为什么使用"mgcv :: s"?在"gam(y〜mgcv :: s ...)"中导致错误? [英] Why does using "mgcv::s" in "gam(y ~ mgcv::s...)" result in an error?
问题描述
我想清楚一点,并在各行中使用::
表示法来适合mgcv::gam
.在模型调用mgcv::s
中使用表示法时,我偶然发现了一件事情.带有可复制示例/错误的代码如下所示.
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.
原因可能是因为我在模型公式中使用了这种表示法,但是我无法弄清楚为什么它不起作用/不允许这样做.这可能是有关语法的非常具体的内容(我想可能不是特定于mgcv的内容),但是也许有人可以帮助我理解这一点以及我对R的理解.
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"
推荐答案
说明
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()
interpret.gam0
源代码的第四行揭示了该问题:
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")
由于不匹配"mgcv::s"
,因此出现了问题.但是mgcv
确实为您提供了解决此问题的空间,方法是通过参数extra.special
传递"mgcv::s"
:
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
在高级例程中这不是用户可控制的:
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
我同意本·博克(Ben Bolker)的观点.找出内部发生的事情是一个很好的练习,但是将其视为错误并修复它是一种过度反应.
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.
更多见解:
s
,te
等与stats::poly
和splines::bs
的逻辑不同.
s
, te
, etc. in mgcv
does not work in the same logic with stats::poly
and splines::bs
.
- 例如,当您执行
X <- splines::bs(x, df = 10, degree = 3)
时,它将评估x
并直接创建设计矩阵X
. - 执行
s(x, bs = 'cr', k = 10)
时,不会进行评估;它被解析.
- When you do for example,
X <- splines::bs(x, df = 10, degree = 3)
, it evaluatesx
and create a design matrixX
directly. - When you do
s(x, bs = 'cr', k = 10)
, no evaluation is made; it is parsed.
mgcv
中的平滑构建过程分为几个阶段:
Smooth construction in mgcv
takes several stages:
- 由
mgcv::interpret.gam
进行解析/解释,从而生成更平滑的轮廓; - 由
mgcv::smooth.construct
进行的初始构造,它建立了基础/设计矩阵和惩罚矩阵(主要在C级完成); -
mgcv::smoothCon
的二次构造,它拾取"by"变量(例如,将因子"by"平滑复制),线性函数项,空空间罚分(如果使用select = TRUE
),罚分重定比例,居中约束等;
mgcv:::gam.setup
进行的最终积分,它将所有平滑器组合在一起,返回模型矩阵等.
- parsing / interpretation by
mgcv::interpret.gam
, which generates a profile for a smoother; - initial construction by
mgcv::smooth.construct
, which sets up basis / design matrix and penalty matrix (mostly done at C-level); - secondary construction by
mgcv::smoothCon
, which picks up "by" variable (duplicating smooth for factor "by", for example), linear functional terms, null space penalty (if you useselect = TRUE
), penalty rescaling, centering constraint, etc; - final integration by
mgcv:::gam.setup
, which combines all smoothers together, returning a model matrix, etc.
所以,这是一个复杂得多的过程.
So, it is a far more complicated process.
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