confint.fderiv | R Documentation |
Calculates point-wise confidence or simultaneous intervals for the first derivatives of smooth terms in a fitted GAM.
## S3 method for class 'fderiv' confint( object, parm, level = 0.95, type = c("confidence", "simultaneous"), nsim = 10000, ncores = 1L, ... )
object |
an object of class |
parm |
which parameters (smooth terms) are to be given intervals as a vector of terms. If missing, all parameters are considered. |
level |
numeric, |
type |
character; the type of interval to compute. One of |
nsim |
integer; the number of simulations used in computing the simultaneous intervals. |
ncores |
number of cores for generating random variables from a
multivariate normal distribution. Passed to |
... |
additional arguments for methods |
a data frame with components:
term
; factor indicating to which term each row relates,
lower
; lower limit of the confidence or simultaneous interval,
est
; estimated derivative
upper
; upper limit of the confidence or simultaneous interval.
Gavin L. Simpson
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 derivatives at, say over the middle 50% of range # of each covariate middle <- function(x, n = 25, 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) ## first derivatives of all smooths... fd <- fderiv(mod, newdata = new_data) ## point-wise interval ci <- confint(fd, type = "confidence") ci ## simultaneous interval for smooth term of x2 x2_sint <- confint(fd, parm = "x2", type = "simultaneous", nsim = 10000, ncores = 2) x2_sint
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