On Jan 21, 2011, at 3:29 PM, Brahmachary, Manisha wrote:
Hi David,
Thanks a lot for you inputs. I have modified my code accordingly.
There
is one more place that I need some help.
This is my code:
=
=
======================================================================
======
X<- read.table("X.txt",as.is=T,header=T,row.names=1)
Y<- read.table("Y.txt",as.is=T,header=T,row.names=1)
X.mat<- as.matrix(X)
Y.mat<- as.matrix(Y)
# calculating the true correlation values from my original dataset
True.Corrs<- matrix()
for (k in 1:nrow(SNP.mat)){
True.Corrs[k]<- cor.test(X.mat[k,],Y.mat[k,],alternative
=c("greater"),method= c("pearson"))$p.value
}
# Creating the random distribution of Correlation p-values
X.rand <- list()
Y.rand<- list()
X.rand<-replicate(1000,(X[sample(1:ncol(Y))]),simplify=FALSE) #
Randomizing the column values for each row
Y.rand<-replicate(1000,Y,simplify=FALSE) # Creating an equivalent list
of the Y matrix (non-randomised), to be able to do a pair-wise
cor.test
Corrs.rand<- list()
tmp<- list()
for (i in 1:2){
for (j in 1:3){
# How to store a multiple values per element of list?
tmp[[j]] <- cor.test(t(X.rand[[i]][j,]),t(Y.rand[[i]][j,]),alternative
=c("greater"),method= c("pearson"))$p.value
Corrs.rand[[i]] <- rbind(Corrs.rand[[j]],tmp[[j]])
}
}
=
=
======================================================================
At this step:
for (i in 1:length(X.rand)){
for (j in 1:nrow(X.rand[[1]]){
# How to store a multiple values per element of list?
tmp[[j]] <- cor.test(t(X.rand[[i]][j,]),t(Y.rand[[i]][j,]),alternative
=c("greater"),method= c("pearson"))$p.value
Corrs.rand[[i]] <- rbind(Corrs.rand[[j]],tmp[[j]])
}
}
I am not sure how I can store multiple values per element.
The usual way would be to pre-allocate a matrix or a data.frame and
then use "[<-" to assign either a whole row at a time or assign
individual elements one by one. rbind in a loop is definitely going to
slow you down.
I haven't followed through the individual steps of all the for-loops.
I guess I have lost the ability to think that way after learning to
use the matrix and indexing features of R, alas. If Corrs.rand is a
1000 x 12 data.frame (which is just a special square list) then you
can assign the i-th row with:
Corrs.rand[i, ] <- <some-12-element-object>
Or you can assign the i,j-th element with:
Corrs.rand[i,j ] <- <vector-of-length-1>
The same syntax works for matrices.
--
David.
For eg. I
want a list of length 1000 (which is the number of random
permutations I
have generated for my dataset) and in each element of the list I
need to
store 12 p.values where 12 corresponds to the number of rows I have in
my randomized dataset. Eg.
[[1]]
0.23
0.05
0.78
0.78
0.87
0.11
0.003
0.9
0.76
0.11
0.23
0.56
[[2]]
0.08
0.67
0.45
0.23
0.35
0.85
0.99
0.78
0.66
0.45
0.06
0.1
[[3]]
So on...
I maybe going about this in a complicated way and there may be other
ways of storing the p.values for each of my randomized dataset. So if
anybody has ideas please oblige me.
======================================================
X dataset:(sample)
#Probes X10851 X12144 X12155 X11882 X10860 X12762 X12239 X12154
1 1 1 0 0 1 0 2 0
2 0 0 0 0 0 0 0 0
3 2 2 2 2 1 2 1 2
4 0 0 0 0 0 0 0 0
5 2 2 2 2 2 2 2 2
6 0 1 0 0 1 1 1 1
7 2 2 NaN 2 2 2 2 2
8 2 2 2 2 2 2 2 2
9 0 1 0 1 1 NaN 1 2
10 2 2 2 2 2 2 2 2
11 2 0 0 0 0 0 0 0
12 0 1 0 1 1 0 1 1
Y dataset:(sample)
Probes X10851 X12144 X12155 X11882 X10860 X12762 X12239 X12154
1 793.0830793 788.1813828 867.8504057 729.8321265
816.8519963 805.2113707 774.5990003 854.6384306
2 12.8695023 4.312894024 10.69769375 5.872212512
13.79299806 9.394132659 6.297552848 9.307943304
3 699.7791876 826.997429 795.6409729 770.9376141
806.1241089 782.3970486 817.107482 859.7154906
4 892.8217221 869.0481458 806.3386667 812.0431017
873.5565439 794.4752191 813.9587056 814.8681274
5 892.8217221 869.0481458 806.3386667 812.0431017
873.5565439 794.4752191 813.9587056 814.8681274
6 839.7350251 943.4455677 950.7575323 859.0208018
894.246041 853.524053 941.4841508 913.0246205
7 653.1272418 751.5217836 750.1757745 737.382114
757.8486157 758.2407075 724.2185775 770.8669409
8 12.8695023 4.312894024 10.69769375 5.872212512
13.79299806 9.394132659 6.297552848 9.307943304
9 839.7350251 943.4455677 950.7575323 859.0208018
894.246041 853.524053 941.4841508 913.0246205
10 653.1272418 751.5217836 750.1757745 737.382114
757.8486157 758.2407075 724.2185775 770.8669409
11 653.1272418 751.5217836 750.1757745 737.382114
757.8486157 758.2407075 724.2185775 770.8669409
12 839.7350251 943.4455677 950.7575323 859.0208018
894.246041 853.524053 941.4841508 913.0246205
Thanks again
Manisha
-----Original Message-----
From: David Winsemius [mailto:dwinsem...@comcast.net]
Sent: Tuesday, January 18, 2011 11:56 PM
To: Brahmachary, Manisha
Cc: R-help@r-project.org
Subject: Re: [R] Pearson correlation with randomization
On Jan 18, 2011, at 11:23 PM, Brahmachary, Manisha wrote:
Hello,
I will be very obliged if someone can help me with this statistical R
problem:
I am trying to do a Pearson correlation on my datasets X, Y with
randomization test. My X and Y datasets are pairs.
