tests/testthat/test-draw-methods.R

In gratia: Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv'

## Test draw() methods

## load packages
library("testthat")
library("gratia")
library("mgcv")
library("ggplot2")

## Need a local wrapper to allow conditional use of vdiffr
# `expect_doppelganger` <- function(title, fig, ...) {
#   testthat::skip_if_not_installed("vdiffr")
#   vdiffr::expect_doppelganger(title, fig, ...)
# }

test_that("draw.evaluated_1d_smooth() plots the smooth", {
    withr::local_options(lifecycle_verbosity = "quiet")
    sm <- evaluate_smooth(su_m_univar_4, "s(x2)")
    plt <- draw(sm)
    expect_doppelganger("draw 1d smooth for selected smooth", plt)
})

test_that("draw.gam works with numeric select", {
    plt <- draw(su_m_quick_eg1, select = 2)
    expect_doppelganger("draw gam smooth for selected smooth numeric", plt)
    plt <- draw(su_m_quick_eg1, select = c(1,2))
    expect_doppelganger("draw gam smooth for two selected smooths numeric", plt)
})

test_that("draw.gam fails with bad select", {
    expect_error(draw(su_m_univar_4, select = 8),
                 "One or more indices in 'select' > than the number of smooths in the model.",
                 fixed = TRUE)
    expect_error(draw(su_m_univar_4, select = c(1,3,5,6)),
                 "One or more indices in 'select' > than the number of smooths in the model.",
                 fixed = TRUE)
    expect_error(draw(su_m_univar_4, select = c(1,2,3,4,5)),
                 "Trying to select more smooths than are in the model.",
                 fixed = TRUE)
    expect_error(draw(su_m_univar_4, select = TRUE),
                 "When 'select' is a logical vector, 'length(select)' must equal
the number of smooths in the model.", fixed = TRUE)
})

test_that("draw.gam works with character select", {
    plt <- draw(su_m_quick_eg1, select = "s(x1)")
    expect_doppelganger("draw gam smooth for selected smooth character", plt)
    plt <- draw(su_m_quick_eg1, select = c("s(x0)", "s(x1)"))
    expect_doppelganger("draw gam smooth for two selected smooths character",
                        plt)
})

test_that("draw.gam works with logical select", {
    plt <- draw(su_m_quick_eg1, select = c(TRUE, rep(FALSE, 3)))
    expect_doppelganger("draw gam smooth for selected smooth logical", plt)
    plt <- draw(su_m_quick_eg1, select = rep(c(TRUE, FALSE), each = 2))
    expect_doppelganger("draw gam smooth for two selected smooths logical", plt)
})

test_that("draw.gam works with partial_match", {
    plt <- draw(su_m_factor_by, select = 'x2', partial_match = TRUE)
    expect_doppelganger("draw gam with partial match TRUE", plt)
    expect_error(draw(su_m_factor_by, select = "s(x2)", partial_match = FALSE),
                 "Failed to match any smooths in model `su_m_factor_by`.\nTry with 'partial_match = TRUE'?",
                 fixed = TRUE)
})

test_that("draw.gam works with select and parametric", {
    plt <- draw(su_m_factor_by, select = 's(x2)', partial_match = TRUE)
    expect_doppelganger("draw gam with select and parametric is NULL", plt)
    plt <- draw(su_m_factor_by, select = 's(x2)', partial_match = TRUE,
                parametric = FALSE)
    expect_doppelganger("draw gam with select and parametric is FALSE", plt)
    plt <- draw(su_m_factor_by, select = 's(x2)', partial_match = TRUE,
                parametric = TRUE)
    expect_doppelganger("draw gam with select and parametric is TRUE", plt)
    plt <- draw(su_m_factor_by, parametric = TRUE, rug = FALSE)
    expect_doppelganger("draw gam without select and parametric is TRUE", plt)
    plt <- draw(su_m_factor_by, parametric = FALSE, rug = FALSE)
    expect_doppelganger("draw gam without select and parametric is FALSE", plt)
})

test_that("draw.evaluated_2d_smooth() plots the smooth", {
    skip_on_os("mac")
    skip_on_os("win") # failing for trivial diffs in contours
    withr::local_options(lifecycle_verbosity = "quiet")
    expect_silent(sm <- evaluate_smooth(su_m_bivar, "s(x,z)", n = 100))
    expect_silent(plt <- draw(sm))
    expect_doppelganger("draw 2d smooth", plt)
    expect_silent(plt <- draw(sm, contour_col = "red"))
    expect_doppelganger("draw 2d smooth diff contour colour", plt)
})

