Question

I imagine I am missing something quite simple here, or I am barking up the wrong tree completely, however I have been trying to sort this out over a number of days and my novice R skills haven’t been able to crack it.

I am looking for a method to reference an array of values from within a R function. I am creating a simulated population, I have individuals age, sex and ethnicity and I want to simulate the presence of absence of diabetes. I have the prevalence of diabetes by age bracket, gender and ethnicity which I have made into a 2(gender)x11(age bracket)x6(ethnicity) array. What I want to do is the reference the correct cell within the array and used that with a runif called to run a bernoulli trial per individual.

The code below is the current version however I have tried a number of different methods with varying results:

function(AB,sex,eth){

AB<-AB
sex<- sex
eth<-as.numeric(eth)


#make matrix reference
#make 'european' equal to 'other'
eth <- ifelse(eth==7,6,eth)
#change male from a 0 coding to a 2 for array lookup
sex <- ifelse(sex==1,1,2)
#remove seven from AB due to diab data starting at 30-34 age bracket
agebracket <- AB-7
#random number drawn
diabbase <- runif(census$Total.Sex[AB],0,1) 
#census$total.sex gives the total number in each age bracket

#array assignment
arrayvalue <- Darray[agebracket,sex,eth]

diab <- ifelse((diabbase >= (Darray[agebracket,sex,eth])) ,1,0)
return(diab)
}

if i call the function from the command line with “arrayvalue” returned rather than “diab” and individual values submitted rather than variables (ie diabtest <- diabgen(10,1,1) ) it returns the correct value from the array but if I submit the variables(ie diabtest <- diabgen(AB,sex,eth) it returns an empty array.

If I can give further info that might make what i am talking about clearer please let me know I would be more than happy to do so, it seems so easy but it is doing my head in. I am open to any suggestions on other/better ways of doing the same thing, any hints appreciated.

Answer 1

This maybe doesn’t solve your problem (I’ll update as needed), but it is a simple simulated dataframe for your conditions (2x11x6 factors)

brackets <- round(seq(15, 85, length.out = 12))
brlabels <- character()
for (i in 1:11) {
  brlabels[i] <- paste(brackets[i], "to", brackets[i + 1], sep = " ")
}
AB <- cut(round(runif(100, 18, 80)), breaks = brackets, labels = brlabels)

sex <- factor(sample(c(1,2), 100, replace = TRUE), levels = c(1,2), labels = c("Male", "Female"))

eth <- factor(sample(c(1:6), 100, replace = TRUE), levels = c(1:6), labels = c("French", "German", "Swedish", "Polish", "Greek", "Italian"))

somerandombusiness <- rnorm(100, 50, 4)

sim.df <- data.frame(somerandombusiness)
sim.df$AB <- AB
sim.df$sex <- sex
sim.df$eth <- eth

It may be more cumbersome to select a specific intersection of the three at first, but most of the tools to deal with factor variables expect a dataframe.

Edit 1

You could do something like:

runif(1,0) >= (sim.df[which(sim.df$AB=="34 to 40"&sim.df$sex=="Male"&sim.df$eth=="German"), 1])

But I’m still not sure why you would want to. For one, with my method there is no way to be sure that all possible combinations are enumerated. You could up the sample size to a few thousand without much trouble but that would only make it really really likely that every combination existed. In this case I’ve chose one that does exist.

You could do this more easily w/ something like table(sim.df$eth, sim.df[, 1] > 60) which will give a cross-tab of all the somerandombusiness values > 60 and various ethnicities.