Taxonomy.Taxon — TypeTaxon is the supertype of all taxons.
using Taxonomy, AbstractTrees
+ UUID("552ef675-5c7b-4ce1… => Record…using Taxonomy, AbstractTrees
AbstractTrees.children(d::DataType) = subtypes(d)
print_tree(Taxonomy.Taxon)
Taxon
@@ -27,7 +27,7 @@
├─ AbstractPathmodel
│ └─ Structural
└─ NoAbstractTaxon
- └─ NoTaxonTaxonomy.Taxon — TypeTaxon is the supertype of all taxons.
Taxonomy.Measurement — TypeMeasurement AbstractCFA. Building Block for Taxonomy. Multiple Measurements can be combined to a Taxon.
Arguments
n_variables: Number of variables (possibly observed/manifest). If items are parceled, this is the number of parcels.loadings: Vector of loadings, one for each item. factor_variance: Variance of the factor.error_variances: Vector of variances of the respective errorserror_covariances_within: Vector of covariances within factor. If unknown, set to missing, if there are no covariances, set to Float64[].error_covariances_between: Vector of covariances the factor shares with a different factor. If unknown, set to missing, if there are no covariances, set to Float64[].crossloadings_incoming: Vector of crossloadings coming from other factors. They should be lower than the loading coming to the item from this factor. If unknown, set to missing, if there are none, set to Float64[]. crossloadings_outgoing: Vector of crossloadings going to other items which have higher loadings from other factors. If unknown, set to missing, if there are none, set to Float64[].quest_scale: Scale of the questionnaire. Anything more than ten is Inf. E.g: 5 point likert scale -> 5. Measurement(n_variables = 2, loadings = [1, 0.4], factor_variance = 0.6, quest_scale = 5)
+ └─ NoTaxonTaxonomy.Taxon — TypeTaxon is the supertype of all taxons.
Taxonomy.Measurement — TypeMeasurement AbstractCFA. Building Block for Taxonomy. Multiple Measurements can be combined to a Taxon.
Arguments
n_variables: Number of variables (possibly observed/manifest). If items are parceled, this is the number of parcels.loadings: Vector of loadings, one for each item. factor_variance: Variance of the factor.error_variances: Vector of variances of the respective errorserror_covariances_within: Vector of covariances within factor. If unknown, set to missing, if there are no covariances, set to Float64[].error_covariances_between: Vector of covariances the factor shares with a different factor. If unknown, set to missing, if there are no covariances, set to Float64[].crossloadings_incoming: Vector of crossloadings coming from other factors. They should be lower than the loading coming to the item from this factor. If unknown, set to missing, if there are none, set to Float64[]. crossloadings_outgoing: Vector of crossloadings going to other items which have higher loadings from other factors. If unknown, set to missing, if there are none, set to Float64[].quest_scale: Scale of the questionnaire. Anything more than ten is Inf. E.g: 5 point likert scale -> 5. Measurement(n_variables = 2, loadings = [1, 0.4], factor_variance = 0.6, quest_scale = 5)
# output
@@ -40,14 +40,14 @@
error_covariances_between: JudgementVecNumber{Missing}
crossloadings_incoming: JudgementVecNumber{Missing}
crossloadings_outgoing: JudgementVecNumber{Missing}
- quest_scale: JudgementNumber{Int64}Taxonomy.Structural — TypeStructural AbstractPathmodel. Consists of a graph from StenoGraphs (structural model).
Arguments
structural_model: Graph from StenoGraphs package. Defines the latent relations between the factors of measurement_model. using StenoGraphs
+ quest_scale: JudgementNumber{Int64}Taxonomy.Structural — TypeStructural AbstractPathmodel. Consists of a graph from StenoGraphs (structural model).
