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partialling_out

Source: vignettes/partialling_out.Rmd
partialling_out.Rmd

Getting started

Let’s make a simple linear model first.

library(partialling.out)
library(tinytable)
library(tinyplot)
library(palmerpenguins)
library(fwlplot)
model <- lm(bill_length_mm ~ bill_depth_mm + species, data = penguins)
summary(model)
#> 
#> Call:
#> lm(formula = bill_length_mm ~ bill_depth_mm + species, data = penguins)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -8.0300 -1.5828  0.0733  1.6925 10.0313 
#> 
#> Coefficients:
#>                  Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)       13.2164     2.2475    5.88 9.83e-09 ***
#> bill_depth_mm      1.3940     0.1220   11.43  < 2e-16 ***
#> speciesChinstrap   9.9390     0.3678   27.02  < 2e-16 ***
#> speciesGentoo     13.4033     0.5118   26.19  < 2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 2.518 on 338 degrees of freedom
#>   (2 observations deleted due to missingness)
#> Multiple R-squared:  0.7892, Adjusted R-squared:  0.7874 
#> F-statistic: 421.9 on 3 and 338 DF,  p-value: < 2.2e-16

Using the partialling_out function, you can get the residualised variable of interest (bill_length_mm) and of the first explanatory variable (bill_depth_mm), i.e. it would return the residuals of the following two regressions.


modely <- lm(bill_length_mm ~ species, data = penguins)

modelx <- lm(bill_depth_mm ~ species, data = penguins)
res <- partialling_out(model, data = penguins)

tt(head(res)) |>
  format_tt(digits = 2) |>
  style_tt(align = "c")
res_bill_length_mm res_bill_depth_mm
0.31 0.35
0.71 -0.95
1.51 -0.35
-2.09 0.95
0.51 2.25
0.11 -0.55

Accordingly, the coefficient of res_bill_depth_mm in the model lm(res_bill_length_mm ~ res_bill_depth_mm) will be the same of the coefficient of bill_depth_mm in the original model.

resmodel <- lm(res_bill_length_mm ~ res_bill_depth_mm, data = res)

print(c(model$coefficients[2], resmodel$coefficients[2]))
#>     bill_depth_mm res_bill_depth_mm 
#>          1.394011          1.394011

If both is set to FALSE, the function will return the actual Y values and the residualised X values.

tt(head(partialling_out(model, penguins, both = FALSE))) |>
  format_tt(digits = 2) |>
  style_tt(align = "c")
bill_length_mm res_bill_depth_mm
39 0.35
40 -0.95
40 -0.35
37 0.95
39 2.25
39 -0.55

Weighted models

If weights are specified in partialling_out they will be used to weight the underlying partial models. The weights will also be returned as a column in the result data.frame. If the original model is weighted but the partial models aren’t, the function will throw a warning (also if the original model is not weighted but partial models are).

model <- lm(
  bill_length_mm ~ bill_depth_mm + species,
  data = penguins,
  weights = penguins$body_mass_g
)
res <- partialling_out(model, data = penguins, weights = penguins$body_mass_g)


tt(head(res)) |>
  format_tt(digits = 2) |>
  style_tt(align = "c")
res_bill_length_mm res_bill_depth_mm weights
0.129 0.27 3750
0.529 -1.03 3800
1.329 -0.43 3250
-2.271 0.87 3450
0.329 2.17 3650
-0.071 -0.63 3625

Note that, for the FWL theorem to hold, if the partial models are weighted, the linear model between the two residualised variables must be weighted as well.

unweighted_model <- lm(res_bill_length_mm ~ res_bill_depth_mm, data = res)

weighted_model <- lm(
  res_bill_length_mm ~ res_bill_depth_mm,
  weights = res$weights,
  data = res
)


data.frame(
  "original model" = model$coefficients[2],
  "unweighted model" = unweighted_model$coefficients[2],
  "weighted_model" = weighted_model$coefficients[2]
) |>
  tt() |>
  format_tt(digits = 4) |>
  style_tt(align = "c")
original.model unweighted.model weighted_model
1.442 1.394 1.442

