Derivatives of estimated smooths via finite differences
Derivatives of estimated smooths via finite differences
derivatives(object, ...)
## Default S3 method:
derivatives(object, ...)
## S3 method for class 'gamm'
derivatives(object, ...)
## S3 method for class 'gam'
derivatives(
object,
term,
newdata,
order = 1L,
type = c("forward", "backward", "central"),
n = 200,
eps = 1e-07,
interval = c("confidence", "simultaneous"),
n_sim = 10000,
level = 0.95,
unconditional = FALSE,
frequentist = FALSE,
offset = NULL,
ncores = 1,
partial_match = FALSE,
...
)object |
an R object to compute derivatives for. |
... |
arguments passed to other methods. |
term |
character; vector of one or more smooth terms for which
derivatives are required. If missing, derivatives for all smooth terms
will be returned. Can be a partial match to a smooth term; see argument
|
newdata |
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
|
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
|
n_sim |
integer; the number of simulations used in computing the simultaneous intervals. |
level |
numeric; |
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 |
partial_match |
logical; should smooths be selected by partial matches
with |
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 of var at which the derivative was evaluated,
derivative: the estimated derivative,
se: the standard error of the estimated derivative,
crit: the critical value such that derivative ± (crit * se) gives
the upper and lower bounds of the requested confidence or simultaneous
interval (given level),
lower: the lower bound of the confidence or simultaneous interval,
upper: the upper bound of the confidence or simultaneous interval.
Gavin L. Simpson
load_mgcv() dat <- gamSim(1, n = 400, dist = "normal", scale = 2, verbose = FALSE) 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") ## derivatives for a selected smooth derivatives(mod, type = "central", term = "s(x1)") ## or via a partial match derivatives(mod, type = "central", term = "x1", partial_match = TRUE)
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