Visualization: Partial residual plots

[mattansb]

Are we allowed to steal from ourselves?

https://strengejacke.github.io/ggeffects/reference/residualize_over_grid.html

[DominiqueMakowski]

library(ggeffects)
#> Warning: package 'ggeffects' was built under R version 4.0.5
set.seed(1234)
x <- rnorm(200)
z <- rnorm(200)
# quadratic relationship
y <- 2 * x + x^2 + 4 * z + rnorm(200)

d <- data.frame(x, y, z)
model <- lm(y ~ x + z, data = d)

pr <- ggpredict(model, c("x [all]", "z"))
head(residualize_over_grid(pr, model))
#>          x group predicted
#> 53  -1.207  0.07 -1.797239
#> 402  0.277  1.08  4.888712
#> 518  1.084  0.07  3.232202
#> 9   -2.346  1.08  4.133561
#> 428  0.429  0.07  1.801594
#> 441  0.506  1.08  5.659527

^{Created on 2021-07-06 by the reprex package (v2.0.0)}

I don't understand what does it do

[mattansb]

Here’s a basic explanation: https://en.wikipedia.org/wiki/Partial_residual_plot (I’m sure @bwiernik would looooveee to elaborate!)

S <- diag(1, 3, 3)
S[1,2] <- S[2,1] <- 0.6
S[1,3] <- S[3,1] <- 0.8
S[2,3] <- S[3,2] <- 0.8

dat <- MASS::mvrnorm(500, c(10,20,30), S, empirical = TRUE)
colnames(dat) <- c("X","Y","Z")
dat <- data.frame(dat)

fit <- lm(Y ~ X + Z, data = dat)

What the hell is this???

plot(ggeffects::ggpredict(fit, "X"), add.data = TRUE, jitter = 0)
#> Loading required namespace: ggplot2

This is because the raw data can only show us the marginal association. Instead, we can look at the residualized data (where the covariance with Z is “controlled” for):

plot(ggeffects::ggpredict(fit, "X [all]"), residuals = TRUE, jitter = 0)

^{Created on 2021-07-06 by the reprex package (v2.0.0)}

[DominiqueMakowski]

Oh I see, so that'd be mostly for plotting of the data points overlay right?

[mattansb]

Yes, that would be only for data overlay.

[bwiernik]

(I’m sure @bwiernik would looooveee to elaborate!)

👀 😝

So, we might consider making four types of plots for a regression model relating a single predictor to the response variable. Two of them are confusingly named something with "partial" in the title.

