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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
- 2: Model module libraries
- 3: Find open access model data
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:
- view the vignette on the library website (adds useful hyperlinks to code blocks)
- view the source file from that article, and;
- edit its contents (requires a GitHub account).
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 This function retrieves an archived version (and therefore potentially less up to date) of the modules summary table.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 |
Related content
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") -> xOne 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 |
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 |
|