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MixedModelsSim.jl
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Simulation tools for Mixed Models
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# MixedModelsSim [](https://zenodo.org/badge/latestdoi/210783029) [](https://www.repostatus.org/#active) [](https://RePsychLing.github.io/MixedModelsSim.jl/stable) [](https://RePsychLing.github.io/MixedModelsSim.jl/dev) [](https://github.com/RePsychLing/MixedModelsSim.jl/actions/workflows/CI.yml) [](https://codecov.io/gh/RePsychLing/MixedModelsSim.jl) [](https://github.com/jrevels/YASGuide) This package provides some utility functions for generating experimental designs, especially those with crossed factors. ## Installation `MixedModelsSim` is registered in the Julia package registry and can be installed via the pkg REPL: ```julia (@v1.5) pkg> add MixedModelsSim ``` ## Purpose This package provides functions to facilitate generating experimental designs, especially designs with crossed grouping factors such as "Subject" and "Item" in addition to experimental factors. The experimental factors can be within-subject or within-item or between-subject and between-item. This package uses structures from the [`Tables` package](https://github.com/JuliaData/Tables). In particular, a data table can be viewed as a `rowtable`, which is a vector of `NamedTuple`s, or a `columntable` which is a `NamedTuple` of vectors (or something similar). For those with experience in [`R`](https://www.r-project.org) just think of a `NamedTuple` as being like R's `list` type. It's an ordered, named collection. ## Changes in v0.2 Version 0.2 brings extensive changes to the API, both to take better advantage of new features in MixedModels.jl 3.0 and to eliminate the direct dependency on DataFrames.jl. - There is now extensive use of row tables instead of DataFrames. - `simulate_waldtests` has been removed. This functionality is now provided by the `coefpvalues` property provided of `MixedModelBootstrap`. - Similarly, `sim_to_df` has been removed because `DataFrame(bootstrapsim.coefpvalues)` provides the same content. ## Examples To create a design with each of five subjects, three old and two young, tested on each of three items, first create the subject table ```julia julia> using MixedModelsSim, DataFrames, Tables julia> subject = (subj = ["S1","S2","S3","S4","S5"], age=["O","O","O","Y","Y"]); julia> typeof(subject) NamedTuple{(:subj, :age),Tuple{Array{String,1},Array{String,1}}} julia> rowtable(subject) 5-element Array{NamedTuple{(:subj, :age),Tuple{String,String}},1}: (subj = "S1", age = "O") (subj = "S2", age = "O") (subj = "S3", age = "O") (subj = "S4", age = "Y") (subj = "S5", age = "Y") ``` then create the design as the product of an item table (defined inline here) and the `subject` table ```julia julia> design = factorproduct((item = ["I1","I2","I3"],), subject) 15-element Array{NamedTuple{(:item, :subj, :age),Tuple{String,String,String}},1}: (item = "I1", subj = "S1", age = "O") (item = "I2", subj = "S1", age = "O") (item = "I3", subj = "S1", age = "O") (item = "I1", subj = "S2", age = "O") (item = "I2", subj = "S2", age = "O") (item = "I3", subj = "S2", age = "O") (item = "I1", subj = "S3", age = "O") (item = "I2", subj = "S3", age = "O") (item = "I3", subj = "S3", age = "O") (item = "I1", subj = "S4", age = "Y") (item = "I2", subj = "S4", age = "Y") (item = "I3", subj = "S4", age = "Y") (item = "I1", subj = "S5", age = "Y") (item = "I2", subj = "S5", age = "Y") (item = "I3", subj = "S5", age = "Y") ``` The design can be converted to a `DataFrame` and the strings pooled to save storage. ```julia julia> design |> DataFrame |> pooled! 15×3 DataFrame │ Row │ item │ subj │ age │ │ │ String │ String │ String │ ├─────┼────────┼────────┼────────┤ │ 1 │ I1 │ S1 │ O │ │ 2 │ I2 │ S1 │ O │ │ 3 │ I3 │ S1 │ O │ │ 4 │ I1 │ S2 │ O │ │ 5 │ I2 │ S2 │ O │ │ 6 │ I3 │ S2 │ O │ │ 7 │ I1 │ S3 │ O │ │ 8 │ I2 │ S3 │ O │ │ 9 │ I3 │ S3 │ O │ │ 10 │ I1 │ S4 │ Y │ │ 11 │ I2 │ S4 │ Y │ │ 12 │ I3 │ S4 │ Y │ │ 13 │ I1 │ S5 │ Y │ │ 14 │ I2 │ S5 │ Y │ │ 15 │ I3 │ S5 │ Y │ julia> describe(ans) 3×8 DataFrame │ Row │ variable │ mean │ min │ median │ max │ nunique │ nmissing │ eltype │ │ │ Symbol │ Nothing │ String │ Nothing │ String │ Int64 │ Nothing │ DataType │ ├─────┼──────────┼─────────┼────────┼─────────┼────────┼─────────┼──────────┼──────────┤ │ 1 │ item │ │ I1 │ │ I3 │ 3 │ │ String │ │ 2 │ subj │ │ S1 │ │ S5 │ 5 │ │ String │ │ 3 │ age │ │ O │ │ Y │ 2 │ │ String │ ``` ## Background on tables and tuples In Julia tuples are created by listing the contents, surrounded by parentheses and separated by commas. ```julia julia> Tables.istable(subject) true julia> Tables.schema(subject) Tables.Schema: :subj String :age String julia> DataFrame(subject) 5×2 DataFrame │ Row │ subj │ age │ │ │ String │ String │ ├─────┼────────┼────────┤ │ 1 │ S1 │ Y │ │ 2 │ S2 │ Y │ │ 3 │ S3 │ Y │ │ 4 │ S4 │ O │ │ 5 │ S5 │ O │ ``` ### The curiously trailing comma To distinguish creating a named tuple of length 1 from an assignment with parentheses around it a comma is required after the first named element. To create an item table with only three item identifiers, the expression must be written ```julia julia> items = (item = ["I1", "I2", "I3"],) (item = ["I1", "I2", "I3"],) ``` with that curiously trailing comma. In general, trailing commas are allowed in the creation of tuples or in argument lists but in this case the trailing comma is mandatory. ## Generating factors with n levels The `nlevels` utility function can be used to generate a vector of length `n` with a given tag. For example, the vector of subject levels can be generated as ```julia julia> show(nlevels(5, 'S')) ["S1", "S2", "S3", "S4", "S5"] ``` The default tag is `S` so this sequence could be generated more simply as ```julia julia> show(nlevels(5)) ["S1", "S2", "S3", "S4", "S5"] ``` ## Acknowledgements The development of this package was supported by the Center for Interdisciplinary Research, Bielefeld (ZiF) Cooperation Group "Statistical models for psychological and linguistic data".
