceleration before or after the SIB for the nap to be considered. By default this will allow all possible naps.
HDCZA_threshold
Numeric (default = c()) If HASPT.algo is set to “HDCZA” and HDCZA_threshold is NOT NULL, (e.g., HDCZA_threshold = 0.2), then that value will be used as threshold in the 6th step in the diagram of Figure 1 in van Hees et al. 2018 Scientific Report (doi: 10.1038/s41598-018-31266-z). However, doing so has not been supported by research yet and is only intended to facilitate methodological research, so we advise sticking with the default in line with the publication. Further, if HDCZA_threshold is set to a numeric vector of length 2, e.g. c(10, 15), that will be used as percentile and multiplier for the above mentioned 6th step.
Physical activity Parameters
mvpathreshold
Numeric (default = 100). Acceleration threshold for MVPA estimation in GGIR g.part2. This can be a single number or an vector of numbers, e.g., mvpathreshold = c(100, 120). In the latter case the code will estimate MVPA separately for each threshold. If this variable is left blank, e.g., mvpathreshold = c(), then MVPA is not estimated.
boutcriter
Numeric (default = 0.8). A number between 0 and 1, it defines what fraction of a bout needs to be above the mvpathreshold, only used in GGIR g.part2.
mvpadur
Numeric (default = 10). The bout duration(s) for which MVPA will be calculated. Only used in GGIR g.part2.
boutcriter.in
Numeric (default = 0.9). A number between 0 and 1, it defines what fraction of a bout needs to be below the threshold.lig.
boutcriter.lig
Numeric (default = 0.8). A number between 0 and 1, it defines what fraction of a bout needs to be between the threshold.lig and the threshold.mod.
boutcriter.mvpa
Numeric (default = 0.8). A number between 0 and 1, it defines what fraction of a bout needs to be above the threshold.mod.
threshold.lig
Numeric (default = 40). In g.part5: Threshold for light physical activity to separate inactivity from light. Value can be one number or an vector of multiple numbers, e.g., threshold.lig =c(30,40). If multiple numbers are entered then analysis will be repeated 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.
threshold.mod
Numeric (default = 100). In g.part5: Threshold for moderate physical activity to separate light from moderate. Value can be one number or an vector of multiple numbers, e.g., threshold.mod = c(100, 120). If multiple numbers are entered then analysis will be repeated 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.
threshold.vig
Numeric (default = 400). In g.part5: Threshold for vigorous physical activity to separate moderate from vigorous. Value can be one number or an vector of multiple numbers, e.g., threshold.vig =c(400,500). If multiple numbers are entered then analysis will be repeated 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.
boutdur.mvpa
Numeric (default = c(1, 5, 10)). Duration(s) of MVPA bouts in minutes to be extracted. It will start with the identification of the longest to the shortest duration. In the default setting, it will start with the 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.
boutdur.in
Numeric (default = c(10, 20, 30)). Duration(s) of inactivity 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. It will start with the identification of the longest to the shortest duration. In the default setting, it 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. Note that we use the term inactivity instead of sedentary behaviour for the lowest intensity level of behaviour. The reason for this is that GGIR does not attempt to classifying the activity type sitting at the moment, by which we feel that using the term sedentary behaviour would fail to communicate that.
boutdur.lig
Numeric (default = c(1, 5, 10)). Duration(s) of light activity 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. It will start with the identification of the longest to the shortest duration. In the default setting, 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.
