Visualization of the relationship between x and y, after partialling out

Makes a bivariate graph to visualize the data and relationship between the outcome and the variable of interest, after having partialLed out controls.

Usage

idid_viz_bivar(reg, var_interest, partial_iv = TRUE, ...)

Arguments

reg

A regression object.

var_interest

A string. The name of the main variable of interest.

partial_iv

A boolean. If TRUE, partials out the part of the endogenous variables that are not explained by the instruments. If the regression is not an IV (or not generated by AER::ivreg or fixest::feols with an instrumented part), the value of partial_iv has no impact on the outcome of the function.

...

Additional elements to pass to the regression function when partialling out controls.

Value

A ggplot2 scatter plot and regression line of the relationship between the outcome and the variable of interest, after partialling out controls.

Examples

reg_few_ctrl <- ggplot2::txhousing |>
  lm(formula = log(sales) ~ median)

idid_viz_bivar(reg_few_ctrl, "median")
#> Warning: Removed 617 rows containing non-finite outside the scale range
#> (`stat_smooth()`).
#> Warning: Removed 617 rows containing missing values or values outside the scale range
#> (`geom_point()`).


reg_more_ctrl <- ggplot2::txhousing |>
  lm(formula = log(sales) ~ median + listings + city + as.factor(date))

idid_viz_bivar(reg_more_ctrl, "median")
#> Warning: Removed 1434 rows containing non-finite outside the scale range
#> (`stat_smooth()`).
#> Warning: Removed 1434 rows containing missing values or values outside the scale range
#> (`geom_point()`).