Derivatives of estimated smooths via finite differences
Usage
derivatives(object, ...)
# Default S3 method
derivatives(object, ...)
# S3 method for class 'gamm'
derivatives(object, ...)
# S3 method for class 'gam'
derivatives(
object,
select = NULL,
term = deprecated(),
data = newdata,
order = 1L,
type = c("forward", "backward", "central"),
n = 100,
eps = 1e-07,
interval = c("confidence", "simultaneous"),
n_sim = 10000,
level = 0.95,
unconditional = FALSE,
frequentist = FALSE,
offset = NULL,
ncores = 1,
partial_match = FALSE,
...,
newdata = NULL
)Arguments
- object
an R object to compute derivatives for.
- ...
arguments passed to other methods.
- select
character; select which smooth's posterior to draw from. The default (
NULL) means the posteriors of all smooths inmodelwil be sampled from. If supplied, a character vector of requested terms. Can be a partial match to a smooth term; see argumentpartial_matchbelow.- term
![[Deprecated]](figures/lifecycle-deprecated.svg)
Use selectinstead.- data
a data frame containing the values of the model covariates at which to evaluate the first derivatives of the smooths.
- order
numeric; the order of derivative.
- type
character; the type of finite difference used. One of
"forward","backward", or"central".- n
numeric; the number of points to evaluate the derivative at.
- eps
numeric; the finite difference.
- interval
character; the type of interval to compute. One of
"confidence"for point-wise intervals, or"simultaneous"for simultaneous intervals.- n_sim
integer; the number of simulations used in computing the simultaneous intervals.
- level
numeric;
0 < level < 1; the confidence level of the point-wise or simultaneous interval. The default is0.95for a 95% interval.- unconditional
logical; use smoothness selection-corrected Bayesian covariance matrix?
- frequentist
logical; use the frequentist covariance matrix?
- offset
numeric; a value to use for any offset term
- ncores
number of cores for generating random variables from a multivariate normal distribution. Passed to
mvnfast::rmvn(). Parallelization will take place only if OpenMP is supported (but appears to work on Windows with currentR).- partial_match
logical; should smooths be selected by partial matches with
term? IfTRUE,termcan only be a single string to match against.- newdata
Deprecated: use
datainstead.
Value
A tibble, currently with the following variables:
smooth: the smooth each row refers to,var: the name of the variable involved in the smooth,data: values ofvarat which the derivative was evaluated,derivative: the estimated derivative,se: the standard error of the estimated derivative,crit: the critical value such thatderivative±(crit * se)gives the upper and lower bounds of the requested confidence or simultaneous interval (givenlevel),lower: the lower bound of the confidence or simultaneous interval,upper: the upper bound of the confidence or simultaneous interval.
Examples
load_mgcv()
dat <- data_sim("eg1", n = 400, dist = "normal", scale = 2, seed = 42)
mod <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = "REML")
## first derivatives of all smooths using central finite differences
derivatives(mod, type = "central")
#> # A tibble: 400 x 12
#> .smooth .by .fs .derivative .se .crit .lower_ci .upper_ci x0 x1
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 s(x0) NA NA 7.41 3.33 1.96 0.874 13.9 2.39e-4 NA
#> 2 s(x0) NA NA 7.40 3.33 1.96 0.884 13.9 1.03e-2 NA
#> 3 s(x0) NA NA 7.39 3.30 1.96 0.929 13.8 2.04e-2 NA
#> 4 s(x0) NA NA 7.36 3.24 1.96 1.01 13.7 3.04e-2 NA
#> 5 s(x0) NA NA 7.32 3.15 1.96 1.14 13.5 4.05e-2 NA
#> 6 s(x0) NA NA 7.26 3.04 1.96 1.30 13.2 5.06e-2 NA
#> 7 s(x0) NA NA 7.18 2.90 1.96 1.49 12.9 6.06e-2 NA
#> 8 s(x0) NA NA 7.09 2.76 1.96 1.69 12.5 7.07e-2 NA
#> 9 s(x0) NA NA 6.99 2.61 1.96 1.87 12.1 8.07e-2 NA
#> 10 s(x0) NA NA 6.87 2.47 1.96 2.03 11.7 9.08e-2 NA
#> # i 390 more rows
#> # i 2 more variables: x2 <dbl>, x3 <dbl>
## derivatives for a selected smooth
derivatives(mod, type = "central", select = "s(x1)")
#> # A tibble: 100 x 9
#> .smooth .by .fs .derivative .se .crit .lower_ci .upper_ci x1
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 s(x1) NA NA -0.907 3.12 1.96 -7.02 5.20 0.000405
#> 2 s(x1) NA NA -0.906 3.11 1.96 -7.01 5.20 0.0105
#> 3 s(x1) NA NA -0.898 3.10 1.96 -6.97 5.17 0.0205
#> 4 s(x1) NA NA -0.880 3.06 1.96 -6.88 5.12 0.0306
#> 5 s(x1) NA NA -0.849 3.00 1.96 -6.73 5.03 0.0406
#> 6 s(x1) NA NA -0.803 2.92 1.96 -6.52 4.92 0.0507
#> 7 s(x1) NA NA -0.740 2.81 1.96 -6.25 4.77 0.0607
#> 8 s(x1) NA NA -0.659 2.69 1.96 -5.93 4.61 0.0708
#> 9 s(x1) NA NA -0.557 2.56 1.96 -5.57 4.46 0.0809
#> 10 s(x1) NA NA -0.436 2.42 1.96 -5.19 4.32 0.0909
#> # i 90 more rows
## or via a partial match
derivatives(mod, type = "central", select = "x1", partial_match = TRUE)
#> # A tibble: 100 x 9
#> .smooth .by .fs .derivative .se .crit .lower_ci .upper_ci x1
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 s(x1) NA NA -0.907 3.12 1.96 -7.02 5.20 0.000405
#> 2 s(x1) NA NA -0.906 3.11 1.96 -7.01 5.20 0.0105
#> 3 s(x1) NA NA -0.898 3.10 1.96 -6.97 5.17 0.0205
#> 4 s(x1) NA NA -0.880 3.06 1.96 -6.88 5.12 0.0306
#> 5 s(x1) NA NA -0.849 3.00 1.96 -6.73 5.03 0.0406
#> 6 s(x1) NA NA -0.803 2.92 1.96 -6.52 4.92 0.0507
#> 7 s(x1) NA NA -0.740 2.81 1.96 -6.25 4.77 0.0607
#> 8 s(x1) NA NA -0.659 2.69 1.96 -5.93 4.61 0.0708
#> 9 s(x1) NA NA -0.557 2.56 1.96 -5.57 4.46 0.0809
#> 10 s(x1) NA NA -0.436 2.42 1.96 -5.19 4.32 0.0909
#> # i 90 more rows