Arguments
structural_model: Graph from StenoGraphs package. Defines the latent relations between the factors of measurement_model. using StenoGraphs
graph = @StenoGraph begin
# latent regressions
fac1 → fac2
end
-Structural(structural_model = graph)Taxonomy.LatentPathmodel — TypeCreate a new LatentPathmodel instance.
for indexing: mymodel.structuralmodel.structuralmodel mymodel.measurement_model[:fac2]
using StenoGraphs
+Structural(structural_model = graph)Taxonomy.LatentPathmodel — TypeCreate a new LatentPathmodel instance.
for indexing: mymodel.structuralmodel.structuralmodel mymodel.measurement_model[:fac2]
using StenoGraphs
graph = @StenoGraph begin
# latent regressions
fac1 → fac2
@@ -65,7 +65,7 @@
LatentPathmodel
structural_model: Structural
- measurement_model: Dict{Symbol, Measurement}Taxonomy.SimpleLGCM — TypeSimpleLGCM AbstractLGCM. Taxon for Linear Growth Curve Model.
## Argumentsn_timepoints: Number of measurement timepoints.timecoding: Vector containing the coding of the measurement time points (loadings of the slope onto the timepoints).intercept: Intercept constant.slope: Slope constant. nonlinear_timecoding: Vector for the timecodings introduced by a nonlinear function. variance_intercept: Variance of the intercept.variance_slope: Variance of the slope. covariance_intercept_slope: Covariance between intercept and slope.variances_timepoints: Vector with variances of the timepoint variables. n_predictors: Number of predictors on intercept and slope. predictor_paths_intercept: Vector for the predictor-paths to the intercept.predictor_paths_slope: Vector for the predictor-paths to the slope.SimpleLGCM(n_timepoints = 6, timecoding = [0, 1, 2, 3, 4, 5], intercept = 10.2,
+ measurement_model: Dict{Symbol, Measurement}Taxonomy.SimpleLGCM — TypeSimpleLGCM AbstractLGCM. Taxon for Linear Growth Curve Model.
## Argumentsn_timepoints: Number of measurement timepoints.timecoding: Vector containing the coding of the measurement time points (loadings of the slope onto the timepoints).intercept: Intercept constant.slope: Slope constant. nonlinear_timecoding: Vector for the timecodings introduced by a nonlinear function. variance_intercept: Variance of the intercept.variance_slope: Variance of the slope. covariance_intercept_slope: Covariance between intercept and slope.variances_timepoints: Vector with variances of the timepoint variables. n_predictors: Number of predictors on intercept and slope. predictor_paths_intercept: Vector for the predictor-paths to the intercept.predictor_paths_slope: Vector for the predictor-paths to the slope.SimpleLGCM(n_timepoints = 6, timecoding = [0, 1, 2, 3, 4, 5], intercept = 10.2,
slope = 0.96, nonlinear_timecoding = [1, 2, 4, 9, 16, 25], variance_intercept = 1, variance_slope = 1, covariance_intercept_slope = 0.1,
n_predictors = 2, predictor_paths_intercept = [2, 4], predictor_paths_slope = [3, 5])
@@ -82,16 +82,16 @@
variances_timepoints: JudgementNumber{Missing}
n_predictors: JudgementInt{Int64}
predictor_paths_intercept: JudgementVecNumber{Vector{Int64}}
- predictor_paths_slope: JudgementVecNumber{Vector{Int64}}Taxonomy.NoTaxonEver — TypeNoTaxonEver()
A Taxon to show, that there is no SEM to code in this paper.
julia> NoTaxonEver()
+ predictor_paths_slope: JudgementVecNumber{Vector{Int64}}Taxonomy.NoTaxonEver — TypeNoTaxonEver()
A Taxon to show, that there is no SEM to code in this paper.
julia> NoTaxonEver()
NoTaxonEver
julia> NoTaxon()
NoTaxonEver
-Taxonomy.NoTaxonYet — TypeNoTaxonYet()A Taxon to show, that there is in fact a model to be coded, but this is at the current point not possible. NoTaxonYet requires a timestamp 'accessdate' to further specify at what point there were no possibility to code the respective model. NoTaxonYet gives you the option to name the 'modeltype', you were not able to code, to spare your future self the work of going everything through.
julia> NoTaxonYet()
Taxonomy.factor_variance — FunctionFunction to extract factor variance.
