indicators = FALSE behavior

[DavisVaughan]

These should all throw warnings of some kind.

Maybe when checking the formula RHS with indicators = FALSE, we should look for only + and names, and warn about anything else! (rather than special casing everything)

library(hardhat)
library(gapminder)
gapminder <- gapminder[1:5,]
mold(year ~ year*continent, gapminder, indicators = FALSE)
#> $predictors
#> # A tibble: 5 x 2
#>    year continent
#>   <int> <fct>    
#> 1  1952 Asia     
#> 2  1957 Asia     
#> 3  1962 Asia     
#> 4  1967 Asia     
#> 5  1972 Asia     
#> 
#> $outcomes
#> # A tibble: 5 x 1
#>    year
#>   <int>
#> 1  1952
#> 2  1957
#> 3  1962
#> 4  1967
#> 5  1972
#> 
#> $preprocessor
#> Formula Preprocessor: 
#>  
#> # Predictors: 2 
#>   # Outcomes: 1 
#>    Intercept: FALSE 
#>   Indicators: FALSE 
#> 
#> $offset
#> NULL
mold(year ~ year*continent - year, gapminder, indicators = FALSE)
#> $predictors
#> # A tibble: 5 x 2
#>    year continent
#>   <int> <fct>    
#> 1  1952 Asia     
#> 2  1957 Asia     
#> 3  1962 Asia     
#> 4  1967 Asia     
#> 5  1972 Asia     
#> 
#> $outcomes
#> # A tibble: 5 x 1
#>    year
#>   <int>
#> 1  1952
#> 2  1957
#> 3  1962
#> 4  1967
#> 5  1972
#> 
#> $preprocessor
#> Formula Preprocessor: 
#>  
#> # Predictors: 2 
#>   # Outcomes: 1 
#>    Intercept: FALSE 
#>   Indicators: FALSE 
#> 
#> $offset
#> NULL
mold(year ~ (year+continent+pop)^2, gapminder, indicators = FALSE)
#> $predictors
#> # A tibble: 5 x 3
#>    year continent      pop
#>   <int> <fct>        <int>
#> 1  1952 Asia       8425333
#> 2  1957 Asia       9240934
#> 3  1962 Asia      10267083
#> 4  1967 Asia      11537966
#> 5  1972 Asia      13079460
#> 
#> $outcomes
#> # A tibble: 5 x 1
#>    year
#>   <int>
#> 1  1952
#> 2  1957
#> 3  1962
#> 4  1967
#> 5  1972
#> 
#> $preprocessor
#> Formula Preprocessor: 
#>  
#> # Predictors: 3 
#>   # Outcomes: 1 
#>    Intercept: FALSE 
#>   Indicators: FALSE 
#> 
#> $offset
#> NULL

^{Created on 2019-02-16 by the reprex package (v0.2.1.9000)}

[DavisVaughan]

Maybe not because we still want to do things like log(var)

[DavisVaughan]

Hard because you might want to allow I(x * y) to be computed algebraically and not represent interactions

[DavisVaughan]

Adding some notes on what led me to the right answer:

when indicators = FALSE:

• check that factor columns are only by themselves (factorcol + x) not factorcol:x or paste0(factorcol)
  • uses terms() directly on the formula, along with original factor classes to determine this
  • throws error if factor columns are used not by themselves
• remove the factor columns from the data
• remove the factor columns from the formula? (append - factorcol)
• run model.matrix(formula_no_factors, data_no_factors)
• matrix -> tibble
• add the factor columns back onto the data
  • Original positions are not kept, but this seems ok

[DavisVaughan]

Closed by #42

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