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Yuan Chun Ding

2024-Jul-27 19:54 UTC

[R] please help generate a square correlation matrix

Hi Richard,

Nice to know you had similar experience.
Yes, your understanding is right.  all correlations are negative after removing
double-zero rows.
It is consistent with a heatmap we generated.
1 is for a cancer patient with a specific mutation.  0 is no mutation for the
same mutation type in a patient.
a pair of mutation type (two different mutations) are exclusive for most of
patients in heatmap or oncoplots.
 If we include all 1000 patients, 900 of patients with no mutations in both
mutation types, then the correlation is not significant at all.
But eyeball the heatmap (oncoplots) for mutation (row) by patient (column),
mutations are exclusive for most of patients,
so I want to measure how strong the exclusiveness between two specific mutation
types across those patients with at least one mutation type.
Then put the pair of mutations with strong negative mutations on the top rows by
order of negative mutation values.

Regarding a final application,  maybe there are some usage for my case.
 If one develops two drugs specific to the two negative correlated mutations,
the drug treatment for cancer patients is usually only for those patients
carrying the specific mutation,
then it is informative to know how strong the negative correlation when
considering different combination of treatment strategies.

Ding





From: R-help <r-help-bounces at r-project.org> On Behalf Of Richard
O'Keefe
Sent: Saturday, July 27, 2024 4:47 AM
To: Bert Gunter <bgunter.4567 at gmail.com>
Cc: r-help at r-project.org
Subject: Re: [R] please help generate a square correlation matrix

Curses, my laptop is hallucinating again. Hope I can get through this. So
we're talking about correlations between binary variables. Suppose we have
two 0-1-valued variables, x and y. Let A <- sum(x*y) # number of cases where
x and y are


Curses, my laptop is hallucinating again.  Hope I can get through this.

So we're talking about correlations between binary variables.

Suppose we have two 0-1-valued variables, x and y.

Let A <- sum(x*y)  # number of cases where x and y are both 1.

Let B <- sum(x)-A  # number of cases where x is 1 and y is 0

Let C <- sum(y)-A # number of cases where y is 1 and x is 0

Let D <- sum(!x * !y) # number of cases where x and y are both 0.

(also D = length(x)-A-B-C)



All the information is summarised in the 2-by-2 contingency table.

Some years ago, Nathan Rountree and I supervised Yung-Sing Koh's

data-mining PhD.

She surveyed the data mining literature and found some 37 different

"interestingness measures" for two-variable associations  -- if I

remember correctly; there were a lot of them.  They fell into a much

smaller number of qualitatively similar groups.

At any rate, the Pearson correlation between x and y is

(A*D - B*C)/sqrt((A+B)*(C+D)*(A+C)*(B+D))



So what happens when we delete the rows where x = 0 and y = 0?

Right, it forces D to 0, leaving A B C unchanged.

And looking at the numerator,

  If you delete rows with x = 0 y = 0 you MUST get a negative correlation.



Quite a modest "true" correlation (based on all the data) like -0.2

can masquerade as quite a strong "zero-suppressed" correlation like

-0.6.  Even +0.2 can turn into -0.4.   (These figures are from a

particular simulation run and may not apply in your case.)



Now one of the reasons why Yun-Sing Koh, Nathan Rountree, and I were

interested in interestingness measures is perhaps coincidentally

related to the file drawer/underreporting problem: it's quite common

for rows where x = 0 and y = 0 never to have been reported to you, so

we were hoping there were measures immune to that.  I have argued for

years that "till record analysis" for supermarkets &c is badly
flawed

by two facts: (a) it is hard to measure how much of a product people

WOULD have bought if only you had offered it for sale (although you

can make educated guesses) and (b) till records provide no evidence on

what the people who walked out without buying anything wanted (was the

price too high?  could they not find it?).  Problem (a) leads to a

commercial variant of the Signor-Lipps effect: "when x and/or y were

available for purchase" is not the same as "the period for which data

were recorded", thus inflating D, perhaps massively.  Methods

developed for handling the Signor-Lipps effect in paleontology can be

used to estimate when x and y were available helping you to recover a

more realistic N=A+B+C+D.  I really should have published that.



All of which is a long-winded way of saying that

- Pearson correlations on binary columns can be computed very efficiently

- the rows with x=0 and y=0 may be very informative, even essential for analysis

- delete them at your peril.

- really, delete them at your peril.