See them here below:

``` r library(ggplot2) mf <- lm(mpg ~ hp + wt, data = transform(mtcars, cyl = factor(cyl))) mr <- lm(mpg ~ hp, data = transform(mtcars, cyl = factor(cyl))) mx <- lm( wt ~ hp, data = transform(mtcars, cyl = factor(cyl))) plot_dat <- mtcars |> subset(select = c(mpg, cyl, hp, wt)) |> transform( fit_f = predict(mf), res_f = resid(mf), fit_r = predict(mr), res_r = resid(mr), fit_x = predict(mx), res_x = resid(mx), par_x = coef(mf)['wt'] * wt, cmf_y = as.vector(colMeans(model.matrix(mf)[,names(coef(mf)) != "wt"]) %*% coef(mf)[names(coef(mf)) != "wt"]) ) base_plot <- ggplot(plot_dat) + geom_point() + geom_smooth(method = "lm", formula = y ~ x) + see::theme_modern(plot.title.space = 1) fit_resid_plot <- base_plot + aes(x = wt, y = res_f) + labs(title = "(A) Predictor - Residuals Plot", caption = "Is there misspecified nonlinearity/endogeneity?", x = "X", y = "Residual Y | full") par_regre_plot <- base_plot + aes(x = res_x, y = res_r) + labs(title = "(B) Residual Predictor - Residuals Plot", subtitle = "Partial Regression Plot / Added Variable Plot", caption = "Does X add predictive power above and beyond covariates?", x = "Residual X | covariates", y = "Residual Y | covariates") fit_effect_plot <- ggplot(plot_dat) + geom_point(aes(x = wt, y = mpg)) + geom_smooth( aes(x = wt, y = res_f + par_x + cmf_y), method = "lm", formula = y ~ x ) + see::theme_modern(plot.title.space = 1) + labs(title = "(C) Predictor - Response Plot", subtitle = "Predictor effects plot", caption = "What is the relationship of X with Y, controlling for covariates?", x = "X", y = "Y") par_resid_plot <- base_plot + aes(x = wt, y = res_f + par_x) + labs(title = "(D) Predictor - Partial Residuals Plot", caption = "What is the relationship of X with Y, controlling for covariates?", x = "X", y = "Residual Y | full + Effect_X") ```

fit_resid_plot

par_regre_plot

fit_effect_plot

par_resid_plot

1. (A) Predictor vs Residuals (full model): Most common diagnostic plot--detect nonlinearity, omitted variables, omitted interactions, etc.
2. (B) Residual predictor vs residual response (from covariates-only model): "added variable plot"--how does the unique part of X predict the left-over unique part of Y after the covariates?
3. (C) Predictor vs Response plot: "predictor effects plot" -- points are raw response, fit line is partial slope for X controlling for covariates (computed as residual + x effect (X * β~x~ + covariate mean effects)-- used to illustrate the partial effect of X on Y
4. (D) Predictor vs partial residuals plot: points computed as residual + e effect; fit line is lm for that. This is used to illustrate partial effect of X (like C; but more confusingly). Can also be used for misspecification diagnostics by adding a loess line and seeing if there is nonlinearity/interactions missing from the model (see https://cran.r-project.org/web/packages/effects/vignettes/partial-residuals.pdf)

[DominiqueMakowski]

I have a hard time understanding what should be a separate function, what should be a plotting option and what should be an option to estimate_predicted ^^ where should we start?

Also, I'm thinking we should add an argument to datawizard::adjust() to be able to pass a pre-specified model to adjust with, rather than letting it build the model.

[bwiernik]

I don't think overloading adjust is a good idea. Instead, I think separate functions make sense here.

[mattansb]

(like C; but more confusingly)

Obviously I completely disagree 😅 - D is the plot that corresponds the closest to the slope's coefficient - it gives the slope (as the conditional regression line) + it visualizes the errors from the overall regression hyper-plane across that line/dimension. That is, for each data point, you can see it's predicted value (on the reg line) and it's error. And it can be used for diagnostics - linearity, missing interactions... A thing of beauty!

[DominiqueMakowski]

so plot(estimate_predicted) should be able to produce either C (default - as is implemented now) or D? i.e., we should try implementing D?

[mattansb]

I think so, yes. I can prob get the code I wrote for ggeffects to work here too (the one that takes data + grid = residualized).

[bwiernik]

Hmm, I wonder if a separate estimate_partial() function would make more sense? With the option for either B or D above? To do that, it could have partial.x and partial.y arguments.

I admit I don't really get D at all--I'm not really following what you are saying above Mattan? I mostly use A and B for diagnostics/probing

[mattansb]

Take the data from data from plot A - each point is (X, residual). Now take the regression line, and along X, add it residual to the line, and you have plot D. This is why D has the properties of plot A, and non of the weirdness going on on the x-axis of plot B. And if you draw a vertical line from each point (X, pred Y + residual), you can see what the predicted value for that point would be if all other vars were held constant. This is why it's (IMO) the closest visual representation of the data around the regression hyper-plane - if you were to "flatten" all other (non X or Y) of the multi-variable hyper space to a point (fix them to a constant), the resulting X,Y plot would be D.

I just find it an elegant way to present results when there are multiple predictors involved 🤷‍♂️ [It's also computationally easy, compared to B that required extra model fitting]

[bwiernik]

(B is a completely different thing than A--not intended for the same purpose--so let's not worry about that comparison)

[DominiqueMakowski]

B is indeed different in the sense that it requires multiple models, it's a thing on its own. A is pretty much obtained via performance:check if I'm not mistaken. C is the current modelbased default. Which leaves D:

For the following model, how would you do to get the partial residuals?

m <- lm(Sepal.Length ~ Petal.Width + Species, data = iris)

pred <- modelbased::estimate_expectation(m, target = "Petal.Width")
pred
#> Model-based Expectation
#> 
#> Petal.Width | Species | Predicted |   SE |       95% CI
#> -------------------------------------------------------
#> 0.10        |  setosa |      4.87 | 0.07 | [4.73, 5.02]
#> 0.37        |  setosa |      5.12 | 0.07 | [4.97, 5.26]
#> 0.63        |  setosa |      5.36 | 0.10 | [5.16, 5.56]
#> 0.90        |  setosa |      5.61 | 0.14 | [5.32, 5.89]
#> 1.17        |  setosa |      5.85 | 0.19 | [5.47, 6.23]
#> 1.43        |  setosa |      6.09 | 0.24 | [5.62, 6.57]
#> 1.70        |  setosa |      6.34 | 0.29 | [5.77, 6.91]
#> 1.97        |  setosa |      6.58 | 0.34 | [5.91, 7.26]
#> 2.23        |  setosa |      6.83 | 0.39 | [6.06, 7.60]
#> 2.50        |  setosa |      7.07 | 0.44 | [6.20, 7.95]
#> 
#> Variable predicted: Sepal.Length
#> Predictors modulated: Petal.Width
#> Predictors controlled: Species

plot(pred, show_data = "none")

^{Created on 2021-07-09 by the reprex package (v2.0.0)}

[mattansb]

Okay, this is the code for this issue and for #130.

***The functions***

