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 plot. If

`NULL`

, the default, then all model smooths are drawn. 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.- smooth
- 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)
```