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Find model modules and datasets

How to find individual modules, module libraries, dataset collections and datasets.

1 - Finding specific modules and sub-modules

How to find individual readyforwhatsnext modules and sub-modules.

This below section renders a vignette article from the ready4 library. You can use the following links to:

Motivation

When considering whether to use a model module, it is useful to first see tutorials about appropriate use of that module.

Implementation

A table itemising individual model modules and sub-modules authored with ready4 can be generated using make_modules_tb. This function scrapes relevant data from the websites of module libraries that have been developed within a specified project’s GitHub organisation.

Use

In this example, we are going to examine modules from the readyforwhatsnext model. The value supplied to the gh_repo_1L_chr argument specifies the repository in which a dataset of readyforwhatsnext module libraries is stored. Note, the following command may take a couple of minutes to execute.

modules_tb <-  make_modules_tb(gh_repo_1L_chr = "ready4-dev/ready4")

A slightly quicker method to achieve a similar result is to use the get_modules_tb function. This function retrieves an archived version (and therefore potentially less up to date) of the modules summary table.

# Not run
# modules_tb <- get_modules_tb(gh_repo_1L_chr = "ready4-dev/ready4")

The modules_tb object itemises both model modules (which always use R’s “S4” class type) and sub-modules (“S3” class type).

To display a HTML summary of just model modules, you can use the print_modules function.

print_modules(modules_tb, what_1L_chr = "S4")
Class Description Examples
AusACT Meta data for processing ACT population projections
AusHeadspace Meta data for constructing Headspace Centre geometries
AusLookup Lookup tables for Australian geometry and spatial attribute data
AusOrygen Meta data for constructing OYH Specialist Mental Health Catchment geometries
AusProjections Meta data for constructing custom Australian population projections boundary
AusTasmania Meta data for processing Tasmanian population projections
CostlyCorrespondences Collection of input, standards definition and results datasets for projects to generate standardised costing datasets 1, 2
CostlyCountries Collection of input, standards definition and results datasets for projects to generate standardised country data for use in costing datasets 1, 2
CostlyCurrencies Collection of input, standards definition and results datasets for projects to generate standardised currency data for use in costing datasets 2
CostlySeed Original (non-standardised) dataset (and metadata) 1, 2
CostlySource Input dataset (and metadata) for generating standardised costing datasets
CostlyStandards Dataset (and metadata) defining the allowable values of specified variables 1, 2
ScorzAqol6 A dataset and metadata to support implementation of an AQoL-6D scoring algorithm
ScorzAqol6Adol A dataset and metadata to support implementation of a scoring algorithm for the adolescent version of AQoL-6D 3
ScorzAqol6Adult A dataset and metadata to support implementation of a scoring algorithm for the adult version of AQoL-6D
ScorzEuroQol5 A dataset and metadata to support implementation of an EQ-5D scoring algorithm 4
ScorzProfile A dataset to be scored, its associated metadata and details of the scoring instrument
SpecificConverter Container for seed objects used for creating SpecificModels modules 5
SpecificFixed Modelling project dataset, input parameters and complete fixed models results
SpecificInitiator Modelling project dataset, input parameters and empty results placeholder
SpecificMixed Modelling project dataset, input parameters and complete mixed models results
SpecificModels Modelling project dataset, input parameters and model comparison results
SpecificParameters Input parameters that specify candidate models to be explored
SpecificPredictors Modelling project dataset, input parameters and predictor comparison results
SpecificPrivate Analysis outputs not intended for public dissemination
SpecificProject Modelling project dataset, parameters and results
SpecificResults Analysis results
SpecificShareable Analysis outputs intended for public dissemination
SpecificSynopsis Input, Output and Authorship Data For Generating Reports
TTUProject Input And Output Data For Undertaking and Reporting Utility Mapping Studies 6
TTUReports Metadata to produce utility mapping study reports
TTUSynopsis Input, Output and Authorship Data For Generating Utility Mapping Study Reports
VicinityArguments Function arguments for constructing a spatial object
VicinityLocal Object defining data to be saved in local directory
VicinityLocalProcessed Object defining data to be saved in local directory in a processed (R) format
VicinityLocalRaw Object defining data to be saved in local directory in a raw (unprocessed) format
VicinityLookup Look up tables for spatiotemporal data
VicinityMacro Macro level context
VicinityMesoArea Meso level context - area
VicinityMesoRegion Meso level context - region
VicinityMicro Micro level context
VicinityProfile Information to create a profiled area object
VicinitySpaceTime Spatiotemporal environment
YouthvarsDescriptives Metadata about descriptive statistics to be generated
YouthvarsProfile A dataset and its associated dictionary, descriptive statistics and metadata 8
YouthvarsSeries A longitudinal dataset and its associated dictionary, descriptive statistics and metadata 8

