abc2e9dcb982ffbea556cafaf02d7f7011af7e83
[morpheus.git] / reports / printResults.R
1 # NOTE: discard top 2% of highest values
2 prms <- function(name, idx)
3 {
4 load(name)
5 d <- nrow(mr[[1]][[1]])-2
6 if (idx > length(mr))
7 mr[[idx]] = mr[[1]]
8 p <- colMeans(do.call(rbind, lapply(mr[[idx]], function(m) m[1,])))
9 bVects <- lapply(mr[[idx]], function(m) m[2+d,])
10 q98 <- Inf #quantile(sapply(bVects, function(bv) sum(abs(bv))), 0.98)
11 bFiltered <- Filter(function(bv) sum(abs(bv)) < q98, bVects)
12 b <- colMeans(do.call(rbind, bFiltered))
13 betaMatrices <- lapply(mr[[idx]], function(m) m[2:(d+1),])
14 q98 <- Inf #quantile(sapply(betaMatrices, function(bm) sum(abs(bm))), 0.98)
15 bmFiltered <- Filter(function(bm) sum(abs(bm)) < q98, betaMatrices)
16 beta <- (1/length(bmFiltered)) * Reduce("+", bmFiltered)
17 list(p, beta, b, mr_params)
18 }
19
20 pprms <- function(link, prefix="./")
21 {
22 toprint <- matrix(nrow=0, ncol=13) #13=1+2+1 + 1+2+1 + 1+3+1
23 for (n in c("5000", "10000", "100000", "500000", "1000000"))
24 {
25 for (method in 1:2)
26 {
27 row <- c()
28 for (d in c(2,5,10))
29 {
30 name <- paste0(prefix, "res_", n, "_", d, "_", link, ".RData")
31 params <- prms(name, method)
32 row <- c( row,
33 sum(abs(params[[1]] - params[[4]]$p)),
34 colSums(abs(params[[2]] - params[[4]]$beta)),
35 sum(abs(params[[3]] - params[[4]]$b)) )
36 }
37 toprint <- rbind(toprint, row)
38 }
39 }
40 print(formatC(toprint, format="e", digits=1)) #for reporting
41 return (toprint)
42 }