Odd plots for categorical variable only models

It attempting to look at lm() objects with only categorical predictors and evaluating fitted v. residuals (for linearity) or sqrt(abs(std residuals)), I get some very odd plots - particularly relative to good old plot(). Also some very interesting errors. This seems like non-ideal behavior.

library(palmerpenguins)
library(performance)

mod <- lm(body_mass_g ~ sex, data = penguins)

check_model(mod, check = "linearity") |> plot()
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : pseudoinverse used at 3858.9
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : neighborhood radius 686.83
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : reciprocal condition number 1.3398e-15
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : There are other near singularities as well. 4.7173e+05
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
#> 3858.9
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
#> 686.83
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
#> number 1.3398e-15
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : There are other near
#> singularities as well. 4.7173e+05

check_model(mod, check = "homogeneity") |> plot()
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : pseudoinverse used at 3858.9
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : neighborhood radius 686.83
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : reciprocal condition number 1.3398e-15
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : There are other near singularities as well. 4.7173e+05
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
#> 3858.9
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
#> 686.83
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
#> number 1.3398e-15
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : There are other near
#> singularities as well. 4.7173e+05

^{Created on 2022-10-13 with reprex v2.0.2}

Compare this to plot()

library(palmerpenguins)
library(performance)

mod <- lm(body_mass_g ~ sex, data = penguins)

plot(mod, which = 1)

^{Created on 2022-10-13 with reprex v2.0.2}

Start by reproducing the categorical-predictor example with lm(), check_model(mod, check = "linearity"), and check_model(mod, check = "homogeneity"). Compare both outputs and warnings with plot(mod, which = 1); done means the diagnostic plots handle categorical-only predictors without the reported loess warnings and produce sensible results.