You can use the same function to display only model sub-modules.

print_modules(modules_tb, what_1L_chr = "S3")
Class Description Examples
specific_models Candidate models lookup table
specific_predictors Candidate predictors lookup table
vicinity_abbreviations ready4 submodule class for tibble object lookup table for spatial data abbreviations
vicinity_identifiers ready4 submodule class for tibble object lookup table of unique feature identifiers used for different spatial objects
vicinity_mapes ready4 submodule class for tibble object that stores spatial simulation parameters relating to Mean Absolute Prediction Errors
vicinity_parameters ready4 submodule class for tibble object that stores simulation structural parameters relating to the spatial environment
vicinity_points ready4 submodule class for tibble object lookup table of the longitude and latitude cordinates of sites of services / homes
vicinity_processed ready4 submodule class for tibble object lookup table of meta-data for spatial data packs (imported and pre-processed data)
vicinity_raw ready4 submodule class for tibble object lookup table of metadata about raw (un-processed) spatial data to import
vicinity_resolutions ready4 submodule class for tibble object lookup table of the relative resolutions of different spatial objects
vicinity_templates ready4 submodule class for tibble object lookup table for base file used in creation of certain spatial objects
vicinity_values ready4 submodule class for tibble object that stores simulation parameter values for each iteration
youthvars_aqol6d_adol youthvars ready4 sub-module (S3 class) for Assessment of Quality of Life Six Dimension Health Utility - Adolescent Version (AQoL6d Adolescent) 7
youthvars_bads youthvars ready4 sub-module (S3 class) for Behavioural Activation for Depression Scale (BADS) scores 7
youthvars_chu9d_adolaus youthvars ready4 sub-module (S3 class) for Child Health Utility Nine Dimension Health Utility - Australian Adolescent Scoring (CHU-9D Australian Adolescent) 7
youthvars_gad7 youthvars ready4 sub-module (S3 class) for Generalised Anxiety Disorder Scale (GAD-7) scores 7
youthvars_k10 youthvars ready4 sub-module (S3 class) for Kessler Psychological Distress Scale (K10) - US Scoring System scores 7
youthvars_k10_aus youthvars ready4 sub-module (S3 class) for Kessler Psychological Distress Scale (K10) - Australian Scoring System scores 7
youthvars_k6 youthvars ready4 sub-module (S3 class) for Kessler Psychological Distress Scale (K6) - US Scoring System scores 7
youthvars_k6_aus youthvars ready4 sub-module (S3 class)for Kessler Psychological Distress Scale (K6) - Australian Scoring System scores 7
youthvars_oasis youthvars ready4 sub-module (S3 class) for Overall Anxiety Severity and Impairment Scale (OASIS) scores 7
youthvars_phq9 youthvars ready4 sub-module (S3 class) for Patient Health Questionnaire (PHQ-9) scores 7
youthvars_scared youthvars ready4 sub-module (S3 class) for Screen for Child Anxiety Related Disorders (SCARED) scores 7
youthvars_sofas youthvars ready4 sub-module (S3 class) for Social and Occupational Functioning Assessment Scale (SOFAS) 7

Details of how to search for themed collections of modules is described in another article.

2 - Model module libraries

Bundles of readyforwhatsnext modules are distributed as R libraries.

readyforwhatsnext model modules are intended to be both transferable (they are tools that can be used in multiple decision contexts) and modular (they are comprised of self-contained components, each of which performs a narrow sub-set of tasks). For these reasons, readyforwhatsnext model modules are developed and distributed as libraries of modules.

The three types of readyforwhatsnext module libraries are:

  • - modules for describing and quality assuring model data;

  • - modules to specify, assess and report statisitical models; and

  • - modules for making predictions.

A table summarising currently available readyforwhatsnext module libraries can be retrieved from an online repository by using the get_libraries_tb function from the ready4 framework library.

library(ready4)
libraries_tb <- get_libraries_tb() %>% update_libraries_tb(include_1L_chr = "modules")#make_libraries_tb("modules")

Module libraries are thematically grouped under one of four “sub-models” of readyforwhatsnext, one each for modelling People (collectively, the “Spring To Life” sub-model), Places (the “Springtides” sub-model), Platforms (collectively, the “First Bounce” sub-model) and Programs (the “On Target” sub-model). We can use the print_packages function to display the module libraries currently available for each section (currently, there are no publicly available libraries of readyforwhatsnext modules for modelling platforms).

