Compare smooths across models

Usage

compare_smooths(
  model,
  ...,
  select = NULL,
  smooths = deprecated(),
  n = 100,
  n_2d = 50,
  data = NULL,
  unconditional = FALSE,
  overall_uncertainty = TRUE,
  partial_match = FALSE,
  envir = NULL
)

Arguments

model: Primary model for comparison.
...: Additional models to compare smooths against those of model.
select: character; select which smooths to compare. The default (NULL) means all smooths in model will be compared. Numeric select indexes the smooths in the order they are specified in the formula and stored in model. Character select matches the labels for smooths as shown for example in the output from summary(object). Logical select operates as per numeric select in the order that smooths are stored.
smooths: Use select instead.
n: numeric; the number of points over the range of the covariate at which to evaluate a univariate smooth.
n_2d: numeric; the number of points along each of the first two axes of a smooth surface, including surface panels of higher-dimensional smooths. The default is 50 in plotting and plot-preparation functions. If NULL, use n instead. Ignored when evaluation data are supplied. Factor levels are retained, and curves with only one continuous covariate use n.
data: a data frame of covariate values at which to evaluate the smooth.
unconditional: logical; if TRUE (and only if frequentist == FALSE) then the bayesian smoothing parameter uncertainty-corrected covariance matrix is returned, if available. Whether it is available depends on which smoothness selection method was used to fit the model.
overall_uncertainty: logical; should the uncertainty in the model constant term be included in the standard error of the evaluate values of the smooth?
partial_match: logical; should smooths be selected by partial matches with select? If TRUE, select can only be a single string to match against.
envir: an optional environment supplying functions and constants used in model expressions. The available model formula environment is used when NULL. Covariate observations should be supplied in data.

Examples

load_mgcv()
dat <- data_sim("eg1", seed = 2)

## models to compare smooths across - artificially create differences
m1 <- gam(y ~ s(x0, k = 5) + s(x1, k = 5) + s(x2, k = 5) + s(x3, k = 5),
  data = dat, method = "REML"
)
m2 <- gam(y ~ s(x0, bs = "ts") + s(x1, bs = "ts") + s(x2, bs = "ts") +
  s(x3, bs = "ts"), data = dat, method = "REML")

## build comparisons
comp <- compare_smooths(m1, m2)
comp
#> # A tibble: 8 x 5
#>   .model .smooth .type         .by   data              
#>   <chr>  <chr>   <chr>         <chr> <list>            
#> 1 m1     s(x0)   TPRS          NA    <tibble [100 x 3]>
#> 2 m2     s(x0)   TPRS (shrink) NA    <tibble [100 x 3]>
#> 3 m1     s(x1)   TPRS          NA    <tibble [100 x 3]>
#> 4 m2     s(x1)   TPRS (shrink) NA    <tibble [100 x 3]>
#> 5 m1     s(x2)   TPRS          NA    <tibble [100 x 3]>
#> 6 m2     s(x2)   TPRS (shrink) NA    <tibble [100 x 3]>
#> 7 m1     s(x3)   TPRS          NA    <tibble [100 x 3]>
#> 8 m2     s(x3)   TPRS (shrink) NA    <tibble [100 x 3]>
## notice that the result is a nested tibble

draw(comp)