On Sat, 27 Jul 2024 at 23:07, Richard O'Keefe <raoknz at
gmail.com<mailto:raoknz at gmail.com>> wrote:
>
> Let's go back to the original posting.
>
> > >
> > >> in each column, less than 10% values are 1, most of them are
0;
> > >
> > >
> > >
> > >> so I want to remove a  row with value of zero in both columns
when calculate correlation between two columns.
> > >
>
> So we're talking about correlations between binary variables.
> Suppose we have two 0-1-valued variables, x and y.
> Let A <- sum(x*y)  # number of cases where x and y are both 1.
> Let B <- sum(x)-a  # number of cases where x is 1 and y is 0
> Let C <- sum(y)-a # number of cases where y is 1 and x is 0
> Let D <- sum(!x * !y) # number of cases where x and y are both 0.
>
> N
>
> On Fri, 26 Jul 2024 at 12:07, Bert Gunter <bgunter.4567 at
gmail.com<mailto:bgunter.4567 at gmail.com>> wrote:
> >
> > If I have understood the request, I'm not sure that omitting all 0
> > pairs for each pair of columns makes much sense, but be that as it
> > may, here's another way to do it by using the 'FUN'
argument of combn
> > to encapsulate any calculations that you do. I just use cor() as the
> > calculation -- you can use anything you like that takes two vectors of
> > 0's and 1's and produces fixed length numeric results (or
fromm which
> > you can extract such).
> >
> > I encapsulated it all in a little function. Note that I first
> > converted the data frame to a matrix. Because of their generality,
> > data frames carry a lot of extra baggage that can slow purely numeric
> > manipulations down.
> >
> > Anyway, here's the function, 'somecors' (I'm a bad
name picker :(  ! )
> >
> >    somecors <- function(dat, func = cor){
> >       dat <- as.matrix(dat)
> >       indx <- seq_len(ncol(dat))
> >          combn(indx, 2, FUN = \(z) {
> >             i <- z[1]; j <- z[2]
> >             k <- dat[, i ] | dat[, j ]
> >             c(z,func(dat[k,i ], dat[k,j ]))
> >          })
> >    }
> >
> > Results come out as a matrix with combn(ncol(dat),2) columns, the
> > first 2 rows giving the pair of column numbers for each column,and
> > then 1 or more rows (possibly extracted) from whatever func you use.
> > Here's the results for your data formatted to 2 decimal places:
> >
> > > round(somecors(dat),2)
> >      [,1]  [,2]  [,3]  [,4] [,5]  [,6]
> > [1,]  1.0  1.00  1.00  2.00    2  3.00
> > [2,]  2.0  3.00  4.00  3.00    4  4.00
> > [3,] -0.5 -0.41 -0.35 -0.41   NA -0.47
> > Warning message:
> > In func(dat[k, i], dat[k, j]) : the standard deviation is zero
> >
> > The NA and warning comes in the 2,4 pair of columns because after
> > removing all zero rows in the pair, dat[,4] is all 1's, giving a
zero
> > in the denominator of the cor() calculation -- again, assuming I have
> > correctly understood your request. If so, this might be something you
> > need to worry about.
> >
> > Again, feel free to ignore if  I have misinterpreterd or this does not
suit.
> >
> > Cheers,
> > Bert
> >
> >
> > On Thu, Jul 25, 2024 at 2:01?PM Rui Barradas <ruipbarradas at
sapo.pt<mailto:ruipbarradas at sapo.pt>> wrote:
> > >
> > > ?s 20:47 de 25/07/2024, Yuan Chun Ding escreveu:
> > > > Hi Rui,
> > > >
> > > > You are always very helpful!! Thank you,
> > > >
> > > > I just modified your R codes to remove a row with zero
values in both column pair as below for my real data.
> > > >
> > > > Ding
> > > >
> > > > dat<-gene22mut.coded
> > > > r <- P <- matrix(NA, nrow = 22L, ncol = 22L,
> > > >                   dimnames = list(names(dat), names(dat)))
> > > >
> > > > for(i in 1:22) {
> > > >    #i=1
> > > >    x <- dat[[i]]
> > > >    for(j in (1:22)) {
> > > >      #j=2
> > > >      if(i == j) {
> > > >        # there's nothing to test, assign correlation 1
> > > >        r[i, j] <- 1
> > > >      } else {
> > > >        tmp <-cbind(x,dat[[j]])
> > > >        row0 <-rowSums(tmp)
> > > >        tem2 <-tmp[row0!=0,]
> > > >        tmp3 <- cor.test(tem2[,1],tem2[,2])
> > > >        r[i, j] <- tmp3$estimate
> > > >        P[i, j] <- tmp3$p.value
> > > >      }
> > > >    }
> > > > }
> > > > r<-as.data.frame(r)
> > > > P<-as.data.frame(P)
> > > >
> > > > From: R-help <r-help-bounces at
r-project.org<mailto:r-help-bounces at r-project.org>> On Behalf Of
Yuan Chun Ding via R-help
> > > > Sent: Thursday, July 25, 2024 11:26 AM
> > > > To: Rui Barradas <ruipbarradas at
sapo.pt<mailto:ruipbarradas at sapo.pt>>; r-help at
r-project.org<mailto:r-help at r-project.org>
> > > > Subject: Re: [R] please help generate a square correlation
matrix
> > > >
> > > > HI Rui, Thank you for the help! You did not remove a row if
zero values exist in both column pair, right? Ding From: Rui Barradas
<ruipbarradas@?sapo.?pt> Sent: Thursday, July 25, 2024 11:?15 AM To: Yuan
Chun Ding <ycding@?coh.?org>;
> > > >
> > > >
> > > > HI Rui,
> > > >
> > > >
> > > >
> > > > Thank you for the  help!
> > > >
> > > >
> > > >
> > > > You did not remove a row if zero values exist in both column
pair, right?
> > > >
> > > >
> > > >
> > > > Ding
> > > >
> > > >
> > > >
> > > > From: Rui Barradas <ruipbarradas at
sapo.pt<mailto:ruipbarradas at sapo.pt<mailto:ruipbarradas at
sapo.pt%3cmailto:ruipbarradas at sapo.pt>>>
> > > >
> > > > Sent: Thursday, July 25, 2024 11:15 AM
> > > >
> > > > To: Yuan Chun Ding <ycding at coh.org<mailto:ycding at
coh.org<mailto:ycding at coh.org%3cmailto:ycding at coh.org>>>;
r-help at r-project.org<mailto:r-help at r-project.org<mailto:r-help at
r-project.org%3cmailto:r-help at r-project.org>>
> > > >
> > > > Subject: Re: [R] please help generate a square correlation
matrix
> > > >
> > > >
> > > >
> > > > ?s 17:?39 de 25/07/2024, Yuan Chun Ding via R-help escreveu:
> Hi R users, > > I generated a square correlation matrix for the dat
dataframe below; > dat<-data.?frame(g1=c(1,0,0,1,1,1,0,0,0), >
g2=c(0,1,0,1,0,1,1,0,0), > g3=c(1,1,0,0,0,1,0,0,0),
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > ?s 17:39 de 25/07/2024, Yuan Chun Ding via R-help escreveu:
> > > >
> > > >
> > > >
> > > >> Hi R users,
> > > >
> > > >
> > > >
> > > >>
> > > >
> > > >
> > > >
> > > >> I generated a square correlation matrix for the dat
dataframe below;
> > > >
> > > >
> > > >
> > > >> dat<-data.frame(g1=c(1,0,0,1,1,1,0,0,0),
> > > >
> > > >
> > > >
> > > >>                   g2=c(0,1,0,1,0,1,1,0,0),
> > > >
> > > >
> > > >
