parse_fixedeffect(data, formula::FormulaTerm)
parse_fixedeffect(data, ts::NTuple{N, AbstractTerm})

Construct any FixedEffect specified with a FixedEffectTerm.

Returns

• Vector{FixedEffect}: a collection of all FixedEffects constructed.
• Vector{Symbol}: names assigned to the fixed effects (can be used as column names).
• FormulaTerm or NTuple{N, AbstractTerm}: formula or ts without any term related to fixed effects (an intercept may be explicitly omitted if necessary).

Partial out variables in a Dataframe

Arguments

• df: A table
• formula::FormulaTerm: A formula created using @formula
• add_mean::Bool: Should the initial mean added to the returned variable?
• method::Symbol: A symbol for the method. Default is :cpu. Alternatively, :gpu requires CuArrays. In this case, use the option double_precision = false to use Float32.
• maxiter::Integer: Maximum number of iterations
• double_precision::Bool: Should the demeaning operation use Float64 rather than Float32? Default to true.
• tol::Real: Tolerance
• align::Bool: Should the returned DataFrame align with the original DataFrame in case of missing values? Default to true

Returns

• ::DataFrame: a dataframe with as many columns as there are dependent variables and as many rows as the original dataframe.
• ::Vector{Int}: a vector of iterations for each column
• ::Vector{Bool}: a vector of success for each column

Details

partial_out returns the residuals of a set of variables after regressing them on a set of regressors. The syntax is similar to reg - but it accepts multiple dependent variables. It returns a dataframe with as many columns as there are dependent variables and as many rows as the original dataframe. The regression model is estimated on only the rows where none of the dependent variables is missing. With the option add_mean = true, the mean of the initial variable is added to the residuals.

Examples

using  RDatasets, DataFrames, FixedEffectModels, Gadfly
df = dataset("datasets", "iris")
result = partial_out(df, @formula(SepalWidth + SepalLength ~ fe(Species)), add_mean = true)
plot(layer(result[1], x="SepalWidth", y="SepalLength", Stat.binmean(n=10), Geom.point),
   layer(result[1], x="SepalWidth", y="SepalLength", Geom.smooth(method=:lm)))