# StatsModels.jl API

## Formulae and terms

StatsModels.@formulaMacro
@formula(ex)

Capture and parse a formula expression as a Formula struct.

A formula is an abstract specification of a dependence between left-hand and right-hand side variables as in, e.g., a regression model. Each side specifies at a high level how tabular data is to be converted to a numerical matrix suitable for modeling. This specification looks something like Julia code, is represented as a Julia Expr, but uses special syntax. The @formula macro takes an expression like y ~ 1 + a*b, transforms it according to the formula syntax rules into a lowered form (like y ~ 1 + a + b + a&b), and constructs a Formula struct which captures the original expression, the lowered expression, and the left- and right-hand-side.

Operators that have special interpretations in this syntax are

• ~ is the formula separator, where it is a binary operator (the first argument is the left-hand side, and the second is the right-hand side.
• + concatenates variables as columns when generating a model matrix.
• & representes an interaction between two or more variables, which corresponds to a row-wise kronecker product of the individual terms (or element-wise product if all terms involved are continuous/scalar).
• * expands to all main effects and interactions: a*b is equivalent to a+b+a&b, a*b*c to a+b+c+a&b+a&c+b&c+a&b&c, etc.
• 1, 0, and -1 indicate the presence (for 1) or absence (for 0 and -1) of an intercept column.

The rules that are applied are

• The associative rule (un-nests nested calls to +, &, and *).
• The distributive rule (interactions & distribute over concatenation +).
• The * rule expands a*b to a+b+a&b (recursively).
• Subtraction is converted to addition and negation, so x-1 becomes x + -1 (applies only to subtraction of literal 1).
• Single-argument & calls are stripped, so &(x) becomes the main effect x.
source
StatsModels.termFunction
term(x)

Wrap argument in an appropriate AbstractTerm type: Symbols and AbstractStrings become Terms, and Numbers become ConstantTerms. Any AbstractTerms are unchanged. AbstractStrings are converted to symbols before wrapping.

Example

julia> ts = term.((1, :a, "b"))
1
a(unknown)
b(unknown)

julia> typeof(ts)
Tuple{ConstantTerm{Int64},Term,Term}
source
StatsBase.coefnamesFunction
coefnames(term::AbstractTerm)

Return the name(s) of column(s) generated by a term. Return value is either a String or an iterable of Strings.

source
StatsModels.modelcolsFunction
modelcols(t::AbstractTerm, data)

Create a numerical "model columns" representation of data based on an AbstractTerm. data can either be a whole table (a property-accessible collection of iterable columns or iterable collection of property-accessible rows, as defined by Tables.jl or a single row (in the form of a NamedTuple of scalar values). Tables will be converted to a NamedTuple of Vectors (e.g., a Tables.ColumnTable).

source
modelcols(ts::NTuple{N, AbstractTerm}, data) where N

When a tuple of terms is provided, modelcols broadcasts over the individual terms. To create a single matrix, wrap the tuple in a MatrixTerm.

Example

julia> using StableRNGs; rng = StableRNG(1);

julia> d = (a = [1:9;], b = rand(rng, 9), c = repeat(["d","e","f"], 3));

julia> ts = apply_schema(term.((:a, :b, :c)), schema(d))
a(continuous)
b(continuous)
c(DummyCoding:3→2)

julia> cols = modelcols(ts, d)
([1, 2, 3, 4, 5, 6, 7, 8, 9], [0.5851946422124186, 0.07733793456911231, 0.7166282400543453, 0.3203570514066232, 0.6530930076222579, 0.2366391513734556, 0.7096838914472361, 0.5577872440804086, 0.05079002172175784], [0.0 0.0; 1.0 0.0; … ; 1.0 0.0; 0.0 1.0])

julia> reduce(hcat, cols)
9×4 Array{Float64,2}:
1.0  0.585195   0.0  0.0
2.0  0.0773379  1.0  0.0
3.0  0.716628   0.0  1.0
4.0  0.320357   0.0  0.0
5.0  0.653093   1.0  0.0
6.0  0.236639   0.0  1.0
7.0  0.709684   0.0  0.0
8.0  0.557787   1.0  0.0
9.0  0.05079    0.0  1.0

julia> modelcols(MatrixTerm(ts), d)
9×4 Array{Float64,2}:
1.0  0.585195   0.0  0.0
2.0  0.0773379  1.0  0.0
3.0  0.716628   0.0  1.0
4.0  0.320357   0.0  0.0
5.0  0.653093   1.0  0.0
6.0  0.236639   0.0  1.0
7.0  0.709684   0.0  0.0
8.0  0.557787   1.0  0.0
9.0  0.05079    0.0  1.0
source

