I wanted to be clear and use the :: notation in the lines for fitting an mgcv::gam. I stumbled over one thing when using the notation within the model call for mgcv::s. The code with a reproducible example / error is shown below.
The reason is probably because I am using this notation within the model formula, but I could not figure out why this does not work / is not allowed. This is probably something quite specific concerning syntax (probably not mgcv specific, I guess), but maybe somebody can help me in understanding this and my understanding of R. Thank you in advance.
library(mgcv)
dat <- data.frame(x = 1:10, y = 101:110)
# this results in an error: invalid type (list)...
mgcv::gam(y ~ mgcv::s(x, bs = "cs", k = -1), data = dat)
# after removing the mgcv:: in front of s everything works fine
mgcv::gam(y ~ s(x, bs = "cs", k = -1), data = dat)
# outside of the model call, both calls return the desired function
class(s)
# [1] "function"
class(mgcv::s)
# [1] "function"
Explanation
library(mgcv)
#Loading required package: nlme
#This is mgcv 1.8-24. For overview type 'help("mgcv-package")'.
f1 <- ~ s(x, bs = 'cr', k = -1)
f2 <- ~ mgcv::s(x, bs = 'cr', k = -1)
OK <- mgcv:::interpret.gam0(f1)$smooth.spec
FAIL <- mgcv:::interpret.gam0(f2)$smooth.spec
str(OK)
# $ :List of 10
# ..$ term : chr "x"
# ..$ bs.dim : num -1
# ..$ fixed : logi FALSE
# ..$ dim : int 1
# ..$ p.order: logi NA
# ..$ by : chr "NA"
# ..$ label : chr "s(x)"
# ..$ xt : NULL
# ..$ id : NULL
# ..$ sp : NULL
# ..- attr(*, "class")= chr "cr.smooth.spec"
str(FAIL)
# list()
The 4th line of the source code of interpret.gam0 reveals the issue:
head(mgcv:::interpret.gam0)
1 function (gf, textra = NULL, extra.special = NULL)
2 {
3 p.env <- environment(gf)
4 tf <- terms.formula(gf, specials = c("s", "te", "ti", "t2",
5 extra.special))
6 terms <- attr(tf, "term.labels")
Since "mgcv::s" is not to be matched, you get the problem. But mgcv does allow you the room to work around this, by passing "mgcv::s" via argument extra.special:
FIX <- mgcv:::interpret.gam0(f, extra.special = "mgcv::s")$smooth.spec
all.equal(FIX, OK)
# [1] TRUE
It is just that this is not user-controllable at high-level routine:
head(mgcv::gam, n = 10)
#1 function (formula, family = gaussian(), data = list(), weights = NULL,
#2 subset = NULL, na.action, offset = NULL, method = "GCV.Cp",
#3 optimizer = c("outer", "newton"), control = list(), scale = 0,
#4 select = FALSE, knots = NULL, sp = NULL, min.sp = NULL, H = NULL,
#5 gamma = 1, fit = TRUE, paraPen = NULL, G = NULL, in.out = NULL,
#6 drop.unused.levels = TRUE, drop.intercept = NULL, ...)
#7 {
#8 control <- do.call("gam.control", control)
#9 if (is.null(G)) {
#10 gp <- interpret.gam(formula) ## <- default to extra.special = NULL
I agree with Ben Bolker. It is a good exercise to dig out what happens inside, but is an over-reaction to consider this as a bug and fix it.
More insight:
s, te, etc. in mgcv does not work in the same logic with stats::poly and splines::bs.
X <- splines::bs(x, df = 10, degree = 3), it evaluates x and create a design matrix X directly.s(x, bs = 'cr', k = 10), no evaluation is made; it is parsed.Smooth construction in mgcv takes several stages:
mgcv::interpret.gam, which generates a profile for a smoother;mgcv::smooth.construct, which sets up basis / design matrix and penalty matrix (mostly done at C-level);mgcv::smoothCon, which picks up "by" variable (duplicating smooth for factor "by", for example), linear functional terms, null space penalty (if you use select = TRUE), penalty rescaling, centering constraint, etc;mgcv:::gam.setup, which combines all smoothers together, returning a model matrix, etc.So, it is a far more complicated process.