Why is my function not recognising one particular argument when using future_map?

Question

This code is designed to generate 1000 landscapes according to a poisson Matern cluster distribution algorithm, with the random allocation of points included to create dynamic landscapes.

When running this code, the numerical object "radiusCluster" is not recognised, hence NAs are being produced and the landscapes are not fully realised.

I have tried many simple things to ensure this error is removed such as included the "radiusCluster" in both the function description and the future_map() call; this leads to "radiusCluster" not being recognised as an argument.

What is causing this error? How can I correct it and include "radiusCluster"?

library("spatstat")
library("furrr")
library("tidyverse")


#create a list for storing the epidemics
numsims<-1000

episet<-as.list(seq(1:numsims))

#function to generate landscape
generate_landscape<-function(radiusCluster=50,
                             lambdaParent,
                             lambdaDaughter,
                             randmod,
                             hosts,
                             dim){
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)



thetaLandscape<-2*pi*runif(sumdaughter)

rho<-radiusCluster*sqrt(runif(sumdaughter))



xx0=rho*cos(thetaLandscape)
yy0=rho*sin(thetaLandscape)


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)),]
while (nrow(cds)<hosts){
  dif<-hosts-nrow(cds)
  extraparentxx<-sample(xxParent,dif,replace = TRUE)
  extraparentyy<-sample(yyParent,dif,replace = TRUE)
  extrathetaLandscape<-2*pi*runif(dif)
  extrarho<-radiusCluster*sqrt(runif(dif))
  newextracoodsxx<-extrarho*cos(extrathetaLandscape)
  newextracoodsyy<-extrarho*sin(extrathetaLandscape)
  extraxx<-extraparentxx+newextracoodsxx
  extrayy<-extraparentyy+newextracoodsyy
  cdsextra<-data.frame(xx=extraxx,yy=extrayy)
  cds<-rbind(cds,cdsextra)
}

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)))
landscapestore<-data.frame(landscape)
landscapestore
}

# Use n-2 cores to generate the landscapes in parallel
cores <- availableCores()-2
plan(multisession, workers=cores, seed=TRUE)

# Generate the landscapes in parallel
landscapes <- future_map(episet, generate_landscape,
#when the hashtag is removed from "radiusCluster", it becomes #an unused argument and the code does not run
                         #radiusCluster=50,
                         lambdaParent=.05,
                         lambdaDaughter=25,
                         randmod=0,
                         hosts=1000,
                         dim=1000)

ggplot(landscapes[[1]])+geom_point(aes(x=x,y=y))

In runif(numbparents, 0 + radiusCluster, dim - radiusCluster) : NAs produced