optimization - Optimize routine in R -


i'm new programming in r , have problem summarize in small routine. seems simple time takes... around 15 minutes 500 iterations. how speed up? in advance!.

edit: routine calculation of probabilities simulates sample (500) of product of conditional probabilities (on pd.max.s1.i). problem has large dimensions due combinatorics of d, , s. why looked idea structures (data.frame, for) use in routine because seems slow run, , possibility other structures more optimal.

ncd = 3; nud = 4; nua = 2 pd.max.s1.i <- matrix( c( 0.00, 0.95, 0.99,                                                                        0.00, 0.80, 0.95,                           0.00, 0.50, 0.70,                           0.00, 0.25, 0.30,                           0.00, 0.00, 0.10),                           nrow = nud + 1, ncol = nua + 1, byrow = t)    # probabilidades rownames(pd.max.s1.i) = paste("d", 0 : nud, sep = "") colnames(pd.max.s1.i) = paste("a", 0 : nua, sep = "") pd.max.s1.i=as.data.frame(pd.max.s1.i)  d = expand.grid(0 : 4, 0 : 4, 0 : 4) d = d[rowsums (d) == nud,] rownames(d) = paste0("d", apply(d, 1, paste0, collapse=""))  = expand.grid(0 : 2 ,0 : 2 ,0 : 2) = a[rowsums (a) <= nua,] rownames(a) = paste0("a", apply(a, 1, paste0, collapse=""))       s = expand.grid(0 : 1, 0 : 1, 0 : 1) rownames(s) = paste0("s", apply(s, 1, paste0, collapse=""))  fun.prd.pa = function(pa, idp, idr, idc, ncd){ # productorio de probabilidades     ppa = 1     (i in 1 : ncd) {         ppi = pa[idr[1,i] + 1, idc[1,i] + 1]         if (idp[1,i] == 0) ppi = 1 - ppi         ppa = ppa * ppi     }     return(ppa) }  (ite in 1: 500) {     (xd1 in rownames(d)){          (xaa in rownames(a)){             (xs1 in rownames(s)){                 p1 = fun.prd.pa (pd.max.s1.i, s[xs1,], d[xd1,], a[xaa,], ncd)             }         }     } }


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