Module libraries for modelling people

print_packages(libraries_tb %>% dplyr::filter(Section == "People"))
Type Package Purpose Documentation Code Examples
Describe and Validate Youth Mental Health Dataset Variables Citation , Website , Manual - Short (PDF) , Manual - Full (PDF) Dev , Archive 12, 13
Score Multi-Attribute Utility Instruments Citation , Website , Manual - Short (PDF) , Manual - Full (PDF) Dev , Archive 14, 15
Model Youth Choice Behaviours Citation , Website , Citation Dev , Archive
Implement Transfer to Utility Mapping Algorithms Citation , Website , Manual - Short (PDF) , Manual - Full (PDF) Dev , Archive 16
Explore and Characterise Heterogeneity in Quality of Life Data Citation , Website , Manual - Short (PDF) , Manual - Full (PDF) Dev , Archive
Specify Models to Solve Inverse Problems Citation , Website , Manual - Short (PDF) , Manual - Full (PDF) Dev , Archive 17
Transform Youth Outcomes to Health Utility Predictions Citation , Website , Manual - Short (PDF) , Manual - Full (PDF) Dev , Archive 18

Module libraries for modelling places

print_packages(libraries_tb %>% dplyr::filter(Section == "Places"))
Type Package Purpose Documentation Code Examples
Model Australian Spatial Data Citation , Website , Manual - Short (PDF) , Manual - Full (PDF) Dev , Archive
Model Spatial Features of Health Systems Citation , Website , Manual - Short (PDF) , Manual - Full (PDF) Dev , Archive

Module libraries for modelling programs

print_packages(libraries_tb %>% dplyr::filter(Section == "Programs"))
Type Package Purpose Documentation Code Examples
Undertake Health Economic Budget Impact Analysis. Citation , Website , Manual - Short (PDF) , Manual - Full (PDF) Dev , Archive
Develop, Use and Share Unit Cost Datasets for Health Economic Citation , Website , Manual - Short (PDF) , Manual - Full (PDF) Dev , Archive 19, 20

3 - Find open access model data

Tools from the ready4 framework library can be used to search for relevant open access readyforwhatsnext model data collections and datasets.

The make_datasets_tb function from the ready4 library can be used to create a summary table of the open access datasets we curate in our ready4 Dataverse Collection.

make_datasets_tb("ready4") -> x

One way to inspect this information is to group contents by Dataverse Collections using the print-data function.

print_data(x,
           by_dv_1L_lgl = T) %>%
  kableExtra::scroll_box(width = "100%")
Dataverse Name Description Creator Datasets
TTU Transfer to Utility A collection of transfer to utility datasets developed with the ready4 open science framework. Orygen 1, 2, 3
fakes Fake Data For Instruction And Illustration Fake data used to illustrate toolkits developed with the ready4 open science framework. Orygen 4 , 5 , 6 , 7 , 8 , 9 , 10, 11
firstbounce First Bounce A ready4 framework model of platforms. Aims to identify opportunities to improve the efficiency and equity of mental health services. Orygen
ready4fw ready4 Framework A collection of datasets that support implementation of the ready4 framework for open science computational models of mental health systems. Orygen 12
readyforwhatsnext readyforwhatsnext Data collections for the readyforwhatsnext mental health systems model. Orygen 13, 14
springtides Springtides A ready4 framework model of places. Synthesises geometry (boundary, coordinate) and spatial attribute (e.g. population counts, environmental characteristics, service identifier and model coefficients associated with areas) data. Orygen 15
springtolife Spring To Life A ready4 framework model of people. Models the characteristics, behaviours, relationships and outcomes of groups of individuals relevant to policymakers and service planners aiming to improve population mental health. Orygen 16

Alternatively, we can itemise individual Dataverse Datasets. When doing so, it makes sense to prepare separate views for toy datasets designed for instruction and real datasets appropriate for use in modelling.