### Higher-order terms

StatsModels.FormulaTermType
FormulaTerm{L,R} <: AbstractTerm

Represents an entire formula, with a left- and right-hand side. These can be of any type (captured by the type parameters).

Fields

• lhs::L: The left-hand side (e.g., response)
• rhs::R: The right-hand side (e.g., predictors)
source
StatsModels.InteractionTermType
InteractionTerm{Ts} <: AbstractTerm

Represents an interaction between two or more individual terms.

Generated by combining multiple AbstractTerms with & (which is what calls to & in a @formula lower to)

Fields

• terms::Ts: the terms that participate in the interaction.

Example

julia> using StableRNGs; rng = StableRNG(1);

julia> d = (y = rand(rng, 9), a = 1:9, b = rand(rng, 9), c = repeat(["d","e","f"], 3));

julia> t = InteractionTerm(term.((:a, :b, :c)))
a(unknown) & b(unknown) & c(unknown)

julia> t == term(:a) & term(:b) & term(:c)
true

julia> t = apply_schema(t, schema(d))
a(continuous) & b(continuous) & c(DummyCoding:3→2)

julia> modelcols(t, d)
9×2 Array{Float64,2}:
0.0       0.0
1.88748   0.0
0.0       1.33701
0.0       0.0
0.725357  0.0
0.0       0.126744
0.0       0.0
4.93994   0.0
0.0       4.33378

julia> modelcols(t.terms, d)
([1, 2, 3, 4, 5, 6, 7, 8, 9], [0.236781883208121, 0.9437409715735081, 0.4456708824294644, 0.7636794266904741, 0.14507148958283067, 0.021124039581375875, 0.15254507694061115, 0.617492416565387, 0.48153065407402607], [0.0 0.0; 1.0 0.0; … ; 1.0 0.0; 0.0 1.0])
source
StatsModels.FunctionTermType
FunctionTerm{Forig,Fanon,Names} <: AbstractTerm

Represents a call to a Julia function. The first type parameter is the type of the function as originally specified (e.g., typeof(log)), while the second is the type of the anonymous function that will be applied element-wise to the data table.

The FunctionTerm also captures the arguments of the original call and parses them as if they were part of a special DSL call, applying the rules to expand *, distribute & over +, and wrap symbols in Terms.

By storing the original function as a type parameter and pessimistically parsing the arguments as if they're part of a special DSL call, this allows custom syntax to be supported with minimal extra effort. Packages can dispatch on apply_schema(f::FunctionTerm{typeof(special_syntax)}, schema, ::Type{<:MyModel}) and pull out the arguments parsed as terms from f.args_parsed to construct their own custom terms.

Fields

• forig::Forig: the original function (e.g., log)
• fanon::Fanon: the generated anonymous function (e.g., (a, b) -> log(1+a+b))
• exorig::Expr: the original expression passed to @formula
• args_parsed::Vector: the arguments of the call passed to @formula, each parsed as if the call was a "special" DSL call.

Type parameters

• Forig: the type of the original function (e.g., typeof(log))
• Fanon: the type of the generated anonymous function
• Names: the names of the arguments to the anonymous function (as a NTuple{N,Symbol})

Example

julia> f = @formula(y ~ log(1 + a + b))
FormulaTerm
Response:
y(unknown)
Predictors:
(a,b)->log(1 + a + b)

julia> typeof(f.rhs)
FunctionTerm{typeof(log),var"#1#2",(:a, :b)}

julia> f.rhs.forig(1 + 3 + 4)
2.0794415416798357

julia> f.rhs.fanon(3, 4)
2.0794415416798357

julia> modelcols(f.rhs, (a=3, b=4))
2.0794415416798357

julia> modelcols(f.rhs, (a=[3, 4], b=[4, 5]))
2-element Array{Float64,1}:
2.0794415416798357
2.302585092994046
source

### Concrete terms

These are all generated by apply_schema.