> > > >>                   g3=c(1,1,0,0,0,1,0,0,0),
> > > >
> > > >
> > > >
> > > >>                   g4=c(0,1,0,1,1,1,1,1,0))
> > > >
> > > >
> > > >
> > > >> library("Hmisc")
> > > >
> > > >
> > > >
> > > >> dat.rcorr = rcorr(as.matrix(dat))
> > > >
> > > >
> > > >
> > > >> dat.r <-round(dat.rcorr$r,2)
> > > >
> > > >
> > > >
> > > >>
> > > >
> > > >
> > > >
> > > >> however, I want to modify this correlation calculation;
> > > >
> > > >
> > > >
> > > >> my dat has more than 1000 rows and 22 columns;
> > > >
> > > >
> > > >
> > > >> in each column, less than 10% values are 1, most of them
are 0;
> > > >
> > > >
> > > >
> > > >> so I want to remove a  row with value of zero in both
columns when calculate correlation between two columns.
> > > >
> > > >
> > > >
> > > >> I just want to check whether those values of 1 are
correlated between two columns.
> > > >
> > > >
> > > >
> > > >> Please look at my code in the following;
> > > >
> > > >
> > > >
> > > >>
> > > >
> > > >
> > > >
> > > >> cor.4gene <-matrix(0,nrow=4*4, ncol=4)
> > > >
> > > >
> > > >
> > > >> for (i in 1:4){
> > > >
> > > >
> > > >
> > > >>     #i=1
> > > >
> > > >
> > > >
> > > >>     for (j in 1:4) {
> > > >
> > > >
> > > >
> > > >>       #j=1
> > > >
> > > >
> > > >
> > > >>       d <-dat[,c(i,j)]%>%
> > > >
> > > >
> > > >
> > > >>         filter(eval(as.symbol(colnames(dat)[i]))!=0 |
> > > >
> > > >
> > > >
> > > >>                  eval(as.symbol(colnames(dat)[j]))!=0)
> > > >
> > > >
> > > >
> > > >>       c <-cor.test(d[,1],d[,2])
> > > >
> > > >
> > > >
> > > >>      
cor.4gene[i*j,]<-c(colnames(dat)[i],colnames(dat)[j],
> > > >
> > > >
> > > >
> > > >>                           c$estimate,c$p.value)
> > > >
> > > >
> > > >
> > > >>     }
> > > >
> > > >
> > > >
> > > >> }
> > > >
> > > >
> > > >
> > > >> cor.4gene<-as.data.frame(cor.4gene)%>%filter(V1
!=0)
> > > >
> > > >
> > > >
> > > >>
colnames(cor.4gene)<-c("gene1","gene2","cor","P")
> > > >
> > > >
> > > >
> > > >>
> > > >
> > > >
> > > >
> > > >> Can you tell me what mistakes I made?
> > > >
> > > >
> > > >
> > > >> first, why cor is NA when calculation of correlation for
g1 and g1, I though it should be 1.
> > > >
> > > >
> > > >
> > > >>
> > > >
> > > >
> > > >
> > > >> cor.4gene$cor[is.na(cor.4gene$cor)]<-1
> > > >
> > > >
> > > >
> > > >> cor.4gene$cor[is.na(cor.4gene$P)]<-0
> > > >
> > > >
> > > >
> > > >> cor.4gene.sq <-pivot_wider(cor.4gene, names_from =
gene1, values_from = cor)
> > > >
> > > >
> > > >
> > > >>
> > > >
> > > >
> > > >
> > > >> Then this line of code above did not generate a square
matrix as what the HMisc library did.
> > > >
> > > >
> > > >
> > > >> How to fix my code?
> > > >
> > > >
> > > >
> > > >>
> > > >
> > > >
> > > >
> > > >> Thank you,
> > > >
> > > >
> > > >
> > > >>
> > > >
> > > >
> > > >
> > > >> Ding
> > > >
> > > >
> > > >
> > > >>
> > > >
> > > >
> > > >
> > > >>
> > > >
> > > >
> > > >
> > > >>
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> >
> > > >  
<https://urldefense.com/v3/__http:/www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb880tLw0$%3chttps:/urldefense.com/v3/__http:/www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb880tLw0$%3e%3e><https://urldefense.com/v3/__http:/www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb880tLw0$%3chttps:/urldefense.com/v3/__http:/www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb880tLw0$%3e%3e%3e%3e>
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> >
> > > >>
<https://urldefense.com/v3/__http:/www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb880tLw0$%3chttps:/urldefense.com/v3/__http:/www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb880tLw0$%3e%3e>and<https://urldefense.com/v3/__http:/www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb880tLw0$%3chttps:/urldefense.com/v3/__http:/www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb880tLw0$%3e%3e%3eand>provide
commented, minimal, self-contained, reproducible code.
> > > >
> > > >
> > > >
> > > > Hello,
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > You are complicating the code, there's no need for
as.symbol/eval, the
> > > >
> > > >
> > > >
> > > > column numbers do exactly the same.
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > # create the two results matrices beforehand
> > > >
> > > >
> > > >
> > > > r <- P <- matrix(NA, nrow = 4L, ncol = 4L, dimnames =
list(names(dat),
> > > >
> > > >
> > > >
> > > > names(dat)))
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > for(i in 1:4) {
> > > >
> > > >
> > > >
> > > >     x <- dat[[i]]
> > > >
> > > >
> > > >
> > > >     for(j in (1:4)) {
> > > >
> > > >
> > > >
> > > >       if(i == j) {
> > > >
> > > >
> > > >
> > > >         # there's nothing to test, assign correlation 1
> > > >
> > > >
> > > >
> > > >         r[i, j] <- 1
> > > >
> > > >
> > > >
> > > >       } else {
> > > >
> > > >
> > > >
> > > >         tmp <- cor.test(x, dat[[j]])
> > > >
> > > >
> > > >
> > > >         r[i, j] <- tmp$estimate
> > > >
> > > >
> > > >
> > > >         P[i, j] <- tmp$p.value
> > > >
> > > >
> > > >
> > > >       }
> > > >
> > > >
> > > >
> > > >     }
> > > >
> > > >
> > > >
> > > > }
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > # these two results are equal up to floating-point precision
> > > >
> > > >
> > > >
> > > > dat.rcorr$r
> > > >
> > > >
> > > >
> > > > #>           g1        g2        g3        g4
> > > >
> > > >
> > > >
> > > > #> g1 1.0000000 0.1000000 0.3162278 0.1581139
> > > >
> > > >
> > > >
> > > > #> g2 0.1000000 1.0000000 0.3162278 0.6324555
> > > >
> > > >
> > > >
> > > > #> g3 0.3162278 0.3162278 1.0000000 0.0000000
> > > >
> > > >
> > > >
> > > > #> g4 0.1581139 0.6324555 0.0000000 1.0000000
> > > >
> > > >
> > > >
> > > > r
> > > >
> > > >
> > > >
> > > > #>           g1        g2           g3           g4
> > > >
> > > >
> > > >
> > > > #> g1 1.0000000 0.1000000 3.162278e-01 1.581139e-01
> > > >
> > > >
> > > >
> > > > #> g2 0.1000000 1.0000000 3.162278e-01 6.324555e-01
> > > >
> > > >
> > > >
> > > > #> g3 0.3162278 0.3162278 1.000000e+00 1.355253e-20
> > > >
> > > >
> > > >
> > > > #> g4 0.1581139 0.6324555 1.355253e-20 1.000000e+00
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > # these two results are equal up to floating-point precision
> > > >
> > > >
> > > >
> > > > dat.rcorr$P
> > > >
> > > >
> > > >