Datasets appropriate for use in modelling projects can be returned by supplying the value “real” to the what_1L_chr argument of print_data.

print_data(x,
           what_1L_chr = "real") %>%
  kableExtra::scroll_box(width = "100%")
Title Description Dataverse DOI
Transfer to AQoL-6D Utility Mapping Algorithms Catalogues of models (and the programs that produced them) that can be used in conjunction with the youthu R package to predict AQoL-6D health utility (and thus, derive QALYs) from measures collected in youth mental health services. TTU
Transfer to AQoL-6D From Measures Collected In Primary Youth Mental Health Services This is a work in progress dataset to support the implementation and reporting of a study to map measures collected in Australian primary youth mental health services to AQoL-6D health utility. TTU
Transfer to CHU-9D From Measures Collected In Primary Youth Mental Health Services This is a work in progress dataset to support the implementation and reporting of a study to map measures collected in Australian primary youth mental health services to CHU-9D health utility TTU
ready4 Framework Abbreviations and Definitions This dataset contains resources that help ready4 Framework Developers adopt common standards and workflows. ready4fw
readyforwhatsnext posters A collection of poster summaries about the readyforwhatsnext project and its outputs. readyforwhatsnext
Australian demographic input parameters for Springtides model Geometry, spatial attribute and metadata inputs for the demographic module of the readyforwhatsnext model. The demographic module is a systems dynamics spatial simulation of area demographic characteristics. The current version of the model is quite rudimentary and is designed to be extended by other models developped with the ready4 open science mental health modelling tools. readyforwhatsnext
Springtides reports for Local Government Areas in the North West of Melbourne This dataset is a collection of reports generated by a development version of the Springtides Model Of Places. Each report summarises prevalence projections for a specified mental disorder / mental health condition for a Local Government Area that is wholly or partially within the catchment area of the Orygen youth mental health service in North West Melbourne. As these reports were generated by a development version of the Springtides Model, these projections should be regarded as exploratory. springtides
Modelling the online helpseeking choice of socially anxious young people

Models to predict the online helpseeking choices of socially anxious young people in Australia and replication code and documentation to implement the discrete choice experiment that generated the models.

All study outputs were created with the aid of the mychoice R package (https://ready4-dev.github.io/mychoice).

springtolife

To view toy datasets, instead supply the value “fakes”.

print_data(x,
           what_1L_chr = "fakes") %>%
  kableExtra::scroll_box(width = "100%")
Title Description Dataverse DOI
TTU (Transfer to Utility) R package - AQoL-6D vignette output This dataset has been generated from fake data as an instructional aid. It is not to be used to inform decision making. fakes
TTU (Transfer to Utility) R package - EQ-5D vignette output This dataset is provided as a teaching aid. It is the output of tools from the TTU R package, applied to a synthetic dataset (Fake Data) of psychological distress and psychological wellbeing. It is not to be used to support decision-making. fakes
Synthetic (fake) youth mental health datasets and data dictionaries The datasets in this collection are entirely fake. They were developed principally to demonstrate the workings of a number of utility scoring and mapping algorithms. However, they may be of more general use to others. In some limited cases, some of the included files could be used in exploratory simulation based analyses. However, you should read the metadata descriptors for each file to inform yourself of the validity and limitations of each fake dataset. To open the RDS format files included in this dataset, the R package ready4use needs to be installed (see ). It is also recommended that you install the youthvars package ( ) as this provides useful tools for inspecting and validating each dataset. fakes
ready4use R package vignette output This dataset is provided so that others can compare the output they generate when implementing vignette code with that generated by the authors. fakes
Replication Data For Quality of Life Heterogeneity Analysis In A Clinical Youth Mental Health Sample This dataset is provided so that others can apply and test the analysis algorithms we have developed. It includes synthetic (fake) data that was generated for the sole purpose of enabling users to rerun our analysis algorithm. fakes
Specific R Package - AQoL-6D Vignette Output This dataset is provided so that others can apply the algorithms we have developed, consistent with the principles of the ready4 open science framework for data synthesis and simulation in mental health. fakes
Synthetic (fake) dataset for hypothetical replication of study mapping psychological distress and functioning measures to AQoL-6D health utility This dataset is comprised of fake data that has been created to illustrate the potential transfer of a study algorithm for creating utility mapping models to new data. Outputs in this dataset are for instructional purposes only and should not be used to inform decision making. fakes
Synthetic (fake) dataset for hypothetical replication of study mapping psychological distress and functioning measures to CHU-9D health utility This dataset is comprised of fake data that has been created to illustrate the potential transfer of a study algorithm for creating CHU-9D utility mapping models to new data. Outputs in this dataset are for instructional purposes only and should not be used to inform decision making fakes