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Data slices

Having fitted a GAM or other model containing penalised splines, we often want to evaluate the model at some pre-specified values of the covariates. For more complex models, this will typically involve holding some covariates at fixed, representative values while visualising the change in the response or effect of a smooth over supplied values of one or more other covariates. The values of the covariates at which we evaluate a smooth or a model are called a data slice1.

This article will explain how to create data slices with {gratia} and its data_slice() function, and how to use them to visualise features of your fitted GAMs.

We’ll need the following packages for this article

library("mgcv")
#> Loading required package: nlme
#> This is mgcv 1.9-1. For overview type 'help("mgcv-package")'.
library("gratia")
library("dplyr")
#> 
#> Attaching package: 'dplyr'
#> The following object is masked from 'package:nlme':
#> 
#>     collapse
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library("ggplot2")
library("forcats")
library("datasets")

Carbon Dioxide Uptake in Grass Plants

The first example uses a small data set from an experimental study of the cold tolerance of the grass Echinochloa crusgalli. The data are in data frame CO2 and provided with the {datasets} package that ships with R.

## data load and prep
data(CO2, package = "datasets")
plant <- CO2 |>
  as_tibble() |>
  rename(plant = Plant, type = Type, treatment = Treatment) |>
  mutate(plant = factor(plant, ordered = FALSE))
plant_ylab <- expression(CO[2] ~ uptake ~ (mu * mol ~ m^{-3}))
plant_xlab <- expression(CO[2] ~ concentration ~ (mL ~ L^{-1}))

plant |>
  ggplot(aes(x = conc, y = uptake, group = plant, colour = treatment)) +
  geom_point() +
  geom_line() +
  facet_wrap(~type) +
  labs(y = plant_ylab, x = plant_xlab, colour = "Treatment")

One way to model these data is to allow for different smooths for all combinations of the treatment and type covariates

plant <- plant |>
  mutate(tt = fct_cross(treatment, type))
m_plant <- gam(uptake ~ treatment * type + s(conc, by = tt, k = 6) +
    s(plant, bs = "re"),
  data = plant, method = "REML", family = Gamma(link = "log")
)
overview(m_plant)
#> 
#> Generalized Additive Model with 8 terms
#> 
#>   term                             type              k   edf statistic p.value  
#>   <chr>                            <chr>         <dbl> <dbl>     <dbl> <chr>    
#> 1 treatment                        parametric       NA  1         1.59 0.2124864
#> 2 type                             parametric       NA  1        11.2  0.0014830
#> 3 treatment:type                   parametric       NA  1         7.45 0.0085489
#> 4 s(conc):ttnonchilled:Quebec      TPRS              5  4.72     69.7  < 0.001  
#> 5 s(conc):ttchilled:Quebec         TPRS              5  4.71     86.5  < 0.001  
#> 6 s(conc):ttnonchilled:Mississippi TPRS              5  4.62     74.1  < 0.001  
#> 7 s(conc):ttchilled:Mississippi    TPRS              5  4.39     25.3  < 0.001  
#> 8 s(plant)                         Random effect    12  7.40     12.8  < 0.001

We can look at the fitted smooths using draw()

draw(m_plant, residuals = TRUE, scales = "fixed")

We might want to compare model fitted values for the treatment for each of the types (origins), ignoring the random effect component. For this we want to evaluate the model at a range of values of covariate conc for some combinations of the other factors. This is a data slice through the covariate space, which we can create using data_slice(). To create a data slice for conc for the Quebec type in the chilled treatment we would use

ds1 <- data_slice(m_plant,
  conc = evenly(conc, n = 100),
  type = level(type, "Quebec"), treatment = level(treatment, "chilled")
)
ds1
#> # A tibble: 100 × 5
#>     conc type   treatment tt                plant
#>    <dbl> <fct>  <fct>     <fct>             <fct>
#>  1   95  Quebec chilled   nonchilled:Quebec Qn1  
#>  2  104. Quebec chilled   nonchilled:Quebec Qn1  
#>  3  113. Quebec chilled   nonchilled:Quebec Qn1  
#>  4  122. Quebec chilled   nonchilled:Quebec Qn1  
#>  5  132. Quebec chilled   nonchilled:Quebec Qn1  
#>  6  141. Quebec chilled   nonchilled:Quebec Qn1  
#>  7  150. Quebec chilled   nonchilled:Quebec Qn1  
#>  8  159. Quebec chilled   nonchilled:Quebec Qn1  
#>  9  168. Quebec chilled   nonchilled:Quebec Qn1  
#> 10  177. Quebec chilled   nonchilled:Quebec Qn1  
#> # ℹ 90 more rows

