Yes, the cause is memory use patterns, but the price is steep nonetheless.
E.g.:
rate<-log(400*1.1^(1:30)) # runs about 27x times as fast as the following
(test via 'microbenchmark')
rate<-numeric(30)
for (i in 1:30){
rate[i]<-log(400*1.1^i)
}
When manipulating large arrays, the difference
This is not true. The steep price has to do with memory use patterns like
result <- c( result, new value ). Vectorization is cleaner, easier to read, and
somewhat faster, but for loops are not the monster that they have a reputation
for being if the memory is allocated before the loop and elemen
Also, any time you write "for" in R, you pay a steep price in performance. In
a short, simple loop it may not be noticeable, but in a more challenging
problem it can be a huge issue.
A more efficient way to write your loop would be:
infectrate = 400*1.1^(1:30) # calculation
cbind(1:30,log(infectra
The code has an error so it won't run as written.
Instead of:
infectrate[n]= (400)(1.1)^(n);
try:
infectrate[n]= 400*1.1^n;
What I get after making this change looks right.
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This has nothing to do with your question, but instead of using
>class=c(rep(1,3),rep(2,3),rep(3,3))
>
It's probably easier to use class = rep(1:3, each =3)
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In addition to what Jeff and David have said...
If you really want to create a separate data frame for each subgroup then
you need use the assign function, and also data.split[[i]] instead of
data.split[1].
for (i in 1:3) {
assign( paste('sub',i,sep='') , data.split[[i]]
}
Jeff showed you how
On Apr 9, 2012, at 5:33 PM, Christopher Desjardins wrote:
Hi,
I am having trouble with syntax for a for loop. Here is what I am
trying to
do.
class=c(rep(1,3),rep(2,3),rep(3,3))
out1=rnorm(length(class))
out2=rnorm(length(class))
out3=rnorm(length(class))
data=data.frame(class,out1,out2,out
class1 <- data[1==data$class,]
gets you a subset of the data into a dedicated object, but if you want to
handle arbitrarily large amounts of data or class values then the list output
of split is really much better to stay with.
Also, "data" is a predefined function, so it is not a good idea to
Hi,
I am having trouble with syntax for a for loop. Here is what I am trying to
do.
class=c(rep(1,3),rep(2,3),rep(3,3))
out1=rnorm(length(class))
out2=rnorm(length(class))
out3=rnorm(length(class))
data=data.frame(class,out1,out2,out3)
dat.split=split(data,data$class)
for(i in 1:3){
sub[i]=da
Hello.
I've just started using R and am trying to figure out if the two codes
snippets below have the same output
gBest<-floor(runif(popsize,min=1,max=top))
velocity[i,j]<<-.4* velocity[i,j] + 1 * runif(1) * (pbestsVar[i,j] -
popVar[i,j]) + 1 * runif(1) * (archiveVar[gBest,j] - popVar[i,j])
and
Hi, all.
I need help on improving the efficiency of my R
simulations. Below is a function that simulates a
change point model. It first generates a sequence of
three dimensional ARMA(1,1) observations, then
calculates the one step ahead prediction errors, some
statistic is calcualted and compare
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