如何修复R聚类函数调用问题并实现多参数批量运行输出至数据表

我已经写好了一段能针对单组参数计算聚类指标的R代码，具体代码如下：

library(spatstat)
library(ggplot2)
library(dplyr)
library(tidyr)
# 生成聚类景观
dim <- 2000
radiusCluster<-100
lambdaParent<-.02
lambdaDaughter<-30
hosts<-900
randmod<-0
numbparents<-rpois(1,lambdaParent*dim)
xxParent<-runif(numbparents,0+radiusCluster,dim-radiusCluster)
yyParent<-runif(numbparents,0+radiusCluster,dim-radiusCluster)
numbdaughter<-rpois(numbparents,(lambdaDaughter))
sumdaughter<-sum(numbdaughter)
theta<-2*pi*runif(sumdaughter)
rho<-radiusCluster*sqrt(runif(sumdaughter))
xx0=rho*cos(theta)
yy0=rho*sin(theta)
xx<-rep(xxParent,numbdaughter)
yy<-rep(yyParent,numbdaughter)
xx<-xx+xx0
yy<-yy+yy0
cds<-data.frame(xx,yy)
is_outlier<-function(x){
 x > dim| x < 0
}
cds<-cds[!(is_outlier(cds$xx)|is_outlier(cds$yy)),]
sampleselect<-sample(1:nrow(cds),hosts,replace=F)
cds<-cds%>%slice(sampleselect)
randfunction<-function(x){
 x<-runif(length(x),0,dim)
}
randselect<-sample(1:nrow(cds),floor(hosts*randmod),replace=F)
cds[randselect,]<-apply(cds[randselect,],1,randfunction)
landscape<-ppp(x=cds$xx,y=cds$yy,window=owin(xrange=c(0,dim),yrange=c(0,dim)))
ggplot(data.frame(landscape))+geom_point(aes(x=x,y=y))+coord_equal()+theme_minimal()
# 计算聚类指标
kk<-Kest(landscape)
plot(kk)
kk_iso<-kk$iso
kk_pois<-kk$theo
kk_div_na<-kk_iso/kk_pois
kk_div_0<-replace_na(kk_div_na,0)
kk_mean<-round(mean(kk_div_0),3)

这段代码能正常针对单组参数（比如radiusCluster=100、randmod=0）计算出kk_mean这个聚类指标。

接下来我想把randmod和radiusCluster作为变量，批量运行多组实验，把结果存入提前生成的parameter_table数据表中：

random_parameter<-rep(c(0,.5,1),3)
radiusCluster_parameter<-rep(c(100,300,600),each=3)
Cluster_metric<-rep(NA,length(radiusCluster_parameter))
parameter_table<-data.frame(random_parameter,radiusCluster_parameter,Cluster_metric)
colnames(parameter_table)<-c("r", "rho", "sigma")

这里r对应randmod，rho对应radiusCluster，sigma要用来存储计算得到的kk_mean。

于是我把之前的代码封装成了cluster_function函数：

cluster_function <- function (dim, lambdaParent, lambdaDaughter, hosts, randmod, radiusCluster) {
 numbparents <- rpois(1, lambdaParent * dim)
 xxParent <- runif(numbparents, 0 + radiusCluster, dim - radiusCluster)
 yyParent <- runif(numbparents, 0 + radiusCluster, dim - radiusCluster)
 numbdaughter <- rpois(numbparents, (lambdaDaughter))
 sumdaughter <- sum(numbdaughter)
 theta <- 2 * pi * runif(sumdaughter)
 rho <- radiusCluster * sqrt(runif(sumdaughter))
 xx0 = rho * cos(theta)
 yy0 = rho * sin(theta)
 xx <- rep(xxParent, numbdaughter)
 yy <- rep(yyParent, numbdaughter)
 xx <- xx + xx0
 yy <- yy + yy0
 cds <- data.frame(xx, yy)
 is_outlier <- function(x) {
 x > dim | x < 0
 }
 cds <- cds[!(is_outlier(cds$xx) | is_outlier(cds$yy)), ]
 sampleselect <- sample(1:nrow(cds), hosts, replace = F)
 cds <- cds %>% slice(sampleselect)
 randfunction <- function(x) {
 x <- runif(length(x), 0, dim)
 }
 randselect <- sample(1:nrow(cds), floor(hosts * randmod), replace = F)
 cds[randselect, ] <- apply(cds[randselect, ], 1, randfunction)
 landscape<-ppp(x=cds$xx,y=cds$yy,window=owin(xrange=c(0,dim),yrange=c(0,dim)))
 ggplot(data.frame(landscape))+geom_point(aes(x=x,y=y))+coord_equal()+theme_minimal()
 kk<-Kest(landscape)
 plot(kk)
 kk_iso<-kk$iso
 kk_pois<-kk$theo
 kk_div_na<-kk_iso/kk_pois
 kk_div_0<-replace_na(kk_div_na,0)
 kk_mean<-round(mean(kk_div_0),3)
}

但是调用这个函数的时候（比如cluster_function(dim <- 2000, lambdaParent <-.02, lambdaDaughter<-30, hosts<-900, randmod<-0, radiusCluster<-0)），完全没有任何反应。就算我把里面的绘图语句删掉，调用函数后也得不到任何输出。

现在我想请教大家两个问题：

• 如何修复这个函数的调用问题，让它能正常返回kk_mean的值？
• 如何实现针对parameter_table里的多组参数批量运行，把计算得到的kk_mean存入sigma列中？

我原本设想的批量运行代码是这样的，但显然也有问题：

for (i in length(parameter_table)){
 cluster_function(dim <- 2000, lambdaParent <-.02, lambdaDaughter<-30, hosts<-900, randmod<-parameter_table[i,"r"], radiusCluster<-parameter_table[i,"rho"])
}

内容的提问来源于stack exchange，提问作者OpenSauce