Notice how data_slice() has filled in something for the remaining covariates that we didn’t mention? In this case, data_slice() doesn’t know how tt was created, so it has chosen the modal level for the tt factor, which is not the correct choice in this case. Instead, we need to specify the correct level explicitly for tt

ds1 <- data_slice(m_plant,
  conc = evenly(conc, n = 100),
  treatment = level(treatment, "chilled"), type = level(type, "Quebec"),
  tt = level(tt, "chilled:Quebec")
)
ds1
#> # A tibble: 100 × 5
#>     conc treatment type   tt             plant
#>    <dbl> <fct>     <fct>  <fct>          <fct>
#>  1   95  chilled   Quebec chilled:Quebec Qn1  
#>  2  104. chilled   Quebec chilled:Quebec Qn1  
#>  3  113. chilled   Quebec chilled:Quebec Qn1  
#>  4  122. chilled   Quebec chilled:Quebec Qn1  
#>  5  132. chilled   Quebec chilled:Quebec Qn1  
#>  6  141. chilled   Quebec chilled:Quebec Qn1  
#>  7  150. chilled   Quebec chilled:Quebec Qn1  
#>  8  159. chilled   Quebec chilled:Quebec Qn1  
#>  9  168. chilled   Quebec chilled:Quebec Qn1  
#> 10  177. chilled   Quebec chilled:Quebec Qn1  
#> # ℹ 90 more rows

Having created the data slice, we can predict from the model using the combination of covariate values specified in our slice. We could use predict.gam() for this, but the fitted_values() function in {gratia} is easier to use, especially for non-Gaussian models

fv1 <- fitted_values(m_plant, data = ds1, scale = "response", exclude = "s(plant)")
fv1
#> # A tibble: 100 × 10
#>     .row  conc treatment type   tt      plant .fitted    .se .lower_ci .upper_ci
#>    <int> <dbl> <fct>     <fct>  <fct>   <fct>   <dbl>  <dbl>     <dbl>     <dbl>
#>  1     1   95  chilled   Quebec chille… Qn1      13.0 0.0783      11.2      15.2
#>  2     2  104. chilled   Quebec chille… Qn1      14.1 0.0757      12.1      16.3
#>  3     3  113. chilled   Quebec chille… Qn1      15.2 0.0737      13.1      17.5
#>  4     4  122. chilled   Quebec chille… Qn1      16.3 0.0722      14.2      18.8
#>  5     5  132. chilled   Quebec chille… Qn1      17.6 0.0714      15.3      20.2
#>  6     6  141. chilled   Quebec chille… Qn1      18.9 0.0711      16.4      21.7
#>  7     7  150. chilled   Quebec chille… Qn1      20.2 0.0712      17.6      23.3
#>  8     8  159. chilled   Quebec chille… Qn1      21.6 0.0716      18.8      24.9
#>  9     9  168. chilled   Quebec chille… Qn1      23.0 0.0721      20.0      26.5
#> 10    10  177. chilled   Quebec chille… Qn1      24.4 0.0726      21.2      28.1
#> # ℹ 90 more rows

Notice how we excluded the random effect term; even though we had to specify something for the plant covariate we can ignore this term in the model using the exclude argument. fitted_values() creates the credible interval on the scale of the link function and then back-transforms to the response scale when scale = "response", which is also the default.