``` r get_data_for_grid <- function(grid, residuals = FALSE, collapse.by = NULL) { #' @param grid For modelbased #' @param residuals [FALSE (default) | TRUE] Should the partial residuals be #' returned? #' @param collapse.by [NULL (default) | TRUE | char] The name of a random #' grouping factor to collapse by. If TRUE will select (the first) from the #' model. model <- attr(grid, "model") data <- insight::get_data(model) pred_name <- attr(grid, "response", exact = TRUE) X_names <- attr(grid, "target", exact = TRUE) data if (residuals) { data_r <- .residualize_over_grid( grid = grid[union("Predicted", X_names)], model = model, data = data, pred_name = "Predicted", collapse.by = collapse.by ) colnames(data_r)[colnames(data_r)=="Predicted"] <- pred_name if(is.null(collapse.by)) { add <- setdiff(colnames(data), colnames(data_r)) data_r[add] <- data[add] } data <- data_r[intersect(colnames(data), colnames(data_r))] } else if (!is.null(collapse.by)) { data_r <- .collapse_by_group( data = data, model = model, pred_name = pred_name, X_names = X_names, collapse.by = collapse.by ) data <- data_r[intersect(colnames(data), colnames(data_r))] } return(as.data.frame(data)) } .residualize_over_grid <- function (grid, model, data, pred_name, collapse.by = NULL, ...) { old_d <- insight::get_predictors(model) fun_link <- insight::link_function(model) inv_fun <- insight::link_inverse(model) predicted <- grid[[pred_name]] grid[[pred_name]] <- NULL is_fixed <- sapply(grid, function(x) length(unique(x))) == 1 grid <- grid[, !is_fixed, drop = FALSE] old_d <- old_d[, colnames(grid)[colnames(grid) %in% colnames(old_d)], drop = FALSE] if (!.is_grid(grid)) { stop("Grid for partial residuals must be a fully crossed grid.") } best_match <- NULL for (p in colnames(old_d)) { if (is.factor(old_d[[p]]) || is.logical(old_d[[p]]) || is.character(old_d[[p]])) { grid[[p]] <- as.character(grid[[p]]) old_d[[p]] <- as.character(old_d[[p]]) } else { grid[[p]] <- .validate_num(grid[[p]]) } best_match <- .closest(old_d[[p]], grid[[p]], best_match = best_match) } idx <- apply(best_match, 2, which) idx <- sapply(idx, "[", 1) res <- tryCatch(stats::residuals(model, type = "working"), error = function(e) NULL) if (is.null(res)) { warning("Could not extract residuals.", call. = FALSE) return(NULL) } points <- grid[idx, , drop = FALSE] points[[pred_name]] <- fun_link(predicted[idx]) + res if (!is.null(collapse.by)) { data[[pred_name]] <- points[[pred_name]] points <- .collapse_by_group( data = data, model = model, pred_name = pred_name, X_names = colnames(grid), collapse.by = collapse.by ) } points[[pred_name]] <- inv_fun(points[[pred_name]]) points } .collapse_by_group <- function (data, model, pred_name, X_names, collapse.by = TRUE) { if (!insight::is_mixed_model(model)) { stop("This function only works with mixed effects models.", call. = FALSE) } if (isTRUE(collapse.by)) { collapse.by <- insight::find_random(model, flatten = TRUE) } if (length(collapse.by) > 1) { collapse.by <- collapse.by[1] warning("More than one random grouping variable found.", "\n Using `", collapse.by, "`.", call. = FALSE) } if (!collapse.by %in% colnames(data)) { stop("Could not find `", collapse.by, "` column.", call. = FALSE) } agg_data <- stats::aggregate(data[[pred_name]], by = data[union(collapse.by, X_names)], FUN = mean) colnames(agg_data)[ncol(agg_data)] <- pred_name agg_data } # Utils ------------------------------------------------------------------- .is_grid <- function (df) { unq <- lapply(df, unique) if (prod(sapply(unq, length)) != nrow(df)) { return(FALSE) } df2 <- do.call(expand.grid, args = unq) df2$..1 <- 1 res <- merge(df, df2, by = colnames(df), all = TRUE) return(sum(res$..1) == sum(df2$..1)) } .validate_num <- function(x) { if (!is.numeric(x)) { x <- as.numeric(as.character(x)) } x } .closest <- function (x, target, best_match) { if (is.numeric(x)) { AD <- abs(outer(x, target, FUN = `-`)) idx <- apply(AD, 1, function(x) x == min(x)) } else { idx <- t(outer(x, target, FUN = `==`)) } if (is.matrix(best_match)) { idx <- idx & best_match } idx } ```

