In bvegetabile/ccber: Estimation of Behavioral Entropy Rate
knitr::opts_chunk$set(echo = TRUE)
Overview of Software
$\texttt{ccber}$ is an R package for the estimation of behavioral entropy rate, developed for the Conte Center @ UCI. The package reads in an individual, or set of, excel files and processes the file to estimate the entropy rate from the supplied data. This document describes usage and input formats that are expected with this software.
See reference: Davis, E.P., Stout, S.A., Molet, J., Vegetabile, B., Glynn, L.M., Sandman, C.A., Heins, K., Stern, H., Baram, T.Z. (2017). Exposure to unpredictable maternal sensory signals influences cognitive development across-species. Proceedings of the National Academy of Sciences. September 26, 2017. 114 (39) 10390-10395
An overview of behavioral entropy rate estimation in the context of human behavior is found in the supporting information (SI) for the reference above.
The source files for ccber are found at github.com/bvegetabile/ccber
Installation of ccber
The package devtools is required to install this R package from this Github repository. Install this package first if it is not already installed.
From within an R console, enter the following:
install.packages('devtools', dependencies = TRUE)
Once that package has been installed, use the following to install ccber
devtools::install_github('bvegetabile/ccber')
Load the package to begin analysis!
library('ccber')
Quick Start
Download files from github.com/bvegetabile/ccber/tree/master/testfiles/testfiles.zip.
Navigate to the directory where the files are located using the following R command. The setwd command sets the working directory for R. ( Note in the below, the path should be changed to the location of where the files have been uncompressed )
setwd('~/git/ccber/testfiles/')
Then run the following,
test_output <- ccber::ber_analyze_dir('.')
By setting the working directory in the first step, any output files will be put in the directory specified.
If successful, you fill will see the following:
> ccber::ber_analyze_dir('.')
Completed without issue : Entropy_6m - 88888HE - Event Logs.xlsx
Completed without issue : Entropy_6m - 99999LE - Event Logs.xlsx
Script total run time: 0.013 minutes
-------------------- Check the log for files below --------------------
The object test_output contains the entropy rates and some additional measures. The output will look as follows:
> test_output
SubjectID CanEstimateEntropy EntropyRate
1 88888HE TRUE 1.2755499
2 99999LE TRUE 0.6442886
TotalNumberOfTransitions CombinedVideoDuration PercentMissing
1 119 600.027 0
2 69 600.027 0
AuditoryCounts AuditoryTotalTime AuditoryAverageTime
1 23 25.001 1.087
2 15 15.000 1.000
VisualCounts VisualTotalTime VisualAverageTime TactileCounts
1 16 309.9636 19.37273 21
2 11 185.0270 16.82064 10
TactileTotalTime TactileAverageTime
1 362.9628 17.28394
2 285.0273 28.50273
To save the output data to a .csv file to be read into excel later, use the following:
write.csv(test_output,
file = file = paste(Sys.Date(),
'-ber-estimates.csv',
sep=''),
row.names = F)
The command Sys.Date() prepends the date to document when the data was created
Function Overviews
Ordinary use of the package would not require any of the information below this point in the description.
These function overviews provide additional information that may be helpful if there is a need to modify the settings (e.g., an error has occurred, there is a new data type or you want to try an alternative padding in event definitions).
Function : ber_analyze_file
The following function from ccber is one of the primary functions for the estimation of entropy rate:
ber_analyze_file(f_loc,
plot_all=F,
plots_to_file=F,
tactile_padding = 1.0,
auditory_padding = 1.0,
behavior_types=list(
"mom_auditory_types" = c('Vocal'),
"mom_tactile_types" = c('TouchBaby',
'HoldingBaby'),
"mom_visual_types" = c('ManipulatingObject'),
"baby_visual_types" = c('LookAtMomActivity'),
"missing_types" = c('CantTellHolding',
'ActivityNotVisible',
'CantTellLooking')),
missing_threshold = 0.1)
Below is a more detailed description of each input:
| Input | Input Type | Description
|:-------------------:|:-------------:|:--------------------------------------------------
| f_loc | String | String indicating the location of the file of interest
| plot_all | Logical | Indicator of whether to provide visualization to the user. Values : True or False
| plots_to_file | Logical | Indicator of whether or not to save visuals. Currently not implemented.