Fixed effects models

As stated, the model will also work with feols or felm models

library(fixest)

model_fixest <- feols(bill_length_mm ~ bill_depth_mm | species, data = penguins)

res_fixest <- partialling_out(model_fixest, data = penguins)
tt(head(res_fixest)) |>
  format_tt(digits = 2) |>
  style_tt(align = "c")
res_bill_length_mm res_bill_depth_mm
0.31 0.35
0.71 -0.95
1.51 -0.35
-2.09 0.95
0.51 2.25
0.11 -0.55 
library(lfe)

model_lfe <- felm(bill_length_mm ~ bill_depth_mm | species, data = penguins)

res_lfe <- partialling_out(model_lfe, data = penguins)


tt(head(res_lfe)) |>
  format_tt(digits = 2) |>
  style_tt(align = "c")
res_bill_length_mm res_bill_depth_mm
0.31 0.35
0.71 -0.95
1.51 -0.35
-2.09 0.95
0.51 2.25
0.11 -0.55

Interaction terms and varying slopes

So far, interaction terms with :, *, or ^ (in feols) as well as varying slopes in feols with [ or [[ are supported only for variables other than the main explanatory variable, i.e.

feols(bill_depth_mm ~ bill_length_mm | species^sex, data = penguins) |>
  partialling_out()

is supported, but

feols(bill_depth_mm ~ i(bill_length_mm, species) | sex, data = penguins) |>
  partialling_out()

will yield an error. Therefore, if your main explanatory variable is an interaction, it should be built outside the model if you intend to partial it out afterwards.

AsIs and polynomial terms

So far, AsIs expressions, and polynomial terms in the formula are not yet supported, so these two expressions will yield an error.

partialling_out(lm(bill_depth_mm ~ I(bill_length_mm^2) + species,
                   data = penguins))

partialling_out(lm(bill_depth_mm ~  poly(bill_length_mm, 2) + species,
                   data = penguins[!is.na(penguins$bill_length_mm), ]))

Plotting the results

Results can then be displayed in a scatterplot either regular or binned.


tinytheme("tufte")
par(mfrow = c(2, 1))


tinyplot(res_fixest$res_bill_length_mm ~ res_fixest$res_bill_depth_mm)
tinyplot_add(
  res_fixest$res_bill_length_mm ~ res_fixest$res_bill_depth_mm,
  type = "lm"
)

res_fixest$qnt <- cut(
  res_fixest$res_bill_depth_mm,
  breaks = quantile(res_fixest$res_bill_depth_mm, probs = seq(0, 1, .02)),
  include.lowest = TRUE
)


res_qnt <- aggregate(
  cbind(res_bill_depth_mm, res_bill_length_mm) ~ qnt,
  data = res_fixest,
  FUN = mean
)

tinyplot(res_qnt$res_bill_length_mm ~ res_qnt$res_bill_depth_mm)
tinyplot_add(
  res_fixest$res_bill_length_mm ~ res_fixest$res_bill_depth_mm,
  type = "lm"
)

Plotting all residuals is equivalent to the following approach in fwlplot


fwlplot(bill_length_mm ~ bill_depth_mm | species, data = penguins)

Adding other parameters to the model

Any parameters that could be passed to lm(), feols(), or felm(), can be passed to partialling_out().

model_fixest <- feols(
  bill_length_mm ~ bill_depth_mm | species + island,
  data = penguins,
  cluster = ~species
)


tt(head(partialling_out(model_fixest, data = penguins, cluster = ~species))) |>
  format_tt(digits = 2) |>
  style_tt(align = "c")
res_bill_length_mm res_bill_depth_mm
0.149 0.27
0.549 -1.03
1.349 -0.43
-2.251 0.87
0.349 2.17
-0.051 -0.63

Acknowledgements

To the authors of the fwlplot package, Kyle Butts and Grant McDermott, which has provided inspiration and ideas for this project. To my colleague Andreu Arenas-Jal for his insight and guiding.