test_that("draw.evaluated_2d_smooth() plots the smooth without contours", {
    skip_on_os("mac")
    withr::local_options(lifecycle_verbosity = "quiet")
    sm <- evaluate_smooth(su_m_bivar, "s(x,z)", n = 100)
    plt <- draw(sm, contour = FALSE)
    expect_doppelganger("draw 2d smooth without contours", plt)
})

test_that("draw.evaluated_2d_smooth() plots the smooth with different contour bins", {
    skip_on_os("mac")
    skip_on_os("windows")
    withr::local_options(lifecycle_verbosity = "quiet")
    sm <- evaluate_smooth(su_m_bivar, "s(x,z)", n = 100)
    plt <- draw(sm, n_contour = 5)
    expect_doppelganger("draw 2d smooth with 5 contour bins", plt)
    plt <- draw(sm, n_contour = 20)
    expect_doppelganger("draw 2d smooth with 20 contour bins", plt)
})

test_that("draw.evaluated_2d_smooth() plots the SE", {
    skip_on_os("mac")
    skip_on_os("windows")
    withr::local_options(lifecycle_verbosity = "quiet")
    sm <- evaluate_smooth(su_m_bivar, "s(x,z)", n = 100)
    plt <- draw(sm, show = "se")
    expect_doppelganger("draw std error of 2d smooth", plt)
})

test_that("draw.gam() plots a simple multi-smooth AM", {
    plt <- draw(su_m_quick_eg1)
    expect_doppelganger("draw simple multi-smooth AM", plt)

    plt <- draw(su_m_quick_eg1, scales = "fixed")
    expect_doppelganger("draw simple multi-smooth AM with fixed scales", plt)
})

test_that("draw.gam() can draw partial residuals", {
    plt <- draw(su_m_quick_eg1, residuals = TRUE, rug = FALSE)
    expect_doppelganger("draw simple partial residuals", plt)

    plt <- draw(su_m_quick_eg1, residuals = TRUE, scales = "fixed", rug = FALSE)
    expect_doppelganger("draw simple partial residuals with fixed scales", plt)
})

test_that("draw.gam() plots an AM with a single 2d smooth", {
    skip_on_os("mac")
    skip_on_os("windows")
    withr::local_options(lifecycle_verbosity = "quiet")
    plt <- draw(su_m_bivar, n = 50, rug = FALSE)
    expect_doppelganger("draw AM with 2d smooth", plt)

    # sm <- evaluate_smooth(su_m_bivar, smooth = "s(x,z)")
    # plt <- draw(sm, show = "se")
    # expect_doppelganger("draw evalated 2d smooth standard errors", plt)
})

test_that("draw.gam() plots an AM with a single factor by-variable smooth", {
    plt <- draw(su_m_factor_by)
    expect_doppelganger("draw AM with factor by-variable smooth", plt)

    plt <- draw(su_m_factor_by, scales = "fixed")
    expect_doppelganger("draw factor by-variable smooth with fixed scales", plt)
})

## simulate date from y = f(x2)*x1 + error
#dat <- gamSim(3, n = 400, verbose = FALSE)
mod <- gam(y ~ s(x2, by = x1), data = su_eg3)

test_that("draw() works with continuous by", {
    plt <- draw(mod)
    expect_doppelganger("draw with continuous by-variable smooth", plt)
})

test_that("draw() works with continuous by and fixed scales", {
    plt <- draw(mod, scales = "fixed")
    expect_doppelganger("draw with continuous by-var fixed scale", plt)
})

test_that("draw() works with random effect smooths (bs = 're')", {
    withr::local_options(lifecycle_verbosity = "quiet")
    ## simulate example... from ?mgcv::random.effects
    ## data are in su_re
    ## model is rm1 with definition:
    ## rm1 <- gam(y ~ s(fac, bs = "re") + s(x0) + s(x1) + s(x2) +
    ##                s(x3), data = new_df, method = "ML")

    sm <- evaluate_smooth(rm1, "s(fac)")
    expect_s3_class(sm, "evaluated_re_smooth")

    p1 <- draw(sm)
    expect_doppelganger("draw.evaluated_re_smooth", p1)

    p2 <- draw(rm1, ncol = 3, rug = FALSE)
    expect_doppelganger("draw.gam model with ranef smooth", p2)

