Bill Dunlap
Spotfire, TIBCO Software
wdunlap tibco.com
-Original Message-
From: r-help-boun...@r-project.org
[mailto:r-help-boun...@r-project.org] On Behalf Of chipmaney
Sent: Wednesday, October 21, 2009 2:58 PM
To: r-help@r-project.org
Subject: [R] How do I vectorize this loop
Basically I need to use the following data to calculate a
squared error for
each Sample based on the expected Survival for the zone.
Basically, this code has Live/Dead for each sample, and I
need to calculate
the square error based on the Expected Mean (ie, Survival).
The code looks
up the expectation for each zone and applies for each sample
in the zone
using a loop:
Data1 - data.frame(Sample=1:6, Live =c(24,25,30,31,22,23), Baseline =
c(35,35,35,32,34,33),Zone = c(rep(Cottonwood,3),rep(OregonAsh,3)))
Data2 - data.frame(Zone = c(Cottonwood,OregonAsh), Survival =
c(0.83,0.76))
for (i in 1:nrow(Data1)) #(Yi -Ybar*Yo)^2
Data1$SquaredError[i] - (Data1$Live[i] -
Data2$Survival[which(Data1$Zone[i]==Data2$Zone)]*Data1$Baseline[i])^2
My question is, can I vectorize this code to avoid the loop?
Does the following do what you want?
(Data1$Live -
Data2$Survival[match(Data1$Zone,Data2$Zone)]*Data1$Baseline)^2
[1] 25.5025 16.4025 0.9025 44.6224 14.7456 4.3264
Obviously, I
could merge the 2 datasets first, but that would still
require 2 steps and
Data1 would have a bunch of redundant data.
Why worry about a bunch of 'redundant' data? Its space is
freed after it is no longer referenced, so if you do the
processing in a function and don't put all the temporarily
needed data in a permanent dataset it won't take up space
for long. If you are running into the memory limits of your
hardware then you have to work harder to avoid using more
memory that you need. (Note that vec[which(logical)] generally
gives the same result as vec[logical] but uses more space doing
so.)
Bill Dunlap
Spotfire, TIBCO Software
wdunlap tibco.com
So, is there a better
alternative? Is there some way I improve indexing syntax
efficiency by
using rownames instead of a column vector?
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