> > > > #>           g1         g2        g3         g4
> > > >
> > > >
> > > >
> > > > #> g1        NA 0.79797170 0.4070838 0.68452834
> > > >
> > > >
> > > >
> > > > #> g2 0.7979717         NA 0.4070838 0.06758329
> > > >
> > > >
> > > >
> > > > #> g3 0.4070838 0.40708382        NA 1.00000000
> > > >
> > > >
> > > >
> > > > #> g4 0.6845283 0.06758329 1.0000000         NA
> > > >
> > > >
> > > >
> > > > P
> > > >
> > > >
> > > >
> > > > #>           g1         g2        g3         g4
> > > >
> > > >
> > > >
> > > > #> g1        NA 0.79797170 0.4070838 0.68452834
> > > >
> > > >
> > > >
> > > > #> g2 0.7979717         NA 0.4070838 0.06758329
> > > >
> > > >
> > > >
> > > > #> g3 0.4070838 0.40708382        NA 1.00000000
> > > >
> > > >
> > > >
> > > > #> g4 0.6845283 0.06758329 1.0000000         NA
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > You can put these two results in a list, like Hmisc::rcorr
does.
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > lst_rcorr <- list(r = r, P = P)
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > Hope this helps,
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > Rui Barradas
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > --
> > > >
> > > >
> > > >
> > > > Este e-mail foi analisado pelo software antiv?rus AVG para
verificar a presen?a de v?rus.
> > > >
> > > >
> > > >
> > > >
https://urldefense.com/v3/__http://www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$<https://urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$><https://urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3chttps:/urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3e%09%5b%5balternative><https://urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3chttps:/urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3e%3chttps:/urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3chttps:/urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3e%09%5b%5balternative%3e%3e>
><https://urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3chttps:/urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3e%3chttps:/urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3chttps:/urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3e%09%5b%5balternative%3e%3e>>
> >
> > > >  
<https://urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3chttps:/urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3e%09%5b%5balternative><https://urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3chttps:/urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3e%09%5b%5balternative%3e%3e>
><https://urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3chttps:/urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3e%09%5b%5balternative%3e%3e>>
> >
> > > >                
[[alternative<https://urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3chttps:/urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3e%09%5b%5balternative>HTML<https://urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3chttps:/urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3e%09%5b%5balternative%3eHTML>version
deleted]]
> > > >
> > > >
> > > >
> > > > ______________________________________________
> > > >
> > > > R-help at r-project.org<mailto:R-help at
r-project.org<mailto:R-help at r-project.org%3cmailto:R-help at
r-project.org>> mailing list -- To UNSUBSCRIBE and more, see
> > > >
> > > >
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><https://urldefense.com/v3/__https:/stat.ethz.ch/mailman/listinfo/r-help__;!!Fou38LsQmgU!s54ahZtZNAIyaIGV3C2p8lhXpYlHksC6XvFKZltf6g3ElJHOO3I1MYFecLQ4QeMO3MpP3qXz-YrhdUE$%3chttps:/urldefense.com/v3/__https:/stat.ethz.ch/mailman/listinfo/r-help__;!!Fou38LsQmgU!s54ahZtZNAIyaIGV3C2p8lhXpYlHksC6XvFKZltf6g3ElJHOO3I1MYFecLQ4QeMO3MpP3qXz-YrhdUE$%3e%3e>>
> >
> > > > PLEASE do read the posting guide
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><https://urldefense.com/v3/__http:/www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!s54ahZtZNAIyaIGV3C2p8lhXpYlHksC6XvFKZltf6g3ElJHOO3I1MYFecLQ4QeMO3MpP3qXzNRZxc6s$%3chttps:/urldefense.com/v3/__http:/www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!s54ahZtZNAIyaIGV3C2p8lhXpYlHksC6XvFKZltf6g3ElJHOO3I1MYFecLQ4QeMO3MpP3qXzNRZxc6s$%3e%3e>>
> >
> > > > and provide commented, minimal, self-contained, reproducible
code.
> > > >
> > > Hello,
> > >
> > > Here are two other ways.
> > >
> > > The first is equivalent to your long format attempt.
> > >
> > >
> > > library(tidyverse)
> > >
> > > dat %>%
> > >    names() %>%
> > >    expand.grid(., .) %>%
> > >    apply(1L, \(x) {
> > >      tmp <- dat[rowSums(dat[x]) > 0, ]
> > >      tmp2 <- cor.test(tmp[[ x[1L] ]], tmp[[ x[2L] ]])
> > >      c(tmp2$estimate, P = tmp2$p.value)
> > >    }) %>%
> > >    t() %>%
> > >    as.data.frame() %>%
> > >    cbind(tmp_df, .) %>%
> > >    na.omit()
> > >
> > >
> > > The second is, in my opinion the one that makes more sense. If
you see
> > > the results, cor is symmetric (as it should) so the calculations
are
> > > repeated. If you only run the cor.tests on the combinations of
> > > names(dat) by groups of 2, it will save a lot of work. But the
output is
> > > a much smaller a data.frame.
> > >
> > >
> > > cbind(
> > >    combn(names(dat), 2L) %>%
> > >      t() %>%
> > >      as.data.frame(),
> > >    combn(dat, 2L, FUN = \(d) {
> > >      d2 <- d[rowSums(d) > 0, ]
> > >      tmp2 <- cor.test(d2[[1L]], d2[[2L]])
> > >      c(tmp2$estimate, P = tmp2$p.value)
> > >    }) %>% t()
> > > ) %>% na.omit()
> > >
> > >
> > >
> > > Hope this helps,
> > >
> > > Rui Barradas
> > >
> > >
> > > ______________________________________________
> > > R-help at r-project.org<mailto:R-help at r-project.org>
mailing list -- To UNSUBSCRIBE and more, see
> > >
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> > > PLEASE do read the posting guide
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> > > and provide commented, minimal, self-contained, reproducible
code.
> >
> > ______________________________________________
> > R-help at r-project.org<mailto:R-help at r-project.org> mailing
list -- To UNSUBSCRIBE and more, see
> >
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> > PLEASE do read the posting guide
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> > and provide commented, minimal, self-contained, reproducible code.