Arguments
x: Measurement or Standalone_Factor.Return
Returns a Judgement
f = Measurement(n_variables = 2, loadings = [1, 0.4], factor_variance = 1.0)
+Taxonomy.NoTaxonYet — TypeNoTaxonYet()A Taxon to show, that there is in fact a model to be coded, but this is at the current point not possible. NoTaxonYet requires a timestamp 'accessdate' to further specify at what point there were no possibility to code the respective model. NoTaxonYet gives you the option to name the 'modeltype', you were not able to code, to spare your future self the work of going everything through.
julia> NoTaxonYet()
Taxonomy.factor_variance — FunctionFunction to extract factor variance.
Arguments
x: Measurement or Standalone_Factor.Return
Returns a Judgement
f = Measurement(n_variables = 2, loadings = [1, 0.4], factor_variance = 1.0)
rating(factor_variance(f))
# output
-1.0Taxonomy.structural_model — FunctionFunction to extract the StenoGraphs structural model from [Structural].
Arguments
x: Strucutral.Return
Returns a Judgement
using Taxonomy
+1.0Taxonomy.structural_model — FunctionFunction to extract the StenoGraphs structural model from [Structural].
Arguments
x: Strucutral.Return
Returns a Judgement
using Taxonomy
using StenoGraphs
graph = @StenoGraph begin
@@ -101,14 +101,14 @@
struct_model = Structural(structural_model = graph)
-structural_model(struct_model)Extract Structural part from a Taxon.
Taxonomy.Record — TypeRecord(j...; rater = missing, id = missing, location = missing, meta = missing)Record represents every paper(like) thing that is coded. It contains who is rating it, what is being rated (uniquely identified by an id), where to find it (location) and other metadata (usually automatically infered). If id is missing, a warning will be generated and an ID will be suggested. This ID is linked to the location (either a DOI or URL). If no location is provided, the ID will be generated at random. You can use this suggested ID or generate your own using generate_id(DOI("yourdoi")) or generate_id(url("yoururl")).
Taxonomy.Study — TypeStudy()Use this function to group together multiple Judgements and/or Taxons on the Study level.
Return: Output value will be a dictionary containing StudyJudgements and/or Taxons.
Taxonomy.Model — TypeModel()Use this function to group together multiple Judgements and/or Taxons on the Model level.
Return: Output value will be a dictionary containing ModelJudgements and/or Taxons.
Taxonomy.generate_id — FunctionGenerate an Entry ID
To create links between entries we need a stable reference point. This ID is generated initially from url(location) and if the url is missing, it is generated randomly. After the ID is generated once, it is saved with the Record and should not be changed.
Taxonomy.Judgements.Judgement — TypeJudgement(r::Union{<: Any, Missing}, c = 1.0, l = missing})Level: Taxonomy.Judgements.AnyLevelJudgement
A generic judgment without any checks on content.
Arguments
rating: The rating, e.g. "Structural" or 1.0.certainty: If uncertain, a number between 0.0 and 1.0 (0-100%)comment: information on why the judgement was made, may contain information about the source within the paper, e.g., section, page, table number, figure number.julia> Judgement(1.0, .99, "Figure 1");Taxonomy.Judgements.J — TypeShorthand for Judgement
Taxonomy.Judgements.NoJudgement — FunctionAbstaining from any judgement.
This implies that your best guess is missing and you are absolutely uncertain about this judgement.
julia> NoJudgement()
-Judgement{Missing}(missing, 0.0, missing)Taxonomy.Judgements.rating — FunctionExtract rating from Judgement.
If rating is called on a Judgement it returns the rating, on everything it returns identity. If rating is called on a JudgementLevel together with a field name, it returns the rating of that field.
Taxonomy.Judgements.certainty — FunctionExtract certainty from Judgement.
Taxonomy.MetaData — FunctionSave metadata.