| tactile_padding | Numeric | Value (in seconds) to right pad each tactile point event
| auditory_padding | Numeric | Value (in seconds) to right pad each auditory point event
| behavior_types | List | List outlining the behavioral states expected. Required : mom_auditory_types, mom_tactile_types, mom_visual_types, baby_visual_types, missing_types. These categories define the sensory domains as in the original paper. See the example code above for the default values expected in each category.
| missing_threshold | Numeric | Value (a proportion) that indicators how much missingness is acceptable. This threshold is defined to be the percentage of the behavioral sequence that is represented by missing types defined in missing_types.
Example Usage
ber_analyze_file('./testfiles/Entropy_6m - 88888HE - Event Logs.xlsx')
Output
ber_analyze_file returns an R list whose first element is a data.frame named estimates and a second element that is a list called file_checks. The element estimates is the primary object of interest and has the following column headings
| Column Header | Description
|:-------------------------- |:--------------------------------------------------
| SubjectID | Subject ID found during analysis of file
| CanEstimateEntropy | Indicator of success or failure in estimating entropy rate
| EntropyRate | Estimate of entropy rate between 0 and $log_2(K)$ where $K$ is the total number of states
| TotalNumberOfTransitions | The total number of events considered in the final event sequence
| CombinedVideoDuration | Total duration of the video (endtime + final duration)
| PercentMissing | Proportion of time represented by missing_types category
| AuditoryCounts | Total number of events in auditory category
| AuditoryTotalTime | Total time representing events in auditory category
| AuditoryAverageTime | Average duration of events in auditory category
| VisualCounts | Total number of events in visual category
| VisualTotalTime | Total time representing events in visual category
| VisualAverageTime | Average duration of events in visual category
| TactileCounts | Total number of events in tactile category
| TactileTotalTime | Total time representing events in tactile category
| TactileTotalTime | Average duration of events in tactile category
During the course of running, the script performs a series of quality assurance tests of the file that has been input. Specifically it performs the following tests:
| Test Name | Description
|:------------- |:--------------------------------------------------
| header_pass | Checks for the following column headers : c('Observation', 'Behavior', 'Time_Relative_sf', 'Duration_sf', 'Event_Type')
| subjid_pass | Checks for an entry in the first cell of the column Observation. Does not check format of subject IDs
| misdat_pass | Checks for empty cells in the columns list above using sum(is.na(behavior_data$Behavior)) > 0
| blabel_pass | Finds the unique entries in the column Behavior, then compares that with the labels in behavior_types. Reports unused labels, i.e., labels found in Behavior that were not including in behavior_types.
| elabel_pass | Finds the unique entries in the column Event_Type, then compares with the following list : c('State start', 'State point', 'Point', 'State stop', 'State Stop'). These are currently supported events types.
| misnes_pass | Checks the proportion of time representing missing data based off of the inputs to missing_types within behavior_types.