24/7 Parameters
qwindow
Numeric or character (default = c(0, 24)). To specify windows over which all variables are calculated, e.g., acceleration distribution, number of valid hours, LXMX analysis, MVPA. If numeric, qwindow should have length two, e.g., qwindow = c(0, 24), all variables will only be calculated over the full 24 hours in a day. If qwindow = 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 in each day then you can set qwindow to be the full path to activity diary file (character). Expected format of the activity diary is: First column headers followed by one row per recording, first column is recording ID, which needs to match with the ID GGIR extracts from the accelerometer file. Followed by date column in format “23-04-2017”, where date format is specified by argument qwindow_dateformat (below). Use the character combination date, Date or DATE in the column name. This is followed by one or multiple columns with start times for the activity types in that day format in hours:minutes:seconds. The header of the column will be used as label for each activity type. Insert a new date column before continuing with activity types for next day. Leave missing values empty. If an activity log is used then individuals who do not appear in the activity log will still be processed with value qwindow = c(0, 24). Dates with no activity log data can be skipped, no need to have a column with the date followed by a column with the next date. If times in the activity diary are not multiple of the short window size (epoch length), the next epoch is considered (e.g., with epoch of 5 seconds, 8:00:02 will be redefined as 8:00:05 in the activity log). When using the qwindow functionality in combination with GGIR part 5 then make sure to check that arguments segmentWEARcrit.part5 and segmentDAYSPTcrit.part5 are specified to your research needs.
qlevels
Numeric (default = NULL). Vector 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. Argument qlevels can for example be used for the MX-metrics (e.g. Rowlands et al) as discussed in the ://cran.r-project.org/package=GGIR/vignettes/GGIR.htmlmain package vignette
qwindow_dateformat
Character (default = “%d-%m-%Y”). To specify the date format used in the activity log as used by [base]strptime.
ilevels
Numeric (default = NULL). Levels for acceleration value frequency distribution in m, e.g., ilevels = c(0,100,200). There is no limit to the number of levels. If left empty then the intensity levels will not be extracted. Distribution will be derived from short epoch metric data.
MX.ig.min.dur
Numeric (default = 10). Minimum MX duration needed in order for intensity gradient to be calculated.
winhr
Numeric (default = 5). Vector of window size(s) (unit: hours) of LX and MX analysis, where look for least and most active consecutive number of X hours.
iglevels
Numeric (default = NULL). Levels for acceleration value frequency distribution in mused 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 iglevels = c(seq(0,4000,by=25), 8000) or the user could specify their own distribution. There is no constriction to the number of levels.
LUXthresholds
Numeric (default = c(0, 100, 500, 1000, 3000, 5000, 10000)). Vector with numeric sequence corresponding to the thresholds used to calculate time spent in LUX ranges.
LUX_cal_constant
Numeric (default = NULL). If both LUX_cal_constant and LUX_cal_exponent are provided LUX values are converted based on formula y = constant * exp(x * exponent)
LUX_cal_exponent
Numeric (default = NULL). If both LUX_cal_constant and LUX_cal_exponent are provided LUX LUX values are converted based on formula y = constant * exp(x * exponent)
LUX_day_segments
Numeric (default = NULL). Vector with hours at which the day should be segmented for the LUX analysis.
L5M5window
Argument deprecated 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.
cosinor
Boolean (default = FALSE). Whether to apply the cosinor analysis from the ActCR package in part 2. In part 6 cosinor analysis is applied by default and cannot be turned off.
part6CR
Boolean (default = FALSE) to indicate whether circadian rhythm
analysis should be run by part 6, this includes: cosinor analysis,
extended cosinor analysis, IS, IV, and phi. Optionally this can be
expanded with detrended fluctutation analysis which is controlled by
parameter part6DFA.
part6HCA
Boolean (default = FALSE) to indicate whether Household Co Analysis should be run by part 6.
part6Window
Character vector with length two (default = c(“start”, “end”)) to indicate the start and the end of the time series to be used for circadian rhythm analysis in part 6. In other words, this parameters is not used for Household co-analysis. Alternative values are: “Wx”, “Ox”, “Hx”, where “x” is a number to indicat the xth wakeup, onset or hour of the recording. Negative values for “x” are also possible and will count relative to the end of the recording. For example, c(“W1”, “W-1”) goes from the first till the last wakeup, c(“H5”, “H-5”) ignores the first and last 5 hours, and c(“O2”, “W10”) goes from the second onset till the 10th wakeup time.