    p3 <- draw(rm1, ncol = 3, scales = "fixed", rug = FALSE)
    expect_doppelganger("draw model with ranef smooth fixed scales", p3)
})

test_that("draw() with random effect smooths (bs = 're') & factor by variable ",
{
    withr::local_options(lifecycle_verbosity = "quiet")
    # simulate example...
    # data are in su_re2
    # model is rm2 with definition:
    # gam(y ~ fac + s(ranef, bs = "re", by = fac) + s(x0) + s(x1) + s(x2),
    #     data = su_re2, method = "ML")

    sm <- evaluate_smooth(rm2, "s(ranef)")
    expect_s3_class(sm, "evaluated_re_smooth")
    p1 <- draw(sm)
    expect_doppelganger("draw.evaluated_re_smooth with factor by", p1)
    p2 <- draw(rm2, ncol = 3, rug = FALSE)
    expect_doppelganger("draw.gam model with ranef smooth factor by", p2)
    p3 <- draw(rm2, ncol = 3, scales = "fixed", rug = FALSE)
    expect_doppelganger("draw with ranef smooth factor by fixed scales", p3)
})

test_that("draw() can handle non-standard names -- a function call as a name", {
    df <- data.frame(y = c(0.15,0.17,0.07,0.17,0.01,0.15,0.18,0.04,-0.06,-0.08,
                           0, 0.03,-0.27,-0.93,0.04,0.12,0.08,0.15,0.04,0.15,
                           0.03,0.09,0.11,0.13,-0.11,-0.32,-0.7,-0.78,0.07,0.04,
                           0.06,0.12,-0.15,0.05,-0.08,0.14,-0.02,-0.14,-0.24,
                           -0.32,-0.78,-0.81,-0.04,-0.25,-0.09,0.02,-0.13,-0.2,
                           -0.04,0,0.02,-0.05,-0.19,-0.37,-0.57,-0.81),
                    time =  rep(2^c(-1, 0, 1, 1.58,2, 2.58, 3, 3.32, 3.58, 4.17,
                                     4.58, 5.58, 6.17, 7.39), 4))
    ## the smooth is of `log2(time)` but this needs special handling
    ## in the `ggplot()` to avoid `ggplot()` looking incorrectly for `time` and
    ## not the correct `log2(time)`
    fit <- gam(y ~ s(log2(time)), data = df, method = "REML")
    p1 <- draw(fit)
    expect_doppelganger("draw.gam model with non-standard names", p1)
})

## simulate example... from ?mgcv::factor.smooth.interaction
set.seed(0)
## simulate data...
f0 <- function(x) 2 * sin(pi * x)
f1 <- function(x, a=2, b=-1) exp(a * x)+b
f2 <- function(x) 0.2 * x^11 * (10 * (1 - x))^6 + 10 *
                      (10 * x)^3 * (1 - x)^10
n <- 500
nf <- 10
fac <- sample(1:nf, n, replace=TRUE)
x0 <- runif(n)
x1 <- runif(n)
x2 <- runif(n)
a <- rnorm(nf) * .2 + 2;
b <- rnorm(nf) * .5
f <- f0(x0) + f1(x1, a[fac], b[fac]) + f2(x2)
fac <- factor(fac)
y <- f + rnorm(n) * 2

df <- data.frame(y = y, x0 = x0, x1 = x1, x2 = x2, fac = fac)
mod_fs <- gam(y~s(x0) + s(x1, fac, bs = "fs", k = 5) + s(x2, k = 20),
              method = "ML")

test_that("draw() works with factor-smooth interactions (bs = 'fs')", {
    withr::local_options(lifecycle_verbosity = "quiet")
    skip_if(packageVersion("mgcv") < "1.8.36")
    sm <- evaluate_smooth(mod_fs, "s(x1,fac)")
    expect_s3_class(sm, "evaluated_fs_smooth")

    p1 <- draw(sm)
    expect_doppelganger("draw.evaluated_fs_smooth", p1)

    p2 <- draw(mod_fs, ncol = 2, rug = FALSE)
    expect_doppelganger("draw.gam model with fs smooth", p2)

    p3 <- draw(mod_fs, ncol = 2, scales = "fixed", rug = FALSE)
    expect_doppelganger("draw model with fs smooth fixed scales", p3)
})