StatsModels.ContinuousTermType
ContinuousTerm <: AbstractTerm

Represents a continuous variable, with a name and summary statistics.

Fields

• sym::Symbol: The name of the variable
• mean::T: Mean
• var::T: Variance
• min::T: Minimum value
• max::T: Maximum value
source
StatsModels.CategoricalTermType
CategoricalTerm{C,T,N} <: AbstractTerm

Represents a categorical term, with a name and ContrastsMatrix

Fields

• sym::Symbol: The name of the variable
• contrasts::ContrastsMatrix: A contrasts matrix that captures the unique values this variable takes on and how they are mapped onto numerical predictors.
source
StatsModels.InterceptTermType
InterceptTerm{HasIntercept} <: AbstractTerm

Represents the presence or (explicit) absence of an "intercept" term in a regression model. These terms are generated from ConstantTerms in a formula by apply_schema(::ConstantTerm, schema, ::Type{<:StatisticalModel}). A 1 yields InterceptTerm{true}, and 0 or -1 yield InterceptTerm{false} (which explicitly omits an intercept for models which implicitly includes one via the implicit_intercept trait).

source
ShiftedArrays.leadFunction
    lead(term, nsteps::Integer)

This @formula term is used to introduce lead variables.
For example lead(x,1) effectively adds a new column containing
the value of the x column from the next row.
If there is no such row (e.g. because this is the last row),
then the lead column will contain missing for that entry.

Note: this is only a basic row-wise lead operation.
It is up to the user to ensure that data is sorted by the temporal variable,
and that observations are spaced with regular time-steps.
(Which may require adding extra-rows filled with missing values.)
source
ShiftedArrays.lagFunction
    lag(term, nsteps::Integer)

This @formula term is used to introduce lagged variables.
For example lag(x,1) effectively adds a new column containing
the value of the x column from the previous row.
If there is no such row (e.g. because this is the first row),
then the lagged column will contain missing for that entry.

Note: this is only a basic row-wise lag operation.
It is up to the user to ensure that data is sorted by the temporal variable,
and that observations are spaced with regular time-steps.
(Which may require adding extra-rows filled with missing values.)
source
StatsModels.collect_matrix_termsFunction
collect_matrix_terms(ts::TupleTerm)
collect_matrix_terms(t::AbstractTerm) = collect_matrix_term((t, ))

Depending on whether the component terms are matrix terms (meaning they have is_matrix_term(T) == true), collect_matrix_terms will return

1. A single MatrixTerm (if all components are matrix terms)
2. A tuple of the components (if none of them are matrix terms)
3. A tuple of terms, with all matrix terms collected into a single MatrixTerm in the first element of the tuple, and the remaining non-matrix terms passed through unchanged.

By default all terms are matrix terms (that is, is_matrix_term(::Type{<:AbstractTerm}) = true), the first case is by far the most common. The others are provided only for convenience when dealing with specialized terms that can't be concatenated into a single model matrix, like random effects terms in MixedModels.jl.

source
StatsModels.is_matrix_termFunction
is_matrix_term(::Type{<:AbstractTerm})

Does this type of term get concatenated with other matrix terms into a single model matrix? This controls the behavior of the collect_matrix_terms, which collects all of its arguments for which is_matrix_term returns true into a MatrixTerm, and returns the rest unchanged.

Since all "normal" terms which describe one or more model matrix columns are matrix terms, this defaults to true for any AbstractTerm.

An example of a non-matrix term is a random effect term in MixedModels.jl.

source

## Schema

StatsModels.SchemaType
StatsModels.Schema

Struct that wraps a Dict mapping Terms to their concrete forms. This exists mainly for dispatch purposes and to support possibly more sophisticated behavior in the future.

