similar to: Regression slopes

Hello, I''m trying to fit a linear mixed effects model of the form: lme(y ~ x * Sex * Year, random=x|subject) where Sex and Year are factors with two and three levels respectively. I want to compare the fixed effects for each level to the overall mean, but the default in R is to compare to the first level. This can be changed by adding the term -1 to the righthand side of the model

Hello, I'm using the nls function and would like to increase the number of iterations. According to the documentation as well as other postings on R-help, I've tried to do this using the "control" argument: nls(y ~ SSfpl(x, A, B, xmid, scal), data=my.data, control=nls.control(maxiter=200)) but no matter how much I increase "maxiter", I get the following error

Hello, Once again, I posted a message without a subject line. Sorry.... here is the question again. Is there a simple way to modify the circ.mean function in the CircStats package to include a vector of weights to obtain a weighted average angle? Thanks! Martin -- Martin Biuw Sea Mammal Research Unit Gatty Marine Laboratory, University of St Andrews St Andrews, Fife KY16 8PA Scotland Ph:

G'Day, I'm a little confused why the c function has the followng effect on classes - is this a feature ? My workround [ class(cc) <- c("POSIXt", "POSIXct") ] seems to do the job. Many thanks Bernie McConnell "R version 1.3.1, 2001-08-31" on NT > aa <- as.POSIXct("2001-09-23") > bb <- as.POSIXct("2001-09-24") > cc

G'Day again, I am attempting to read a large MSAccess file into R, but get memory problems. With the first 100 rows of the table ("Macca99") things, as shown below, are fine and the resulting object is 33,780 bytes. But when I read the entire table ("MaccaDiv99") which is 218,000 rows R falls over with the message: Rgui.exe - Application error The instruction at

Hello all, Does anyone know of any already written functions for carrying out stochastic dynamic programming in R? Thanks! Martin -- Martin Biuw Sea Mammal Research Unit Gatty Marine Laboratory, University of St Andrews St Andrews, Fife KY16 8PA Scotland Ph: +44-(0)1334-462637 Fax: +44-(0)1334-462632 Web: http://smub.st.and.ac.uk

Hi, Is there a function in R to calculate the nth root, similar to the MATLAB function NTHROOT()? Thanks, Martin Biuw [[alternative HTML version deleted]]

Hello, I've written a simple (although probably overly roundabout) function to test whether two regression slope coefficients from two linear models on independent data sets are significantly different. I'm a bit concerned, because when I test it on simulated data with different sample sizes and variances, the function seems to be extremely sensitive both of these. I am wondering if

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it seems that word can not read encapsupalted postscripts generated by R I used this command postscript("output.eps",horizontal=F,onefile=TRUE) since onefile=TRUE produces an encapsualted postscript actually what I'm trying to do is to insert the postsript file into a word document since other formats like jpeg and bmp do not reproduce the same quality like postscript formats any

Hi, This may or may not be slightly off topic, but I'll stick my neck out... I want to launch an external application from R while retaining R as the active application window (with R console as the active R window). I'm using shell.exec() to sequentially open and evaluate the contents in all jpeg images in a specific folder, using the viewer specified by the file association (in my case

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

Can anyone show me how to test for significant simple slopes of a 3-way interaction, with covariates. my equation tmod<-(glm(PCL~ rank.f + gender.f + MONTHS + CEXPOSE.M + bf.m + MONTHS*CEXPOSE.M*bf.m, data=mhatv, family=gaussian ,na.action=na.omit)) Thank you Mike [[alternative HTML version deleted]]

Hi I have (8 measures * 96 groups) = 768 datasets for which I did linear regressions using lm(). Now I want to compare the slopes for each of the 8 measures in each of the 96 groups. As I understand , I can not use > anova(lm1, ..., lm8) as the lm1 ... lm8 are based on different datasets. I also read in previous discussions in this list, that I can see if the slope +- stddev(slope)

Hi, all, I am thinking to compare a group of slopes from regression lines to see if they are different overall, and then make specific comparisons between groups. How can I achieve that in R? I searched the archives and there are only discussions about comparing two lines a time. Thanks. A sample data set is like the following. I would like to compare the regression slopes between the five

Hi all: I perform the linear mixed model for 300 persons, y is CD4 count,x is time. I randomized slope and intercept,so I can get 300 slopes and 300 intercepts.Now I wanna test wheter the variance of 300 slopes and 300 intercepts differs from zero. If the variance of 300 slopes(or intercepts) differs from zero at 0.05 significant level,I should randomize the slope(or intercept), and if not,I

Dear all I have the following data (a shortened extract shown; some replictates of time deleted) to which I fitted the linear model given below: time group mass 11 control 0.019 11 control 0.014 14 control 0.0306 14 control 0.0289 14 control 0.0236 17 control 0.0469 17 control 0.0709 11 five 0.0077 11 five

I want to specify a 2-level mixed model using the lme function in order to test an a priori hypothesis about the between-group values of the slopes but don't know how to do this . Here is the problem. Consider first the case of a single group. The model is: Y_i= a +bX_i + error where I indexes the different values of X and Y in this group . The a priori hypothesis of the slope is: b=K.

Dear all, I am trying to estimate the prediction from a fixed effects model and their confidence intervals as well. Though I do not want to include in the prediction and at the confidence intervals the intercept. For that reason I used the argument incl.non.slopes=FALSE. But either if it is TRUE or FALSE it does not have any difference and also the system does not provide any warning. I really