如果我有这样的功能：
te()我正在尝试实现FlattenDeep：
library(mgcv)
set.seed(540)
df <- gamSim(2, n = 300, scale = 0.15)[[1]]
df$x <- scales::rescale(df$x, to = c(-180,180))
df$y <- scales::rescale(df$y, to = c(-90,90))
head(df)
# works fine using s()
m1 <- gam(z ~ s(x, y, bs = "gp", m = c(2, 5, 2), k = 30), data = df)
summary(m1)
Family: gaussian
Link function: identity
Formula:
z ~ s(x, y, bs = "gp", m = c(2, 5, 2), k = 30)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.46143 0.01581 29.19 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(x,y) 8.055 12.73 0.333 0.983
R-sq.(adj) = 0.0248 Deviance explained = 5.11%
GCV = 0.077292 Scale est. = 0.074959 n = 300
# Fails
# pass list as per example in ?te for multivariate marginal
mp <- list(c(2, 5, 2))
m2 <- gam(z ~ te(x, y, bs = "gp", m = mp, k = 30), data = df)
Error in gpE(knt, knt, object$p.order) :
incorrect arguments to GP smoother
In addition: Warning message:
In mgcv::te(x, y, bs = "gp", m = mp, k = 30) : m wrong length and ignored.
# try passing directly
m2 <- gam(z ~ te(x, y, bs = "gp", m = c(2,5,2), k = 30), data = df) # FAILS
Error in gpE(knt, knt, object$p.order) :
incorrect arguments to GP smoother
In addition: Warning message:
In mgcv::te(x, y, bs = "gp", m = c(2, 5, 2), k = 30) :
m wrong length and ignored.
# don't pass m
m2 <- gam(z ~ te(x, y, bs = "gp", k = 10), data = df) # works
summary(m2)
Family: gaussian
Link function: identity
Formula:
z ~ te(x, y, bs = "gp", k = 10)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.46143 0.01299 35.52 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
te(x,y) 14.69 20.14 7.633 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.341 Deviance explained = 37.4%
GCV = 0.05341 Scale est. = 0.050618 n = 300
gam.check(m2) # k has been ignored!?
Method: GCV Optimizer: magic
Smoothing parameter selection converged after 15 iterations.
The RMS GCV score gradient at convergence was 6.499192e-07 .
The Hessian was positive definite.
Model rank = 100 / 100
Basis dimension (k) checking results. Low p-value (k-index<1) may
indicate that k is too low, especially if edf is close to k'.
k' edf k-index p-value
te(x,y) 99.0 14.7 1.08 0.94
但是我不知道如何一次将多个项目添加到列表中。我应该只遍历FlattenDeep的结果，还是可以分散结果并将其附加到列表中？
这可能有效：
func AcceptsAnything(v ...interface{}){
args =: FlattenDeep(v); // flatten any arrays or slices
}
但是我正在寻找一些不太详细的东西
答案 0 :(得分：2)
以下是将任意切片和数组展平为[]接口{}的方法：
"如果函数必须处理具有任意元素类型的切片和数组类型，则应用程序必须使用反射API遍历切片或数组，以将值放入[]接口{}。
如果仅需要展平[] interface {}，则不需要反射API：
let currentQuestion = 0;
let score = 1;
function renderQuestion() {
document.getElementById("quiz").innerHTML =
`
<fieldset>
<legend> ${questions[currentQuestion].question} </legend>
<input type="radio" name="option"
value="A">${questions[currentQuestion].options[0].option}<br>
<input type="radio" name="option"
value="B">${questions[currentQuestion].options[1].option}<br>
<input type="radio" name="option"
value="C">${questions[currentQuestion].options[2].option}<br>
<input type="radio" name="option"
value="D">${questions[currentQuestion].options[3].option}<br>
<button id="button" class="submitAnswer"> Submit answer </button>
<div id="errorMessage">
</div>
<div id="feed">
</div>
</fieldset>
`;
document.getElementById("button").addEventListener("click",
function(event) {
event.preventDefault();
let hasAnswer = false;
for (let el of document.querySelectorAll("input")) {
if (el.checked) {
hasAnswer = true;
if (el.value === questions[currentQuestion].answer)
{
score++;
}
}
}
if (hasAnswer) {
if (currentQuestion === questions.length - 1) {
document.getElementById("quiz").innerHTML = `Your score is
${score}`;
}
else {
currentQuestion++;
renderQuestion();
}
}
else {
document.getElementById("errorMessage").innerHTML = "Please
select an answer!";
}
});
}
renderQuestion();