A Schema behaves for all intents and purposes like an immutable Dict, and delegates the constructor as well as getindex, get, merge!, merge, keys, and haskey to the wrapped Dict.

source
StatsModels.schemaFunction
schema([terms::AbstractVector{<:AbstractTerm}, ]data, hints::Dict{Symbol})
schema(term::AbstractTerm, data, hints::Dict{Symbol})

Compute all the invariants necessary to fit a model with terms. A schema is a dict that maps Terms to their concrete instantiations (either CategoricalTerms or ContinuousTerms. "Hints" may optionally be supplied in the form of a Dict mapping term names (as Symbols) to term or contrast types. If a hint is not provided for a variable, the appropriate term type will be guessed based on the data type from the data column: any numeric data is assumed to be continuous, and any non-numeric data is assumed to be categorical.

Returns a StatsModels.Schema, which is a wrapper around a Dict mapping Terms to their concrete instantiations (ContinuousTerm or CategoricalTerm).

Example

julia> using StableRNGs; rng = StableRNG(1);

julia> d = (x=sample(rng, [:a, :b, :c], 10), y=rand(rng, 10));

julia> ts = [Term(:x), Term(:y)];

julia> schema(ts, d)
StatsModels.Schema with 2 entries:
x => x
y => y

julia> schema(ts, d, Dict(:x => HelmertCoding()))
StatsModels.Schema with 2 entries:
x => x
y => y

julia> schema(term(:y), d, Dict(:y => CategoricalTerm))
StatsModels.Schema with 1 entry:
y => y

Note that concrete ContinuousTerm and CategoricalTerm and un-typed Terms print the same in a container, but when printed alone are different:

julia> sch = schema(ts, d)
StatsModels.Schema with 2 entries:
x => x
y => y

julia> term(:x)
x(unknown)

julia> sch[term(:x)]
x(DummyCoding:3→2)

julia> sch[term(:y)]
y(continuous)
source
StatsModels.concrete_termFunction
concrete_term(t::Term, data[, hint])

Create concrete term from the placeholder t based on a data source and optional hint. If data is a table, the getproperty is used to extract the appropriate column.

The hint can be a Dict{Symbol} of hints, or a specific hint, a concrete term type (ContinuousTerm or CategoricalTerm), or an instance of some <:AbstractContrasts, in which case a CategoricalTerm will be created using those contrasts.

If no hint is provided (or hint==nothing), the eltype of the data is used: Numbers are assumed to be continuous, and all others are assumed to be categorical.

Example

julia> concrete_term(term(:a), [1, 2, 3])
a(continuous)

julia> concrete_term(term(:a), [1, 2, 3], nothing)
a(continuous)

julia> concrete_term(term(:a), [1, 2, 3], CategoricalTerm)
a(DummyCoding:3→2)

julia> concrete_term(term(:a), [1, 2, 3], EffectsCoding())
a(EffectsCoding:3→2)

julia> concrete_term(term(:a), [1, 2, 3], Dict(:a=>EffectsCoding()))
a(EffectsCoding:3→2)

julia> concrete_term(term(:a), (a = [1, 2, 3], b = [0.0, 0.5, 1.0]))
a(continuous)
source
StatsModels.apply_schemaFunction
apply_schema(t, schema::StatsModels.Schema[, Mod::Type = Nothing])

Return a new term that is the result of applying schema to term t with destination model (type) Mod. If Mod is omitted, Nothing will be used.

When t is a ContinuousTerm or CategoricalTerm already, the term will be returned unchanged unless a matching term is found in the schema. This allows selective re-setting of a schema to change the contrast coding or levels of a categorical term, or to change a continuous term to categorical or vice versa.

When defining behavior for custom term types, it's best to dispatch on StatsModels.Schema for the second argument. Leaving it as ::Any will work in most cases, but cause method ambiguity in some.

source
apply_schema(t::AbstractTerm, schema::StatsModels.FullRank, Mod::Type)

Apply a schema, under the assumption that when a less-than-full rank model matrix would be produced, categorical terms should be "promoted" to full rank (where a categorical variable with $k$ levels would produce $k$ columns, instead of $k-1$ in the standard contrast coding schemes). This step is applied automatically when Mod <: StatisticalModel, but other types of models can opt-in by adding a method like

StatsModels.apply_schema(t::FormulaTerm, schema::StatsModels.Schema, Mod::Type{<:MyModelType}) =
apply_schema(t, StatsModels.FullRank(schema), mod)

See the section on Modeling categorical data in the docs for more information on how promotion of categorical variables works.

source

## Modeling

StatsBase.fitFunction
fit(Mod::Type{<:StatisticalModel}, f::FormulaTerm, data, args...;
contrasts::Dict{Symbol}, kwargs...)

