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Dear R-users I analysed the same data set by two different ways; analysis of covariance by using lm and anova functions and multiple comparison by using simtest function in the multcomp library. The output from the analysis of covariance is; > y<-lm(D~Cond+Q1,data=x) > anova(y) Analysis of Variance Table Response: D Df Sum Sq Mean Sq F value Pr(>F) Cond 2

Hello I am using R 2.2.0 with Windows XP. I've got a five element list object, each element containing two dataframes of equivalent size. > str(mylist) List of 1 $ data1:List of 2 ..$ data1a :`data.frame': 77 obs. of 63 variables: .. ..$ var1 : num [1:77] 0.41375 0.00056 1.43040 1.43528 0.61730 ... .. ..$ var2 : num [1:77] 1.154 1.686 0.673 0.800 0.760 ... ..

Dear R Helpers: I am trying to annotate a plot. The following code snippet works, but it is kind of a kludge since it adds the partial derivative symbols after creating the plotmath frac(). Is there a more elegant way to write a partial derivative? dev.off() plot(NA, xlim=c(-3,3), ylim=c(0,1.6), xlab="", ylab="", tck=-0.015) text(1.6, 1, expression(paste("slope =

Dear R-philes, I am having some trouble averaging across multiple runs of a classifier in an ROC Curve. I am using the ROCR package and the plot() method. First, I initialize a list with two elements where each element is a list of predictions and labels: vowel.ROC <- list(predictions=list(), labels=list()) For every run of the classifier, I append the scores and labels to their

Hi all. Belove is the example from the cluster-help page wtih the output. I simply cannot figure out how to relate the estimate and robust Std. Err to the p-value. I am aware this a marginal model applying the sandwich estimator using (here I guess) an emperical (unstructered/exchangeable?) ICC. Shouldent it be, at least to some extend, comparable to the robust z-test, for rx :

Hi, I would like to add a key to my trellis plot using draw.key. Here is what I want: 3 x 6 key where the first row is a header. row 1: empty, "S-R Mapping", "R^2" row 2: pch=17, "Color", 0.951 row 2: pch=17, "Shape", 0.934 ect... The problem is that I would like the cell in the upper left corner to be empty (a placeholder) with the remaining

Dear Thomas, The head of my dataset > head(wsuv) parcel sp time censo treatment species 1 S8 Poecilanthe effusa ( Hub. ) Ducke. 1 1 1 1 2 S8 Poecilanthe effusa ( Hub. ) Ducke. 1 1 1 1 3 S8 Poecilanthe effusa ( Hub. ) Ducke. 1 1 1 1 4 S8 Poecilanthe effusa ( Hub. ) Ducke. 1 1 1

Hello all: I am confused about the output from a lm() model with an incomplete design/missing level. I have two categorical predictors and a continuous covariate (day) that I am using to model larval mass (l.mass): leaf.species has three levels - map, syc, and oak cond.time has two levels - 30 and 150. There are no response values for Map-150, so that entire, two-way, level is missing.

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, When I run the survfit function, I want to get the restricted mean value and the standard error also. I found out using the "print" function to do so, as shown below, print(km.fit,print.rmean=TRUE) Call: survfit(formula = Surv(diff, status) ~ 1, type = "kaplan-meier") records n.max n.start events *rmean *se(rmean) median 200.000

Dear list, I am confused about two functions in R: AIC(fm) and extractAIC(fm). What is the difference between two and when do I have to use one over the other? I have found the similar question previously and still not clear for me to understand. I also looked at '?AIC' and '?extractAIC' in R, which is also unclear. I pasted faked data set, fitting summary, and AICs. Thank

Hi Xavier, the reason you observe this feature is that in the 'constraint' argument you should specify the values under the additive parameterization, i.e., when in the second column of the matrix supplied in 'constraint' you specify 2, then you need to provide the easiness parameters (not the difficulty parameters) in the third column. Check the Details section of ?tpm() and

Hi ! We study the effect of several variables on fruit set for 44 individuals (plants). For each individual, we have the number of fruits, the number of flowers and a value for each variable. Here is our first model in R : y <- cbind(indnbfruits,indnbflowers); model1 <-glm(y~red*yellow+I(red^2)+I(yellow^2)+densite8+I(densite8^2)+freq8_4+I (freq8_4^2), quasibinomial); - We have

hello: my data looks like: time1  time2   event  catagoria 2004    2006        1            C 2004    2005        0            C 2005    2010        1            E 2007    2009        1            C 2006    2007        0            E 2008    2010        0            C 2008    2010        1            E ... and the census interval is 1 year I have tried  this

We collected more data to address some of the questions from the reviewers. Note this time we use clang itself as the benchmark. We choose clang because we think it's a typical C++ program and the reviewers here have good knowledge of the code base. What we measure is running time for clang to compile a large preprocessed source file (4.98M lines of .ii file), using different compilation

Justin, Sean and other people interested in this proposal, I'm wondering if you have chances to read the new experiment results in my last email sent 2 weeks ago. Can you share you thoughts, or you have other tests that you want to to run? I'm in the final stage of preparing the patch. If you are OK, I can sent out the patch soon. Thanks, -Rong On Wed, Aug 19, 2015 at 5:18 PM, Philip

Dear all, I'm trying to make multiple comparisons for an lme-object. The data is for an experiment on parental work load in birds, in which adults at different sites were induced to work at one of three levels ('treat'; H, M, L). The response is 'feedings', which is a quantitative measure of nest provisioning per parent per chick per hour. Site is included as a random effect

Hello all, I'm trying to replicate a confirmatory factor analysis done in Amos. The idea is to compare a one-factor and a two-factor model. I get the following warning message when I run either model: "Could not compute QR decomposition of Hessian. Optimization probably did not converge." I have no idea what to do here. I believe posters reported the same problem. It seems

Hello together, this is ma first post, so please aplogize me if post this in the wrong section. I have problem concerning ma two matrizes. After a regressione and so on, I got two matrizes Matrixres contains the results of ma calculation. Matrixr contains my detiene, which where Aldo used for the regression. Please ser the following code: #Datei einlesen residual =

One aspect of this that I have not seen discussed is that middle-end instrumentation enables PGO optimizations to front-ends other than Clang. While I agree that FE instrumentation could be improved, it still requires every FE to implement essentially the same common functionality. Having PGO instrumentation generated in the middle-end, allows us every FE to automatically take advantage of PGO.