Can be from complete minimal metadata, incomplete metadata or preferably from DOI.
julia> min = MetaData("Peikert, Aaron", 2022, "Journal of Statistical Software");
+structural_model(struct_model)Extract Structural part from a Taxon.
Taxonomy.Record — TypeRecord(j...; rater = missing, id = missing, location = missing, meta = missing)Record represents every paper(like) thing that is coded. It contains who is rating it, what is being rated (uniquely identified by an id), where to find it (location) and other metadata (usually automatically infered). If id is missing, a warning will be generated and an ID will be suggested. This ID is linked to the location (either a DOI or URL). If no location is provided, the ID will be generated at random. You can use this suggested ID or generate your own using generate_id(DOI("yourdoi")) or generate_id(url("yoururl")).
Taxonomy.Study — TypeStudy()Use this function to group together multiple Judgements and/or Taxons on the Study level.
Return: Output value will be a dictionary containing StudyJudgements and/or Taxons.
Taxonomy.Model — TypeModel()Use this function to group together multiple Judgements and/or Taxons on the Model level.
Return: Output value will be a dictionary containing ModelJudgements and/or Taxons.
Taxonomy.generate_id — FunctionGenerate an Entry ID
To create links between entries we need a stable reference point. This ID is generated initially from url(location) and if the url is missing, it is generated randomly. After the ID is generated once, it is saved with the Record and should not be changed.
Taxonomy.Judgements.Judgement — TypeJudgement(r::Union{<: Any, Missing}, c = 1.0, l = missing})Level: Taxonomy.Judgements.AnyLevelJudgement
A generic judgment without any checks on content.
Arguments
rating: The rating, e.g. "Structural" or 1.0.certainty: If uncertain, a number between 0.0 and 1.0 (0-100%)comment: information on why the judgement was made, may contain information about the source within the paper, e.g., section, page, table number, figure number.julia> Judgement(1.0, .99, "Figure 1");Taxonomy.Judgements.J — TypeShorthand for Judgement
Taxonomy.Judgements.NoJudgement — FunctionAbstaining from any judgement.
This implies that your best guess is missing and you are absolutely uncertain about this judgement.
julia> NoJudgement()
+Judgement{Missing}(missing, 0.0, missing)Taxonomy.Judgements.rating — FunctionExtract rating from Judgement.
If rating is called on a Judgement it returns the rating, on everything it returns identity. If rating is called on a JudgementLevel together with a field name, it returns the rating of that field.
Taxonomy.Judgements.certainty — FunctionExtract certainty from Judgement.
Taxonomy.MetaData — FunctionSave metadata.
Can be from complete minimal metadata, incomplete metadata or preferably from DOI.
julia> min = MetaData("Peikert, Aaron", 2022, "Journal of Statistical Software");
julia> incomplete = MetaData("Peikert, Aaron", 2022, missing);
julia> extensive = MetaData(DOI("10.5281/zenodo.6719627"));
-Taxonomy.apa — FunctionGet an APA citation.
julia> apa(DOI("10.5281/zenodo.6719627"))
-"Ernst, M. S., & Peikert, A. (2022). <i>StructuralEquationModels.jl</i> (Version v0.1.0) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.6719627"Taxonomy.json — FunctionGet a Citeproc JSON.
CSL JSON can be read by Zotero and automatically generated by doi.org from DOI. All availible information are included and saved in a Dict.
julia> json(DOI("10.5281/zenodo.6719627"))
+Taxonomy.apa — FunctionGet an APA citation.
julia> apa(DOI("10.5281/zenodo.6719627"))
+"Ernst, M. S., & Peikert, A. (2022). <i>StructuralEquationModels.jl</i> (Version v0.1.0) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.6719627"Taxonomy.json — FunctionGet a Citeproc JSON.
