similar to: Finding proportion of observations that are outliers from the left tail of the normal distribution

hi all! i want to mix a dataset that is build up from 2 distribution: an exponential and a normal. I can' figure out how can i use Rmix package to do the fitting of my data. Pheraps it si the wrong package? any suggestion? thanks, nelson

Hello, I'm seeking ideas on how to remove outliers from a non-normal distribution predictor variable. We wish to reset points deemed outliers to a truncated value that is less extreme. (I've seen many posts requesting outlier removal systems. It seems like most of the replies center around "why do you want to remove them", "you shouldn't remove them", "it

Dear All, In Storey's estimator of the proportion of true nulls, the estimator depends on the tuning parameter lamda. Suppose now that an estimator of this proportion has been obtained by the qvalue package, what is the lamda that corresponds to the estimate? How to get this lamda? Thanks, -Chee [[alternative HTML version deleted]]

Dear list, I have been trying to run the function "qvalue" under the package qvalue on a vector with about 20 million values. > asso_p.qvalue<-qvalue(asso_p.vector) Error: cannot allocate vector of size 156513 Kb > sessionInfo() Version 2.3.1 (2006-06-01) i686-pc-linux-gnu attached base packages: [1] "methods" "stats" "graphics"

Recently, I have been working with some data that look like two overlapping gaussian distributions. I would like to either 1) determine the mean and SD for each of the two distributions OR 2) get some (bayesian ?) statistic that estimates how likely an observation is to belong to the left-hand or right-hand distribution In case I'm using the wrong language, my data looks something like

Hi, I am trying to install qvalue, however its giving installation error -> Error : package 'tcltk' does not have a name space ERROR: lazy loading failed for package ?qvalue? * removing ?/home/sbw/R/x86_64-unknown-linux-gnu-library/2.12/qvalue? The downloaded packages are in ?/tmp/RtmpKnS1X4/downloaded_packages? Warning message: In install.packages(pkgs = pkgs, repos = repos, ...)

Hi, I have a huge dataset (53 million records). I have to calculate the p and q values of my data. How can I do it in R or perl? I have downloaded R (I'm completely new to R). and the package qvalue but I don't understand how can I call/use qvalue package with R. When I type library(qvalue), it gives me an error that this package doesn't exist. What should I do? Thanks! -- View this

I am using storey's qvalue package but I keep on getting errors. Why is this? > qvalue(p, lambda=0.5)$pi0 [1] "ERROR: p-values not in valid range." Error in qvalue(p, lambda = 0.5)$pi0 : $ operator is invalid for atomic vectors -- Thanks, Jim. [[alternative HTML version deleted]]

Good Morning Everyone, I was going through my list of FTBFS packages today and I fixed all my R packages but 2: qvalue and xtable. qvalue requires ggplot2 xtable requires: lsmeans, spdep, splm, sphet, plm I am not doing any R anymore these days and in fact spot has been the one maintaining most of my R packages these days (thanks spot!!), so I am not really interested in maintaining more R

Dear colleagues, This is not strictly a R question, but I hope it is ok to ask on the list. I fed a vector of p-values from about 20 million anova tests to the package q-value and obtained this output: > qsummary(asso_p.qvalue) Call: qvalue(p = asso_p.vec) pi0: 1 Cumulative number of significant calls: <1e-04 <0.001 <0.01 <0.025 <0.05 <0.1 <1

Hello, I've been searching for a method for identify outliers for quite some time now. The complication is that I cannot assume that my data is normally distributed nor symmetrical (i.e. some distributions might have one longer tail) so I have not been able to find any good tests. The Walsh's Test (http://www.statistics4u.info/ fundsta...liertest.html#), as I understand assumes that the

Hi, Dear all, i encounted a problem with tcl/tk installation actually i want to install q-value package i use the R CMD INSTALL command: [root@bioinfo ~]# R CMD INSTALL qvalue_1.20.0.tar.gz * Installing to library �usr/local/lib64/R/library� * Installing *source* package ‘qvalue�... ** R ** data ** inst ** preparing package for lazy loading Error in firstlib(which.lib.loc, package) :

Hi, All I got trouble on installing the qvalue package. Error message: package 'tcltk' does not have a name space [cchen1 at ibibmem Yale_CB]$ R CMD INSTALL qvalue.tar.gz * installing to library '/cchome/cchen1/R/x86_64-unknown-linux-gnu-library/2.10' * installing *source* package 'qvalue' ... ** R ** data ** inst ** preparing package for lazy loading Error : package

You asked the same question on the Bioconductor mailing list back in August. At that time, you suggested yourself a solution for how the adjusted p-values should be interpreted. I answered your query and told you that your interpretation was correct. So I'm not sure what more can be said, except that you should read the article Wright (1992), which is cited in the help entry for p.adjust(),

dear all, could somebody tell me how I can plot mild outliers as a circle(?) and extreme outliers as an asterisk(*) in a box-whisker plot? Thanks very much in advance -- View this message in context: http://www.nabble.com/mild-and-extreme-outliers-in-boxplot-tp25040545p25040545.html Sent from the R help mailing list archive at Nabble.com.

Happy holidays all! I know it's very subjective to determine whether some data is outlier or not... But are there reasonally good and realistic methods of identifying outliers in R? Thanks a lot! [[alternative HTML version deleted]]

I am posting this to both R and BioC communities because I believe there is a lot of confusion on this topic in both communities (having searched the mail archives of both) and I am hoping that someone will have information that can be shared with both communities. I have seen countless questions on the BioC list regarding limma (Bioconductor) and its calculation of FDR. Some of them involved

I am posting this to both R and BioC communities because I believe there is a lot of confusion on this topic in both communities (having searched the mail archives of both) and I am hoping that someone will have information that can be shared with both communities. I have seen countless questions on the BioC list regarding limma (Bioconductor) and its calculation of FDR. Some of them involved

Hi All, I am working on the dataset in which some of the variables have more than one observations with outliers . I am using below mentioned sample script library(outliers) x1 <- c(10, 10, 11, 12, 13, 14, 14, 10, 11, 13, 12, 13, 10, 19, 18, 17, 10099, 10099, 10098) outlier_tf1 = outlier(x1,logical=TRUE) find_outlier1 = which(outlier_tf1==TRUE, arr.ind=TRUE) beh_input_ro1 =

Hello, I'm analysing reaction time data from a linguistic experiment (a variant of a lexical decision task). To ascertain that the data was normally distributed, I used *shapiro.test *for each participant (see commands below), but only one out of 21 returns a p value above p.0 05. > f = function(dfr) return(shapiro.test(dfr$Target.RTinv)$p.value) > p = as.vector(by(newdat,