test_that("draw() works with parametric terms", {
    set.seed(0)
    ## fake some data...
    f1 <- function(x) {exp(2 * x)}
    f2 <- function(x) {
        0.2*x^11*(10*(1-x))^6+10*(10*x)^3*(1-x)^10
    }
    f3 <- function(x) {x*0}

    n <- 200
    sig2 <- 4
    x0 <- rep(1:4,50)
    x1 <- runif(n, 0, 1)
    x2 <- runif(n, 0, 1)
    x3 <- runif(n, 0, 1)
    e <- rnorm(n, 0, sqrt(sig2))
    y <- 2*x0 + f1(x1) + f2(x2) + f3(x3) + e
    df <- data.frame(x0 = x0, x1 = x1, x2 = x2, x3 = x3, y = y)

    ## fit
    mod <- gam(y ~ x0 + s(x1) + s(x2) + s(x3), data = df)

    ## evaluate parametric terms directly
    e1 <- evaluate_parametric_term(mod, term = "x0")
    expect_s3_class(e1, "evaluated_parametric_term")
    expect_equal(ncol(e1), 5L)
    expect_named(e1, c("term", "type", "value", "partial", "se"))
    p1 <- draw(e1)
    expect_doppelganger("draw with linear parametric term", p1)

    ## check evaluate_parametric_term works
    p2 <- draw(mod)
    expect_doppelganger("draw.gam with linear parametric term", p2)

    ## factor parametric terms
    x0 <- factor(x0)
    df <- data.frame(x0 = x0, x1 = x1, x2 = x2, x3 = x3, y = y)
    ## fit
    mod <- gam(y ~ x0 + s(x1) + s(x2) + s(x3), data = df)

    ## check evaluate_parametric_term works
    p3 <- draw(mod)
    expect_doppelganger("draw.gam with factor parametric term", p3)

    ## evaluate parametric terms directly
    e2 <- evaluate_parametric_term(mod, term = "x0")
    expect_s3_class(e2, "evaluated_parametric_term")

    expect_error(evaluate_parametric_term(mod, term = "x1"),
                 "Term is not in the parametric part of model: <x1>",
                 fixed = TRUE)

    expect_warning(evaluate_parametric_term(mod, term = c('x0', 'x1')),
                   "More than one `term` requested; using the first <x0>",
                   fixed = TRUE)
})

test_that("component-wise CIs work without seWithMean", {
    plt <- draw(su_m_univar_4, overall_uncertainty = FALSE, rug = FALSE)
    expect_doppelganger("draw gam with overall_uncertainty false", plt)
})

test_that("draw.derivates() plots derivatives for a GAM", {
    d1 <- derivatives(su_m_univar_4, type = "central")
    plt <- draw(d1)
    expect_doppelganger("draw derivatives for a GAM", plt)

    plt <- draw(d1, scales = "fixed")
    ## skip_on_ci()
    expect_doppelganger("draw derivatives for a GAM with fixed scales", plt)
})

test_that("draw.derivates() plots derivatives for a GAM rotated labels", {
    skip_on_cran()
    d1 <- derivatives(su_m_univar_4, type = "central")
    plt <- draw(d1, angle = 45)
    expect_doppelganger("draw derivatives for a GAM rotated labels", plt)

    plt <- draw(d1, scales = "fixed", angle = 45)
    expect_doppelganger("draw derivatives for a GAM with fixed scales rotated",
        plt)
})

## test that issue 39 stays fixed
test_that("draw.gam doesn't create empty plots with multiple parametric terms", {
    plt <- draw(m_2_fac, rug = FALSE)
    expect_doppelganger("draw issue 39 empty plots", plt)
})

test_that("draw.mgcv_smooth() can plot basic smooth bases", {
    bs <- basis(s(x0), data = su_eg1)
    plt <- draw(bs)
    expect_doppelganger("draw basic tprs basis", plt)
})

test_that("draw.mgcv_smooth() can plot basic smooth bases with rotated labels",
{
    skip_on_cran()
    bs <- basis(s(x0), data = su_eg1)
    plt <- draw(bs, angle = 45)
    expect_doppelganger("draw basic tprs basis rotated", plt)
})

test_that("draw.mgcv_smooth() can plot by factor basis smooth bases", {
    bs <- basis(s(x2, by = fac), data = su_eg4)
    plt <- draw(bs)
    expect_doppelganger("draw by factor basis", plt)
})

test_that("draw() works with a ziplss models; issue #45", {
    ## simulate some data...
    f0 <- function(x) 2 * sin(pi * x); f1 <- function(x) exp(2 * x)
    f2 <- function(x) 0.2 * x^11 * (10 * (1 - x))^6 + 10 * 
                          (10 * x)^3 * (1 - x)^10
    n <- 500
    set.seed(5)
    x0 <- runif(n)
    x1 <- runif(n)
    x2 <- runif(n)
    x3 <- runif(n)

