similar to: lme code help

Hi R experts, I'm using the xyplot function in lattice to draw a multipanel plot consiting of 5x6 scatterplots. Now I need to link single points in each of those scatterplots (=panel),but the points, that need linking are different for each panel. I tried to use the panel.segments function for that, but I can't address each panel separately. Links right for panel 1, show up in all other

Dear R users, Has anyone implemented a "matrix log" function in R similar to the function logm() in Matlab? I did a quick R site search and browsed the contributed packages to no avail. The octave function is far too simplistic and fails for the Matlab test matrix. Ideally, the code of Cheng, Higham, and Laub (2001) or something similar could be utilized. Just checking before I

The marilith boxes use a conserver (http://www.conserver.com/) setup for serial access. Our installation exports the logs via http allowing us to grab them with wget. Sending debug keys with is handled separately via xenuse. xenuse ultimately speaks to the conserver too but it abstracts away the IP and port to use so this is preferred. With these changes the correct Serial hostprop for a

Is there any way to put an argument into an object name. For example, say I have 5 objects, model1, model2, model3, model4 and model5. I would like to make a vector of the r.squares from each model by code such as this: rsq <- summary(model1)$r.squared for(i in 2:5){ rsq <- c(rsq, summary(model%i%)$r.squared) } So I assign the first value to rsq then cycle through models 2 through

I am running an lme model with the main effects of four fixed variables (3 continuous and one categorical – see below) and one random variable. The data describe the densities of a mite species – awsm – in relation to four variables: adh31 (temperature related), apsm (another plant feeding mite) awpm (a predatory mite), and orien (sampling location within plant – north or south). I have read

Hello, I am using the following model model1=lmer(PairFrequency~MatingPair+(1|DrugPair)+(1|DrugPair:MatingPair), data=MateChoice, REML=F) 1. After reading around through the R help, I have learned that the above code is the right way to analyze a mixed model with the MatingPair as the fixed effect, DrugPair as the random effect and the interaction between these two as the random effect as well.

Dear R users, I am using lme and nlme to account for spatially correlated errors as random effects. My basic question is about being able to correct F, p, R2 and parameters of models that do not take into account the nature of such errors using gls, glm or nlm and replace them for new F, p, R2 and parameters using lme and nlme as random effects. I am studying distribution patterns of 50 tree

Dear R users, Could somebody please help me to find a way of comparing nonlinear, non-nested models in R, where the number of parameters is not necessarily different? Here is a sample (growth rates, y, as a function of internal substrate concentration, x): x <- c(0.52, 1.21, 1.45, 1.64, 1.89, 2.14, 2.47, 3.20, 4.47, 5.31, 6.48) y <- c(0.00, 0.35, 0.41, 0.49, 0.58, 0.61, 0.71, 0.83, 0.98,

#I am trying to understand how R fits models for contrasts in a #simple one-way anova. This is an example, I am not stupid enough to want #to simultaneously apply all of these contrasts to real data. With a few #exceptions, the tests that I would compute by hand (or by other software) #will give the same t or F statistics. It is the contrast estimates that R produces #that I can't seem to

hello all, I am trying to use the function getCovariateFormula(nlme) in conjunction with the library lme4. When I load both packages I get the following message and the getCovariateFormula function no longer works: library(nlme) library(lme4) Attaching package 'lme4': The following object(s) are masked from package:nlme : contr.SAS getCovariateFormula

Dear all, I was wondering if anyone could help solve a problem of a missing interaction effect!! I carried out a 2 x 2 factorial experiment to see if eggs from 2 different locations (Origin = 1 or 2) had different hatching success under 2 different incubation schedules (Treat = 1 or 2). Six eggs were taken from 10 females (random = Female) at each location and split between the treatments,

Hello list, I have a linear regression: mylm = lm(y~x-1) I've been reading old mail postings as well as the plotmath demo and I came up with a way to print an equation resulting from a linear regression: model = substitute(list("y"==slope%*%"x", R^2==rsq), list(slope=round(mylm$coefficients[[1]],2),rsq=round(summary(mylm)$adj.r.squared, 2))) I have four models and I

If I have a model line = lm(y~x1) and I want to use a for loop to change the number of explanatory variables, how would I do this? So for example I want to store the model objects in a list. model1 = lm(y~x1) model2 = lm(y~x1+x2) model3 = lm(y~x1+x2+x3) model4 = lm(y~x1+x2+x3+x4) model5 = lm(y~x1+x2+x3+x4+x5)... model10. model_function = function(x){ for(i in 1:x) { } If x =1, then the list

Dear all, For an introductory course on glm?s I would like to create an example to show the difference between glm and transformation of the response. For this, I tried to create a dataset where the variance increases with the mean (as is the case in many ecological datasets): poissondata=data.frame( response=rpois(40,1:40), explanatory=1:40) attach(poissondata) However, I have run into

Dear all, I have a big data frame: str(data1) 'data.frame': 18272 obs. of 11 variables: $ tag : int 100001 100002 100003 100005 100007 100008 100009 100011 100012 100014 ... $ sp : Factor w/ 18 levels "acassp","acocar",..: 13 5 7 14 14 18 3 11 13 10 ... $ gx : num 20 10 35 68 88 63 123 115 137 136 ... $ gy : num 30 25 24 1 10 40 45 25 23 45 ...

Hello, I am trying to replicate example 4 in the package 'scatterplot3d': s3d.dat_data.frame(cols=as.vector(col(my.model4)), rows=as.vector(row(my.model4)), value=as.vector(my.model4)) scatterplot3d(s3d.dat, type="h", lwd=5, pch=" ", x.ticklabs=colnames(my.model4), y.ticklabs=rownames(my.model4), main="Conditional probabilities, Model 3") Nice! but,

Dear R-Community, I am relatively new with R, so sorry for things which for you might be obvious... I am trying to automatically update lmer formulae. the variables of the model are: depM= my dependent measure Sb2= a random factor OS = a predictor VR= another predictor So, I am building the first model with random intercept only: model = lmer(depM ~ (1 |Sb2)) then I update the formula

hello everybody. i need to log "who did what" on some models. i was reading recipe 59 of rails recipes and i created something like that: class LogSweeper < ActionController::Caching::Sweeper observe :model1, :model2, :model3, :model4, :model5, ... after_save(model) save_log(model, "save") end after_destroy(model) save_log(model, "destroy) end

flight 22184 xen-unstable real [real] http://www.chiark.greenend.org.uk/~xensrcts/logs/22184/ Regressions :-( Tests which did not succeed and are blocking, including tests which could not be run: test-amd64-amd64-xl-qemuu-winxpsp3 7 windows-install fail REGR. vs. 22106 test-amd64-i386-xl-win7-amd64 9 guest-localmigrate fail REGR. vs. 22106 Regressions which are regarded as

I have 5 GAMs ( model1, model2, model3, model4 and model5) Before I use some data X(predictor -January to June data) to form a equation and calculate the expected value of Y (predictand -January to June). After variable selection, GAMs (Model 1)were bulit up! R-square :0.40 NOW, I want to use new X'( predictor -July - December data) and put into Model 1, then get the expected value of Y'