如何在R中使用“ hclust”作为函数调用 [英] How to use 'hclust' as function call in R

问题描述

我尝试通过以下方式将聚类方法构造为函数：

I tried to construct the clustering method as function the following ways:

mydata <- mtcars

# Here I construct hclust as a function
hclustfunc <- function(x) hclust(as.matrix(x),method="complete")

# Define distance metric
distfunc <- function(x) as.dist((1-cor(t(x)))/2)

# Obtain distance
d <- distfunc(mydata)

# Call that hclust function
fit<-hclustfunc(d)

# Later I'd do
# plot(fit)

但是为什么会出现以下错误：

But why it gives the following error:

Error in if (is.na(n) || n > 65536L) stop("size cannot be NA nor exceed 65536") : 
  missing value where TRUE/FALSE needed

什么是正确的方法？

推荐答案

请阅读所用功能的帮助。 ？hclust 很清楚，第一个参数 d 是一个不相似的对象，而不是矩阵：

Do read the help for functions you use. ?hclust is pretty clear that the first argument d is a dissimilarity object, not a matrix:

Arguments:

       d: a dissimilarity structure as produced by ‘dist’.

更新

因为OP已经存在更新了他们的问题，需要的是

Update

As the OP has now updated their question, what is need is

hclustfunc <- function(x) hclust(x, method="complete")
distfunc <- function(x) as.dist((1-cor(t(x)))/2)
d <- distfunc(mydata)
fit <- hclustfunc(d)

---

原始

您想要的是

---

Original

What you want is

hclustfunc <- function(x, method = "complete", dmeth = "euclidean") {    
    hclust(dist(x, method = dmeth), method = method)
}

然后

fit <- hclustfunc(mydata)

可以正常工作。请注意，您现在可以将差异系数方法作为 dmeth 和聚类方法进行传递。

works as expected. Note you can now pass in the dissimilarity coefficient method as dmeth and the clustering method.

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