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Dear R-developers, when tapply() is invoked on factors that have empty levels, it returns NA. This behaviour is in accord with the tapply documentation, and is reasonable in many cases. However, when FUN is sum, it would also seem reasonable to return 0 instead of NA, because "the sum of an empty set is zero, by definition." I'd like to raise a discussion of the possibility of an

Hi Community, I'm trying to reconcile Cook's Distances computed in glm. The following snippet of code shows that the Cook's Distances contours on the plot of Residuals v Leverage do not seem to be the same as the values produced by cooks.distance() or in the Cook's Distance against observation number plot. counts <- c(18,17,15,20,10,20,25,13,12) outcome <- gl(3,1,9)

Dear R community, I'm curious to know how people go about estimating standard errors for parameter estimates after model selection by ridge regression and the lasso. Do you have any practical or theoretical advice? Warmly, Andrew -- Andrew Robinson Department of Mathematics and Statistics Tel: +61-3-8344-9763 University of Melbourne, VIC 3010 Australia Fax:

Dear R-helpers, when in the browser, is it possible to change the environment, so as to be able to easily access (print, manipulate) objects in the parent, or elsehwere? I know that it is possible to evaluate expressions in different environments, using eval(), but I would prefer to avoid that if possible. Thanks, Andrew -- Andrew Robinson Department of Mathematics and Statistics

Dear all, I'm curious about how to interpret the results of the following code. The first model is directly from the help page of lmer; the second is the same model but using the Gamma family with log link. The fixed effects make sense, because y = 251.40510 + 10.46729 * Days is about the same as log(y) = 5.53613298 + 0.03502057 * Days but the random effects seem quite

Dear Friends and Colleagues, I'm puzzling over how to interpret or use some bootstrap intervals. I think that I know what I should do, but I want to check with knowledgeable people first! I'm using a studentized non-parametric bootstrap to estimate 95% confidence intervals for three parameters. I estimate the variance of the bootstrap replicates using another bootstrap. The script

Hi everyone, I'm involved in a discussion with a colleague. He suggested a sample design for a finite-sized process that (to all intents and purposes) involves tossing a coin and examining the unit if the coin shows Heads. I should emphasize that we're both approaching the problem from a design-based sampling theory point of view. So I have no argument about the appropriateness of the

Dear R colleagues, a friend and I are trying to develop a modest workflow for the problem of decomposing tests of higher-order terms into interpretable sets of tests of lower order terms with conditioning. For example, if the interaction between A (3 levels) and C (2 levels) is significant, it may be of interest to ask whether or not A is significant at level 1 of C and level 2 of C. The

Hi, Does anyone know if: with R can you take a set of numbers and aggregate them like you can in SPSS? For example, could you calculate the percentage of people who smoke based on a dataset like the following: smoke = 1 non-smoke = 2 variable 1 1 1 2 2 1 1 1 2 2 2 2 2 2 When aggregated, SPSS can tell you what percentage of persons are smokers based on the frequency of 1's and 2's. Can

Dear community, can anyone provide a snippet of code to force the lm() to fit a model with terms in the formula in an arbitrary order? I am interested in something like: lm(y ~ A * B + C, data=data) where the interaction of A and B should be in the formula before C. My goal is to simplify my presentation of models using the anova() statement. I have found that this should be possible using

Hi, Can anyone explain how the standard error in arima() is calculated? Also, how can I extract it from the Arima object? I don't see it in there. > x <- rnorm(1000) > a <- arima(x, order = c(4, 0, 0)) > a Call: arima(x = x, order = c(4, 0, 0)) Coefficients: ar1 ar2 ar3 ar4 intercept -0.0451 0.0448 0.0139 -0.0688 0.0010 s.e.

Hi everyone, I was wondering if anyone has any code they could share for creating thumbnail plots in Sweave. For example, I'd like a plot like the following: y <- c(40, 46, 39, 44, 23, 36, 70, 39, 30, 73, 53, 74) x <- c(6, 4, 3, 6, 1, 5, 6, 2, 1, 8, 4, 6) opar <- par(mar=c(3,3,0,0)) plot(x, y, xlab="", ylab="") abline(h=mean(y), col="red") par(opar)

I read the posts about augPred with lme, but does anyone know if there is a correlate for augPred for lmer? Specifically, I want to be able to use it to plot projections for all groups in an lmer class object using plot(augPred(lmer.object)). Thanks, Kyle *************************************** J. Kyle Roberts, Ph.D. Baylor College of Medicine Center for Educational Outreach One Baylor Plaza,

Dear R community, I'm trying to figure out if there is any way to split the printed output of some commands, for example summary.lme, so that I can intersperse comments in Sweave. I don't mind running the command numerous times and masking various portions of the output, or saving the output as an object and printing it, but I can't figure out how to do either. Does anyone have any

Dear R-community, my data set looks like 'mat' below. Plant<-c(NA,1,1,1,NA,NA,NA,NA,NA,1); Value1<-rnorm(1:10); Value2<-rnorm(1:10); mat<-cbind(Plant,Value1,Value2); I receive data from two different sites. One site is identified by an interger number, the other site has no data in column Plant=NA. My pb: I'm trying to assign labels "A" or "B" to

Dear R-users, I was wondering if anybody knows if it's possible to obtain a p value for the full model of a GLMM with the lme4 package. I was told that I should check whether the full model including all the predictor variables is significant before doing stepwise regression or further analysis, but I can't figure out how to do this. I also wanted to know if there's a way of

Using the weight argument with a variance function in lme (nlme), you can allow for heteroscedasticity of the within-group error. Is there a way to do this for the other variance components? For example, suppose you had subjects, days nested within subjects, and visits nested within days within subjects (a fully nested two-way design) and you had, say men and women subjects. Could you allow for

Hi all, Does anybody have the experience of using optim to estimate variables with integral forms? here the code: trun.mean<- function(x) # t is the threshold { mu=x[1]; sigma=x[2]; t=x[3]; f <- function(x) (1/(sigma*sqrt(2*pi)))*exp(-(x-mu)^2/(2*sigma^2)); pdf.fun <- function(x) x*f(x); integrate(f,thre,upper=Inf)$value/integrate(pdf.fun,thre,upper=Inf)$value ; } when I

Dear All, I tried to use the boot function, provided in the boot package, in such a simple task as to create a bootstrap distribution of the mean of a vector x. I wrote: b <- boot(x, mean, R=200) Well, it doesn't work. I suspect it has something to do with what is called "second argument" of the "statistic" in the help page of boot. What is the second argument of

Hi, I have to build a logistic regression model on a data set that I have. I have three input variables (x1, x2, x3) and one output variable (y). The syntax of lrm function looks like this lrm(formula, data, subset, na.action=na.delete, method="lrm.fit", model=FALSE, x=FALSE, y=FALSE, linear.predictors=TRUE, se.fit=FALSE, penalty=0, penalty.matrix, tol=1e-7,