Hi Peter,
Thank you so much!!! I will use complete linkage clustering because
Mendelian Randomization function
(https://cran.r-project.org/web/packages/MendelianRandomization/vignettes/Vignette_MR.pdf)
I plan to use allows for correlations but not as high as 0.9 or more.
I got 40 SNPs out of 246 s
Try hclust(as.dist(1-calc.rho), method = "average").
Peter
On Fri, Nov 15, 2019 at 10:02 AM Ana Marija wrote:
>
> HI Peter,
>
> Thank you for getting back to me and shedding light on this. I see
> your point, doing Jim's method:
>
> > keeprows<-apply(calc.rho,1,function(x) return(sum(x>0.8)<3))
While the remedy for your dissatisfaction with my previous solution
should be obvious, I will make it explicit.
# that is rows containing at most one value > 0.8
# ignoring the diagonal
keeprows<-apply(ro246,1,function(x) return(sum(x>0.8)<2))
ro246.lt.8<-ro246[keeprows,keeprows]
Jim
___
if it is of any help my correlation matrix (calc.rho) was done here,
under LDmatrix tab https://ldlink.nci.nih.gov/?tab=ldmatrix
and dataset of 246 is bellow
rs56192520
rs3764410
rs145984817
rs1807401
rs1807402
rs35350506
rs2089177
rs12325677
rs62064624
rs62064631
rs2349295
rs2174369
rs7218554
rs6
HI Peter,
Thank you for getting back to me and shedding light on this. I see
your point, doing Jim's method:
> keeprows<-apply(calc.rho,1,function(x) return(sum(x>0.8)<3))
> ro246.lt.8<-calc.rho[keeprows,keeprows]
> ro246.lt.8[ro246.lt.8 == 1] <- NA
> (mmax <- max(abs(ro246.lt.8), na.rm=TRUE))
[1
I suspect that you want to identify which variables are highly
correlated, and then keep only "representative" variables, i.e.,
remove redundant ones. This is a bit of a risky procedure but I have
done such things before as well sometimes to simplify large sets of
highly related variables. If your
HI Jim,
This:
colnames(calc.jim)[colSums(abs(calc.jim)>0.8)<3]
was the master take!
Thank you so much!!!
On Thu, Nov 14, 2019 at 3:39 PM Jim Lemon wrote:
>
> I thought you were going to trick us. What I think you are asking now
> is how to get the variable names in the columns that have at mos
I thought you were going to trick us. What I think you are asking now
is how to get the variable names in the columns that have at most one
_absolute_ value greater than 0.8. OK:
# I'm not going to try to recreate your correlation matrix
calc.jim<-matrix(runif(100,min=-1,max=1),nrow=10)
for(i in 1
Hi Ana,
Rather than addressing the question of why you want to do this, Let's
get make the question easier to answer:
calc.rho<-matrix(c(0.903,0.268,0.327,0.327,0.327,0.582,
0.928,0.276,0.336,0.336,0.336,0.598,
0.975,0.309,0.371,0.371,0.371,0.638,
0.975,0.309,0.371,0.371,0.371,0.638,
0.975,0.309,0
what would be the approach to remove variable that has at least 2
correlation coefficients >0.8?
this is the whole output of the head()
> head(calc.rho)
rs56192520 rs3764410 rs145984817 rs1807401 rs1807402 rs35350506
rs56192520 1.000 0.976 0.927 0.927 0.927
That's assuming your data was returned by head().
__
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and provide c
> I basically want to remove all entries for pairs which have value in
> between them (correlation calculated not in R, bit it is correlation,
> r2)
> so for example I would not keep: rs883504 because it has r2>0.8 for
> all those rs...
I'm still not sure what "remove all entries" means?
In your e
Sorry, but I don't understand your question.
When I first looked at this, I thought it was a correlation (or
covariance) matrix.
e.g.
> cor (quakes)
> cov (quakes)
However, your row and column variables are different, implying two
different data sets.
Also, some of the (correlation?) coefficien
I don't understand. I have to keep only pairs of variables with
correlation less than 0.8 in order to proceed with some calculations
On Thu, Nov 14, 2019 at 2:09 PM Bert Gunter wrote:
>
> Obvious advice:
>
> DON'T DO THIS!
>
> Bert Gunter
>
> "The trouble with having an open mind is that people k
Obvious advice:
DON'T DO THIS!
Bert Gunter
"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Thu, Nov 14, 2019 at 10:50 AM Ana Marija
wrote:
> Hello,
>
> I have a data fra
Hello,
I have a data frame like this (a matrix):
head(calc.rho)
rs9900318 rs8069906 rs9908521 rs9908336 rs9908870 rs9895995
rs56192520 0.903 0.268 0.327 0.327 0.327 0.582
rs3764410 0.928 0.276 0.336 0.336 0.336 0.598
rs145984817 0.
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