similar to: likelihood from test result

Could anyone recommend a package for visualizing a likelihood function of two scalar parameters? I would prefer a three-dimensional plot similar to the kind Mathematica is known for, perhaps generated by a package not specific to likelihood. David ______________________________ David R. Bickel Ottawa Institute of Systems Biology BMI Dept., University of Ottawa http://www.oisb.ca/members.htm

What are some reliable R functions that can compute the value of a convergent series? David -- David R. Bickel, PhD Associate Professor Ottawa Institute of Systems Biology Biochem., Micro. and I. Department Mathematics and Statistics Department University of Ottawa 451 Smyth Road Ottawa, Ontario K1H 8M5 http://www.statomics.com Office Tel: (613) 562-5800 ext. 8670 Office Fax: (613) 562-5185

Neither biocLite nor the GUI menus can install packages on my system. Here is relevant output: > version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 2 minor 11.1 year 2010 month 05 day 31 svn rev 52157 language R version.string R version 2.11.1 (2010-05-31) > source("http://bioconductor.org/biocLite.R") BioC_mirror =

o <- (structure(list(sand.silt = structure(list(statistic = structure(185, .Names = "W"), parameter = NULL, p.value = 0.0478835773838087, null.value = structure(0, .Names = "location shift"), alternative = "two.sided", method = "Wilcoxon rank sum test with continuity correction", data.name = ".column by site"), .Names =

Hello, I've posted a question about this subject yesterday, but since there was no R code to comment, no one did. I'm trying to have the print method for class 'htest' print some extra information common in some test, like the time series linearity related tests. Many of them have an 'order' parameter, representing a lag or embedding dimension, and it would be a nice

Dear R users and Dear authors of the distr package and sequels I am trying to use the (very nice) package distrMod as I want to implement maximum likelihood (ML) fit of some univariate data for which I have derived a theoretical continuous density (pdf). As it is a parametric density, I guess that I should implement myself a new distribution of class AbscontDistributions (as stated in the pdf

Hello! First of all I must appologize if this has been raised previously, but search provided by Robert King at the University of Newcastle seems to be down these days. Additionally let me know if such a question should be sent to R-help. I did a contribution to function hwe.hardy in package 'gap' during the weekend. That functions performs Hardy-Weinberg equilibrium test using MCMC. The

Hello, everyone, I don't quite understand the following message: > TTT <- t.test(1:10, y=c(7:20)) > class(TTT) [1] "htest" > TTT@p.value Error: Trying to get slot "p.value" from an object whose class ("htest") is not defined > TTT$p.value [1] 1.855282e-05 Why the message says the class of TTT is not defined while class(TTT) gets

Hi all, I just used power.t.test() in Version 1.9.0 beta (2004-03-31) today under FC1. The following is the output: > power.t.test(delta = .5, power = .95, type = "paired") $n [1] 53.94062 $delta [1] 0.5 $sd [1] 1 $sig.level [1] 0.05 $power [1] 0.95 $alternative [1] "two.sided" $note [1] "n is number of *pairs*, sd is std.dev. of *differences* within

To help me make choices regarding a platform for running high-memory R processes in parallel, I would appreciate any responses to these questions: 1. Does the amount of RAM available to an R session depend on the processor (Intel vs. Sun) or on the OS (various Linux distributions vs. Solaris)? 2. Does R have any built-in limitations of RAM available to a session? For example, could it make use

Dear list, I am fitting a logistic multi-level regression model and need to test the difference between the ordinary logistic regression from a glm() fit and the mixed effects fit from glmer(), basically I want to do a likelihood ratio test between the two fits. The data are like this: My outcome is a (1,0) for health status, I have several (1,0) dummy variables RURAL, SMOKE, DRINK, EMPLOYED,

I'd like to learn the process of revising R functions & packages and then submitting proposed patches to the R Core team. Would someone be willing to mentor me through one example? For starters, consider an example. I'd like to revise the t.test function to return the stderr value to the user. We only need to change the "rval" in the third-from-the end line of

Dear all How does print.htest display the p-value in scientific notation? > (x <- cor.test(iris[[1]], iris[[3]])) Pearson's product-moment correlation data: iris[[1]] and iris[[3]] t = 21.65, df = 148, p-value < 2.2e-16 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: 0.8270 0.9055 sample estimates: cor 0.8718 Above the p-value comes

I sort of asked this before, but perhaps not explicitly enough. In my ctest collection, there are several chisquare-based tests. For some of them, it may be useful to also return information on expected (and observed) counts. The question is, how should this be done. Of course, there is no problem adding the corresponding components to the list returned by the functions. However, as these are

Hello, I am running Kruskal-Walis test in R. When I try to save results using write.table it gives me the following error : Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors) : cannot coerce class "htest" into a data.frame The overall code is as follows : >data_file = read.table("~/DATA.dir/data_file.txt", header=T)

Hi, I have a function that generates a set of data but I am having problems determining the parameters using the nls fitting procedure. #### "MH"<-function(field,diameter,mu=10e-7,sig=0.1,Ms=100,chi=0){ #variables mu, sig, chi, Ms #input: field and diameter #all in CGS rho <- 5 kb <- 1.38e-16 t <- 300 length.d<-length(diameter) length.H<-length(field)

Is there a function that provides the Fisher information matrix for a generalized linear model? I do not see how to access the off-diagonal matrix elements of the value returned by glm. (I'm particularly interested in logistic regression.) If not, what is a good way to use R to compute Hessians or other partial derivatives of log likelihoods? I would appreciate any guidance. David

Dear List, I am trying to get R's terminal output to a file and to the terminal at the same time, so that I can walk through some tests and keep a log concurrently. The function 'sink' with the option split=TRUE seems to do just that. It works fine for most output but for objects of class htest, the terminal output is incomplete (the lines are there but empty). Here is an

>>>>> peter dalgaard >>>>> on Fri, 22 Feb 2019 12:38:14 +0100 writes: > It's not a problem per se to put additional information > into class htest objects (hey, it's S3 after all...) and > there is a precedent in chisq.test which returns $observed > and $expected. It seems the consent is to simply return the SE but *not*

What are some effective ways to leverage the strengths of R and Mathematica for the analysis of a single data set? More specifically, are there any functions that can assist with any of the following? 1. Calling an R function from Mathematica. 2. Calling a Mathematica function from R. 3. Using XML or another reliable data format to pass vectors, matrices, and/or lists from one environment to