______________________________________________

R-help at r-project.org<mailto:R-help at r-project.org> mailing list -- To
UNSUBSCRIBE and more, see

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and provide commented, minimal, self-contained, reproducible code.

	[[alternative HTML version deleted]]

Bert Gunter

2024-Jul-27 23:49 UTC

head link

[R] please help generate a square correlation matrix

Your expanded explanation helps clarify your intent. Herewith some
comments. Of course, feel free to ignore and not respond. And, as
always, my apologies if I have failed to comprehend your intent.

1. I would avoid any notion of "statistical significance" like the
plague. This is a purely exploratory exercise.

2. My understanding is that you want to know the proportion of rows in
a pair of columns/vectors in which only 1 values of the pair is 1 out
of the number of pairs where 1 or 2 values is 1.  In R syntax, this is
simply:

sum(xor(x, y)) / sum(x | y)  ,
where x and y are two columns of 1's and 0's

Better yet might be to report both this *and* sum(x|y) to help you
judge "meaningfulness".
Here is a simple function that does this

## first, define a function that does above calculation:
assoc <- \(z){
   x <- z[,1]; y <- z[,2]
   n <- sum(x|y)
   c(prop = sum(xor(x, y))/n, N = n)
}

## Now a function that uses it for the various combinations:

somecor <- function(dat, func = assoc){
   dat <- as.matrix(dat)
   indx <- seq_len(ncol(dat))
   rbind(w <- combn(indx,2),
         combn(indx, 2, FUN = \(m)func(dat[,m]) )) |>
     t()  |> round(digits =2) |>
  'dimnames<-'(list(rep.int('',ncol(w)),
c("","", "prop","N")))
}

# Now apply it to your example data:

somecor(dat)
## which gives
     prop N
 1 2 0.67 6
 1 3 0.60 5
 1 4 0.57 7
 2 3 0.60 5
 2 4 0.33 6
 3 4 0.71 7

This seems more interpretable and directly useful to me. Bigger values
of prop for bigger N are the more interesting, assuming I have
interpreted you correctly.

