Solved: [R] piece wise application of functions
Thanks! Especially for pointing out the usage of a 'wrapper' function in conjunction to lapply. In addition, data re-organization was important, too, as you pointed out. My final solution was slightly different than your proposition. See below. Thanks again, Itay
On Fri, 20 Feb 2004, Tom Blackwell wrote:
If it were my problem, I would re-structure the task around the
existing capabilities of lapply(). In particular, I would
concatenate the three lists of functions, then write a wrapper
function which takes three arguments: an index, a list of
functions and a data set. Then I would call lapply() twice
to get the result you appear to be asking for. Here's a rough
example, untested, which may not work exactly for your situation.
miss.all <- c(missgp0, missgp1, missgp2)
wrapper <- function(i, ff, d) {ff[[i]](d)}
result.1 <- unlist(lapply(seq(9), wrapper, miss.all, snps.missgp[["ic"]]))
result.2 <- unlist(lapply(seq(9), wrapper, miss.all, strs.missgp[["ic"]]))
gaps <- c( # nine multiples of 1e+6 which describe the nine functions )
result <- cbind(gaps=gaps, snps=result.1, strs=result.2)
# Make skeleton d.f. for the results
gaps <- data.frame(gap=paste(c(1, 2, 5), "+06", sep="e")
# Re-organize data :-)
# Each interpolation function will operate on a single row
markers <- ( markers <- cbind(strs.missgp[["ic"]],
snps.missgp[["ic"]]) )[1:3,]
# A slight different definition: makes explicit the application
# to rows
wrapper <- function(i, ff, d) {ff[[i]](d[i,])}
# Compute. Note the use of sapply()
case0 <- cbind(gaps, t(sapply(seq(3), wrapper, missgp0,
markers)))
case1 <- cbind(gaps, t(sapply(seq(3), wrapper, missgp1,
markers)))
case2 <- cbind(gaps, t(sapply(seq(3), wrapper, missgp2,
markers)))
gaps was a d.f.; therefore, cbind() coerces the result to d.f.
For example:
case0
gap strs snps
1 1e+06 46145.218 374.3882
2 2e+06 2547.841 494.0718
3 5e+06 1372.235 402.9667
In this case, result is a matrix, not a data frame, but you can easily convert between the two.
HTH - tom blackwell - u michigan medical school - ann arbor -
I tried to loop over the indices of case and missgp, 0,1,2, using assign() but some how failed; and really wanted to go on with the analysis. Later, it occured to me that I might have used some wrapper function in combination with outer(); and the result would be in the diag()onal. That is because what in fact I was looking for was an inner prodcut of a vector of functions, with a vector of arguments. Thanks again, Itay
On Thu, 19 Feb 2004, Itay Furman wrote:
Dear all, After struggling for some time with *apply() and eva() without success, I decided to ask for help. I have 3 lists labeled with, each contains 3 different interpolation functions with identical names:
names(missgp0)
[1] "spl.1mb" "spl.2mb" "spl.5mb"
names(missgp1)
[1] "spl.1mb" "spl.2mb" "spl.5mb"
names(missgp2)
[1] "spl.1mb" "spl.2mb" "spl.5mb"
( In case it matters the functions accept and return one argument: block.size <- spl.1mb(ic) ) Then, I have 2 data frames with identical structure:
snps.missgp
intvl.mb ic 1 1e+06 0.597 2 2e+06 0.504 3 5e+06 0.327 4 1e+07 0.204
strs.missgp
intvl.mb ic 1 1e+06 0.67200 2 2e+06 0.62325 3 5e+06 0.51000 4 1e+07 0.38775
I would like to apply the functions on these data frames piece-wise and create a data frame per function _list_. So I am looking for a final output like this:
case0
gap snps strs 1 1e+06 .. .. 2 2e+06 .. .. 3 5e+06 .. .. Here, case0$snps[1] is, for example, the result of applying the function in missgp0[1] on the entry snps.missgp$ic[1]; and, case0$strs[1] is the result of applying the same function on strs.missgp$ic[1]. Then, I want to repeat the whole thing with missgp1,2 instead of missgp0, generating case1,2 data frames. How should I do it? Thanks in advance, Itay Furman -------------------------------------------------------------------- itayf at fhcrc.org Fred Hutchinson Cancer Research Center
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