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Question: multtest different seed
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gravatar for Stefano Moretti
10.2 years ago by
Stefano Moretti20 wrote:
Hi, I have a problem with the multtest package. Using the following code, for a very simple example, on R version 2.4.0 with multtest version 1.12.0 I didn't get any problem. library(multtest) R<- matrix(0, 2, 6) R[1,]<- c(0,0,0.3,0.2,0,0.16) R[2,]<- c(0, 0.2, 0,0.2,0.3,0) classes<- c(1,1,1,2,2,2) OUTPUT <- MTP(X=R, Y=classes, standardize=FALSE, B=1000) But now, with the R version 2.7.2 with multtest version 1.21.1 I get the following error message running bootstrap... iteration = Error in function (x, w = NULL, samp = Samp) : Only one unique value in bootstrap sample for second group. Cannot calculate variance. This problem may be resolved if you try again with a different seed. Is there anyone who can help me to understand what I do wrong? Many thanks for your attention. Best, Stefano
ADD COMMENTlink modified 10.2 years ago • written 10.2 years ago by Stefano Moretti20
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gravatar for Vincent J. Carey, Jr.
10.2 years ago by
United States
Vincent J. Carey, Jr.6.2k wrote:
> Hi, > > I have a problem with the multtest package. > Using the following code, for a very simple example, on R version 2.4.0 with multtest version 1.12.0 I didn't get any problem. > > library(multtest) > R<- matrix(0, 2, 6) > R[1,]<- c(0,0,0.3,0.2,0,0.16) > R[2,]<- c(0, 0.2, 0,0.2,0.3,0) > classes<- c(1,1,1,2,2,2) > OUTPUT <- MTP(X=R, Y=classes, standardize=FALSE, B=1000) > > But now, with the R version 2.7.2 with multtest version 1.21.1 I get the following error message > running bootstrap... > iteration = Error in function (x, w = NULL, samp = Samp) : > Only one unique value in bootstrap sample for second group. Cannot calculate variance. This problem may be resolved if you try again with a different seed. > > > Is there anyone who can help me to understand what I do wrong? This should have nothing to do with the version of R or the package. Bootstrapping is a procedure that involves computing test statistics repetitively over samples taken with replacement from the original data. If a given sample contains enough copies of a given observation (owing to the sampling with replacement), calculations that require nonuniqueness will fail. It is true that changing the seed will change the collection of samples that your procedure encounters, and _may_ thus avoid a sample with "too many copies", and calculations requiring uniqueness (or dispersion) of values will succeed. This problem is occurring in this case because your base sample size is so small, so that the probability of an underdispersed sample is relatively high, and because some computation fails when variance is zero. Avoiding the problem by changing the seed seems to me to be an unwise approach, but as von Neumann wrote, "Any one who considers arithmetical methods of producing random digits is, of course, in a state of sin". (See Knuth TAOCP v2.) > > Many thanks for your attention. > Best, > Stefano > > > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor >
ADD COMMENTlink written 10.2 years ago by Vincent J. Carey, Jr.6.2k
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gravatar for Stefano Moretti
10.2 years ago by
Stefano Moretti20 wrote:
Hi Vincent, many thanks for your explanation. You convinced me, even if I must say that I really found a different behaviour of the code between the two versions that I reported in my previous message. I also (erroneously) thought that setting 'standardize=FALSE' in the MTP function could avoid calculations which fail when the variance is zero. However, in the future I will try to adopt the 'dominant strategy' to take my software updated. Believe me, in my work I really take into consideration what John von Neumann wrote :-) Best, Stefano ________________________________ From: Vincent Carey 525-2265 [mailto:stvjc@channing.harvard.edu] Sent: Thu 18/09/2008 17:12 To: Stefano Moretti Cc: bioconductor at stat.math.ethz.ch Subject: Re: [BioC] multtest different seed > Hi, > > I have a problem with the multtest package. > Using the following code, for a very simple example, on R version 2.4.0 with multtest version 1.12.0 I didn't get any problem. > > library(multtest) > R<- matrix(0, 2, 6) > R[1,]<- c(0,0,0.3,0.2,0,0.16) > R[2,]<- c(0, 0.2, 0,0.2,0.3,0) > classes<- c(1,1,1,2,2,2) > OUTPUT <- MTP(X=R, Y=classes, standardize=FALSE, B=1000) > > But now, with the R version 2.7.2 with multtest version 1.21.1 I get the following error message > running bootstrap... > iteration = Error in function (x, w = NULL, samp = Samp) : > Only one unique value in bootstrap sample for second group. Cannot calculate variance. This problem may be resolved if you try again with a different seed. > > > Is there anyone who can help me to understand what I do wrong? This should have nothing to do with the version of R or the package. Bootstrapping is a procedure that involves computing test statistics repetitively over samples taken with replacement from the original data. If a given sample contains enough copies of a given observation (owing to the sampling with replacement), calculations that require nonuniqueness will fail. It is true that changing the seed will change the collection of samples that your procedure encounters, and _may_ thus avoid a sample with "too many copies", and calculations requiring uniqueness (or dispersion) of values will succeed. This problem is occurring in this case because your base sample size is so small, so that the probability of an underdispersed sample is relatively high, and because some computation fails when variance is zero. Avoiding the problem by changing the seed seems to me to be an unwise approach, but as von Neumann wrote, "Any one who considers arithmetical methods of producing random digits is, of course, in a state of sin". (See Knuth TAOCP v2.) > > Many thanks for your attention. > Best, > Stefano > > > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor >
ADD COMMENTlink written 10.2 years ago by Stefano Moretti20
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