The first derivative of the smooth functions of a GAM model calculated using finite differences.

fderiv(model, ...)

# S3 method for gam
fderiv(model, newdata, term, n = 200, eps = 1e-07,
unconditional = FALSE, offset = NULL, ...)

# S3 method for gamm
fderiv(model, ...)

## Arguments

model A fitted GAM. Currently only models fitted by mgcv::gam() and mgcv::gamm() are supported. Arguments that are passed to other methods. a data frame containing the values of the model covariates at which to evaluate the first derivatives of the smooths. character; vector of one or more terms for which derivatives are required. If missing, derivatives for all smooth terms will be returned. integer; if newdata is missing the original data can be reconstructed from model and then n controls the number of values over the range of each covariate with which to populate newdata. numeric; the value of the finite difference used to approximate the first derivative. logical; if TRUE, the smoothing parameter uncertainty corrected covariance matrix is used, if available, otherwise the uncorrected Bayesian posterior covariance matrix is used. numeric; value of offset to use in generating predictions.

## Value

An object of class "fderiv" is returned.

## Examples

suppressPackageStartupMessages(library("mgcv"))dat <- gamSim(1, n = 400, dist = "normal", scale = 2)#> Gu & Wahba 4 term additive modelmod <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = "REML")

## first derivatives of all smooths...
fd <- fderiv(mod)

## ...and a selected smooth
fd2 <- fderiv(mod, term = "x1")

## Models with factors
set.seed(2)
dat <- gamSim(4, n = 400, dist = "normal", scale = 2)#> Factor by' variable examplemod <- gam(y ~ s(x0) + s(x1) + fac, data = dat, method = "REML")

## first derivatives of all smooths...
fd <- fderiv(mod)

## ...and a selected smooth
fd2 <- fderiv(mod, term = "x1")`