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#I am trying to understand how R fits models for contrasts in a #simple one-way anova. This is an example, I am not stupid enough to want #to simultaneously apply all of these contrasts to real data. With a few #exceptions, the tests that I would compute by hand (or by other software) #will give the same t or F statistics. It is the contrast estimates that R produces #that I can't seem to

Dear R-help members, Apologies - I am posting on behalf of a colleague, who is a little puzzled as STATA and R seem to be yielding different survival estimates for the same dataset when treating a variable as ordinal. Ordered() is used to represent an ordinal variable) I understand that R's coxph (by default) uses the Efron approximation, whereas STATA uses (by default) the Breslow. but we

?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 =

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> Sent: Thursday, July 25, 2024 11:15 AM To: Yuan Chun Ding <ycding at coh.org>; 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

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;

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) { #

?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),

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

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

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

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

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

HI Bert, Thank you for extra help!! Yes, exactly, your interpretation is perfectly correct and your R code is what I should look for. after generated all those negative values of correlation, I thought about the extremely small p values associated with those negative correlation, which is not meaningful as I truncated my data. When examining the exclusiveness of mutation pairs, what I first