Convert tabular data into a numeric response vector and predictor matrix using the formula f, and then fit the specified model type, wrapping the result in a TableRegressionModel or TableStatisticalModel (as appropriate).

This is intended as a backstop for modeling packages that implement model types that are subtypes of StatsBase.StatisticalModel but do not explicitly support the full StatsModels terms-based interface. Currently this works by creating a ModelFrame from the formula and data, and then converting this to a ModelMatrix, but this is an internal implementation detail which may change in the near future.

source
StatsBase.responseFunction
response(f::FormulaTerm, data; hints=Dict(), mod=StatisticalModel)
response(mf::ModelFrame; data=mf.data)

Return the response (left-hand side) of a formula generated by a data source. If a ModelFrame is provided instead of an AbstractTerm, the wrapped table is used by default.

Like modelmatrix, this will compute and apply a Schema before calling modelcols if necessary. The optional hints and mod keyword arguments are passed to apply_schema.

Note

response is provided as a convenience for interactive use. For modeling packages that wish to support a formula-based interface, it is recommended to use the schemaapply_schemamodelcols pipeline directly

source
StatsBase.modelmatrixFunction
modelmatrix(t::AbstractTerm, data; hints=Dict(), mod=StatisticalModel)
modelmatrix(mf::ModelFrame; data=mf.data)

Return the model matrix based on a term and a data source. If the term t is a FormulaTerm, this uses the right-hand side (predictor terms) of the formula; otherwise all columns are generated. If a ModelFrame is provided instead of an AbstractTerm, the wrapped table is used as the data source by default.

Like response, this will compute and apply a Schema before calling modelcols if necessary. The optional hints and mod keyword arguments are passed to apply_schema.

Note

modelmatrix is provided as a convenience for interactive use. For modeling packages that wish to support a formula-based interface, it is recommended to use the schemaapply_schemamodelcols pipeline directly

source
StatsModels.lrtestFunction
lrtest(mods::StatisticalModel...; atol::Real=0.0)

For each sequential pair of statistical models in mods..., perform a likelihood ratio test to determine if the first one fits significantly better than the next.

A table is returned containing degrees of freedom (DOF), difference in DOF from the preceding model, deviance, difference in deviance from the preceding model, and likelihood ratio and p-value for the comparison between the two models.

Optional keyword argument atol controls the numerical tolerance when testing whether the models are nested.

Examples

Suppose we want to compare the effects of two or more treatments on some result. Our null hypothesis is that Result ~ 1 fits the data as well as Result ~ 1 + Treatment.

julia> using DataFrames, GLM

julia> dat = DataFrame(Result=[1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1],
Treatment=[1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2],
Other=categorical([1, 1, 2, 1, 2, 1, 3, 1, 1, 2, 2, 1]));

julia> nullmodel = glm(@formula(Result ~ 1), dat, Binomial(), LogitLink());

julia> model = glm(@formula(Result ~ 1 + Treatment), dat, Binomial(), LogitLink());

julia> bigmodel = glm(@formula(Result ~ 1 + Treatment + Other), dat, Binomial(), LogitLink());

julia> lrtest(nullmodel, model, bigmodel)
Likelihood-ratio test: 3 models fitted on 12 observations
──────────────────────────────────────────────
DOF  ΔDOF  Deviance  ΔDeviance  p(>Chisq)
──────────────────────────────────────────────
    1         16.3006
    2     1   15.9559    -0.3447     0.5571
    4     2   14.0571    -1.8988     0.3870
──────────────────────────────────────────────

julia> lrtest(bigmodel, model, nullmodel)
Likelihood-ratio test: 3 models fitted on 12 observations
──────────────────────────────────────────────
DOF  ΔDOF  Deviance  ΔDeviance  p(>Chisq)
──────────────────────────────────────────────
    4         14.0571
    2    -2   15.9559     1.8988     0.3870
    1    -1   16.3006     0.3447     0.5571
──────────────────────────────────────────────
source

