What's a good strategy in R to store results in list form to access it later on by names rather than indices?

Question

I would like to automate the results creation process when I apply certain modeling techniques. So I'll have different parameters which will be applied (e.g. hierarchical clustering distances and linkage methods). The results will have a matrix form so that I can access individual results by specifying the model parameters (eg. single, euclidean). In a dataframe I could name the columns and rows and access the elements simply by df[rname[1],cname[1]]. As far I as I read its not possible to use data frame objects to store list results. So I need lists for storing list-results. But in lists I can only specify lst$cname[1] and not both dimensions. Am I correct?

# data frame layout for numeric results does not work with list results
rname<-c("u","v","w")
cname<-c("ave","single")

# dataframe for results but does not work for results which are lists
paste.1<-function(x,y) paste(x,y,sep=".")
df1<-data.frame(lapply(cname,paste.1,x=rname),row.names=rname)
colnames(df1)<-cname

# creating list for results - do not get a good idea to proceed from here Advices??
lst<-(lapply(cname,paste.1,x=rname))
names(lst)<-cname

# results example - could be anything else 
# with a dataframe I could use df1[rname,cname]<-foo(rname,cname)
# with lists I guess its not as easy
require(graphics)
ave.u <- hclust(dist(USArrests,"euclidean"), cname[1])
ave.v <- hclust(dist(USArrests,"maximum"), cname[1])
ave.w <- hclust(dist(USArrests,"manhattan"), cname[1])
single.u <- hclust(dist(USArrests,"euclidean"), cname[2])
single.v <- hclust(dist(USArrests,"maximum"), cname[2])
single.w <- hclust(dist(USArrests,"manhattan"), cname[2])

Well I am not sure if there is a solution which I guess must exist. At the end I just want to access the list results via the row names and column names. I know I could transfer row/column names to numerical ones and then play with assigning proper indexes to find my results in a list of length(rname) x length(cname) but since the data frame is so nicely to use I am assuming it must be an easy way to store it more user friendly. It might also be the case that I did not really get well into the concept of lists since I am just starting to play around with R. So my question is: What would be a good strategy to store the structured results (which are lists) using R?

Answer 1

One can have a matrix of lists:

nr <- length(rname)
nc <- length(cname)

m <- matrix(list(), nr, nc, dimnames = list(rname, cname))

m[["u", "ave"]] <- ave.u

# etc.

For example, form the row names, rnm, and column names, cnm, and a data frame, g, of all combinations of their values. Then create a matrix of lists, m :

rnm <- c("euclidean", "maximum", "manhattan")
cnm <- c("ave", "single")
g <- expand.grid(rnm, cnm)
f <- function(i) hclust(dist(USArrests, g[i,1]), g[i,2])
m <- matrix(lapply(1:nrow(g), f), length(rnm), dimnames = list(rnm, cnm))

We can access elements like this:

> m[["euclidean", "single"]]

Call:
hclust(d = dist(USArrests, g[i, 1]), method = g[i, 2])

Cluster method   : single 
Distance         : euclidean 
Number of objects: 50