similar to: Weird LM behaviour

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

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 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.

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

Dear All, First of all I would like to say I do not have much knowledge about this subject, so most of you can find it really easy. I am doing a linear regression and I want to test if the slope of the curve is 0. R gives the summary statistics: Call: lm(formula = x ~ s) Residuals: Min 1Q Median 3Q Max -0.025096 -0.020316 -0.001203 0.011658 0.044970 Coefficients:

Hello, We've got a dataset with several variables, one of which we're using to split the data into 3 smaller subsets. (as the variable takes 1 of 3 possible values). There are several more variables too, many of which we're using to fit regression models using lm. So I have 3 models fitted (one for each subset of course), each having slope estimates for the predictor variables.

Hi All, I am confused with SMATR's test for common slope. My null hypothesis here is that all slopes are parallel (common slopes?), right? So if I get a p value < 0.05 means that we can have confidence to reject it? That slopes are different? Or the other way around? it means that we have statistical confidence that the slopes are parallel? thanks -- Eugenio Larios PhD Student University

Hi, I'm trying to get confidence intervals to slopes from a linear model and I can't figure out how to get at them. As a cut 'n' paste example: ################# # dummy dataset - regression data for 3 treatments, each treatment with different (normal) variance x <- rep(1:10, length=30) y <- 10 - (rep(c(0.2,0.5,0.8), each=10)*x)+c(rnorm(10, sd=0.1), rnorm(10,

Hello R users, I've used the following help two compare two regression line slopes. Wanted to test if they differ significantly: Hi, I've made a research about how to compare two regression line slopes (of y versus x for 2 groups, "group" being a factor ) using R. I knew the method based on the following statement : t = (b1 - b2) / sb1,b2 where b1 and b2 are the two slope

Not really r-specific: Z = (b1 - b2) / SQRT ( SEb1^2 + SEb2^2) -------Original Message------- From: Gijsbert Stoet <stoet at volition.wustl.edu> Sent: 07/20/03 09:51 PM To: r-help at stat.math.ethz.ch Subject: [R] how to test whether two slopes are sign. different? > > Hi, suppose I do want to test whether the slopes (e.g. determined with lsfit) of two different population are

I have not really used lm before and I was hoping for some help on a simple problem. Here is a toy version of the problem I want to solve. y x grp -.9 1 a -.8 2 a -.7 3 a -.7 1.5 b -.5 2.5 b -.3 3.5 b -.19 2.7 c -.11 3.7 c -.41 4.7 c I want to fit a model that has one y-intercept and three slopes, one for

I am interested in whether the slopes in a linear model are different from 0. I.e. I would like to obtain the slope estimates, and their standard errors, ``relative to 0'' for each group, rather than relative to some baseline. Explicitly I would like to write/represent the model as y = a_i + b_i*x + E i = 1, ..., K, where x is a continuous variate and i indexes groups (levels of a

I'm trying to do a linear regression between the columns of matrices. In example below I want to regress column 1 of matrix xdat with column1 of ydat and do a separate regression between the column 2s of each matrix. But the output I get seems to give correct slopes but incorrect intercepts and another set of slopes with value NA. How do I do this correctly? I'm after the slope and