1. I want to randomize (rearrange) only my X dataset per
row ,while
keeping the my Y dataset as it is.
X <- X[sample(1:nrow(Y)), ]
2. Then Calculate the correlation for this pair, and compare it
to
your true value of correlation.
3. Repeat 2 and 3 maybe a 100 times
You may want to look at the replicate function.
4. If your true p-value is greater than 95% of the random
values,
then you can reject the null hypothesis at p<0.05.
You won't have a very stable estimate of the 95th order statistics
with "maybe" 100 replications.
--
David.
I am stuck at the randomization step. I need some help in
implementing
it the appropriate randomization step in my correlation.
Below is my incomplete code. I will be very obliged if someone could
help:
X <- read.table("X.txt",as.is=T,header=T,row.names=1)
Y <- read.table("Y.txt",as.is=T,header=T,row.names=1)
X.mat<- as.matrix(X)
Y.mat<- as.matrix(Y)
Corrs<- cor.test(X.mat[1,],Y.mat[1,],alternative
=c("greater"),method=
c("pearson"))
Corrs.rand <- list()
for (i in 1:length(X.mat)){
for (j in 1:100){
# This doesnot seem to wrok correctly. How do I run sample function
100
times for the same row?
SNP.rand<- sample(SNP.mat[i,],56, replace = FALSE, prob = NULL)
Corrs.rand[[j]]<- cor.test(SNP.rand,CNV.mat[j,],alternative
=c("greater"),method= c("pearson"))
# need to calculate how many times my pvalue from true p-value>
random
pvalue
}
}
X dataset:
#Probes
X10851
X12144
X12155
X11882
X10860
X12762
X12239
X12154
1
1
1
0
0
1
0
2
0
2
0
0
0
0
0
0
0
0
3
2
2
2
2
1
2
1
2
4
0
0
0
0
0
0
0
0
5
2
2
2
2
2
2
2
2
6
0
1
0
0
1
1
1
1
7
2
2
NaN
2
2
2
2
2
8
2
2
2
2
2
2
2
2
9
0
1
0
1
1
NaN
1
2
10
2
2
2
2
2
2
2
2
11
2
0
0
0
0
0
0
0
12
0
1
0
1
1
0
1
1
Y dataset:
Probes
X10851
X12144
X12155
X11882
X10860
X12762
X12239
X12154
1
793.0831
788.1814
867.8504
729.8321
816.852
805.2114
774.599
854.6384
2
12.8695
4.312894
10.69769
5.872213
13.793
9.394133
6.297553
9.307943
3
699.7792
826.9974
795.641
770.9376
806.1241
782.397
817.1075
859.7155
4
892.8217
869.0481
806.3387
812.0431
873.5565
794.4752
813.9587
814.8681
5
892.8217
869.0481
806.3387
812.0431
873.5565
794.4752
813.9587
814.8681
6
839.735
943.4456
950.7575
859.0208
894.246
853.5241
941.4842
913.0246
7
653.1272
751.5218
750.1758
737.3821
757.8486
758.2407
724.2186
770.8669
8
12.8695
4.312894
10.69769
5.872213
13.793
9.394133
6.297553
9.307943
9
839.735
943.4456
950.7575
859.0208
894.246
853.5241
941.4842
913.0246
10
653.1272
751.5218
750.1758
737.3821
757.8486
758.2407
724.2186
770.8669
11
653.1272
751.5218
750.1758
737.3821
757.8486
758.2407
724.2186
770.8669
12
839.735
943.4456
950.7575
859.0208
894.246
853.5241
941.4842
913.0246
Thanks in advance
Manisha
Mount Sinai School of Medicine
Icahn Medical Institute,
1425 Madison Avenue, Box 1498
NY-10029, NEW-YORK, USA
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PLEASE do read the posting guide
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David Winsemius, MD
West Hartford, CT
David Winsemius, MD
West Hartford, CT
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