Example output from script:
When running ber_analyze_file by itself, the following is an example of what is printed to the console at runtime to help diagnose issues with files:
-----------------------------------------------------------------------
Filename: Entropy_6m - 88888HE - Event Logs.xlsx
Time of Analysis: 2018-06-04 10:33:12
-----------------------------------------------------------------------
*************************** Performing File Check *********************
- Checking for required Columns:
Observation : First cell used to set "SubjectID"
Behavior : Set of used behavior labels
Time_Relative_sf : Sets the start point for each action
Duration_sf : Time_Relative_sf + Duration_sf sets end points
Event_Type : Defines point events and states
--- PASSED : Found all Required Column Headers
- Checking "Observation" Column For Subject ID
--- PASSED: Using Subject ID from Column J, Cell 1: 88888HE
- Checking for Missing Data in Columns
--- "Behavior" : PASSED
--- "Time_Relative_sf" : PASSED
--- "Duration_sf" : PASSED
--- "Event_Type" : PASSED
- Checking "Behavior" Column For Unused Labels:
--- WARNING : Unused Labels in "Behavior" Column, See Below:
Expected Label : "NotHoldingBaby", not used in analysis
Expected Label : "NoObjectInHand", not used in analysis
--- NOTE: Investigate this if these do not look familar
- Checking "Event_Type" Column For Labels:
--- PASSED : No Unused Labels in "Event_Type" Column
- Checking Missingness based on "missing_types"
--- Percent Missingness: 0
--- PASSED : Percent missing less than threshold
-----------------------------------------------------------------------
************************ File Completed Successfully ******************
-----------------------------------------------------------------------
Function : ber_analyze_dir
The following function is another primary functions for the estimation of entropy rate. Given a directory, the function makes multiple calls to ber_analyze_file to analyze the excel files within that directory. Note that there is no plotting capability currently included when operating on directories.
ber_analyze_dir(dir_loc,
tactile_padding = 1.0,
auditory_padding = 1.0,
behavior_types=list(
"mom_auditory_types" = c('Vocal'),
"mom_tactile_types" = c('TouchBaby',
'HoldingBaby'),
"mom_visual_types" = c('ManipulatingObject'),
"baby_visual_types" = c('LookAtMomActivity'),
"missing_types" = c('CantTellHolding',
'ActivityNotVisible',
'CantTellLooking')),
missing_threshold = 0.1,
log_file = paste(Sys.Date(), '-ber-logfile.txt', sep=''))
| Input | Input Type | Description
|:-------------------:|:-------------:|:--------------------------------------------------
| dir_loc | String | String indicating the location of the directory of interest
| tactile_padding | Numeric | Value (in seconds) to right pad each tactile point event
| auditory_padding | Numeric | Value (in seconds) to right pad each auditory point event
| behavior_types | List | List outlining the behavioral states expected. Required : mom_auditory_types, mom_tactile_types, mom_visual_types, baby_visual_types, missing_types. These categories define the sensory domains as in the original paper. See the example code above for the default values expected in each category.
| missing_threshold | Numeric | Value (a proportion) that indicators how much missingness is acceptable. This threshold is defined to be the percentage of the behavioral sequence that is represented by missing types defined in missing_types
| log_file | String | String indicating the location for where to save the diagnostic information from each run of ber_analyze_file
Example Usage
From the directory where the files of interest are located type the following:
ber_analyze_dir('.')
Output
ber_analyze_dir returns a R data.frame with column headings as in ber_analyze_file. Each row of the data.frame represents the results from calling ber_analyze_file on a file.
Input file formats
This section describes the expected input file format required for the software. It is also identifies important columns utilized in the estimation of entropy rate.
File Format Type
At the moment, the required format is an Excel .xlsx file.
Note: In the future this may be modifed to include .csv files, by altering specific lines of code in ber.R. Specifically, line 37 within ber_analyze_dir should be altered to accept different string patterns:
all_files = list.files(dir_loc, pattern="*.xlsx")
and line 116 within ber_analyze_file should be changed read .csv files:
behavior_data <- data.frame(readxl::read_xlsx(f_loc))
File Organization
Below is a visualization of a current input file that is located within the testfiles subdirectory of the source files and was used earlier in the software demonstration:
The expected column headings are:
| Column Heading | Used? | Description
|:-----------------------------:|:-----:|:--------------------------------------------------
| Date_Time_Absolute_dmy_hmsf | No | Absolute Time - Day-Month-Year Hour:Min:Sec.Millisec (Military Time)
| Date_dmy | No | Absolute Time - Day-Month-Year
| Time_Absolute_hms | No | Absolute Time - Hour:Min:Sec
| Time_Absolute_f | No | ??? Appears to be millisecond part ???
