I realize this should be simple, but even after reading over the several
help pages several times, I still cannot decide between the myriad "apply"
functions to address it. I simply want to apply a function to all the rows
(or columns) of the same index from two (or more) identically sized arrays
(or data frames).
For example:
> a=matrix(1:50,nrow=10)
> a2=floor(jitter(a,amount=50))
> a
[,1] [,2] [,3] [,4] [,5]
[1,] 1 11 21 31 41
[2,] 2 12 22 32 42
[3,] 3 13 23 33 43
[4,] 4 14 24 34 44
[5,] 5 15 25 35 45
[6,] 6 16 26 36 46
[7,] 7 17 27 37 47
[8,] 8 18 28 38 48
[9,] 9 19 29 39 49
[10,] 10 20 30 40 50
> a2
[,1] [,2] [,3] [,4] [,5]
[1,] 31 56 -29 -13 10
[2,] 38 61 71 55 9
[3,] -29 38 47 12 38
[4,] 12 2 43 39 93
[5,] -43 23 -23 62 1
[6,] -13 61 55 11 2
[7,] -42 1 38 12 8
[8,] -13 -6 -18 16 95
[9,] -19 -2 78 33 1
[10,] 20 -16 -11 19 17
if I try the following for example:
apply(a,1,function(x) lm(a~a2))
I get 10 identical repeats (except for the list indexer) of the following:
[[1]]
Call:
lm(formula = a ~ a2)
Coefficients:
[,1] [,2] [,3] [,4] [,5]
(Intercept) 8.372135 18.372135 28.372135 38.372135 48.372135
a21 -0.006163 -0.006163 -0.006163 -0.006163 -0.006163
a22 -0.093390 -0.093390 -0.093390 -0.093390 -0.093390
a23 0.009315 0.009315 0.009315 0.009315 0.009315
a24 -0.015143 -0.015143 -0.015143 -0.015143 -0.015143
a25 -0.026761 -0.026761 -0.026761 -0.026761 -0.026761
...Which is clearly very wrong, in a number of ways. If I try by columns:
apply(a,2,function(x) lm(a~a2))
...I get exactly the same result.
So, which is the appropriate apply-type function when two arrays (or
d.f.'s?) are involved like this? Or none of them and some other approach
(other than looping which I can do but which I assume is not optimal)?
Thanks for any help.
--
Jim Bouldin, PhD
Research Ecologist
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