### Traits

StatsModels.implicit_interceptFunction
implicit_intercept(T::Type)
implicit_intercept(x::T) = implicit_intercept(T)

Return true if models of type T should include an implicit intercept even if none is specified in the formula. Is true by default for all T<:StatisticalModel, and false for others. To specify that a model type T includes an intercept even if one is not specified explicitly in the formula, overload this function for the corresponding type: implicit_intercept(::Type{<:T}) = true

If a model has an implicit intercept, it can be explicitly excluded by using 0 in the formula, which generates InterceptTerm{false} with apply_schema.

source
StatsModels.drop_interceptFunction
drop_intercept(T::Type)
drop_intercept(x::T) = drop_intercept(T)

Define whether a given model automatically drops the intercept. Return false by default. To specify that a model type T drops the intercept, overload this function for the corresponding type: drop_intercept(::Type{<:T}) = true

Models that drop the intercept will be fitted without one: the intercept term will be removed even if explicitly provided by the user. Categorical variables will be expanded in the rank-reduced form (contrasts for n levels will only produce n-1 columns).

source

### Wrappers

Warning

These are internal implementation details that are likely to change in the near future. In particular, the ModelFrame and ModelMatrix wrappers are dispreferred in favor of using terms directly, and can in most cases be replaced by something like

# instead of ModelMatrix(ModelFrame(f::FormulaTerm, data, model=MyModel))
sch = schema(f, data)
f = apply_schema(f, sch, MyModel)
response, predictors = modelcols(f, data)
StatsModels.ModelFrameType
ModelFrame(formula, data; model=StatisticalModel, contrasts=Dict())

Wrapper that encapsulates a FormulaTerm, schema, data table, and model type.

This wrapper encapsulates all the information that's required to transform data of the same structure as the wrapped data frame into a model matrix (the FormulaTerm), as well as the information about how that formula term was instantiated (the schema and model type)

Creating a model frame involves first extracting the schema for the data (using any contrasts provided as hints), and then applying that schema with apply_schema to the formula in the context of the provided model type.

Constructors

ModelFrame(f::FormulaTerm, data; model::Type{M} = StatisticalModel, contrasts::Dict = Dict())

Fields

• f::FormulaTerm: Formula whose left hand side is the response and right hand side are the predictors.
• schema::Any: The schema that was applied to generate f.
• data::D: The data table being modeled. The only restriction is that data is a table (Tables.istable(data) == true)
• model::Type{M}: The type of the model that will be fit from this model frame.

Examples

julia> df = (x = 1:4, y = 5:8)
julia> mf = ModelFrame(@formula(y ~ 1 + x), df)
source
StatsModels.ModelMatrixType
ModelMatrix(mf::ModelFrame)

Convert a ModelFrame into a numeric matrix suitable for modeling

Fields

• m::AbstractMatrix{<:AbstractFloat}: the generated numeric matrix
• assign::Vector{Int} the index of the term corresponding to each column of m.

Constructors

ModelMatrix(mf::ModelFrame)
# Specify the type of the resulting matrix (default Matrix{Float64})
ModelMatrix{T <: AbstractMatrix{<:AbstractFloat}}(mf::ModelFrame)
source
StatsModels.TableStatisticalModelType

Wrapper for a StatisticalModel that has been fit from a @formula and tabular data.

Most functions from the StatsBase API are simply delegated to the wrapped model, with the exception of functions like fit, predict, and coefnames where the tabular nature of the data means that additional processing is required or information provided by the formula.

Fields

• model::M the wrapped StatisticalModel.
• mf::ModelFrame encapsulates the formula, schema, and model type.
• mm::ModelMatrix{T} the model matrix that the model was fit from.
source
StatsModels.TableRegressionModelType

Wrapper for a RegressionModel that has been fit from a @formula and tabular data.

Most functions from the StatsBase API are simply delegated to the wrapped model, with the exception of functions like fit, predict, and coefnames where the tabular nature of the data means that additional processing is required or information provided by the formula.

Fields

• model::M the wrapped RegressioModel.
• mf::ModelFrame encapsulates the formula, schema, and model type.
• mm::ModelMatrix{T} the model matrix that the model was fit from.
source