Plotting the fitted values for the data slice now only requires some simple {ggplot2} knowledge

fv1 |>
  ggplot(aes(x = conc, y = .fitted)) +
  geom_point(
    data = plant |>
      filter(type == "Quebec", treatment == "chilled"),
    mapping = aes(y = uptake),
    alpha = 0.8, colour = "steelblue"
  ) +
  geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci), alpha = 0.2) +
  geom_line() +
  labs(
    x = plant_xlab, y = plant_ylab,
    title = expression(Estimated ~ CO[2] ~ uptake),
    subtitle = "Chilled plants of the Quebec type"
  )

Next, let’s compare the fitted effects of the treatment in the Mississippi origin plants

ds2 <- data_slice(m_plant,
  conc = evenly(conc, n = 100),
  treatment = evenly(treatment), type = level(type, "Mississippi")
) |>
  mutate(tt = fct_cross(treatment, type, keep_empty = TRUE))
ds2
#> # A tibble: 200 × 5
#>     conc treatment  type        tt                     plant
#>    <dbl> <fct>      <fct>       <fct>                  <fct>
#>  1   95  nonchilled Mississippi nonchilled:Mississippi Qn1  
#>  2   95  chilled    Mississippi chilled:Mississippi    Qn1  
#>  3  104. nonchilled Mississippi nonchilled:Mississippi Qn1  
#>  4  104. chilled    Mississippi chilled:Mississippi    Qn1  
#>  5  113. nonchilled Mississippi nonchilled:Mississippi Qn1  
#>  6  113. chilled    Mississippi chilled:Mississippi    Qn1  
#>  7  122. nonchilled Mississippi nonchilled:Mississippi Qn1  
#>  8  122. chilled    Mississippi chilled:Mississippi    Qn1  
#>  9  132. nonchilled Mississippi nonchilled:Mississippi Qn1  
#> 10  132. chilled    Mississippi chilled:Mississippi    Qn1  
#> # ℹ 190 more rows

Here, we replaced the automatically-generated tt variable with the correctly specified call to fct_cross(), retaining the levels of the type and treatment factors. This insures that we have the correct combinations corresponding to the treatment and type factors but also that we preserve the original levels of the tt covariate we created.

We can again visualise the fitted values for this data slice

fitted_values(m_plant,
  data = ds2, scale = "response",
  exclude = "s(plant)"
) |>
  ggplot(aes(x = conc, y = .fitted, group = treatment)) +
  geom_point(
    data = plant |> filter(type == "Mississippi"),
    mapping = aes(y = uptake, colour = treatment),
    alpha = 0.8
  ) +
  geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci, fill = treatment),
    alpha = 0.2
  ) +
  geom_line(aes(colour = treatment)) +
  labs(
    x = plant_xlab, y = plant_ylab,
    title = expression(Estimated ~ CO[2] ~ uptake),
    subtitle = "Comparison of treatment in plants of the Mississippi type",
    colour = "Treatment", fill = "Treatment"
  )

When we were creating our data slices, we used some helper functions to specify covariate values for the slice. {gratia} provides several such helper functions:

In all cases involving factors, the helper functions set the levels of the factor to match those in the original model fit2.

The second argument to data_slice() is ...

args(gratia:::data_slice.gam)
#> function (object, ..., data = NULL, envir = NULL) 
#> NULL

The ... argument allows you to provide expressions to create the covariate values you want for your data slice. Expressions passed to ... are evaluated within the model frame of the fitted model (argument object) or in data (if supplied). You are not restricted either to using only the helper functions provide by {gratia}; any R function could be used as long as it makes sense in the context of the model frame, and it returns something that can be combined using tidyr::expand_grid().

Slices through a 2D smooth

In the second example, I’ll use the bivariate example data set from {mgcv} but fit a tensor product of covariates x and z

# simulate data from the bivariate surface
df <- data_sim("eg2", n = 1000, scale = 0.25, seed = 2)

# fit the GAM
m_biv <- gam(y ~ te(x, z), data = df, method = "REML")

The aim of the example will be to create a univariate data slice through the 2D smooth at user-specified values of x while holding z at one or more fixed values. We could visualise the effect at the smooth level, using smooth_estimates(), or at the response level, as we did above using fitted_values().

