[R] code optimization

[james.holtman@convergys.com]

Try this:

 split(delta,cut(delta, breaks=c(0,2,4,7,10,max(delta)),include.lowest=T))
$"[0,2]"
[1] 0.0 1.4 2.0 2.0

$"(2,4]"
[1] 2.3 2.3

$"(4,7]"
[1] 4.5 6.4

$"(7,10]"
[1] 8.9 7.4

$"(10,12.4]"
[1] 12.4 11.5





"Peter B. Mandeville" <mandevip at uaslp.mx>@stat.math.ethz.ch on 06/24/2002
13:11:41

Sent by:  owner-r-help at stat.math.ethz.ch


To:   r-help at stat.math.ethz.ch
cc:
Subject:  [R] code optimization


I have a function "eval.delta" which does what I want but isn't very
elegant. I have consulted the R documents, MASS, and S Programming. Is
there a practical way to optimize the code? Thank you very much.

Peter B.

Function:

eval.delta <- function(delta){
     cat("VALUES\n")
     vlr <- NULL
     k <- 0
     for(j in 1:length(delta)) if(delta[j] <= 2){
         k <- k+1
         vlr[k] <- delta[j]
     }
     cat("<= 2 (no credible evidence)\t",k,"\t",vlr,"\n")
     vlr <- NULL
     k <- 0
     for(j in 1:length(delta)) if(delta[j]>2 & delta[j] <= 4){
         k <- k+1
         vlr[k] <- delta[j]
     }
     cat("> 2 y <= 4 (weak evidence)\t",k,"\t",vlr,"\n")
     vlr <- NULL
     k <- 0
     for(j in 1:length(delta)) if(delta[j]>4 & delta[j] <= 7){
         k <- k+1
         vlr[k] <- delta[j]
     }
     cat("> 4 y <= 7 (definite evidence)\t",k,"\t",vlr,"\n")
     vlr <- NULL
     k <- 0
     for(j in 1:length(delta)) if(delta[j]>7 & delta[j] <= 10){
         k <- k+1
         vlr[k] <- delta[j]
     }
     cat("> 7 y <= 10 (strong evidence)\t",k,"\t",vlr,"\n")
     vlr <- NULL
     k <- 0
     for(j in 1:length(delta)) if(delta[j]>10){
         k <- k+1
         vlr[k] <- delta[j]
     }
     cat("> 10 (very strong evidence)\t",k,"\t",vlr,"\n")
     cat("\nMODELS\n")
     vlr <- NULL
     k <- 0
     for(j in 1:length(delta)) if(delta[j] <= 2){
         k <- k+1
         vlr[k] <- j
     }
     cat("<= 2 (no credible evidence)\t",k,"\t",vlr,"\n")
     vlr <- NULL
     k <- 0
     for(j in 1:length(delta)) if(delta[j]>2 & delta[j] <= 4){
         k <- k+1
         vlr[k] <- j
     }
     cat("> 2 y <= 4 (weak evidence)\t",k,"\t",vlr,"\n")
     vlr <- NULL
     k <- 0
     for(j in 1:length(delta)) if(delta[j]>4 & delta[j] <= 7){
         k <- k+1
         vlr[k] <- j
     }
     cat("> 4 y <= 7 (definite evidence)\t",k,"\t",vlr,"\n")
     vlr <- NULL
     k <- 0
     for(j in 1:length(delta)) if(delta[j]>7 & delta[j] <= 10){
         k <- k+1
         vlr[k] <- j
     }
     cat("> 7 y <= 10 (strong evidence)\t",k,"\t",vlr,"\n")
     vlr <- NULL
     k <- 0
     for(j in 1:length(delta)) if(delta[j]>10){
         k <- k+1
         vlr[k] <- j
     }
     cat("> 10 (very strong evidence)\t",k,"\t",vlr,"\n")
}

Data:

 > delta <- c(0,1.4,2.3,4.5,2.3,8.9,12.4,6.4,7.4,11.5,2,2)

Function Call and Output:

 > eval.delta(delta)
VALUES
<= 2 (no credible evidence)      4       0 1.4 2 2
 > 2 y <= 4 (weak evidence)       2       2.3 2.3
 > 4 y <= 7 (definite evidence)   2       4.5 6.4
 > 7 y <= 10 (strong evidence)    2       8.9 7.4
 > 10 (very strong evidence)      2       12.4 11.5

MODELS
<= 2 (no credible evidence)      4       1 2 11 12
 > 2 y <= 4 (weak evidence)       2       3 5
 > 4 y <= 7 (definite evidence)   2       4 8
 > 7 y <= 10 (strong evidence)    2       6 9
 > 10 (very strong evidence)      2       7 10



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