similar to: NaN when using dffits, stemming from lm.influence call

R-users E-mail: r-help@r-project.org Hi! R-users. I am just wondering what the definition of "dffits" in R language is. Let me show you an simple example. function() { library(MASS) xx <- c(1,2,3,4,5) yy <- c(1,3,4,2,4) data1 <- data.frame(x=xx, y=yy) lm.out <- lm(y~., data=data1, x=T) lev1 <- lm.influence(lm.out)$hat sig1 <-

All - My question is a bit involved, so bear with me. I have some data that looks like: Lake LL LW 81 2.176091259 1.342422681 81 2.176091259 1.414973348 81 2.176091259 1.447158031 81 2.181843588 1.414973348 81 2.181843588 1.447158031 81 2.184691431 1.462397998 81 2.187520721 1.447158031 81 2.187520721 1.477121255 81 2.187520721 1.505149978 ... [truncated] I'm trying to: 1) fit a simple

This is a question regarding the 'regTermTest' function in the 'survey' package. Imagine Z as a three level factor variable, and code ZB and ZC as the two corresponding dummy variables. X is a continuous variable. In a 'glm' of Y on Z and X, say, how do the two test specifications test.terms = c("ZB:X","ZC:X") # and test.terms = ~ ZB:X + ZC:X in

Hi all, In a simulation context, I'm applying some my function, "myfun" say, to a list of glm obj, "list.glm": >length(list.glm) #number of samples simulated [1] 1000 >class(list.glm[[324]]) #any component of the list [1] "glm" "lm" >length(list.glm[[290]]$y) #sample size [1] 1000 Because length(list.glm) and the sample size are rather large,

This is a question about removing the intercept in a binomial glm() model with categorical predictors. V&R (3rd Ed. Ch7) and Chambers & Hastie (1993) were very helpful but I wasn't sure I got all the answers. In a simplistic example suppose I want to explore how disability (3 levels, profound, severe, and mild) affects the dichotomized outcome. The glm1 model (see below) is

I am attempting to run a glm with a binomial model to analyze proportion data. I have been following Crawley's book closely and am wondering if there is an accepted standard for how much is too much overdispersion? (e.g. change in AIC has an accepted standard of 2). In the example, he fits several models, binomial and quasibinomial and then accepts the quasibinomial. The output for residual

Dear R-List users, Can anyone explain exactly the difference between Weights options in lm glm and gls? I try the following codes, but the results are different. > lm1 Call: lm(formula = y ~ x) Coefficients: (Intercept) x 0.1183 7.3075 > lm2 Call: lm(formula = y ~ x, weights = W) Coefficients: (Intercept) x 0.04193 7.30660 > lm3 Call:

Hi! I would like to perform an F-Test over more than one variable within a generalized mixed model with Gamma-distribution and log-link function. For this purpose, I use the package mgcv. Similar tests may be done using the function "anova", as for example in the case of a normal distributed response. However, if I do so, the error message "error in eval(expr, envir, enclos) :

Hi, Suppose I have a vector: > names.select [1] "Idd13" "Idd14" "Idd8.12" "Idd7" automatically generated by some selection criteria. Now, if I have a data frame with many variables, of which the variables in "names.select" are also variables from the data frame. e.g. > all.df[1:5,] Mouse Idd5 Idd6.19.20 Idd13 Idd14 Idd8.12

Dear R-users When running a glm polynomial model with one explanatory variable (example Y~X+X^2), with a poisson or binomial error distribution, the predicted values obtained from using the predict() function and those obtained from using the coefficients from the summary table "as is" in an equation of the form Y=INTERCEPT+ XCoef x X + XCoef x X^2, differ considerably. The former are

Ted Harding said: > I can get the estimated RRs from > RRs <- exp(summary(GLM)$coef[,1]) > but do not see how to implement confidence intervals based > on "robust error variances" using the output in GLM. Thanks for the link to the data. Here's my best guess. If you use the following approach, with the HC0 type of robust standard errors in the

Hi i would like to use some graphs or tables to explore the data and make some sensible guesses of what to expect to see in a glm model to assess if toxin concentration and sex have a relationship with the kill rate of rats. But i cant seem to work it out as i have two predictor variables~help?Thanks.:) Here's my data. >

Problems with predict and lines in plotting binomial glm Dear R-helpers I have found quite a lot of tips on how to work with glm through this mailing list, but still have a problem that I can't solve. I have got a data set of which the x-variable is count data and the y-variable is proportional data, and I want to know what the relationship between the variables are. The data was

Hello: I'd like to know how and if the GLM convergence problems are addressed in gamlss. For simplicity, let's focus on Normal and Negative Binomial with log link. The convergence issues of the glm() function were alleviated in 2011 when glm2 package was released. Package gamlss was released in 2012, so it might still use the glm-like solution or call glm() directly. Is that the case or

Dear all, I am comparing logistic regression models to evaluate if one predictor explains additional variance that is not yet explained by another predictor. As far as I understand Baron and Li describe how to do this, but my question is now: how do I report this in an article? Can anyone recommend a particular article that shows a concrete example of how the results from te following simple

dear list! i have run several glm analysises to estimate a mean rate of dung decay for independent trials. i would like to compare these results statistically but can't find any solution. the glm calls are: dung.glm1<-glm(STATE~DAYS, data=o_cov, family="binomial(link="logit")) dung.glm2<-glm(STATE~DAYS, data=o_cov_T12, family="binomial(link="logit")) as

This note is to r-devel rather than r-announce because it notes an experimental package that addresses issues that intersect with broader developmental issues in R. I have posted the package cremo = Comparative REndering of Modeling Outputs for retrival at http://www.biostat.harvard.edu/~carey/cremo.html This package addresses the problem of assembling and rendering results of multiple

Hello, I use a type III anova ("car" package) to analyse an unbalanced data design. I have two factors and I would have the effect of the interaction. I read that the result could be strongly influenced by the contrasts. I am really not an expert and I am not sure to understand indeed about what it is... Consequently, I failed to properly used the fit.contrast function (gregmisc

Hi all, inside my * is a E400P. The machine is a PII 400Mhz with 256MB Ram. OS is Debian woody. * is the newest cvs co. I have written a little callgen script which make outgoing calls through my *: #! /bin/sh set -e n=$1 # Nummer anz=$2 # Anzhal der Versuche anz2=$3 # Kan?le sle=$4 # Timeout bis zum n?chsten Versuch if [ -z $4 ]; then sle=0 fi s=1

We have several optimizations in InstCombine for bitwise logic ops (and/or/xor) that fail to handle compare patterns with the equivalent bitwise logic. Example: define i8 @or_and_not(i8 %a, i8 %b) { %nota = xor i8 %a, -1 %and = and i8 %nota, %b %res = or i8 %and, %a ret i8 %res } define i1 @or_and_cmp_not(i32 %a, i32 %b, i1 %c) { %cmp = icmp sgt i32 %a, %b %cmp_inv = icmp sle i32 %a,