similar to: Documentation for Pearson Residuals

I am running a series of candidate mixed models using lmer (package lme4) and I'd like to be able to compile a list of the AIC scores for those models so that I can quickly summarize and rank the models by AIC. When I do logistic regression, I can easily generate this kind of list by creating the model objects using glm, and doing: > md <- c("md1.lr", "md2.lr",

Hi, I try to make a plot like this: Y | | | o | o o | o o | o o |o o o |-------------------------------- 0 10 20 30 40 50 60 70 80 90 100 (A) 100 90 80 70 60 50 40 30 20 10 0 (B) or B 100 90 80 70 60 50 40 30 20 10 0 Y |-------------------------------- | | o

I'm using R version 1.6.2 under Windows 2000 Prof. I've recently had a problem with R terminating with an error message that tells me that an error log has been generated. However, I've been unable to find this error log. Could someone tell me where it should have been written to and/or what the name of the file should be? Thanks, Dave

I've been experimenting with the lattice graphics in chapter 3 of MASS. The code to produce figure 3.14 is: data(crabs) library(mva) lcrabs.pc <- predict(princomp(log(crabs[,4:8]))) crabs.grp <- c("B", "b", "O", "o")[rep(1:4, rep(50,4))] splom( ~lcrabs.pc[, 1:3], groups = crabs.grp, panel = panel.superpose, key = list(text =

Hi all, This is a very silly question or something escapes me: Let obj a simple gam poisson model. Let >obj<-gam(....,family=poisson) >obj1<-update(obj, family=quasi(link="log", var="mu")) >From summary.glm(obj1) the dispersion parameter is estimated 1.165; In fact it is: > (predict(obj1, se.fit=T)$se.fit[1:5]/predict(obj, se.fit=T)$se.fit[1:5])^2 4

Hi Peter and others, If it helps, I wrote a small function glm.scoretest() for the statmod package on CRAN to compute score tests from glm fits. The score test for adding a covariate, or any set of covariates, can be extracted very neatly from the standard glm output, although you probably already know that. Regards Gordon --------------------------------------------- Professor Gordon K

The function wtd.var(x,w) in Hmisc calculates the weighted variance of x where w are the weights. It appears to me that wtd.var(x,w) = var(x) if all of the weights are equal, but this does not appear to be the case. Can someone point out to me where I am going wrong here? Thanks. Tom La Bone [[alternative HTML version deleted]]

Dear Sirs What is the best aproximation to the standardized normal distribution: necessidade = c("sem necessidade","com necessidade") tipo =c("CE-1", "CE-2", "CE-3") dados=c(20,34,44,69,9,3) Tabela =cbind(expand.grid(list(Necessidade=necessidade, Tipo=tipo)), count=dados) Tabela.array=tapply(Tabela$count, Tabela[,1:2], sum) ni =

Hi Everyone, I did a quick search of the list and it looks like this may not have been asked before... I'm trying to generate a matrix of random numbers between 0 and 1, with 6 columns, 10000 rows. About all I know is that runif(1) gives me the random number I'm looking for. Any help would be great! thanks, -Max

Hi Everyone, I did a quick search of the list and it looks like this may not have been asked before... I'm trying to generate a matrix of random numbers between 0 and 1, with 6 columns, 10000 rows. About all I know is that runif(1) gives me the random number I'm looking for. Any help would be great! thanks, -Max

After installing rggobi, I get the following error when I try to load it: > local({pkg <- select.list(sort(.packages(all.available = TRUE))) + if(nchar(pkg)) library(pkg, character.only=TRUE)}) Loading required package: RGtk2 Error in dyn.load(x, as.logical(local), as.logical(now)) : unable to load shared library

Hi, 1. How do I construct 95% prediction interval for new x values, for example - x = 30000? 2. How do I construct 95% confidence interval? my dataframe is as follows : >dt structure(list(y = c(26100000, 60500000, 16200000, 30700000, 70100000, 57700000, 46700000, 8600000, 10000000, 61800000, 30200000, 52200000, 71900000, 55000000, 12700000 ), x = c(108000, 136000,

Full_Name: Bin Nan Version: 1.3.0 OS: Win32 Submission from: (NULL) (141.211.15.110) The function resid(fit, "pearson") seems not giving the same Pearson's residuals for logistic regression as what Splus does. I found the problem when I fitted glm with family = binomial using Crowder's seed data.

Hi I have used a polycor package for categorical correlation coefficients. I run the following script. But there were no results. Could you tell me how to correct the script? Thanks in advance, vars <- names(sdi) for (i in 1:length(vars)) { for (j in 1:length(vars)) { paste(vars[i]," and ", vars[j]) polychor(vars[i], vars[j]) # corr } } -- Kum-Hoe Hwang, Ph.D.Phone :

Hi, I would like to estimate something like y = a + b*d2*y + c*d3*y where the dummies are created from some vector d with three (actually many more) levels using factor(). But either there is included the variable y or d1*y. How could I get rid of these? Example: x = c(1,2,3,4,5,6,7,8) y = c(3,6,2,8,7,6,2,4) d = c(1,1,1,2,3,2,3,3) fd = factor(d) lm(x ~ fd*y) gives: Coefficients: (Intercept)

Full_Name: Carmen Fernandez Version: 1.3.1 OS: Submission from: (NULL) (138.251.202.115) I think there is a problem when computing Pearson residuals, in that they seem to be computed at the raw residuals divided by the square root of the corresponding diagonal element of the weight matrix W evaluated at the last step of the iterative model fitting procedure (IWLS), instead of dividing by the

Dear all, I am new to R. I wish to use R's multiple imputation to deal with missing data. I have a data set with the size around 300 observations and 150 variables. I checked the help function in R and could not locate how to write the codes for this. can anyone give a hand? Do appreciate your time and kindness! Q. [[alternative HTML version deleted]]

Hi, I try to calculate Kendall's W coefficient and I have a bizarre error. little.app.mat<-matrix(c(1,3,4,2,6,5,2,4,3,1,5,6,3,2,5,1,5,4),nrow=3,byrow=TRUE) print(kendall.w(little.app.mat[-1,])) >>> Kendall's W for ordinal data >>> W = 0.7753623Error in if (is.na(x$p.table)) { : argument is of length zero

Hi. Please, how can I put together 2 or more lines at the same scatterplot ? Example: measures of protein intake (quantitative) of 4 children over 30 days, by day. How to plot all children at same graphic: Protein X Time ? Is there any command like "overlay" ? Thank you, Mauricio

Hello, I am trying to model an intensity function with time-varying covariates. Before, I have successfully defined a log likelihood function for a Power-Law Process (lambda(t)=alpha*beta*t^(beta-1)) with two paramters and no covariates for a repairable systems with failure times (t). This function was maximized with R optim. No problem! But now I want to include a covariate indicating a