Cheers,
Bert


On Sat, Jul 27, 2024 at 12:54?PM Yuan Chun Ding <ycding at coh.org>
wrote:>
> Hi Richard,
>
>
>
> Nice to know you had similar experience.
>
> Yes, your understanding is right.  all correlations are negative after
removing double-zero rows.
>
> It is consistent with a heatmap we generated.
>
> 1 is for a cancer patient with a specific mutation.  0 is no mutation for
the same mutation type in a patient.
>
> a pair of mutation type (two different mutations) are exclusive for most of
patients in heatmap or oncoplots.
>
>  If we include all 1000 patients, 900 of patients with no mutations in both
mutation types, then the correlation is not significant at all.
>
> But eyeball the heatmap (oncoplots) for mutation (row) by patient (column),
mutations are exclusive for most of patients,
>
> so I want to measure how strong the exclusiveness between two specific
mutation types across those patients with at least one mutation type.
>
> Then put the pair of mutations with strong negative mutations on the top
rows by order of negative mutation values.
>
>
>
> Regarding a final application,  maybe there are some usage for my case.
>
>  If one develops two drugs specific to the two negative correlated
mutations, the drug treatment for cancer patients is usually only for those
patients carrying the specific mutation,
>
> then it is informative to know how strong the negative correlation when
considering different combination of treatment strategies.
>
>
>
> Ding
>
>
>
>
>
>
>
>
>
>
>
> From: R-help <r-help-bounces at r-project.org> On Behalf Of Richard
O'Keefe
> Sent: Saturday, July 27, 2024 4:47 AM
> To: Bert Gunter <bgunter.4567 at gmail.com>
> Cc: r-help at r-project.org
> Subject: Re: [R] please help generate a square correlation matrix
>
>
>
> Curses, my laptop is hallucinating again. Hope I can get through this. So
we're talking about correlations between binary variables. Suppose we have
two 0-1-valued variables, x and y. Let A <- sum(x*y) # number of cases where
x and y are
>
> Curses, my laptop is hallucinating again.  Hope I can get through this.
>
> So we're talking about correlations between binary variables.
>
> Suppose we have two 0-1-valued variables, x and y.
>
> Let A <- sum(x*y)  # number of cases where x and y are both 1.
>
> Let B <- sum(x)-A  # number of cases where x is 1 and y is 0
>
> Let C <- sum(y)-A # number of cases where y is 1 and x is 0
>
> Let D <- sum(!x * !y) # number of cases where x and y are both 0.
>
> (also D = length(x)-A-B-C)
>
>
>
> All the information is summarised in the 2-by-2 contingency table.
>
> Some years ago, Nathan Rountree and I supervised Yung-Sing Koh's
>
> data-mining PhD.
>
> She surveyed the data mining literature and found some 37 different
>
> "interestingness measures" for two-variable associations  -- if I
>
> remember correctly; there were a lot of them.  They fell into a much
>
> smaller number of qualitatively similar groups.
>
> At any rate, the Pearson correlation between x and y is
>
> (A*D - B*C)/sqrt((A+B)*(C+D)*(A+C)*(B+D))
>
>
>
> So what happens when we delete the rows where x = 0 and y = 0?
>
> Right, it forces D to 0, leaving A B C unchanged.
>
> And looking at the numerator,
>
>   If you delete rows with x = 0 y = 0 you MUST get a negative correlation.
>
>
>
> Quite a modest "true" correlation (based on all the data) like
-0.2
>
> can masquerade as quite a strong "zero-suppressed" correlation
like
>
> -0.6.  Even +0.2 can turn into -0.4.   (These figures are from a
>
> particular simulation run and may not apply in your case.)
>
>
>
> Now one of the reasons why Yun-Sing Koh, Nathan Rountree, and I were
>
> interested in interestingness measures is perhaps coincidentally
>
> related to the file drawer/underreporting problem: it's quite common
>
> for rows where x = 0 and y = 0 never to have been reported to you, so
>
> we were hoping there were measures immune to that.  I have argued for
>
> years that "till record analysis" for supermarkets &c is
badly flawed
>
> by two facts: (a) it is hard to measure how much of a product people
>
> WOULD have bought if only you had offered it for sale (although you
>
> can make educated guesses) and (b) till records provide no evidence on
>
> what the people who walked out without buying anything wanted (was the
>
> price too high?  could they not find it?).  Problem (a) leads to a
>
> commercial variant of the Signor-Lipps effect: "when x and/or y were
>
> available for purchase" is not the same as "the period for which
data
>
> were recorded", thus inflating D, perhaps massively.  Methods
>
> developed for handling the Signor-Lipps effect in paleontology can be
>
> used to estimate when x and y were available helping you to recover a
>
> more realistic N=A+B+C+D.  I really should have published that.
>
>
>
> All of which is a long-winded way of saying that
>
> - Pearson correlations on binary columns can be computed very efficiently
>
> - the rows with x=0 and y=0 may be very informative, even essential for
analysis
>
> - delete them at your peril.
>
> - really, delete them at your peril.
>
>
>
> On Sat, 27 Jul 2024 at 23:07, Richard O'Keefe <raoknz at
gmail.com> wrote:
>
> >
>
> > Let's go back to the original posting.
>
> >
>
> > > >
>
> > > >> in each column, less than 10% values are 1, most of them
are 0;
>
> > > >
>
> > > >
>
> > > >
>
> > > >> so I want to remove a  row with value of zero in both
columns when calculate correlation between two columns.
>
> > > >
>
> >
>
> > So we're talking about correlations between binary variables.
>
> > Suppose we have two 0-1-valued variables, x and y.
>
> > Let A <- sum(x*y)  # number of cases where x and y are both 1.
>
> > Let B <- sum(x)-a  # number of cases where x is 1 and y is 0
>
> > Let C <- sum(y)-a # number of cases where y is 1 and x is 0
>
> > Let D <- sum(!x * !y) # number of cases where x and y are both 0.
>
> >
>
> > N
>
> >
>
> > On Fri, 26 Jul 2024 at 12:07, Bert Gunter <bgunter.4567 at
gmail.com> wrote:
>
> > >
>
> > > If I have understood the request, I'm not sure that omitting
all 0
>
> > > pairs for each pair of columns makes much sense, but be that as
it
>
> > > may, here's another way to do it by using the 'FUN'
argument of combn
>
> > > to encapsulate any calculations that you do. I just use cor() as
the
>
> > > calculation -- you can use anything you like that takes two
vectors of
>
> > > 0's and 1's and produces fixed length numeric results (or
fromm which
>
> > > you can extract such).
>
> > >
>
> > > I encapsulated it all in a little function. Note that I first
>
> > > converted the data frame to a matrix. Because of their
generality,
>
> > > data frames carry a lot of extra baggage that can slow purely
numeric
>
> > > manipulations down.
>
> > >
>
> > > Anyway, here's the function, 'somecors' (I'm a
bad name picker :(  ! )
>
> > >
>
> > >    somecors <- function(dat, func = cor){
>
> > >       dat <- as.matrix(dat)
>
> > >       indx <- seq_len(ncol(dat))
>
> > >          combn(indx, 2, FUN = \(z) {
>
> > >             i <- z[1]; j <- z[2]
>
> > >             k <- dat[, i ] | dat[, j ]
>
> > >             c(z,func(dat[k,i ], dat[k,j ]))
>
> > >          })
>
> > >    }
>
> > >
>
> > > Results come out as a matrix with combn(ncol(dat),2) columns, the
>
> > > first 2 rows giving the pair of column numbers for each
column,and
>
> > > then 1 or more rows (possibly extracted) from whatever func you
use.
>
> > > Here's the results for your data formatted to 2 decimal
places:
>
> > >
>
> > > > round(somecors(dat),2)
>
> > >      [,1]  [,2]  [,3]  [,4] [,5]  [,6]
>
> > > [1,]  1.0  1.00  1.00  2.00    2  3.00
>
> > > [2,]  2.0  3.00  4.00  3.00    4  4.00
>
> > > [3,] -0.5 -0.41 -0.35 -0.41   NA -0.47
>
> > > Warning message:
>
> > > In func(dat[k, i], dat[k, j]) : the standard deviation is zero
>
> > >
>
> > > The NA and warning comes in the 2,4 pair of columns because after
>
> > > removing all zero rows in the pair, dat[,4] is all 1's,
giving a zero
>
> > > in the denominator of the cor() calculation -- again, assuming I
have
>
> > > correctly understood your request. If so, this might be something
you
>
> > > need to worry about.
>
> > >
>
> > > Again, feel free to ignore if  I have misinterpreterd or this
does not suit.
>
> > >
>
> > > Cheers,
>
> > > Bert
>
> > >
>
> > >
>
> > > On Thu, Jul 25, 2024 at 2:01?PM Rui Barradas <ruipbarradas at
sapo.pt> wrote:
>
> > > >
>
> > > > ?s 20:47 de 25/07/2024, Yuan Chun Ding escreveu:
>
> > > > > Hi Rui,
>
> > > > >
>
> > > > > You are always very helpful!! Thank you,
>
> > > > >
>
> > > > > I just modified your R codes to remove a row with zero
values in both column pair as below for my real data.
>
> > > > >
>
> > > > > Ding
>
> > > > >
>
> > > > > dat<-gene22mut.coded
>
> > > > > r <- P <- matrix(NA, nrow = 22L, ncol = 22L,
>
> > > > >                   dimnames = list(names(dat),
names(dat)))
>
> > > > >
>
> > > > > for(i in 1:22) {
>
> > > > >    #i=1
>
> > > > >    x <- dat[[i]]
>
> > > > >    for(j in (1:22)) {
>
> > > > >      #j=2
>
> > > > >      if(i == j) {
>
> > > > >        # there's nothing to test, assign
correlation 1
>
> > > > >        r[i, j] <- 1
>
> > > > >      } else {
>
> > > > >        tmp <-cbind(x,dat[[j]])
>
> > > > >        row0 <-rowSums(tmp)
>
> > > > >        tem2 <-tmp[row0!=0,]
>
> > > > >        tmp3 <- cor.test(tem2[,1],tem2[,2])
>
> > > > >        r[i, j] <- tmp3$estimate
>
> > > > >        P[i, j] <- tmp3$p.value
>
> > > > >      }
>
> > > > >    }
>
> > > > > }
>
> > > > > r<-as.data.frame(r)
>
> > > > > P<-as.data.frame(P)
