similar to: Influence Diagrams in R

Hello everyone ! I am currently trying to convert a program from S-plus to R, and I am having some trouble with the S-plus function called "influence(data, statistic,...)". This function aims to "calculate empirical influence values and related quantities", and is part of the Resample library that I cannot find for R. However, 2 similar functions are available in R: - the

Hello, Dear SSH developers. I'm currently studying Business Information Technologies at the University of Applied Sciences in Oulu, Finland. I'm about to start my own online computer security related magazine. I would like to make an article about the helpful ssh protocol for security professionals. I have made sequence diagrams on how the SSH 2.0 protocol works, however i'm not sure

I'm in e-mail contact with a geochemist who maintains a well regarded geochemistry web site. He's drawing diagrams either with a Turbo Pascal program running in a DOS window or with Excel. I'm trying to persuade him that R would be a better choice. Something he's particularly keen on is ternary diagrams. I think he is talking about ternary phase diagrams, and if so, it looks as

Dear R-list, I am trying to visualize where the dropout happens in our patient flow. We are currently using traditional flowcharts and it bothers me that I can't visualize both the percentage and the flow in one diagram. The other day I came across some interesting diagrams doing exactly what I wanted, they had both flow and percentages visualized on one diagram. Here is some nice examples

Hello, I am looking for an explanation and/or fix for a discrepancy in the behaviour of the R lm.influence() function [ version R 1.5.0 (2002-04-29) ] and the same function in Splus [ Splus version 5.1 release 1, running on SGI IRIX 6.2]. The discrepancy is of concern because I am migrating some Splus scripts to R and need to ensure consistency of results. Specifically, when I fit a glm()

Hi all Does anyone know if there are any R packages available for creating UML class and object diagrams in R (suitable for use with Latex)? I'm considering developing such a system... Note, I'm aware there are Latex packages (so no one needs to state the obvious). On a side issue I'm thinking about (mostly) abandoning the class system I mentioned earlier. Thank you to Martin

I'm following up on a question I posted 8/10/2010, but my newsreader has lost this thread. > Barrett & Ling, JASA, 1992, v.87(417), pp184-191 define general > classes of influence measures for multivariate > regression models, including analogs of Cook's D, Andrews & Pregibon > COVRATIO, etc. As in univariate > response models, these are based on leverage and

I am looking for a function (or package) to plot histograms of directional data such as wind direction. I believe these are called rose diagrams. Is there an R script for this? If not, can it be constructed in a function calling primitive graphic calls (lines, circles, boxes or polygons)? The stars function is not quite right. -- David Finlayson Geomorphogist and GIS Specialist NearPRISM -

In http://finzi.psych.upenn.edu/R/Rhelp02a/archive/14637.html some rudimentary R functions were given for drawing proportional area venn diagrams with area of each intersection ~ the count in a 2 x 2 x 2 table. I'm interested in this, for another application: showing the correlations among Y, X1, X2 using area ~ r^2 of each pair (sometimes called a Ballantine diagram). Before I attempt

I contacted John Fox about this first, because parts of the file are attributed to him. He says that he didn't write rstandard.glm(), and suggests asking r-devel. As it stands, rstandard.glm() has summary(model)$dispersion outside the sqrt(), while in rstandard.lm(), the sd is already sqrt()ed. This seems to follow stdres() in VR/MASS/R/stdres.R. Of course for the c("poisson",

Hi everybody, I am trying to make up call flow diagrams for for a setup which include ser as a sip proxy/registrar and asteriks as a voicemail server. Is my sequence correct?: UA 1 send an invite to SER. SER forwards this invite to UA2. UA2 sends back a sends back a 100 trying and 180 ringing message. SER forwards these. However UA2 doesnt answer the phone,so what happens then?...is there a

Dear R users, I'm trying to construct a distance matrix based on a nb object created by spdep where sites must have a larger influence in one direction then in the other. Here is an example to better illustrate what I need: Let say I have the following Gabriel connection network library(spdep) library(ade4) data(orbatid) nbgab<-graph2nb(gabrielneigh(as.matrix(oribatid$xy)))

I am a novice R user, and I am having difficulty understanding R's influence plots. I am trying to remove outliers from a particular variable, "sib." I am able to generate influence plots and further outlier information such as below (which is a shortened example). For my analyses, I end up excluding the points R refers to, 7, 18, 26, and 105. However, my question is, how can I

Dear list, There is, I believe, a minor typo in the example section of influence.measures.Rd. In the final example the word `does` appears where I suspect `dose` is required: I couldn't remember exactly what format patches should be in, so here is one as diff would produce: Index: devel/src/library/stats/man/influence.measures.Rd

Hi dear all, I'm wondering about the question that; Does the influence.measures(model) for linear models valid for general linear models such as logistic regression models? That is; If I fit the model like model <- glm( y~X1+X2, family=binomial) Then, if i apply the function "influence.measures(model), i will get the result of influence measures. These result are valid for

(R 0.65.0 and earlier): example(aov) lm.influence(npk.aov) gives Error: NA/NaN/Inf in foreign function call (arg 6) which comes from the lm.obj$coefficients (the last 3-way interaction coefficient is indetermined and accordingly NA) argument passed to .Fortran("lminfl",...) inside lm.influence. The following is a simple correct patch --- lm.influence.R 1999/08/13

I am performing a locally weighted regression model using housing data, where I only include observations within a certain distance of the house in question. For cross-validation of the bandwidth I am collecting elements of the "hat matrix" (where y hat=hat matrix *y). I was convinced I could grab the diagonal elements for the hat matrix using lm.influence()$hat. In

Barrett & Ling, JASA, 1992, v.87(417), pp184-191 define general classes of influence measures for multivariate regression models, including analogs of Cook's D, Andrews & Pregibon COVRATIO, etc. As in univariate response models, these are based on leverage and residuals based on omitting one (or more) observations at a time and refitting, although, in the univariate case, the

Dear list, I've noticed that influence.measures and cooks.distance gives different results for non-gaussian GLMs. For example, using R-1.9.0 alpha (2003-03-17) under Windows: > ## Dobson (1990) Page 93: Randomized Controlled Trial : > counts <- c(18,17,15,20,10,20,25,13,12) > outcome <- gl(3,1,9) > treatment <- gl(3,3) > glm.D93 <- glm(counts ~ outcome +

Hello. I want to get the proper influence function for the glm coefficients in R. This is supposed to be inv(information)*(y-yhat)*x. So I am wondering what is the exact mathematical formula for the output that the functions: dfbeta() OR lm.influence()$coefficients return for a glm model. I am confused because: 1. Their columns don't sum to zero as influences should. 2. They