library(modelbased)

S <- diag(1, 3, 3)
S[1,2] <- S[2,1] <- 0.6
S[1,3] <- S[3,1] <- 0.8
S[2,3] <- S[3,2] <- 0.8

dat <- MASS::mvrnorm(500, c(10, 20, 30), S, empirical = TRUE)
colnames(dat) <- c("X", "Y", "Z")
dat <- data.frame(dat)
dat$X <- cut(dat$X, breaks = 10)
dat$ID <- rep(letters, length.out = 500)

fit <- lme4::lmer(Y ~ X + Z + (1|ID), data = dat)

pred <- modelbased::estimate_expectation(fit, target = "X")

get_data_for_grid(pred) |> head()
#>          Y           X        Z ID
#> 1 21.34821 (10.9,11.8] 31.57896  a
#> 2 19.20351 (9.22,10.1] 29.67882  b
#> 3 19.42736 (9.22,10.1] 29.84396  c
#> 4 19.07498 (8.37,9.22] 28.65095  d
#> 5 19.90768 (9.22,10.1] 29.47125  e
#> 6 20.33612 (11.8,12.6] 31.29219  f
get_data_for_grid(pred, residuals = T) |> head()
#>            Y           X        Z ID
#> 6   19.98276 (10.9,11.8] 31.57896  a
#> 4   19.48057 (9.22,10.1] 29.67882  b
#> 4.1 19.57408 (9.22,10.1] 29.84396  c
#> 3   20.23562 (8.37,9.22] 28.65095  d
#> 4.2 20.36956 (9.22,10.1] 29.47125  e
#> 7   19.18748 (11.8,12.6] 31.29219  f
get_data_for_grid(pred, collapse.by = T) |> head()
#>          Y           X ID
#> 1 18.47561 (6.67,7.52]  p
#> 2 18.56970 (6.67,7.52]  q
#> 3 18.63299 (6.67,7.52]  t
#> 4 17.10856 (6.67,7.52]  v
#> 5 19.41991 (7.52,8.37]  b
#> 6 19.01859 (7.52,8.37]  e
get_data_for_grid(pred, residuals = T, collapse.by = T) |> head()
#>          Y           X ID
#> 1 20.63882 (6.67,7.52]  p
#> 2 21.43813 (6.67,7.52]  q
#> 3 20.17450 (6.67,7.52]  t
#> 4 18.79272 (6.67,7.52]  v
#> 5 20.14677 (7.52,8.37]  b
#> 6 20.48828 (7.52,8.37]  e

^{Created on 2021-07-09 by the reprex package (v2.0.0)}