Conditional predictions from a GAM

Generate predicted values from a GAM, conditional upon supplied values of covariates. conditional_values() is modelled after marginaleffects::plot_predictions(), but with an intentionally simpler, more restrictive functionality. The intended use case is for quickly visualizing predicted values from a fitted GAM on the response scale. For more complex model predictions, you are strongly encouraged to use marginaleffects::plot_predictions().

Usage

conditional_values(
  model,
  condition = NULL,
  data = NULL,
  scale = c("response", "link", "linear_predictor"),
  ...
)

# S3 method for class 'gam'
conditional_values(
  model,
  condition = NULL,
  data = NULL,
  scale = c("response", "link", "linear_predictor"),
  n_vals = 100,
  ci_level = 0.95,
  ...
)

Arguments

model

a fitted GAM object.

condition

either a character vector or a list supplying the names of covariates, and possibly their values, to condition up. The order of the values determines how these are plotted via the draw.conditional_values() method; the first element is mapped to the x channel, the second element to the colour channel, the third to ggplot2::facet_wrap() if no fourth element is present, if present, the fourth element is mapped to the rows and the third element is mapped to the columns of ggplot2::facet_grid().

data

data frame of values at which to predict. If supplied overrides values supplied through condition.

scale

character; which scale should predictions be returned on?

...

arguments passed to fitted_values().

n_vals

numeric; number of values to generate for numeric variables named in condition.

ci_level

numeric; a number on interval (0,1) giving the coverage for credible intervals.

Value

A data frame (tibble) of class "conditional_values".

Author

Gavin L. Simpson

Examples

load_mgcv()
df <- data_sim("eg1", seed = 2)
m1 <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = "REML")

# predictions conditional on values evenly spaced over x2, all other
# variables in model are held at representative values
cv <- conditional_values(
  m1,
  condition = "x2"
)
# plot
cv |> draw()


# as above but condition on `x1` also. When plotted, `x1` is mapped to the
# colour channel, noting that it has been summarised using fivenum()
cv <- conditional_values(
  m1,
  condition = c("x2", "x1")
)
# plot
cv |> draw()


# can pass `condition` a list, allowing for greater flexibility
# For example, here we condition on all four variables in the model,
# summarising:
#   * `x1` at its five number summary,
#   * `x0  at its quartiles
#   * `x3` at its mean a d mean +/- sd
cv <- conditional_values(
  m1,
  condition = list("x2", x1 = "fivenum", x0 = "quartile", x3 = "threenum")
)
# plot
cv |> draw()


# some model terms can be exclude from the conditional predictions using the
# `exclude` mechanism of `predict.gam`. Here we exclude the effects of
# `s(x0)` and `s(x3)` from the conditional predictions. This, in effect,
# treats these smooths as having **0** effect on the conditional predictions
# of the response, even though the two smooths conditioned on (`s(x2)` and
# `s(x1)`) were estimated given the two excluded smooths were in the model
cv <- conditional_values(
  m1,
  condition = list("x2", x1 = "minmax"),
  exclude = c("s(x0)", "s(x3)")
)
# plot
cv |> draw()


# categorical conditions are also handled
df <- data_sim("eg4", seed = 2)
m2 <- gam(y ~ fac + s(x2, by = fac) + s(x0), data = df, method = "REML")
cv <- conditional_values(
  m2,
  condition = list("fac", x2 = "fivenum")
)
# plot - we see a discrete x axis
cv |> draw()


# in this example we condition on `x2` and `fac %in% c(2,3)`
cv <- conditional_values(
  m2,
  condition = list("x2", fac = 2:3)
)
# plot - smooths of `x2` for `fac == 2` and `fac == 3`
cv |> draw()