Differences of factor smooth interactions — difference

Estimates pairwise differences (comparisons) between factor smooth interactions (smooths with a factor by argument) for pairs of groups defined by the factor. The group means can be optionally included in the difference.

Usage

difference_smooths(model, ...)

# S3 method for class 'gam'
difference_smooths(
  model,
  select = NULL,
  smooth = deprecated(),
  n = 100,
  ci_level = 0.95,
  data = NULL,
  group_means = FALSE,
  partial_match = TRUE,
  unconditional = FALSE,
  frequentist = FALSE,
  ...
)

Arguments

model

A fitted model.

...

arguments passed to other methods. Not currently used.

select

character, logical, or numeric; which smooths to compare. If NULL, the default, then all model smooths are factor-smooth interactions are compared. Numeric select indexes the smooths in the order they are specified in the formula and stored in object. 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. Careful selection is needed because it is not allowed to compare smooths of different covariates or of different factor-by variables.

For character select, specific named smooths cane be provided, in which case, the exact names of the smooths (as given by smooths(), for example, can be specified, and partial_match must be set to FALSE.

smooth

Use select instead.

n

numeric; the number of points at which to evaluate the difference between pairs of smooths.

ci_level

numeric between 0 and 1; the coverage of credible interval.

data

data frame of locations at which to evaluate the difference between smooths.

group_means

logical; should the group means be included in the difference?

partial_match

logical; should smooth match partially against smooths? If partial_match = TRUE, smooth must only be a single string, a character vector of length 1. Unlike similar functions, the default here is TRUE because the intention is that users will be matching against factor-by smooth labels.

unconditional

logical; account for smoothness selection in the model?

frequentist

logical; use the frequentist covariance matrix?

Examples


load_mgcv()
df <- data_sim("eg4", seed = 42)
m <- gam(y ~ fac + s(x2, by = fac) + s(x0), data = df, method = "REML")

sm_dif <- difference_smooths(m, select = "s(x2)")
sm_dif
#> # A tibble: 300 x 9
#>    .smooth .by   .level_1 .level_2 .diff   .se .lower_ci .upper_ci      x2
#>    <chr>   <chr> <chr>    <chr>    <dbl> <dbl>     <dbl>     <dbl>   <dbl>
#>  1 s(x2)   fac   1        2        0.386 0.618  -0.824        1.60 0.00359
#>  2 s(x2)   fac   1        2        0.479 0.574  -0.646        1.60 0.0136 
#>  3 s(x2)   fac   1        2        0.572 0.534  -0.474        1.62 0.0237 
#>  4 s(x2)   fac   1        2        0.665 0.497  -0.308        1.64 0.0338 
#>  5 s(x2)   fac   1        2        0.758 0.464  -0.151        1.67 0.0438 
#>  6 s(x2)   fac   1        2        0.850 0.435  -0.00342      1.70 0.0539 
#>  7 s(x2)   fac   1        2        0.941 0.412   0.134        1.75 0.0639 
#>  8 s(x2)   fac   1        2        1.03  0.393   0.262        1.80 0.0740 
#>  9 s(x2)   fac   1        2        1.12  0.378   0.380        1.86 0.0841 
#> 10 s(x2)   fac   1        2        1.21  0.367   0.489        1.93 0.0941 
#> # i 290 more rows

draw(sm_dif)


# include the groups means for `fac` in the difference
sm_dif2 <- difference_smooths(m, select = "s(x2)", group_means = TRUE)
draw(sm_dif2)


# compare specific smooths
sm_dif3 <- difference_smooths(m,
  select = c("s(x2):fac1", "s(x2):fac2"), partial_match = FALSE
)