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Display Smooth Terms

Source: R/conditional_smooths.R
conditional_smooths.brmsfit.Rd

Display smooth s and t2 terms of models fitted with brms.

Usage

# S3 method for class 'brmsfit'
conditional_smooths(
  x,
  smooths = NULL,
  int_conditions = NULL,
  prob = 0.95,
  spaghetti = FALSE,
  surface = TRUE,
  resolution = 100,
  too_far = 0,
  ndraws = NULL,
  draw_ids = NULL,
  nsamples = NULL,
  subset = NULL,
  probs = NULL,
  ...
)

conditional_smooths(x, ...)

Arguments

x

An object of class brmsfit.

smooths

Optional character vector of smooth terms to display. If NULL (the default) all smooth terms are shown.

int_conditions

An optional named list whose elements are vectors of values of the variables specified in effects. At these values, predictions are evaluated. The names of int_conditions have to match the variable names exactly. Additionally, the elements of the vectors may be named themselves, in which case their names appear as labels for the conditions in the plots. Instead of vectors, functions returning vectors may be passed and are applied on the original values of the corresponding variable. If NULL (the default), predictions are evaluated at the \(mean\) and at \(mean +/- sd\) for numeric predictors and at all categories for factor-like predictors.

prob

A value between 0 and 1 indicating the desired probability to be covered by the uncertainty intervals. The default is 0.95.

spaghetti

Logical. Indicates if predictions should be visualized via spaghetti plots. Only applied for numeric predictors. If TRUE, it is recommended to set argument ndraws to a relatively small value (e.g., 100) in order to reduce computation time.

surface

Logical. Indicates if interactions or two-dimensional smooths should be visualized as a surface. Defaults to TRUE. The surface type can be controlled via argument stype of the related plotting method.

resolution

Number of support points used to generate the plots. Higher resolution leads to smoother plots. Defaults to 100. If surface is TRUE, this implies 10000 support points for interaction terms, so it might be necessary to reduce resolution when only few RAM is available.

too_far

Positive number. For surface plots only: Grid points that are too far away from the actual data points can be excluded from the plot. too_far determines what is too far. The grid is scaled into the unit square and then grid points more than too_far from the predictor variables are excluded. By default, all grid points are used. Ignored for non-surface plots.

ndraws

Positive integer indicating how many posterior draws should be used. If NULL (the default) all draws are used. Ignored if draw_ids is not NULL.

draw_ids

An integer vector specifying the posterior draws to be used. If NULL (the default), all draws are used.

nsamples

Deprecated alias of ndraws.

subset

Deprecated alias of draw_ids.

probs

(Deprecated) The quantiles to be used in the computation of uncertainty intervals. Please use argument prob instead.

...

Currently ignored.

Value

For the brmsfit method, an object of class brms_conditional_effects. See conditional_effects for more details and documentation of the related plotting function.

Details

Two-dimensional smooth terms will be visualized using either contour or raster plots.

Examples

# \dontrun{
set.seed(0)
dat <- mgcv::gamSim(1, n = 200, scale = 2)
#> Gu & Wahba 4 term additive model
fit <- brm(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat)
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0.000286 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.86 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
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#> Chain 1:                10.845 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 4.1e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.41 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 4.2e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.42 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 4.2e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.42 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:                10.471 seconds (Total)
#> Chain 4: 
#> Warning: There were 6 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
# show all smooth terms
plot(conditional_smooths(fit), rug = TRUE, ask = FALSE)




# show only the smooth term s(x2)
plot(conditional_smooths(fit, smooths = "s(x2)"), ask = FALSE)


# fit and plot a two-dimensional smooth term
fit2 <- brm(y ~ t2(x0, x2), data = dat)
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 5e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.5 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 3.7e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.37 seconds.
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#> Chain 3: 
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#> 
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#> Chain 4: 
#> Chain 4: Gradient evaluation took 3.7e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.37 seconds.
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#> Chain 4: 
#> Warning: There were 1 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
ms <- conditional_smooths(fit2)
plot(ms, stype = "contour")

plot(ms, stype = "raster")

# }