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kleinschmidt committed Oct 1, 2019
2 parents 05dbb50 + 32c43e7 commit 0a3a5b1
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2 changes: 1 addition & 1 deletion Project.toml
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@@ -1,6 +1,6 @@
name = "StatsModels"
uuid = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
version = "0.6.4"
version = "0.6.5"

[deps]
CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
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87 changes: 53 additions & 34 deletions docs/src/formula.md
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Expand Up @@ -54,7 +54,7 @@ Predictors:
b(unknown) & c(unknown)
julia> df = DataFrame(y = rand(9), a = 1:9, b = rand(9), c = repeat(["d","e","f"], 3))
9×4 DataFrame
9×4 DataFrames.DataFrame
│ Row │ y │ a │ b │ c │
│ │ Float64 │ Int64 │ Float64 │ String │
├─────┼────────────┼───────┼───────────┼────────┤
Expand Down Expand Up @@ -108,6 +108,11 @@ The left-hand side has one term `y` which means that the response variable is
the column from the data named `:y`. The response can be accessed with the
analogous `response(f, df)` function.

!!! note

To make a "one-sided" formula (with no response), put a `0` on the left-hand
side, like `@formula(0 ~ 1 + a + b)`.

The right hand side is made up of a number of different **terms**, separated by
`+`: `1 + a + b + c + b&c`. Each term corresponds to one or more columns in the
generated model matrix:
Expand Down Expand Up @@ -214,34 +219,34 @@ For instance, to fit a linear regression to a log-transformed response:
julia> using GLM
julia> lm(@formula(log(y) ~ 1 + a + b), df)
StatsModels.TableRegressionModel{LinearModel{LmResp{Array{Float64,1}},DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Array{Float64,1}},GLM.DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}
:(log(y)) ~ 1 + a + b
Coefficients:
──────────────────────────────────────────────────────
Estimate Std.Error t value Pr(>|t|)
──────────────────────────────────────────────────────
(Intercept) -4.16168 2.98788 -1.39285 0.2131
a 0.357482 0.342126 1.04489 0.3363
b 2.32528 3.13735 0.741159 0.4866
──────────────────────────────────────────────────────
──────────────────────────────────────────────────────────────────────────────
Estimate Std. Error t value Pr(>|t|) Lower 95% Upper 95%
──────────────────────────────────────────────────────────────────────────────
(Intercept) -4.16168 2.98788 -1.39285 0.2131 -11.4727 3.14939
a 0.357482 0.342126 1.04489 0.3363 -0.479669 1.19463
b 2.32528 3.13735 0.741159 0.4866 -5.35154 10.0021
──────────────────────────────────────────────────────────────────────────────
julia> df[:log_y] = log.(df[:y]);
julia> df.log_y = log.(df.y);
julia> lm(@formula(log_y ~ 1 + a + b), df) # equivalent
StatsModels.TableRegressionModel{LinearModel{LmResp{Array{Float64,1}},DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Array{Float64,1}},GLM.DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}
log_y ~ 1 + a + b
Coefficients:
──────────────────────────────────────────────────────
Estimate Std.Error t value Pr(>|t|)
──────────────────────────────────────────────────────
(Intercept) -4.16168 2.98788 -1.39285 0.2131
a 0.357482 0.342126 1.04489 0.3363
b 2.32528 3.13735 0.741159 0.4866
──────────────────────────────────────────────────────
──────────────────────────────────────────────────────────────────────────────
Estimate Std. Error t value Pr(>|t|) Lower 95% Upper 95%
──────────────────────────────────────────────────────────────────────────────
(Intercept) -4.16168 2.98788 -1.39285 0.2131 -11.4727 3.14939
a 0.357482 0.342126 1.04489 0.3363 -0.479669 1.19463
b 2.32528 3.13735 0.741159 0.4866 -5.35154 10.0021
──────────────────────────────────────────────────────────────────────────────
```

Expand All @@ -262,9 +267,9 @@ julia> modelmatrix(@formula(y ~ 1 + b + identity(1+b)), df)
1.0 0.0203749 1.02037
```

## Constructing a formula programatically
## Constructing a formula programmatically

A formula can be constructed at run-time by creating `Term`s and combining them
A formula can be constructed at runtime by creating `Term`s and combining them
with the formula operators `+`, `&`, and `~`:

```jldoctest 1
Expand All @@ -279,6 +284,20 @@ Predictors:
a(unknown) & b(unknown)
```

!!! warning

Even though the `@formula` macro supports arbitrary julia functions,
runtime (programmatic) formula construction does not. This is because to
resolve a symbol giving a function's _name_ into the actual _function_
itself, it's necessary to `eval`. In practice this is not often an issue,
_except_ in cases where a package provides special syntax by overloading a
function (like `|` for
[MixedModels.jl](https://github.com/dmbates/MixedModels.jl), or `absorb`
for [Econometrics.jl](https://github.com/Nosferican/Econometrics.jl)). In
these cases, you should use the corresponding constructors for the actual
terms themselves (e.g., `RanefTerm` and `FixedEffectsTerm` respectively), as
long as the packages have [implemented support for them](@ref extend-runtime).

The [`term`](@ref) function constructs a term of the appropriate type from
symbols and numbers, which makes it easy to work with collections of mixed type:

Expand Down Expand Up @@ -338,26 +357,26 @@ julia> β_true = 1:8;
julia> ϵ = randn(100)*0.1;
julia> data[:y] = X*β_true .+ ϵ;
julia> data.y = X*β_true .+ ϵ;
julia> mod = fit(LinearModel, @formula(y ~ 1 + a*b), data)
StatsModels.TableRegressionModel{LinearModel{LmResp{Array{Float64,1}},DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Array{Float64,1}},GLM.DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}
y ~ 1 + a + b + a & b
Coefficients:
───────────────────────────────────────────────────
Estimate Std.Error t value Pr(>|t|)
───────────────────────────────────────────────────
(Intercept) 0.98878 0.0384341 25.7266 <1e-43
a 2.00843 0.0779388 25.7694 <1e-43
b: e 3.03726 0.0616371 49.2764 <1e-67
b: f 4.03909 0.0572857 70.5078 <1e-81
b: g 5.02948 0.0587224 85.6484 <1e-88
a & b: e 5.9385 0.10753 55.2264 <1e-71
a & b: f 6.9073 0.112483 61.4075 <1e-75
a & b: g 7.93918 0.111285 71.3407 <1e-81
───────────────────────────────────────────────────
──────────────────────────────────────────────────────────────────────────
Estimate Std. Error t value Pr(>|t|) Lower 95% Upper 95%
──────────────────────────────────────────────────────────────────────────
(Intercept) 0.98878 0.0384341 25.7266 <1e-43 0.912447 1.06511
a 2.00843 0.0779388 25.7694 <1e-43 1.85364 2.16323
b: e 3.03726 0.0616371 49.2764 <1e-67 2.91484 3.15967
b: f 4.03909 0.0572857 70.5078 <1e-81 3.92531 4.15286
b: g 5.02948 0.0587224 85.6484 <1e-88 4.91285 5.14611
a & b: e 5.9385 0.10753 55.2264 <1e-71 5.72494 6.15207
a & b: f 6.9073 0.112483 61.4075 <1e-75 6.6839 7.1307
a & b: g 7.93918 0.111285 71.3407 <1e-81 7.71816 8.16021
──────────────────────────────────────────────────────────────────────────
```

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