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>>>>> Serguei Sokol <sokol at insa-toulouse.fr> >>>>> on Tue, 30 May 2017 16:01:17 +0200 writes: > Le 30/05/2017 ? 09:33, Martin Maechler a ?crit : ... >> However, even after the patch, The example from the SO >> post differs from the result of Richie Cotton's >> function... > The explanation is quite simple.

>>>>> Serguei Sokol <sokol at insa-toulouse.fr> >>>>> on Mon, 29 May 2017 15:28:12 +0200 writes: > Sorry, I have seen it too late that we had different tab > width in the original file and my editor. Here is the > patch with all white spaces instead of mixing tabs and > white spaces. thank you - it still gives quite a few

Hi all -- I have to calculate a likelihood ratio test for a logit model. I found logLik, but I need to calculate the log likelihood for the model without any predictors. How can I specify this in glm? If the full model is glm(y ~ x1), is the one without predictors (y ~ 0)? Or (y ~ 1)? Is there a more direct way of getting this? -- Chris

Dear All, I am attempting to use the FANNY fuzzy clustering function in R (Kaufman & Rousseeuw, 1990), found in the "cluster" package. I have run into a variety of difficulties; the two most crucial difficulties are enumerated below. 1. Where is the 'm' parameter in FANNY? In _Finding Groups in Data: An Introduction to Cluster Analysis_ (1990) by Kaufman & Rousseeuw,

A small comment: The code of Agnes is written in Fortran. Following book give more details: Kaufman, L. and Rousseeuw, P.J. (1990). _Finding Groups in Data: An Introduction to Cluster Analysis_. Wiley, New York. The 'hclust' function is based an Algorithm contributed to STATLIB by F. Murtagh and the code is written in C. Probably the differences between agnes and hclust causes

Hello everybody :-) I have some data that I want to model with a logistic regression, most of the independent variables are numeric and the only dependent is categorical, I was thinking that I could apply a logistic regression using glm but I wanted to deepen my knowledge of this so I tried to do some reading and found the "iris" dataset, now I would like to ask two things, first

Dear all, I'm using the package "Genetics", and I'm interested in the computation of D' statistics for Linkage Disequilibrium, for which the LD() command has been realised. Unfortunately I don't find any reference on "how" the D' is computed by the LD() function. In the package documentation it is generally referred as "MLE" estimation, but

A new version (0.7.1) of package 'subselect' has been uploaded to CRAN. Package 'subselect' provides functions which assess the quality of variable subsets as surrogates for a full data set, in an exploratory data analysis, and search for subsets which are optimal under various criteria. As of version 0.7 a new function 'leaps' has been added. 'Leaps' performs a

A new version (0.7.1) of package 'subselect' has been uploaded to CRAN. Package 'subselect' provides functions which assess the quality of variable subsets as surrogates for a full data set, in an exploratory data analysis, and search for subsets which are optimal under various criteria. As of version 0.7 a new function 'leaps' has been added. 'Leaps' performs a

In short: I didn't take enough stats courses in college. Now I am working on scientific research and I feel somewhat lost when it comes to designing the statistical framework. I have looked through the books at: http://www.r-project.org/doc/bib/R-books.html I even tried to read [17] Julian J. Faraway. Linear Models with R. This book is too advanced. It helped a little bit but I still