    ## Simulate probability of potential presence...
    eta1 <- f0(x0) + f1(x1) - 3
    p <- binomial()$linkinv(eta1) 
    y <- as.numeric(runif(n) < p) ## 1 for presence, 0 for absence

    ## Simulate y given potentially present (not exactly model fitted!)...
    ind <- y > 0
    eta2 <- f2(x2[ind])/3
    y[ind] <- rpois(exp(eta2), exp(eta2))
    df <- data.frame(y, x0, x1, x2, x3)
    b1 <- gam(list(y ~ s(x2) + x3,
                   ~ s(x0) + x1), family = ziplss(), data = df)
    plt <- draw(b1, rug = FALSE)
    expect_doppelganger("draw ziplss parametric terms issue 45", plt)
})

test_that("draw works for sample_smooths objects", {
    sm1 <- smooth_samples(su_m_univar_4, n = 15, seed = 23478, n_vals = 50)
    plt <- draw(sm1, alpha = 0.7, n_samples = 15, seed = 2635)
    expect_doppelganger("draw smooth_samples for GAM m1", plt)

    sm2 <- smooth_samples(su_m_bivar, n = 4, seed = 23478, n_vals = 50)
    plt <- draw(sm2, alpha = 0.7, n_samples = 4, seed = 2635)
    expect_doppelganger("draw smooth_samples for GAM m2", plt)

    sm3 <- smooth_samples(su_m_factor_by, n = 10, seed = 23478, n_vals = 50)
    plt <- draw(sm3, alpha = 0.7, n_samples = 10, seed = 2635, rug = FALSE)
    expect_doppelganger("draw smooth_samples for GAM m3", plt)

    sm3 <- smooth_samples(su_m_factor_by, n = 10, seed = 23478, n_vals = 50)
    plt <- draw(sm3, alpha = 0.7, scales = "fixed", n_samples = 10, seed = 2635,
    rug = FALSE)
    expect_doppelganger("draw smooth_samples for GAM m3 fixed scales", plt)
})

test_that("draw works for sample_smooths objects rotated labels", {
    skip_on_cran()
    sm1 <- smooth_samples(su_m_univar_4, n = 15, seed = 23478, n_vals = 50)
    plt <- draw(sm1, alpha = 0.7, n_samples = 15, seed = 2635, angle = 45,
        rug = FALSE)
    expect_doppelganger("draw smooth_samples for GAM m1 rotated", plt)

    sm2 <- smooth_samples(su_m_bivar, n = 4, seed = 23478, n_vals = 50)
    plt <- draw(sm2, alpha = 0.7, n_samples = 4, seed = 2635, angle = 45,
        rug = FALSE)
    expect_doppelganger("draw smooth_samples for GAM m2 rotated", plt)

    sm3 <- smooth_samples(su_m_factor_by, n = 15, seed = 23478, n_vals = 50)
    plt <- draw(sm3, alpha = 0.7, n_samples = 15, seed = 2635, angle = 45,
        rug = FALSE)
    expect_doppelganger("draw smooth_samples for GAM m3 rotated", plt)

    sm3 <- smooth_samples(su_m_factor_by, n = 15, seed = 23478, n_vals = 50)
    plt <- draw(sm3, alpha = 0.7, scales = "fixed", n_samples = 15, seed = 2635,
        angle = 45, rug = FALSE)
    expect_doppelganger("draw smooth_samples for GAM m3 fixed scales rotated",
        plt)
})

test_that("draw works for sample_smooths objects", {
    skip_on_os("win")
    skip_on_os("mac")
    sm2 <- smooth_samples(su_m_bivar, n = 2, seed = 23478, n_vals = 50)
    plt <- draw(sm2, alpha = 0.7, contour = TRUE)
    expect_doppelganger("draw smooth_samples for bivariate GAM contours", plt)
})

test_that("draw works for sample_smooths objects with n_samples", {
    sm1 <- smooth_samples(su_m_univar_4, n = 15, seed = 23478, n_vals = 50)
    plt <- draw(sm1, alpha = 0.7, n_samples = 6)
    expect_doppelganger("draw smooth_samples for m1 n_samples", plt)

