similar to: Influence.measures

influence.measures gives several measures of influence for each observation (Cook's Distance, etc) and actually flags observations that it determines are influential by any of the measures. Looks good! But how does it discriminate between the influential and non- influential observations by each of the measures? Like does it do a Bonferroni-corrected t on the residuals identified by

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 +

I was wondering if someone might be able to tell me what formula R's influence.measures function uses for determining whether the hat value it computes is influential (i.e., the true/false value in the "hat" column of the returned is.inf data frame). The reason I'm asking is that its results disagree with what I've just learned in my statistics class, namely that a point

Hello, I was asked to try the following code on R, gamma.mles function (xx,shape0,rate0) { n<- length(xx) xbar<- mean(xx) logxbar<- mean(log(xx)) theta<-c(shape0,rate0) repeat { theta0<- theta shape<- theta0[1] rate<- theta0[2] S<- n*matrix(c(log(rate)-digamma(shape)+logxbar,shape/rate-xbar),ncol=1) I<- n*matrix(c(trigamma(shape),-1/rate,-1/rate,shape/rate^2),ncol=2)

Is there a routine in the boot package to get the jackknife-after- bootstrap influence values? That is, the influence values of a jackknife of the bootstrap estimates? I can see how one would go about it from the jack.after.boot code, but that routine only makes pretty pictures. It wouldn't be hard to write, but I find it hard to believe this isn't part of the package already. Thanks

Dear list members, I would like to ask two questions, although the second one must have been answered a thousand times. 1) is there a simple way to estimate the influence of individual variables in PCR and PLS models? What I have in mind is a way to rank the variables according to their influence, using some numerical value of a statistic. 2) what is the proper way to cite a CRAN package? How

Many thanks to those who replied to my message regarding installation of the lattice package. I have now upgraded my version of R. I have a quick question: I am running mixed effects models using the lme command. I am wanting to assess the fit of these models using the usual plots including Cooks Distance plots. I have noticed from the help files that it is possible to run influence measures

Hello, Is it possible to model and solve influence diagrams in R? I would appreciate your help. Pavel.

I make a regression using the lm.rigde function from library MASS. Now i want some measures of influence. Is there a way to use a lm.ridge object into the function influence.measures? -- bertola at fastmail.fm --

I am hoping that some one might be to tell me whether there are any functions that produce influence measures for lme /nlme objects (i.e those suggested by Lesaffre and Verbeke or Langford and Lewis for multilevel models). Thanks in advance. ----------------------------------------------- Anthony A. Pezzola apezzola@uc.cl (02) 354-7823 Profesor de Ciencia Política Instituto de Ciencia

Hi, I know this is the wrong forum for this kind of question and is only ephemerally related to xapian, but maybe someone here knows and will save me some digging. Given a google PR of, say, .15 for page A of site x.com, does google "radiate" any of that PR goodness onto any other pages of site x.com? Irrespective of whether page A links to any other pages in site x.com. In other

I wondered why mdbox_rotate_size is 2MB by default. I thought if I increased it to 16MB, maybe there would be less disk io, but I dont see any difference. Furthermore I read in some thread that increasing it from 2MB, could give problems when deleting messages, can someone explain this? Does anyone have this on a cephfs mount, does it make sense to set this to 4MB? [@test2 ~]# pr -m -t

(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

Hi all, Does lme has function to compute the cook's distance or influence measure like lm? I can't find them. Thanks. Yen Lin [[alternative HTML version deleted]]

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)))

Hello, I'm plotting a data-frame and use plot(......., pch=...) as well as setting pch with par(pch=...) but always get the default symbol (circle) for the plots. Does pch have no effect, when plotting a complete data-frame, instead of certain values/vectors? How to change the behaviour? Do I have to change the print-function? TIA, Oliver

HI, Dear Greg, I have one question about the variable relative influence plot: THE following is the rel.inf value of 25 variables, but wen I plot, not all the variables are labeled. i.e. num_genes, wg, hydrophob_per etc are not labeled on the y-axis. also the variables are labeled vertically, can it be labeled horizontally just like the summary table? Thanks! > summary(gbm1,

A while back I asked about getting a list of points that R considers influential after fitting a linear model, and very quickly got a helpful pointer to influence.measures(). But "it has happened again." The trouble I am having is that points marked on plots are not flagged in the output from influence.measures(), and I can't read them on the plots. I tried some successive

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

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