Thanks Jim and others (and sorry Jim - an early version of this slipped
into your inbox :))
Apologies for not giving some concrete code - I was trying to explain in
words.
What I need to do is to fit a simple linear model to successive sections
of a long matrix.
So far, the best solution I have
Hi John,
With due respect to the other respondents, here is something that might help:
# get a vector of values
foo<-rnorm(100)
# get a vector of increasing indices (aka your "recent" values)
bar<-sort(sample(1:100,40))
# write a function to "clump" the adjacent index values
clump_adj_int<-functio
Hopefully Bert and William won't be offended if I more or less summarize:
Are you assuming a loop will take ages, or have you actually tested it? I
wouldn't assume a loop will take ages, or that it will take much longer
than apply().
What's wrong with
apply( X[ {logical expression } , ] , 1, f
John:
1. Please read and follow the posting guide. In particular, provide a
small reproducible example so that we know what your data and looping
code look like.
2. apply-type commands are *not* vectorized; they are disguised loops
that may or may not offer any speedup over explicit loops.
3. A
>It is easy in a loop but that will take ages. Is there any vectorised
>apply-like solution to this?
If you showed the loop that takes ages, along with small inputs for
it (and an indication of how to expand those small inputs to big ones),
someone might be able to show you some code that does the
Folks
Is there any way to get the row index into apply as a variable?
I want a function to do some sums on a small subset of some very long
vectors, rolling through the whole vectors.
apply(X,1,function {do something}, other arguments)
seems to be the way to do it.
The subset I want is the mos
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