I am working with amino acid sequences changing each letter to numbers.I have a data
from acf transformation called Zm as shown below. I would like to get Indices D1 to
D10 and then create F1 to F10 as indicated below. Is there anyway I can do that in R
without typing each of them one by one. For example to get D1 to D3, I have to type
D1<-c(Zm[[1]])[1][[1]], D2<-c(Zm[[2]])[1][[1]], and D3<-c(Zm[[3]])[1][[1]].
This is Zm was calculated.
ReadZm<-read.table("C:\\Documents and Settings\\stephen\\Desktop\\practice\\Ft.dat",
header=TRUE)
Zm<-by(scale(ReadZm[, 3:5],center=FALSE, scale=TRUE),ReadZm$GI, acf, = lag.max=5,
type="covariance", plot=FALSE)
Zm
INDICES: D1
Autocovariances of series 'data[x, ]', by lag
, , Z1
Z1 Z2 Z3
1.0038341836 ( 0) -0.0689672193 ( 0) 0.1638605166 ( 0)
-0.0484984679 ( 1) -0.1884834898 ( -1) 0.1599671586 ( -1)
0.1129146146 ( 2) -0.0268675914 ( -2) 0.0604863049 ( -2)
0.0580622445 ( 3) 0.0176879067 ( -3) 0.0511021407 ( -3)
Zm
INDICES: D2
Autocovariances of series 'data[x, ]', by lag
, , Z1
Z1 Z2 Z3
1.076612e+00 ( 0) 3.475059e-02 ( 0) 1.728981e-01 ( 0)
-2.639058e-04 ( 1) 2.919360e-03 ( -1) -5.340469e-03 ( -1)
2.669045e-02 ( 2) -8.738303e-02 ( -2) -6.066240e-02 ( -2)
Zm
INDICES: D3
Autocovariances of series 'data[x, ]', by lag
, , Z1
Z1 Z2 Z3
1.0583627071 ( 0) 0.0396194354 ( 0) 0.1507399860 ( 0)
0.0016303943 ( 1) 0.0025233219 ( -1) 0.0354695276 ( -1)
0.0093683974 ( 2) -0.0666452362 ( -2) -0.0721539962 ( -2)
0.0482982100 ( 3) -0.0211955002 ( -3) 0.0740133158 ( -3)
etc.
I would like to convert those indices into arrays as indicated below by typing each
index D1, D2,.....D10.
Is there a way of doing that in R without going through each index? My final results
should appear like example of figure 1 below.
D1<-c(Zm[[1]])[1][[1]]
D2<-c(Zm[[2]])[1][[1]]
D3<-c(Zm[[3]])[1][[1]]
D4<-c(Zm[[4]])[1][[1]]
D5<-c(Zm[[5]])[1][[1]]
D6<-c(Zm[[6]])[1][[1]]
D7<-c(Zm[[7]])[1][[1]]
D8<-c(Zm[[8]])[1][[1]]
D9<-c(Zm[[9]])[1][[1]]
D10<-c(Zm[[10]])[1][[1]]
To calculate F1 to F10 see below.
F1<-data.frame<-c(D1)
F2<-data.frame<-c(D2)
F3<-data.frame<-c(D3)
F4<-data.frame<-c(D4)
F5<-data.frame<-c(D5)
F6<-data.frame<-c(D6)
F7<-data.frame<-c(D7)
F8<-data.frame<-c(D8)
F9<-data.frame<-c(D9)
F10<-data.frame<-c(D10)
K<-rbind(F1,F2,F3,F4,F5,F6,F7,F8,F9,F10)
Figure 1. K
D1 0.0438961122 -0.0850376026 0.003703249 -0.045145163 -0.0137549764
D2 0.0858789555 0.1359138805 0.142618579 0.181216290 0.2144693206
D3 -0.0120998969 0.0538759994 0.043675137 0.001780804 0.0357002270
D4 0.0576657536 0.0255354998 -0.001225369 0.021486156 0.0371668239
D5 -0.0347536925 0.0148086592 0.029135976 0.066354175 -0.0069727461
D6 0.0546508768 0.0293732170 -0.049859976 0.204040093 0.0611271659
D7 0.0507488987 0.1765004194 0.081786138 0.105454843 0.0077602850
D8 -0.0297705333 0.0094467070 0.019988517 0.055050705 0.0002062495
D9 0.0791272355 0.0320509961 0.073237929 0.036325332 0.0302841389
D10 0.0000995983 0.0646743929 0.044791169 0.010257411 0.0478312766
Sincerely,
Stephen Opiyo
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