confint.gam: Point-wise and simultaneous confidence intervals for smooths

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

Point-wise and simultaneous confidence intervals for smooths

Description

Calculates point-wise confidence or simultaneous intervals for the smooth terms of a fitted GAM.

Usage

## S3 method for class 'gam'
confint(
  object,
  parm,
  level = 0.95,
  data = newdata,
  n = 100,
  type = c("confidence", "simultaneous"),
  nsim = 10000,
  shift = FALSE,
  transform = FALSE,
  unconditional = FALSE,
  ncores = 1,
  partial_match = FALSE,
  ...,
  newdata = NULL
)

## S3 method for class 'gamm'
confint(object, ...)

## S3 method for class 'list'
confint(object, ...)

Arguments

object	an object of class "gam" or "gamm".
parm	which parameters (smooth terms) are to be given intervals as a vector of terms. If missing, all parameters are considered, although this is not currently implemented.
level	numeric, 0 < level < 1; the confidence level of the point-wise or simultaneous interval. The default is 0.95 for a 95% interval.
data	data frame; new values of the covariates used in the model fit. The selected smooth(s) wil be evaluated at the supplied values.
n	numeric; the number of points to evaluate smooths at.
type	character; the type of interval to compute. One of "confidence" for point-wise intervals, or "simultaneous" for simultaneous intervals.
nsim	integer; the number of simulations used in computing the simultaneous intervals.
shift	logical; should the constant term be add to the smooth?
transform	logical; should the smooth be evaluated on a transformed scale? For generalised models, this involves applying the inverse of the link function used to fit the model. Alternatively, the name of, or an actual, function can be supplied to transform the smooth and it's confidence interval.
unconditional	logical; if TRUE (and freq == FALSE) then the Bayesian smoothing parameter uncertainty corrected covariance matrix is returned, if available.
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 current R).
partial_match	logical; should matching parm use a partial match or an exact match? Can only be used if length(parm) is 1.
...	additional arguments for methods
newdata	DEPRECATED! data frame; containing new values of the covariates used in the model fit. The selected smooth(s) wil be evaluated at the supplied values.

Value

a tibble with components:

1. smooth; character indicating to which term each row relates,

2. type; the type of smooth,

3. by the name of the by variable if a by smooth, NA otherwise,

4. one or more vectors of values at which the smooth was evaluated, named as per the variables in the smooth,

5. zero or more variables containing values of the by variable,

6. est; estimated value of the smooth,

7. se; standard error of the estimated value of the smooth,

8. crit; critical value for the 100 * level% confidence interval.

9. lower; lower limit of the confidence or simultaneous interval,

10. upper; upper limit of the confidence or simultaneous interval,

Author(s)

Gavin L. Simpson

Examples

load_mgcv()

dat <- data_sim("eg1", n = 1000, dist = "normal", scale = 2, seed = 2)
mod <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = "REML")

# new data to evaluate the smooths at, say over the middle 50% of range
# of each covariate
middle <- function(x, n = 50, coverage = 0.5) {
  v <- (1 - coverage) / 2
  q <- quantile(x, prob = c(0 + v, 1 - v), type = 8)
  seq(q[1], q[2], length = n)
}
new_data <- sapply(dat[c("x0", "x1", "x2", "x3")], middle)
new_data <- data.frame(new_data)

## point-wise interval for smooth of x2
ci <- confint(mod, parm = "s(x2)", type = "confidence", data = new_data)
ci

## simultaneous interval for smooth of x2

si <- confint(mod, parm = "s(x2)", data = new_data,
              type = "simultaneous", nsim = 3000, ncores = 2)
si

gratia documentation built on Feb. 16, 2023, 10:40 p.m.