>
> > > > >
>
> > > > > From: R-help <r-help-bounces at r-project.org> On
Behalf Of Yuan Chun Ding via R-help
>
> > > > > Sent: Thursday, July 25, 2024 11:26 AM
>
> > > > > To: Rui Barradas <ruipbarradas at sapo.pt>;
r-help at r-project.org
>
> > > > > Subject: Re: [R] please help generate a square
correlation matrix
>
> > > > >
>
> > > > > HI Rui, Thank you for the help! You did not remove a
row if zero values exist in both column pair, right? Ding From: Rui Barradas
<ruipbarradas@?sapo.?pt> Sent: Thursday, July 25, 2024 11:?15 AM To: Yuan
Chun Ding <ycding@?coh.?org>;
>
> > > > >
>
> > > > >
>
> > > > > HI Rui,
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > Thank you for the  help!
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > You did not remove a row if zero values exist in both
column pair, right?
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > Ding
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > From: Rui Barradas <ruipbarradas at
sapo.pt<mailto:ruipbarradas at sapo.pt>>
>
> > > > >
>
> > > > > Sent: Thursday, July 25, 2024 11:15 AM
>
> > > > >
>
> > > > > To: Yuan Chun Ding <ycding at
coh.org<mailto:ycding at coh.org>>; r-help at
r-project.org<mailto:r-help at r-project.org>
>
> > > > >
>
> > > > > Subject: Re: [R] please help generate a square
correlation matrix
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > ?s 17:?39 de 25/07/2024, Yuan Chun Ding via R-help
escreveu: > Hi R users, > > I generated a square correlation matrix for
the dat dataframe below; > dat<-data.?frame(g1=c(1,0,0,1,1,1,0,0,0), >
g2=c(0,1,0,1,0,1,1,0,0), > g3=c(1,1,0,0,0,1,0,0,0),
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > ?s 17:39 de 25/07/2024, Yuan Chun Ding via R-help
escreveu:
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> Hi R users,
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> I generated a square correlation matrix for the dat
dataframe below;
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> dat<-data.frame(g1=c(1,0,0,1,1,1,0,0,0),
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>                   g2=c(0,1,0,1,0,1,1,0,0),
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>                   g3=c(1,1,0,0,0,1,0,0,0),
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>                   g4=c(0,1,0,1,1,1,1,1,0))
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> library("Hmisc")
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> dat.rcorr = rcorr(as.matrix(dat))
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> dat.r <-round(dat.rcorr$r,2)
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> however, I want to modify this correlation
calculation;
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> my dat has more than 1000 rows and 22 columns;
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> in each column, less than 10% values are 1, most of
them are 0;
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> so I want to remove a  row with value of zero in
both columns when calculate correlation between two columns.
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> I just want to check whether those values of 1 are
correlated between two columns.
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> Please look at my code in the following;
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> cor.4gene <-matrix(0,nrow=4*4, ncol=4)
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> for (i in 1:4){
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>     #i=1
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>     for (j in 1:4) {
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>       #j=1
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>       d <-dat[,c(i,j)]%>%
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>         filter(eval(as.symbol(colnames(dat)[i]))!=0
|
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>                 
eval(as.symbol(colnames(dat)[j]))!=0)
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>       c <-cor.test(d[,1],d[,2])
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>      
cor.4gene[i*j,]<-c(colnames(dat)[i],colnames(dat)[j],
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>                           c$estimate,c$p.value)
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>     }
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> }
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>
cor.4gene<-as.data.frame(cor.4gene)%>%filter(V1 !=0)
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>
colnames(cor.4gene)<-c("gene1","gene2","cor","P")
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> Can you tell me what mistakes I made?
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> first, why cor is NA when calculation of
correlation for g1 and g1, I though it should be 1.
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> cor.4gene$cor[is.na(cor.4gene$cor)]<-1
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> cor.4gene$cor[is.na(cor.4gene$P)]<-0
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> cor.4gene.sq <-pivot_wider(cor.4gene, names_from
= gene1, values_from = cor)
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> Then this line of code above did not generate a
square matrix as what the HMisc library did.
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> How to fix my code?
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> Thank you,
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> Ding
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>
----------------------------------------------------------------------
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>
------------------------------------------------------------
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> -SECURITY/CONFIDENTIALITY WARNING-
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> This message and any attachments are intended
solely for the individual or entity to which they are addressed. This
communication may contain information that is privileged, confidential, or
exempt from disclosure under applicable law (e.g., personal health information,
research data, financial information). Because this e-mail has been sent without
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not wish to rec
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>    eive further communications via e-mail, please
reply to this message and inform the sender that you do not wish to receive
further e-mail from the sender. (LCP301)
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>
------------------------------------------------------------
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>              [[alternative HTML version deleted]]
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> ______________________________________________
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >> R-help at r-project.org<mailto:R-help at
r-project.org<mailto:R-help at r-project.org%3cmailto:R-help at
r-project.org>> mailing list -- To UNSUBSCRIBE and more, see
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >>
https://urldefense.com/v3/__https://stat.ethz.ch/mailman/listinfo/r-help__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb8338TBM$<https://urldefense.com/v3/__https:/stat.ethz.ch/mailman/listinfo/r-help__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb8338TBM$><https://urldefense.com/v3/__https:/stat.ethz.ch/mailman/listinfo/r-help__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb8338TBM$%3chttps:/urldefense.com/v3/__https:/stat.ethz.ch/mailman/listinfo/r-help__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb8338TBM$%3e%3e>
>
> >> > >
>
> > > > >  
<https://urldefense.com/v3/__https:/stat.ethz.ch/mailman/listinfo/r-help__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb8338TBM$%3chttps:/urldefense.com/v3/__https:/stat.ethz.ch/mailman/listinfo/r-help__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb8338TBM$%3e%3e>
>
> >> > >
>
> > > > >>
<https://urldefense.com/v3/__https:/stat.ethz.ch/mailman/listinfo/r-help__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb8338TBM$%3chttps:/urldefense.com/v3/__https:/stat.ethz.ch/mailman/listinfo/r-help__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb8338TBM$%3e%3e>PLEASEdo
read the posting guide
https://urldefense.com/v3/__http://www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb880tLw0$<https://urldefense.com/v3/__http:/www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb880tLw0$><https://urldefense.com/v3/__http:/www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb880tLw0$%3chttps:/urldefense.com/v3/__http:/www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb880tLw0$%3e%3e>
>
> >> > >
>
> > > > >  
<https://urldefense.com/v3/__http:/www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb880tLw0$%3chttps:/urldefense.com/v3/__http:/www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb880tLw0$%3e%3e>
>
> >> > >
>
> > > > >>
<https://urldefense.com/v3/__http:/www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb880tLw0$%3chttps:/urldefense.com/v3/__http:/www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6Hb880tLw0$%3e%3e>andprovide
commented, minimal, self-contained, reproducible code.
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > Hello,
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > You are complicating the code, there's no need for
as.symbol/eval, the
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > column numbers do exactly the same.