Output Parameters
epochvalues2csv
Boolean (default = FALSE). In g.part2: If TRUE then epoch values are exported to a csv file. Here, non-wear time is imputed where possible.
save_ms5rawlevels
Boolean (default = FALSE). In g.part5: Whether to save the time series classification (levels) as csv or RData files (as defined by save_ms5raw_format). Note that time stamps will be stored in the column timenum in UTC format (i.e., seconds from 1970-01-01). To convert timenum to time stamp format, you need to specify your desired time zone, e.g., as.POSIXct(mdat$timenum, tz = “Europe/London”).
save_ms5raw_format
Character (default = “csv”). In g.part5: To specify how data should be stored: either “csv” or “RData”. Only used if save_ms5rawlevels = TRUE.
save_ms5raw_without_invalid
Boolean (default = TRUE). In g.part5: To indicate whether to remove invalid days from the time series output files. Only used if save_ms5rawlevels = TRUE.
timewindow
Character (default = c(“MM”, “WW”)). In g.part5: Timewindow over which summary statistics are derived. Value can be “MM” (midnight to midnight), “WW” (waking time to waking time), “OO” (sleep onset to sleep onset), or any combination of them.
viewingwindow
Numeric (default = 1). Centre the day as displayed around noon (viewingwindow = 1) or around midnight (viewingwindow = 2) in the visual report generated with visualreport = TRUE.
dofirstpage
Boolean (default = TRUE). To indicate whether a first page with histograms summarizing the whole measurement should be added in the file summary reports generated with visualreport = TRUE.
visualreport
Boolean (default = TRUE). If TRUE, then generate visual report based on combined output from g.part2 and g.part4. Please note that the visual report was initially developed to provide something to show to study participants and not for data quality checking purposes. Over time we have improved the visual report to also be useful for QC-ing the data. However, some of the scorings as shown in the visual report are created for the visual report only and may not reflect the scorings in the main GGIR analyses as reported in the quantitative csv-reports. Most of our effort in the past 10 years has gone into making sure that the csv-report are correct, while the visualreport has mostly been a side project. This is unfortunate and we hope to find funding in the future to design a new report specifically for the purpose of QC-ing the analyses done by GGIR.
week_weekend_aggregate.part5
Boolean (default = FALSE). In g.part5: To indicate whether week and weekend-days aggregates should be stored. This is turned off by default as it generates a large number of extra columns in the output report.
outliers.only
Boolean (default = FALSE). In g.part4: Only used if do.visual = TRUE. If FALSE, all available nights are included in the visual representation of the data and sleeplog. If TRUE, then only nights with a difference in onset or waking time larger than the variable of argument criterror will be included.
criterror
Numeric (default = 3). In g.part4: Only used if 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.
do.visual
Boolean (default = TRUE). In g.part4: If TRUE, 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 visually verify that the sleeplog entries do not come with obvious mistakes.
do.sibreport
Boolean (default = FALSE). In g.part4: To indicate whether to generate report for the sustained inactivity bouts (SIB). If set to TRUE and when an advanced sleep diary is available in part 4 then part 5 will use this to generate summary statistics on the overlap between self-reported nonwear and napping with SIB. Here, SIB can be filter based on argument possible_nap_edge_acc and the first value of possible_nap_dur
sep_reports
Character (default = “,”). Value used as sep argument in [data.table]fwrite for writing csv reports.
sep_config
Character (default = “,”). Value used as sep argument in [data.table]fwrite for writing csv config file.
dec_reports
Character (default = “.”). Value used as dec argument in [data.table]fwrite for writing csv reports.
dec_config
Character (default = “.”). Value used as dec argument in [data.table]fwrite for writing csv config file.
visualreport_without_invalid
Boolean (default = TRUE). If TRUE, then reports generated with visualreport = TRUE only show the windows with sufficiently valid data according to includedaycrit when viewingwindow = 1 or includenightcrit when viewingwindow = 2
require_complete_lastnight_part5
Boolean (default = FALSE). When set to TRUE: The last WW window is excluded if the recording ends between midnight and 3pm, and starts on a date that is on or one day before the recording end date; The last OO and MM window are excluded if recording ends between midnight and 9am, and starts on a date that is on or one day before the recording end date. This to avoid risk that recording end biases the sleep estimates for the last night.