CSL JSON can be read by Zotero and automatically generated by doi.org from DOI. All availible information are included and saved in a Dict.
julia> json(DOI("10.5281/zenodo.6719627"))
Dict{String, Any} with 11 entries:
"publisher" => "Zenodo"
"issued" => Dict{String, Any}("date-parts"=>Any[Any[2022, 6, 24]])
@@ -120,33 +120,33 @@
"URL" => "https://zenodo.org/record/6719627"
"title" => "StructuralEquationModels.jl"
"abstract" => "StructuralEquationModels v0.1.0 This is a package for Structu…
- "type" => "book"Taxonomy.author — FunctionExtract the author.
julia> doi = MetaData(DOI("10.1126/SCIENCE.169.3946.635"));
+ "type" => "book"Taxonomy.author — FunctionExtract the author.
julia> doi = MetaData(DOI("10.1126/SCIENCE.169.3946.635"));
julia> author(doi)
-"Frank, Henry S."Taxonomy.year — FunctionExtract the year.
julia> doi = MetaData(DOI("10.1126/SCIENCE.169.3946.635"));
+"Frank, Henry S."Taxonomy.year — FunctionExtract the year.
julia> doi = MetaData(DOI("10.1126/SCIENCE.169.3946.635"));
julia> year(doi)
-1970Taxonomy.journal — FunctionExtract the journal.
julia> doi = MetaData(DOI("10.1126/SCIENCE.169.3946.635"));
+1970Taxonomy.journal — FunctionExtract the journal.
julia> doi = MetaData(DOI("10.1126/SCIENCE.169.3946.635"));
julia> journal(doi)
-"Science"Taxonomy.MinimalMeta — TypeA representation of the most important metadata.
julia> min = MetaData("Peikert, Aaron", 2022, "Journal of Statistical Software");
+"Science"Taxonomy.MinimalMeta — TypeA representation of the most important metadata.
julia> min = MetaData("Peikert, Aaron", 2022, "Journal of Statistical Software");
julia> typeof(min)
-MinimalMetaTaxonomy.IncompleteMeta — TypeA representation of Metadata when we can not even capture the most important metadata.
julia> incomplete = MetaData(missing, 2022, "Journal of Statistical Software");
+MinimalMetaTaxonomy.IncompleteMeta — TypeA representation of Metadata when we can not even capture the most important metadata.
julia> incomplete = MetaData(missing, 2022, "Journal of Statistical Software");
julia> typeof(incomplete)
-IncompleteMetaTaxonomy.ExtensiveMeta — TypeThe metadata we can gather from doi.org.
julia> doi = MetaData(DOI("10.1126/SCIENCE.169.3946.635"));
+IncompleteMetaTaxonomy.ExtensiveMeta — TypeThe metadata we can gather from doi.org.
julia> doi = MetaData(DOI("10.1126/SCIENCE.169.3946.635"));
julia> typeof(doi)
-ExtensiveMeta{MinimalMeta}Taxonomy.DOI — TypeAlias for UsualDOI.
Taxonomy.UsualDOI — TypeConstruct a validated DOI
Most valid DOIs (not all) can be simply validated via a regular expression.
Arguments
doi::String: a DOI without resolver (e.g. without doi.org), capitalization does not matterfallback::String: an optional fallback link where one maybe can find the content in case the doi failsjulia> DOI("10.5281/zenodo.6719627")
+ExtensiveMeta{MinimalMeta}Taxonomy.DOI — TypeAlias for UsualDOI.
Taxonomy.UsualDOI — TypeConstruct a validated DOI
Most valid DOIs (not all) can be simply validated via a regular expression.
