Evaluate smooths at covariate values — smooth

Evaluate a smooth at a grid of evenly spaced value over the range of the covariate associated with the smooth. Alternatively, a set of points at which the smooth should be evaluated can be supplied. smooth_estimates() is a new implementation of evaluate_smooth(), and replaces that function, which has been removed from the package.

Usage

smooth_estimates(object, ...)

# S3 method for class 'gam'
smooth_estimates(
  object,
  select = NULL,
  smooth = deprecated(),
  n = 100,
  n_3d = 16,
  n_4d = 4,
  data = NULL,
  unconditional = FALSE,
  overall_uncertainty = TRUE,
  dist = NULL,
  unnest = TRUE,
  partial_match = FALSE,
  ...
)

Arguments

object: an object of class "gam" or "gamm".
...: arguments passed to other methods.
select: character; select which smooth's posterior to draw from. The default (NULL) means the posteriors of all smooths in model wil be sampled from. If supplied, a character vector of requested terms.
smooth: Use select instead.
n: numeric; the number of points over the range of the covariate at which to evaluate the smooth.
n_3d, n_4d: numeric; the number of points over the range of last covariate in a 3D or 4D smooth. The default is NULL which achieves the standard behaviour of using n points over the range of all covariate, resulting in n^d evaluation points, where d is the dimension of the smooth. For d > 2 this can result in very many evaluation points and slow performance. For smooths of d > 4, the value of n_4d will be used for all dimensions > 4, unless this is NULL, in which case the default behaviour (using n for all dimensions) will be observed.
data: a data frame of covariate values at which to evaluate the smooth.
unconditional: logical; should confidence intervals include the uncertainty due to smoothness selection? If TRUE, the corrected Bayesian covariance matrix will be used.
overall_uncertainty: logical; should the uncertainty in the model constant term be included in the standard error of the evaluate values of the smooth?
dist: numeric; if greater than 0, this is used to determine when a location is too far from data to be plotted when plotting 2-D smooths. The data are scaled into the unit square before deciding what to exclude, and dist is a distance within the unit square. See mgcv::exclude.too.far() for further details.
unnest: logical; unnest the smooth objects?
partial_match: logical; in the case of character select, should select match partially against smooths? If partial_match = TRUE, select must only be a single string, a character vector of length 1.

Value

A data frame (tibble), which is of class "smooth_estimates".

Examples

load_mgcv()
dat <- data_sim("eg1", n = 400, dist = "normal", scale = 2, seed = 2)
m1 <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = "REML")

## evaluate all smooths
smooth_estimates(m1)
#> # A tibble: 400 x 9
#>    .smooth .type .by   .estimate      .se         x0    x1    x2    x3
#>    <chr>   <chr> <chr>     <dbl>    <dbl>      <dbl> <dbl> <dbl> <dbl>
#>  1 s(x0)   TPRS  NA    -0.966542 0.316118 0.00710904    NA    NA    NA
#>  2 s(x0)   TPRS  NA    -0.925391 0.297170 0.0171157     NA    NA    NA
#>  3 s(x0)   TPRS  NA    -0.884233 0.279256 0.0271224     NA    NA    NA
#>  4 s(x0)   TPRS  NA    -0.843050 0.262594 0.0371291     NA    NA    NA
#>  5 s(x0)   TPRS  NA    -0.801824 0.247376 0.0471358     NA    NA    NA
#>  6 s(x0)   TPRS  NA    -0.760536 0.233728 0.0571425     NA    NA    NA
#>  7 s(x0)   TPRS  NA    -0.719175 0.221701 0.0671492     NA    NA    NA
#>  8 s(x0)   TPRS  NA    -0.677736 0.211261 0.0771559     NA    NA    NA
#>  9 s(x0)   TPRS  NA    -0.636220 0.202303 0.0871626     NA    NA    NA
#> 10 s(x0)   TPRS  NA    -0.594641 0.194685 0.0971693     NA    NA    NA
#> # i 390 more rows

## or selected smooths
smooth_estimates(m1, select = c("s(x0)", "s(x1)"))
#> # A tibble: 200 x 7
#>    .smooth .type .by   .estimate      .se         x0    x1
#>    <chr>   <chr> <chr>     <dbl>    <dbl>      <dbl> <dbl>
#>  1 s(x0)   TPRS  NA    -0.966542 0.316118 0.00710904    NA
#>  2 s(x0)   TPRS  NA    -0.925391 0.297170 0.0171157     NA
#>  3 s(x0)   TPRS  NA    -0.884233 0.279256 0.0271224     NA
#>  4 s(x0)   TPRS  NA    -0.843050 0.262594 0.0371291     NA
#>  5 s(x0)   TPRS  NA    -0.801824 0.247376 0.0471358     NA
#>  6 s(x0)   TPRS  NA    -0.760536 0.233728 0.0571425     NA
#>  7 s(x0)   TPRS  NA    -0.719175 0.221701 0.0671492     NA
#>  8 s(x0)   TPRS  NA    -0.677736 0.211261 0.0771559     NA
#>  9 s(x0)   TPRS  NA    -0.636220 0.202303 0.0871626     NA
#> 10 s(x0)   TPRS  NA    -0.594641 0.194685 0.0971693     NA
#> # i 190 more rows