| Time_Relative_hmsf | No | Relative Time - Hour:Min:Sec.Millisec
| Time_Relative_hms | No | Relative Time - Hour:Min:Sec.Millisec
| Time_Relative_f | No | ??? Appears to be millisecond part ???
| Time_Relative_sf | Yes | Relative Time - Seconds. Used as starting point for each instance in the video
| Duration_sf | Yes | Relative Time - Seconds. Duration of the the instance. The time of an event is Time_Relative_sf + Duration_sf
| Observation | Yes | Typically Subject ID. Cell J1 is used as the Subject ID that is reported from ber_analyze_dir and ber_analyze_file
| Event_Log | No | Unknown
| Behavior | Yes | Used to find and match against behavior_types specified in ber_analyze_dir and ber_analyze_file
| Event_Type | Yes | State start indicates cells which will be used as events. Point instances that are included will be right padded to become events. State stop rows are ignored.
The order of the rows should not matter, but it is best to be safe and keep the organization that is above for ease of processing.
Input File Tests
As stated above the following tests are run using ber_analyze_file and are shown again below:
| Test Name | Description
|:------------- |:--------------------------------------------------
| header_pass | Checks for the following column headers : c('Observation', 'Behavior', 'Time_Relative_sf', 'Duration_sf', 'Event_Type')
| subjid_pass | Checks for an entry in the first cell of the column Observation. Does not check format of subject IDs
| misdat_pass | Checks for empty cells in the columns list above using sum(is.na(behavior_data$Behavior)) > 0
| blabel_pass | Finds the unique entries in the column Behavior, then compares that with the labels in behavior_types. Reports unused labels, i.e., labels found in Behavior that were not including in behavior_types.
| elabel_pass | Finds the unique entries in the column Event_Type, then compares with the following list : c('State start', 'State point', 'Point', 'State stop', 'State Stop'). These are currently supported events types.
| misnes_pass | Checks the proportion of time representing missing data based off of the inputs to missing_types within behavior_types.
Be sure to check the log files and output using ber_analyze_dir to ensure that the files are processed correctly.
bvegetabile/ccber documentation built on May 10, 2019, 1:15 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
knitr::opts_chunk$set(echo = TRUE)
Overview of Software
$\texttt{ccber}$ is an R package for the estimation of behavioral entropy rate, developed for the Conte Center @ UCI. The package reads in an individual, or set of, excel files and processes the file to estimate the entropy rate from the supplied data. This document describes usage and input formats that are expected with this software.
See reference: Davis, E.P., Stout, S.A., Molet, J., Vegetabile, B., Glynn, L.M., Sandman, C.A., Heins, K., Stern, H., Baram, T.Z. (2017). Exposure to unpredictable maternal sensory signals influences cognitive development across-species. Proceedings of the National Academy of Sciences. September 26, 2017. 114 (39) 10390-10395
An overview of behavioral entropy rate estimation in the context of human behavior is found in the supporting information (SI) for the reference above.
The source files for ccber are found at github.com/bvegetabile/ccber
Installation of ccber
The package devtools is required to install this R package from this Github repository. Install this package first if it is not already installed.
From within an R console, enter the following:
install.packages('devtools', dependencies = TRUE)
Once that package has been installed, use the following to install ccber
devtools::install_github('bvegetabile/ccber')
Load the package to begin analysis!
library('ccber')
Quick Start
Download files from github.com/bvegetabile/ccber/tree/master/testfiles/testfiles.zip.
Navigate to the directory where the files are located using the following R command. The setwd command sets the working directory for R. ( Note in the below, the path should be changed to the location of where the files have been uncompressed )
setwd('~/git/ccber/testfiles/')
Then run the following,
test_output <- ccber::ber_analyze_dir('.')
By setting the working directory in the first step, any output files will be put in the directory specified.