    sm2 <- smooth_samples(su_m_bivar, n = 4, seed = 23478, n_vals = 50)
    plt <- draw(sm2, alpha = 0.7, n_samples = 2)
    expect_doppelganger("draw smooth_samples for m2 n_samples", plt)

    sm3 <- smooth_samples(su_m_factor_by, n = 15, seed = 23478, n_vals = 50)
    plt <- draw(sm3, alpha = 0.7, n_samples = 6)
    expect_doppelganger("draw smooth_samples for GAM n_samples", plt)
})

test_that("draw works for sample_smooths objects with user specified smooth", {
    sm3 <- smooth_samples(su_m_factor_by, n = 15, seed = 23478, n_vals = 50)
    plt <- draw(sm3, select = "s(x0)", alpha = 0.7)
    expect_doppelganger("draw selected smooth_samples for GAM m3", plt)

    plt <- draw(sm3, select = "s(x2)", alpha = 0.7, partial_match = TRUE)
    expect_doppelganger("draw selected factor by smooth_samples for GAM m3",
                        plt)
})

## Issue #22
test_that("draw() can handle a mixture of numeric and factor random effects", {
    df <- data_sim("eg4", seed = 42)
    m <- gam(y ~ s(x2, fac, bs = "re"), data = df, method = "REML")
    plt <- draw(m)
    expect_doppelganger("issue 22 draw with mixed random effects", plt)
})

test_that("draw.gam uses fixed scales if asked for them: #73", {
    skip_on_cran()
    skip_on_ci()
    df <- data_sim("eg1", n = 1000, seed = 1)
    m <- gam(y ~ s(x1) + s(x2) + ti(x1, x2), data = su_eg1, method = "REML")
    plt <- draw(m, scales = "fixed", rug = FALSE)
    expect_doppelganger("issue 73 draw uses fixed scales if asked for them",
                                 plt)
})

test_that("draw.gam can take user specified scales", {
    skip_on_os(os = "win")
    skip_on_os(os = "mac") # trivial diffs in contours
    plt <- draw(su_m_bivar, rug = FALSE,
                continuous_fill = scale_fill_distiller(palette = "Spectral",
                                                       type = "div"))
    expect_doppelganger("draw 2d smooth with spectral palette", plt)

    skip_if(packageVersion("mgcv") < "1.8.36")
    plt <- draw(mod_fs, rug = FALSE,
                discrete_colour = scale_colour_viridis_d(option = "plasma"))
    expect_doppelganger("draw fs smooth with discrete plasma palette",
                                 plt)
})

## draw.penalty
test_that("draw.penalty_df works", {
    expect_silent(pen <- penalty(su_m_univar_4))
    plt <- draw(pen)
    expect_doppelganger("draw penalty_df with multiple smooths",
                        plt)

    plt <- draw(penalty(su_m_univar_4, "s(x1)"))
    expect_doppelganger("draw penalty_df with single smooths",
                        plt)
})

test_that("draw.penalty_df gets labels on plot in corrcet order issue 95", {
    skip_on_cran()
    expect_silent(pen <- penalty(su_m_penalty))
    plt <- draw(pen)
    expect_doppelganger("draw penalty_df issue 95 label order",
                        plt)
})

test_that("draw.penalty_df accepts user-specified continuous_fill", {
    expect_silent(pen <- penalty(su_m_univar_4))
    plt <- draw(pen,
                continuous_fill = scale_fill_distiller(palette = "Spectral",
                                                       type = "div"))
    expect_doppelganger("draw penalty multiple smooths user continous fill",
                        plt)

    plt <- draw(penalty(su_m_univar_4, "s(x1)"),
                continuous_fill = scale_fill_distiller(palette = "Spectral",
                                                       type = "div"))
    expect_doppelganger("draw penalty single smooths user continous fill",
                        plt)
})

test_that("draw.penalty_df works with normalization", {
    expect_silent(pen <- penalty(su_m_univar_4))
    plt <- draw(pen, normalize = TRUE)
    expect_doppelganger("draw penalty_df with multiple smooths normalized",
                        plt)

    plt <- draw(penalty(su_m_univar_4, "s(x1)"), normalize = TRUE)
    expect_doppelganger("draw penalty_df with single smooths normalized",
                        plt)
})