fderiv: First derivatives of fitted GAM functions

In gavinsimpson/gratia: Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv'

First derivatives of fitted GAM functions

Description

This function was deprecated because it was limited to first order forward finite differences for derivatives only, but couldn't be improved to offer the needed functionality without breaking backwards compatability with papers and blog posts that already used fderiv(). A replacement, derivatives(), is now available and recommended for new analyses.

Usage

fderiv(model, ...)

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

## S3 method for class '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.
newdata	a data frame containing the values of the model covariates at which to evaluate the first derivatives of the smooths.
term	character; vector of one or more terms for which derivatives are required. If missing, derivatives for all smooth terms will be returned.
n	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.
eps	numeric; the value of the finite difference used to approximate the first derivative.
unconditional	logical; if TRUE, the smoothing parameter uncertainty corrected covariance matrix is used, if available, otherwise the uncorrected Bayesian posterior covariance matrix is used.
offset	numeric; value of offset to use in generating predictions.

Value

An object of class "fderiv" is returned.

Author(s)

Gavin L. Simpson

Examples

load_mgcv()

dat <- data_sim("eg1", seed = 2)
mod <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = "REML")

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

## ...and a selected smooth
fd2 <- fderiv(mod, term = "x1")
## now use -->
fd2 <- derivatives(mod, select = "s(x1)")

## Models with factors
dat <- data_sim("eg4", n = 400, dist = "normal", scale = 2, seed = 2)
mod <- gam(y ~ s(x0) + s(x1) + fac, data = dat, method = "REML")

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

## ...and a selected smooth
fd2 <- fderiv(mod, term = "x1")
## now use -->
fd2 <- derivatives(mod, select = "s(x1)")

gavinsimpson/gratia documentation built on April 24, 2024, 6:29 a.m.