If successful, you fill will see the following:
> ccber::ber_analyze_dir('.') Completed without issue : Entropy_6m - 88888HE - Event Logs.xlsx Completed without issue : Entropy_6m - 99999LE - Event Logs.xlsx Script total run time: 0.013 minutes -------------------- Check the log for files below --------------------
The object test_output contains the entropy rates and some additional measures. The output will look as follows:
> test_output SubjectID CanEstimateEntropy EntropyRate 1 88888HE TRUE 1.2755499 2 99999LE TRUE 0.6442886 TotalNumberOfTransitions CombinedVideoDuration PercentMissing 1 119 600.027 0 2 69 600.027 0 AuditoryCounts AuditoryTotalTime AuditoryAverageTime 1 23 25.001 1.087 2 15 15.000 1.000 VisualCounts VisualTotalTime VisualAverageTime TactileCounts 1 16 309.9636 19.37273 21 2 11 185.0270 16.82064 10 TactileTotalTime TactileAverageTime 1 362.9628 17.28394 2 285.0273 28.50273
To save the output data to a .csv file to be read into excel later, use the following:
write.csv(test_output, file = file = paste(Sys.Date(), '-ber-estimates.csv', sep=''), row.names = F)
The command Sys.Date() prepends the date to document when the data was created
Function Overviews
Ordinary use of the package would not require any of the information below this point in the description.
These function overviews provide additional information that may be helpful if there is a need to modify the settings (e.g., an error has occurred, there is a new data type or you want to try an alternative padding in event definitions).
Function : ber_analyze_file
The following function from ccber is one of the primary functions for the estimation of entropy rate:
ber_analyze_file(f_loc, plot_all=F, plots_to_file=F, tactile_padding = 1.0, auditory_padding = 1.0, behavior_types=list( "mom_auditory_types" = c('Vocal'), "mom_tactile_types" = c('TouchBaby', 'HoldingBaby'), "mom_visual_types" = c('ManipulatingObject'), "baby_visual_types" = c('LookAtMomActivity'), "missing_types" = c('CantTellHolding', 'ActivityNotVisible', 'CantTellLooking')), missing_threshold = 0.1)
Below is a more detailed description of each input:
| Input | Input Type | Description
|:-------------------:|:-------------:|:--------------------------------------------------
| f_loc | String | String indicating the location of the file of interest
| plot_all | Logical | Indicator of whether to provide visualization to the user. Values : True or False
| plots_to_file | Logical | Indicator of whether or not to save visuals. Currently not implemented.
| tactile_padding | Numeric | Value (in seconds) to right pad each tactile point event
| auditory_padding | Numeric | Value (in seconds) to right pad each auditory point event
| behavior_types | List | List outlining the behavioral states expected. Required : mom_auditory_types, mom_tactile_types, mom_visual_types, baby_visual_types, missing_types. These categories define the sensory domains as in the original paper. See the example code above for the default values expected in each category.
| missing_threshold | Numeric | Value (a proportion) that indicators how much missingness is acceptable. This threshold is defined to be the percentage of the behavioral sequence that is represented by missing types defined in missing_types.
Example Usage
ber_analyze_file('./testfiles/Entropy_6m - 88888HE - Event Logs.xlsx')
Output
ber_analyze_file returns an R list whose first element is a data.frame named estimates and a second element that is a list called file_checks. The element estimates is the primary object of interest and has the following column headings
| Column Header | Description
|:-------------------------- |:--------------------------------------------------
| SubjectID | Subject ID found during analysis of file
| CanEstimateEntropy | Indicator of success or failure in estimating entropy rate
| EntropyRate | Estimate of entropy rate between 0 and $log_2(K)$ where $K$ is the total number of states
| TotalNumberOfTransitions | The total number of events considered in the final event sequence
| CombinedVideoDuration | Total duration of the video (endtime + final duration)
| PercentMissing | Proportion of time represented by missing_types category
| AuditoryCounts | Total number of events in auditory category
| AuditoryTotalTime | Total time representing events in auditory category
| AuditoryAverageTime | Average duration of events in auditory category
| VisualCounts | Total number of events in visual category
| VisualTotalTime | Total time representing events in visual category
| VisualAverageTime | Average duration of events in visual category
| TactileCounts | Total number of events in tactile category
| TactileTotalTime | Total time representing events in tactile category
| TactileTotalTime | Average duration of events in tactile category
During the course of running, the script performs a series of quality assurance tests of the file that has been input. Specifically it performs the following tests:
| Test Name | Description
|:------------- |:--------------------------------------------------
| header_pass | Checks for the following column headers : c('Observation', 'Behavior', 'Time_Relative_sf', 'Duration_sf', 'Event_Type')
| subjid_pass | Checks for an entry in the first cell of the column Observation. Does not check format of subject IDs
| misdat_pass | Checks for empty cells in the columns list above using sum(is.na(behavior_data$Behavior)) > 0
| blabel_pass | Finds the unique entries in the column Behavior, then compares that with the labels in behavior_types. Reports unused labels, i.e., labels found in Behavior that were not including in behavior_types.