Using smooth_estimates()

We begin by creating a slice through the data space. We also create a label at this point for a nice axis label.

ds3 <- data_slice(m_biv,
  x = evenly(x, n = 100),
  z = quantile(z, probs = 0.25)
)

z_val <- with(ds3, round(quantile(z, probs = 0.25), 2))
ylab <- bquote(hat(f)(x, .(z_val)))

Then we evaluate the smooth at the desired values and add a confidence interval

sm <- smooth_estimates(m_biv, select = "te(x,z)", data = ds3) |>
  add_confint()
sm
#> # A tibble: 100 × 9
#>    .smooth .type        .by   .estimate    .se       x     z .lower_ci .upper_ci
#>    <chr>   <chr>        <chr>     <dbl>  <dbl>   <dbl> <dbl>     <dbl>     <dbl>
#>  1 te(x,z) Tensor prod… <NA>      0.103 0.0583 6.63e-4 0.245   -0.0107     0.218
#>  2 te(x,z) Tensor prod… <NA>      0.122 0.0548 1.08e-2 0.245    0.0148     0.230
#>  3 te(x,z) Tensor prod… <NA>      0.141 0.0514 2.08e-2 0.245    0.0400     0.242
#>  4 te(x,z) Tensor prod… <NA>      0.159 0.0482 3.09e-2 0.245    0.0648     0.254
#>  5 te(x,z) Tensor prod… <NA>      0.177 0.0451 4.10e-2 0.245    0.0890     0.266
#>  6 te(x,z) Tensor prod… <NA>      0.195 0.0422 5.11e-2 0.245    0.113      0.278
#>  7 te(x,z) Tensor prod… <NA>      0.213 0.0396 6.12e-2 0.245    0.135      0.291
#>  8 te(x,z) Tensor prod… <NA>      0.230 0.0372 7.13e-2 0.245    0.157      0.303
#>  9 te(x,z) Tensor prod… <NA>      0.247 0.0351 8.14e-2 0.245    0.178      0.316
#> 10 te(x,z) Tensor prod… <NA>      0.263 0.0333 9.14e-2 0.245    0.198      0.328
#> # ℹ 90 more rows

We can plot sm using {ggplot2}

sm |>
  ggplot(aes(x = x, y = .estimate)) +
  geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci), alpha = 0.2) +
  geom_line() +
  labs(
    title = "Evaluation of smooth te(x,z) at fixed z",
    y = ylab
  )

Note that the above interval is not the Marra and Wood (2012) interval. It doesn’t include the uncertainty from the model constant term at the moment, but unless the smooth is very close to linear that shouldn’t make too much difference.

This extends to multiple slices by asking for several discrete z

ds4 <- data_slice(m_biv,
  x = evenly(x, n = 100),
  z = round(quantile(z, probs = c(0.25, 0.5, 0.75)), 2)
)

sm <- smooth_estimates(m_biv, select = "te(x,z)", data = ds4) |>
  add_confint() |>
  mutate(fz = factor(z))

sm |>
  ggplot(aes(x = x, y = .estimate, colour = fz, group = fz)) +
  geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci, fill = fz, colour = NULL),
    alpha = 0.2
  ) +
  geom_line() +
  labs(
    title = "Evaluation of smooth te(x,z) at fixed z",
    y = expression(hat(f)(x, z)), colour = "z", fill = "z"
  )

Using fitted_samples()

If you want to evaluate the surface over x at fixed z conditional upon other values of other covariates (model predicted or fitted values) then fitted_samples() is a tidy wrapper to predict.gam().

For single z we have

fitted_values(m_biv, data = ds3) |> # default is response scale, not link
  ggplot(aes(x = x, y = .fitted)) +
  geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci), alpha = 0.2) +
  geom_line() +
  labs(
    title = "Fitted values from model",
    y = expression(hat(y))
  )

And for the multiple z we have

fitted_values(m_biv, data = ds4) |>
  mutate(fz = factor(z)) |>
  ggplot(aes(x = x, y = .fitted, colour = fz, group = fz)) +
  geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci, fill = fz, colour = NULL),
    alpha = 0.2
  ) +
  geom_line() +
  labs(
    title = "Fitted values from model",
    y = expression(hat(y)), colour = "z", fill = "z"
  )

where the only difference here is that now the model constant is included as well as its uncertainty.

  1. at least that’s what I’m calling them.↩︎

  2. Depending on the value of argument drop.unused.levels passed to gam() when you fitted the model. The default will drop any unused levels before fitting the model, and as a result the helpers will not include those levels either.↩︎

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