similar to: Compare effects between lm-models

Hello all, I've become confused by the output produced by a call to anova(model1,model2). First a brief background. My model used to predict final tree height is summarised here: Df Sum Sq Mean Sq F value Pr(>F) Treatment 2 748.35 374.17 21.3096 7.123e-06 *** HeightInitial 1 0.31 0.31 0.0178 0.89519

Deal all, new as I am to developing packages for R-Project, I apologize on beforehand for questions that are too obvious. I am trying to 'add' a PDF document containing some detailed information to a package. The way I understand the Rexts.pdf document, I should add my .PDF document to the /inst/doc/ folder, and links to the files should be build automatically. However, after

Dear helpers, I've come up with what is probably a simple problem, but I cannot find the solution. I have a data-set containing survey-data from several countries. What I want to do is to perform some regression analyses, for each country separately. The question is, how to do this nicely (thus without repeating the same syntax with another `subset' argument). I thought of the

Dear R users, I am trying to get data from the clipboard into R on MacOSX. I tried the following, but got an error message: read.delim("clipboard") Error in file(file, "r") : unable to open connection In addition: Warning message: unable to contact X11 display Obviously, I'm not running R using X11. I'm wondering, can I import data from the clipboard on MacosX?

Dear expeRts, I am trying to plot a lme-object {package nlme) in such a way, that on a selected level the x-axis represents the value on a selected predictor and the y-axis represents the predicted-outcome variable. The graphs would than consist of several lines that each represent one group. I can't find such a plotting function. I could write such a function myself, based on

Dear R users, experimenting with the lm function in R, I've encountered some behaviour I don't understand with my limited knowledge of regression. I made a data-'set' of three measurements (see syntax below). Using lm (linear model) to fit the regression-line, I expected to find an intercept of 2.0 and a slope of 0, but in fact the slope is slightly below zero. Amazed by

Dear ListeRs, I am trying to fit a heteroscedastic multilevel model using lmer{lme4- package). Take, for instance, the (fictive) model below. lmer(test.result ~ homework + Sex -1 + (1 | School)) Suppose that I suspect the error terms in the predicted values to differ between men and women (so, on the first level). In order to model this, I want the 'Sex'-variable to be random on

Dear helpers, I'm estimating multilevel regression models, using the lme-function from the nlme-package. Let's say that I estimated a model and stored it inside the object named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value

Dear helpeRs, I'm working with the map-package and came upon a problem which I couldn't solve. I hope onee of you can. If not, this can be seen as a suggestion for new versions of the package. I'm trying to create a map of some European countries, filled with colors corresponding to some values. Let's say I have the following countries and I assign the following colors

Dear All, I'm trying to model heteroscedasticity using a multilevel model. To do so, I make use of the nlme package and the weigths-parameter. Let's say that I hypothesize that the exam score of students (normexam) is influenced by their score on a standardized LR test (standLRT). Students are of course nested in "schools". These variables are contained in the

LS, I'm estimating multilevel regression models, using the lme-function from the nlme-package. Let's say that I estimated a model and stored it inside the object named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value

Hi, I'm running some data simulations using (mixed effects)* regression models that show difficulty to converge. Therefore, I seek a way of capturing warnings (of false convergence) inside a loop. Inside that loop, I modify data and estimate a model. I do so many times with slightly different modifications of the data. Next, I extract some of the model parameters and store these in a matrix.

I was unable to open this file Bill Venables' excellent "Exegeses on Linear Models" posted at http://www.stats.ox.ac.uk/pub/MASS3/Exegeses.ps.gz I'd be very interested in reading it? Thanks Esther Meenken Biometrician Crop & Food Research Private Bag 4704 Christchurch TEL: (03) 325 9639 FAX: (03) 325 2074 EMAIL:MeenkenE at crop.cri.nz Visit our website at

Hi there, I checked google for aov. usually one uses summary to see whether the p-value is small. but I want to put aov in my script. how can I get the p-value, (0.1115, 0.6665, 0.6665 in the following example)? thanks YU > datafilename="http://personality-project.org/r/datasets/R.appendix2.data" > data.example2=read.table(datafilename,header=T) > aov.ex2 =

Dear all, I'm investigating on how to estimate a specific kind of cross- classified multilevel model. I think it is often referred to as a multiple-membership model. To problem is this: I want to study changing attitudes of people, for which I use (amongst other things) contextual data of the neighborhood they live in. I have two surveys (panel-design) and I know the neighborhood

LS, I'm estimating multilevel regression models, using the lme-function from the nlme-package. Let's say that I estimated a model and stored it inside the object named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value

Dear R-gurus, I have an interpretation problem regarding lme models. I am currently working on dog locomotion, particularly on some variation factors. I try to figure out which limb out of 2 generated more dispersed data. I record a value called Peak, around 20 times for each limb with a record. I repeat the records during a single day, and on several days. I tried to build two models, one

Hi there, as a student sociology, I'm starting to learn about SEM. The course I follow is based on LISREL, but I want to use the SEM-package on R parallel to it. Using LISREL, I found it to be very usable to be able to see the total direct and total indirect effects (standardized and unstandardized) in the output. Can I create these effects using R? I know how to calculate them

Daniel, When Group is entered as a factor, and the factor has two levels, the ANOVA table gives a p value for each level of the factor. What I am looking for is the omnibus p value for the factor, i.e. the test that the factor (with all its levels) improves the prediction of the outcome. You are correct that normally one could rely on the fact that the model

Dear R users I recently migrated from Statistica/SigmaPlot (Windows) to R (Linux), so please excuse if this may sound 'basic'. When running a nonlinear regression (V = Vmax * conc / (Ks + conc), i.e. Michaelis-Menten) on SigmaPlot, I get the output listed below: >>>Begin SigmaPlot Output<<< R = 0.94860969 Rsqr = 0.89986035 Adj Rsqr = 0.89458984 Standard Error of