| elabel_pass | Finds the unique entries in the column Event_Type, then compares with the following list : c('State start', 'State point', 'Point', 'State stop', 'State Stop'). These are currently supported events types.
| misnes_pass | Checks the proportion of time representing missing data based off of the inputs to missing_types within behavior_types.
Example output from script:
When running ber_analyze_file by itself, the following is an example of what is printed to the console at runtime to help diagnose issues with files:
-----------------------------------------------------------------------
Filename: Entropy_6m - 88888HE - Event Logs.xlsx
Time of Analysis: 2018-06-04 10:33:12
-----------------------------------------------------------------------
*************************** Performing File Check *********************
- Checking for required Columns:
Observation : First cell used to set "SubjectID"
Behavior : Set of used behavior labels
Time_Relative_sf : Sets the start point for each action
Duration_sf : Time_Relative_sf + Duration_sf sets end points
Event_Type : Defines point events and states
--- PASSED : Found all Required Column Headers
- Checking "Observation" Column For Subject ID
--- PASSED: Using Subject ID from Column J, Cell 1: 88888HE
- Checking for Missing Data in Columns
--- "Behavior" : PASSED
--- "Time_Relative_sf" : PASSED
--- "Duration_sf" : PASSED
--- "Event_Type" : PASSED
- Checking "Behavior" Column For Unused Labels:
--- WARNING : Unused Labels in "Behavior" Column, See Below:
Expected Label : "NotHoldingBaby", not used in analysis
Expected Label : "NoObjectInHand", not used in analysis
--- NOTE: Investigate this if these do not look familar
- Checking "Event_Type" Column For Labels:
--- PASSED : No Unused Labels in "Event_Type" Column
- Checking Missingness based on "missing_types"
--- Percent Missingness: 0
--- PASSED : Percent missing less than threshold
-----------------------------------------------------------------------
************************ File Completed Successfully ******************
-----------------------------------------------------------------------
Function : ber_analyze_dir
The following function is another primary functions for the estimation of entropy rate. Given a directory, the function makes multiple calls to ber_analyze_file to analyze the excel files within that directory. Note that there is no plotting capability currently included when operating on directories.
ber_analyze_dir(dir_loc, tactile_padding = 1.0, auditory_padding = 1.0, behavior_types=list( "mom_auditory_types" = c('Vocal'), "mom_tactile_types" = c('TouchBaby', 'HoldingBaby'), "mom_visual_types" = c('ManipulatingObject'), "baby_visual_types" = c('LookAtMomActivity'), "missing_types" = c('CantTellHolding', 'ActivityNotVisible', 'CantTellLooking')), missing_threshold = 0.1, log_file = paste(Sys.Date(), '-ber-logfile.txt', sep=''))
| Input | Input Type | Description
|:-------------------:|:-------------:|:--------------------------------------------------
| dir_loc | String | String indicating the location of the directory of interest
| tactile_padding | Numeric | Value (in seconds) to right pad each tactile point event
| auditory_padding | Numeric | Value (in seconds) to right pad each auditory point event
| behavior_types | List | List outlining the behavioral states expected. Required : mom_auditory_types, mom_tactile_types, mom_visual_types, baby_visual_types, missing_types. These categories define the sensory domains as in the original paper. See the example code above for the default values expected in each category.
| missing_threshold | Numeric | Value (a proportion) that indicators how much missingness is acceptable. This threshold is defined to be the percentage of the behavioral sequence that is represented by missing types defined in missing_types
| log_file | String | String indicating the location for where to save the diagnostic information from each run of ber_analyze_file
Example Usage
From the directory where the files of interest are located type the following:
ber_analyze_dir('.')
