similar to: clustering based on most significant pvalues does not separate the groups!

Hello list, I have a similar issue as this post http://tolstoy.newcastle.edu.au/R/e6/help/09/04/11438.html#options2 and I used the suggestion provided by Jorge with modifications to my data do.call(c,lapply(your_list_with_the_t_tests,function(x) x$p.value)) but I am getting the following error after excuting the code B<-by(eo,eo$PlateID, function(.sub) t.test(mcp1~Self_T1D,data=.sub,

dear all- If I have a vector of numbers (not necessarily normally distributed) how can I get the p-value of a number in this distribution. I am interested in the "inverse" of 'quantile' . thank you- Maya

Hi All, I have run many regression analyses (14000 +) and want to collect the coefficients and pvalues into an excel file. I can get the statements below to work up to step 4. I can printout the regressionresults (sample output below). So my hope is to run something like step 5 and 6 and put the pvalues (and then coefficients) into an excel file. Can anyone suggest what I am doing wrong or a

Hi all, My data genes [,1] [,2] [,3] [,4] [1,] 25 72 23 55 [2,] 34 53 41 33 [3,] 26 43 26 44 [4,] 36 64 64 22 [5,] 47 72 67 34 stu<-t.test(genes[,1:2],genes[,3:4]) > stu$p.value [1] 0.4198002 i get 1 pvalue for the entire col1:col2 Vs col3:col4. I am trying to get 5 p values for the 5 rows i have. I am trying to avoid a for loop coz my

Hello all, I have written a for loop to act on a dataframe with close to 3million rows and 6 columns and I would like to pass it to apply() to speed the process up (I let the loop run for 2 days before stopping it and it had only gone through 200,000 rows) but I am really struggling to find a way to pass the arguments. Below are the loop and the head of the dataframe I am working on. Any hints

Dear R list, I think my question maybe easy for you but I really spent entire day to resolve it. Say I have a matrix, rows are 6000 genes, columns(1-6) are 3 genotypes (a,b,c) with 2 repeat. I have to use two groups each time for t-test, a vs. c or b vs. c, but I dont know how to write correct codes. Below is my codes, the last two lines are needed to be corrected....

Hi, How do I generate bootstrap p-values for goodness of fit test. Thank you in advance for your help, Juliet [[alternative HTML version deleted]]

Dear R users, I have developed a model I have compared several options of obtaining p-values for poisson lmer model including Marlov chain monty carlo methods, single term deletions and summary. > > However, I encountered several problems that can be classified as > (1) the p values from the summary command are total different from those derived from Marlov chain monty carlo methods >

Hi, Is in R any "fast" algorithm for correlation? What I mean is: I have very large dataset (microarray) with 55000 rows and 100 columns. I want to count correlation (p-value and cor.coef) between each row of dataset and some vector (of course length of this vector is equal to number of columns of dataset). In short words: For t-test we have: "normal" algorithm - t.test

I made an ANOVA with repeated mesures (aov(Mesure~Distance*Genre*Correct+Error(Sujet/(Distance*Genre*Correct)), data)) and I would like to extract the pvalues. The output is: ----------------------------------------------------------- Error: Sujet Df Sum Sq Mean Sq F value Pr(>F) Residuals 21 97.082 4.623 Error: Sujet:Distance Df Sum Sq Mean Sq F value Pr(>F) Distance

Hi, I have a matrix (pwdiff in the example below) with ~480000 rows and 780 columns. For each row, I want to get the percentage of columns that have an absolute value above a certain threshold "t". I then want to allocate that percentage to matrix 'perc' in the corresponding row. Below is my attempt at doing this, but it does not work: I get 'replacement has length

Hey I'm not really sure what I should put on here, but I am having trouble with my R code. I am trying to get the p-values, R^2s etc for a number of different groups of variables that are all in one dataset. This is the code: #Stand counter st<-1 #Collections stands<-numeric(67) slopes<-numeric(67) intercepts<-numeric(67) mses<-numeric(67) rsquares<-numeric(67)

This has a stats question and a R question. I am sure there are many core statisticians here how would know the answer to this simple question. In determining the significant comparisons using the methods in multcomp, the ones that are designated as significant are the ones that do not intersect the zero line. What is the physical meaning of this and why are those considered significant? I can

Hello, I have some discrete pvalues and I would like to sort them. Then add random noise so that they are ordered the same way as the original pvalues. Off course I don't want any pvalues less than 0 or greater than 1. Any ideas on how to do this in R? -- Thanks, Jim. [[alternative HTML version deleted]]

Dear All, I am trying to combine dependent p-values in R. May you please help me with this? For independent pvalue combination, one of the popular way is fisher's method which I found the R code here (http://r.789695.n4.nabble.com/fisher-s-posthock-test-or-fisher-s-combination-test-td2195964.html#a2305025): fisher.comb <- function (pvalues) { df=length(pvalues)

Full_Name: Jason Polak Version: R version 2.5.0 (2007-04-23) OS: Xubuntu 7.04 Submission from: (NULL) (137.122.144.35) Dear R group, I have noticed a strange anomaly with the shapiro.test() function. Unfortunately I do not know how to calculate the shapiro test P values manually so I don't know if this is an actual bug. So, to produce the results, run the following code: pvalues = 0; for

Hello, i am a newby on R and i am trying to make a backward selection on a binomial-logit glm on a large dataset (69000 lines for 145 predictors). After 3 days working, the stepAIC function did not terminate. I do not know if that is normal but i would like to try computing a "homemade" backward with a repeated glm ; at each step, the predictor with the max pvalue would be

Dear all, I observer a strange behavior of the pvalues of the t-test under the null hypothesis. Specifically, I obtain 2 samples of 3 individuals each from a normal distribution of mean 0 and variance 1. Then, I calculate the pvalue using the t-test (var.equal=TRUE, samples are independent). When I make a histogram of pvalues I see that consistently the bin of the smallest pvalues has a lower

Hi, I was trying to evaluate the pearson correlation and the p-values for an nxm matrix, where each row represents a vector. One way to do it would be to iterate through each row, and find its correlation value( and the p-value) with respect to the other rows. Is there some function by which I can use the matrix as input? Ideally, the output would be an nxn matrix, containing the p-values

Buen día estimados Estoy tratando de hacer un tibble con los resultados de un apply que se muestran en la consola que me da R, no estoy seguro si eso se pueda hacer, pero me gustaría organizar los resultados de esa manera. mi código es: data("mtcars") Mtcars_matriz <- as.matrix(mtcars) apply(Mtcars_matriz, MARGIN =2, FUN = shapiro.test) DF2 <- tibble(Variable = NA, W = NA, Pvalue =