You've already been pointed to options(digits=);
here's another way: since your data appear to be limited to 2 decimals,
why not select your noise from UNIF(0, 0.001)?
More importantly, are you really trying to do correlation between the
values you're showing us? What do you hope to learn from such correlations?
Peter
Manisha Brahmachary wrote:
Hello,
I am trying to do a spearman correlation. My data has tied values. To
overcome this issue, I am adding some random noise (values) to my original
data. However when I add the random noise to the data, the final matrix does
not show the new values. I guess the reason being that the noise I add is
very small and the full value including the decimals gets truncated to show
only the value upto two decimal points. Is there a way to keep the full value
as it is without truncation. Thanks for your suggestions in advance.
My script:
rand.val: stores the random numbers
data: stores the actual values
rand.val<-data.frame(a=runif(11,0.000000001,0.00000001),b=runif(11,0.00000000
1,0.00000001),
c=runif(11,0.000000001,0.00000001),d=runif(11,0.000000001,0.00000001),e=runif
(11,0.000000001,0.00000001),f=runif(11,0.000000001,0.00000001),g=runif(11,0.0
00000001,0.00000001),h=runif(11,0.000000001,0.00000001))
rand.val<- as.matrix(rand.val)
data
X1015 X1238 X1433 X1520 X1847 X1870 X1928 X2422
1-Mar 2 2 2 2.00 2.00 2.00 2 2.00
2-Mar 2 2 2 2.00 2.00 2.00 2 2.00
5-Mar 2 2 2 2.00 4.28 2.00 2 2.00
6-Mar 2 2 2 2.00 2.00 2.00 2 2.72
8-Mar 2 2 2 2.00 2.00 2.00 2 2.00
9-Mar 2 2 2 2.00 2.00 2.00 2 2.00
1-Sep 2 2 2 2.00 2.00 2.00 2 2.00
2-Sep 2 2 2 2.00 2.00 1.39 2 2.00
6-Sep 2 2 2 2.00 2.00 2.00 2 2.00
8-Sep 2 2 2 1.05 2.00 2.00 2 2.00
9-Sep 2 2 2 2.00 2.00 2.00 2 2.00
rand.val
a b c d e
f g h
[1,] 6.066415e-09 7.243185e-09 2.579000e-09 6.140522e-09 6.630778e-09
7.035269e-09 4.122901e-09 1.449599e-09
[2,] 3.623560e-09 9.038553e-09 2.269913e-09 7.762269e-09 5.540944e-09
1.478679e-09 6.159272e-09 7.082123e-09
[3,] 2.085766e-09 3.737900e-09 2.457034e-09 4.939643e-09 3.868569e-09
5.015730e-09 3.320189e-09 3.858882e-09
[4,] 5.037955e-09 8.234747e-09 5.407211e-09 4.136906e-09 9.078506e-09
8.743044e-09 8.026434e-09 7.089469e-09
[5,] 6.254242e-09 7.805144e-09 5.264731e-09 3.993522e-09 4.668103e-09
9.429713e-09 2.144503e-09 4.739684e-09
[6,] 2.051520e-09 3.173583e-09 7.463606e-09 2.799975e-09 6.192137e-09
4.345219e-09 1.301613e-09 2.913670e-09
[7,] 7.278996e-09 4.329527e-09 2.407270e-09 8.421633e-09 4.196166e-09
9.890987e-09 3.783615e-09 4.093923e-09
[8,] 9.046755e-09 5.665165e-09 7.740206e-09 3.482159e-09 6.048917e-09
3.170118e-09 6.764364e-09 5.860817e-09
[9,] 7.766820e-09 5.901440e-09 2.198517e-09 7.349194e-09 6.563453e-09
9.981425e-09 7.768106e-09 4.213753e-09
[10,] 4.493040e-09 5.796008e-09 1.623817e-09 8.136924e-09 1.029656e-09
3.725094e-09 8.767293e-09 7.532047e-09
[11,] 5.974140e-09 4.173989e-09 8.704932e-09 1.929161e-09 5.718819e-09
4.995755e-09 4.723989e-09 9.198158e-09
data+rand.val
X1015 X1238 X1433 X1520 X1847 X1870 X1928 X2422
1-Mar 2 2 2 2.00 2.00 2.00 2 2.00
2-Mar 2 2 2 2.00 2.00 2.00 2 2.00
5-Mar 2 2 2 2.00 4.28 2.00 2 2.00
6-Mar 2 2 2 2.00 2.00 2.00 2 2.72
8-Mar 2 2 2 2.00 2.00 2.00 2 2.00
9-Mar 2 2 2 2.00 2.00 2.00 2 2.00
1-Sep 2 2 2 2.00 2.00 2.00 2 2.00
2-Sep 2 2 2 2.00 2.00 1.39 2 2.00
6-Sep 2 2 2 2.00 2.00 2.00 2 2.00
8-Sep 2 2 2 1.05 2.00 2.00 2 2.00
9-Sep 2 2 2 2.00 2.00 2.00 2 2.00
Manisha
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