Output
ber_analyze_dir returns a R data.frame with column headings as in ber_analyze_file. Each row of the data.frame represents the results from calling ber_analyze_file on a file.
Input file formats
This section describes the expected input file format required for the software. It is also identifies important columns utilized in the estimation of entropy rate.
File Format Type
At the moment, the required format is an Excel .xlsx file.
Note: In the future this may be modifed to include
.csvfiles, by altering specific lines of code inber.R. Specifically, line 37 withinber_analyze_dirshould be altered to accept different string patterns:
all_files = list.files(dir_loc, pattern="*.xlsx")
and line 116 within
ber_analyze_fileshould be changed read.csvfiles:
behavior_data <- data.frame(readxl::read_xlsx(f_loc))
File Organization
Below is a visualization of a current input file that is located within the testfiles subdirectory of the source files and was used earlier in the software demonstration:
The expected column headings are:
| Column Heading | Used? | Description
|:-----------------------------:|:-----:|:--------------------------------------------------
| Date_Time_Absolute_dmy_hmsf | No | Absolute Time - Day-Month-Year Hour:Min:Sec.Millisec (Military Time)
| Date_dmy | No | Absolute Time - Day-Month-Year
| Time_Absolute_hms | No | Absolute Time - Hour:Min:Sec
| Time_Absolute_f | No | ??? Appears to be millisecond part ???
| Time_Relative_hmsf | No | Relative Time - Hour:Min:Sec.Millisec
| Time_Relative_hms | No | Relative Time - Hour:Min:Sec.Millisec
| Time_Relative_f | No | ??? Appears to be millisecond part ???
| Time_Relative_sf | Yes | Relative Time - Seconds. Used as starting point for each instance in the video
| Duration_sf | Yes | Relative Time - Seconds. Duration of the the instance. The time of an event is Time_Relative_sf + Duration_sf
| Observation | Yes | Typically Subject ID. Cell J1 is used as the Subject ID that is reported from ber_analyze_dir and ber_analyze_file
| Event_Log | No | Unknown
| Behavior | Yes | Used to find and match against behavior_types specified in ber_analyze_dir and ber_analyze_file
| Event_Type | Yes | State start indicates cells which will be used as events. Point instances that are included will be right padded to become events. State stop rows are ignored.
The order of the rows should not matter, but it is best to be safe and keep the organization that is above for ease of processing.
Input File Tests
As stated above the following tests are run using ber_analyze_file and are shown again below:
| Test Name | Description
|:------------- |:--------------------------------------------------
| header_pass | Checks for the following column headers : c('Observation', 'Behavior', 'Time_Relative_sf', 'Duration_sf', 'Event_Type')
| subjid_pass | Checks for an entry in the first cell of the column Observation. Does not check format of subject IDs
| misdat_pass | Checks for empty cells in the columns list above using sum(is.na(behavior_data$Behavior)) > 0
| blabel_pass | Finds the unique entries in the column Behavior, then compares that with the labels in behavior_types. Reports unused labels, i.e., labels found in Behavior that were not including in behavior_types.
| elabel_pass | Finds the unique entries in the column Event_Type, then compares with the following list : c('State start', 'State point', 'Point', 'State stop', 'State Stop'). These are currently supported events types.
| misnes_pass | Checks the proportion of time representing missing data based off of the inputs to missing_types within behavior_types.
Be sure to check the log files and output using ber_analyze_dir to ensure that the files are processed correctly.
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