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > # create the two results matrices beforehand
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > r <- P <- matrix(NA, nrow = 4L, ncol = 4L,
dimnames = list(names(dat),
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > names(dat)))
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > for(i in 1:4) {
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >     x <- dat[[i]]
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >     for(j in (1:4)) {
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >       if(i == j) {
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >         # there's nothing to test, assign
correlation 1
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >         r[i, j] <- 1
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >       } else {
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >         tmp <- cor.test(x, dat[[j]])
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >         r[i, j] <- tmp$estimate
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >         P[i, j] <- tmp$p.value
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >       }
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >     }
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > }
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > # these two results are equal up to floating-point
precision
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > dat.rcorr$r
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > #>           g1        g2        g3        g4
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > #> g1 1.0000000 0.1000000 0.3162278 0.1581139
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > #> g2 0.1000000 1.0000000 0.3162278 0.6324555
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > #> g3 0.3162278 0.3162278 1.0000000 0.0000000
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > #> g4 0.1581139 0.6324555 0.0000000 1.0000000
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > r
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > #>           g1        g2           g3           g4
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > #> g1 1.0000000 0.1000000 3.162278e-01 1.581139e-01
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > #> g2 0.1000000 1.0000000 3.162278e-01 6.324555e-01
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > #> g3 0.3162278 0.3162278 1.000000e+00 1.355253e-20
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > #> g4 0.1581139 0.6324555 1.355253e-20 1.000000e+00
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > # these two results are equal up to floating-point
precision
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > dat.rcorr$P
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > #>           g1         g2        g3         g4
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > #> g1        NA 0.79797170 0.4070838 0.68452834
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > #> g2 0.7979717         NA 0.4070838 0.06758329
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > #> g3 0.4070838 0.40708382        NA 1.00000000
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > #> g4 0.6845283 0.06758329 1.0000000         NA
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > P
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > #>           g1         g2        g3         g4
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > #> g1        NA 0.79797170 0.4070838 0.68452834
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > #> g2 0.7979717         NA 0.4070838 0.06758329
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > #> g3 0.4070838 0.40708382        NA 1.00000000
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > #> g4 0.6845283 0.06758329 1.0000000         NA
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > You can put these two results in a list, like
Hmisc::rcorr does.
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > lst_rcorr <- list(r = r, P = P)
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > Hope this helps,
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > Rui Barradas
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > --
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > Este e-mail foi analisado pelo software antiv?rus AVG
para verificar a presen?a de v?rus.
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > >
https://urldefense.com/v3/__http://www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$<https://urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$><https://urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3chttps:/urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3e%09%5b%5balternative>
>
> >> > >
>
> > > > >  
<https://urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3chttps:/urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3e%09%5b%5balternative>
>
> >> > >
>
> > > > >                
[[alternative<https://urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3chttps:/urldefense.com/v3/__http:/www.avg.com__;!!Fou38LsQmgU!tyykZkQmOKcwoWXEpV2ohbnr02thhHMabAcYLL_-7dteKHAabK-eo4rGDnwgSFjniAy8SO00L6HbloMCQMI$%3e%09%5b%5balternative>HTMLversion
deleted]]
>
> > > > >
>
> > > > >
>
> > > > >
>
> > > > > ______________________________________________
>
> > > > >
>
> > > > > R-help at r-project.org<mailto:R-help at
r-project.org> mailing list -- To UNSUBSCRIBE and more, see
>
> > > > >
>
> > > > >
https://urldefense.com/v3/__https://stat.ethz.ch/mailman/listinfo/r-help__;!!Fou38LsQmgU!s54ahZtZNAIyaIGV3C2p8lhXpYlHksC6XvFKZltf6g3ElJHOO3I1MYFecLQ4QeMO3MpP3qXz-YrhdUE$<https://urldefense.com/v3/__https:/stat.ethz.ch/mailman/listinfo/r-help__;!!Fou38LsQmgU!s54ahZtZNAIyaIGV3C2p8lhXpYlHksC6XvFKZltf6g3ElJHOO3I1MYFecLQ4QeMO3MpP3qXz-YrhdUE$>
>
> >> > >
>
> > > > > PLEASE do read the posting guide
https://urldefense.com/v3/__http://www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!s54ahZtZNAIyaIGV3C2p8lhXpYlHksC6XvFKZltf6g3ElJHOO3I1MYFecLQ4QeMO3MpP3qXzNRZxc6s$<https://urldefense.com/v3/__http:/www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!s54ahZtZNAIyaIGV3C2p8lhXpYlHksC6XvFKZltf6g3ElJHOO3I1MYFecLQ4QeMO3MpP3qXzNRZxc6s$>
>
> >> > >
>
> > > > > and provide commented, minimal, self-contained,
reproducible code.
>
> > > > >
>
> > > > Hello,
>
> > > >
>
> > > > Here are two other ways.
>
> > > >
>
> > > > The first is equivalent to your long format attempt.
>
> > > >
>
> > > >
>
> > > > library(tidyverse)
>
> > > >
>
> > > > dat %>%
>
> > > >    names() %>%
>
> > > >    expand.grid(., .) %>%
>
> > > >    apply(1L, \(x) {
>
> > > >      tmp <- dat[rowSums(dat[x]) > 0, ]
>
> > > >      tmp2 <- cor.test(tmp[[ x[1L] ]], tmp[[ x[2L] ]])
>
> > > >      c(tmp2$estimate, P = tmp2$p.value)
>
> > > >    }) %>%
>
> > > >    t() %>%
>
> > > >    as.data.frame() %>%
>
> > > >    cbind(tmp_df, .) %>%
>
> > > >    na.omit()
>
> > > >
>
> > > >
>
> > > > The second is, in my opinion the one that makes more sense.
If you see
>
> > > > the results, cor is symmetric (as it should) so the
calculations are
>
> > > > repeated. If you only run the cor.tests on the combinations
of
>
> > > > names(dat) by groups of 2, it will save a lot of work. But
the output is
>
> > > > a much smaller a data.frame.
>
> > > >
>
> > > >
>
> > > > cbind(
>
> > > >    combn(names(dat), 2L) %>%
>
> > > >      t() %>%
>
> > > >      as.data.frame(),
>
> > > >    combn(dat, 2L, FUN = \(d) {
>
> > > >      d2 <- d[rowSums(d) > 0, ]
>
> > > >      tmp2 <- cor.test(d2[[1L]], d2[[2L]])
>
> > > >      c(tmp2$estimate, P = tmp2$p.value)
>
> > > >    }) %>% t()
>
> > > > ) %>% na.omit()
>
> > > >
>
> > > >
>
> > > >
>
> > > > Hope this helps,
>
> > > >
>
> > > > Rui Barradas
>
> > > >
>
> > > >
>
> > > > ______________________________________________
>
> > > > R-help at r-project.org mailing list -- To UNSUBSCRIBE and
more, see
>
> > > >
https://urldefense.com/v3/__https://stat.ethz.ch/mailman/listinfo/r-help__;!!Fou38LsQmgU!syHSW7xho1Y4ssijZgjysEtoRhDbKljzLKfIYGOzmXQsT7wsjfUQ3n7CZDn7aQ-aUmwxIgqJrg$
>
> > > > PLEASE do read the posting guide
https://urldefense.com/v3/__http://www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!syHSW7xho1Y4ssijZgjysEtoRhDbKljzLKfIYGOzmXQsT7wsjfUQ3n7CZDn7aQ-aUmxc76WB2w$
>
> > > > and provide commented, minimal, self-contained, reproducible
code.
>
> > >
>
> > > ______________________________________________
>
> > > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more,
see
>
> > >
https://urldefense.com/v3/__https://stat.ethz.ch/mailman/listinfo/r-help__;!!Fou38LsQmgU!syHSW7xho1Y4ssijZgjysEtoRhDbKljzLKfIYGOzmXQsT7wsjfUQ3n7CZDn7aQ-aUmwxIgqJrg$
>
> > > PLEASE do read the posting guide
https://urldefense.com/v3/__http://www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!syHSW7xho1Y4ssijZgjysEtoRhDbKljzLKfIYGOzmXQsT7wsjfUQ3n7CZDn7aQ-aUmxc76WB2w$
>
> > > and provide commented, minimal, self-contained, reproducible
code.
>
>
>
> ______________________________________________
>
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
>
>
https://urldefense.com/v3/__https://stat.ethz.ch/mailman/listinfo/r-help__;!!Fou38LsQmgU!syHSW7xho1Y4ssijZgjysEtoRhDbKljzLKfIYGOzmXQsT7wsjfUQ3n7CZDn7aQ-aUmwxIgqJrg$
>
> PLEASE do read the posting guide
https://urldefense.com/v3/__http://www.R-project.org/posting-guide.html__;!!Fou38LsQmgU!syHSW7xho1Y4ssijZgjysEtoRhDbKljzLKfIYGOzmXQsT7wsjfUQ3n7CZDn7aQ-aUmxc76WB2w$
>
> and provide commented, minimal, self-contained, reproducible code.

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