Arguments
doi::String: a DOI without resolver (e.g. without doi.org), capitalization does not matterfallback::String: an optional fallback link where one maybe can find the content in case the doi failsjulia> DOI("10.5281/zenodo.6719627")
UsualDOI{String, Missing}("10.5281/ZENODO.6719627", missing)
julia> DOI("10.5281/zenodo.6719627", "https://github.com/StructuralEquationModels/StructuralEquationModels.jl")
-UsualDOI{String, String}("10.5281/ZENODO.6719627", "https://github.com/StructuralEquationModels/StructuralEquationModels.jl")Taxonomy.UnusualDOI — TypeConstruct an unvalidated DOI
You should prefer an validated UsualDOI but if you have tested the DOI and are sure it links were it supposed to link, go ahead and create an unvalidated doi.
julia> UnusualDOI("weird10.5281doi/zenodo.6719627")
+UsualDOI{String, String}("10.5281/ZENODO.6719627", "https://github.com/StructuralEquationModels/StructuralEquationModels.jl")Taxonomy.UnusualDOI — TypeConstruct an unvalidated DOI
You should prefer an validated UsualDOI but if you have tested the DOI and are sure it links were it supposed to link, go ahead and create an unvalidated doi.
julia> UnusualDOI("weird10.5281doi/zenodo.6719627")
UnusualDOI{String, Missing}("WEIRD10.5281DOI/ZENODO.6719627", missing)
julia> UnusualDOI("weird10.5281doi/zenodo.6719627", "https://github.com/StructuralEquationModels/StructuralEquationModels.jl")
-UnusualDOI{String, String}("WEIRD10.5281DOI/ZENODO.6719627", "https://github.com/StructuralEquationModels/StructuralEquationModels.jl")Taxonomy.NoDOI — TypeWhat to do if there is no doi
Last resort if there is no DOI. Than we save other metadata, similar to BibTex.
Arguments
url::String: an link where one maybe can find the content in case the doi failsauthor::String: like in BibTex, e.g. "Peikert, Aaron and Ernst, Maximilian S. and Bode, Clifford"date::Union{Date, Missing}: optional dateyear::Union{Int64}: optional if date is suppliedjournal::String: The outlet of the publicationother::Dict: more BibTexlike metadataNoDOI(
+UnusualDOI{String, String}("WEIRD10.5281DOI/ZENODO.6719627", "https://github.com/StructuralEquationModels/StructuralEquationModels.jl")Taxonomy.NoDOI — TypeWhat to do if there is no doi
Last resort if there is no DOI. Than we save other metadata, similar to BibTex.
Arguments
url::String: an link where one maybe can find the content in case the doi failsauthor::String: like in BibTex, e.g. "Peikert, Aaron and Ernst, Maximilian S. and Bode, Clifford"date::Union{Date, Missing}: optional dateyear::Union{Int64}: optional if date is suppliedjournal::String: The outlet of the publicationother::Dict: more BibTexlike metadataNoDOI(
url = "https://github.com/StructuralEquationModels/StructuralEquationModels.jl",
author = "Ernst, Maximilian Stefan and Peikert, Aaron",
title = "StructuralEquationModels.jl: A fast and flexible SEM framework",
@@ -162,7 +162,7 @@
title = "StructuralEquationModels.jl: A fast and flexible SEM framework",
year = 2022, # date is omitted
journal = "No Real Journal"
-)Taxonomy.NoLocation — TypeWhen everything fails.
This is a placeholder if really no location can be found.
Taxonomy.url — FunctionGet URL from location.
julia> url(DOI("10.1126/SCIENCE.169.3946.635"))
+)Taxonomy.NoLocation — TypeWhen everything fails.
This is a placeholder if really no location can be found.
Taxonomy.url — FunctionGet URL from location.
julia> url(DOI("10.1126/SCIENCE.169.3946.635"))
"https://doi.org/10.1126/SCIENCE.169.3946.635"location = NoDOI(
url = "https://github.com/StructuralEquationModels/StructuralEquationModels.jl",
author = "Ernst, Maximilian Stefan and Peikert, Aaron",
@@ -175,4 +175,4 @@
# output
-"https://github.com/StructuralEquationModels/StructuralEquationModels.jl"Taxonomy.valid_doi — FunctionValidate DOI via Regex
Regular expression taken from:
https://www.crossref.org/blog/dois-and-matching-regular-expressions/
Settings
This document was generated with Documenter.jl version 0.27.19 on Thursday 11 April 2024. Using Julia version 1.9.4.