similar to: The mathematics inside lme()

Hello all! I have a problem that calls for a better understanding, than mine, of how lme() uses the random part of the call. The dataset consists of eleven field trials (Trial) with three replicates (Block) and four fertiliser treatments (Treat). Analysing for example yield with lme() is easy: m1 <- lme(Yield ~ Treat, data=data, random =~1| Trial/Block) giving estimates of

Thanks a lot to Demitris for a prompt answer some minutes ago on another tread (see below). To avoid excess mails on the list, I move onto next question: I have another small plotting problem that confuses me. I want to plot results from a field trial series, using the numbers of the trials as symbols in the plot. pch = as.character(trial_no) works fine, but truncates the trial number to the

Hello all, R2.1.1, Wk2 I am doing some two-step plotting, first using plot() to illustrate the datapoints and then using abline() to place a trend line from a fitted model into the plot. Everything works well as long as the formula of the fitted model i of the type: m1 <- lm(Dependent ~ Independent) then abline(m1) puts the proper straight line into the plot. But if I use: m2 <-

Hello all, R2.2.1, W2k I posted a similar question last week after having problem with a group update (using update.packages()), without getting any answer. Now I have updated all packages separately, and found that the problem comes from nlme: Everything comes home, the md5 checksums are ok, but then the old version can??t be removed from my computer, leaving a "nlme" library with

Hello all, I posted a similar question recently but I suspect that it was not well enough formulated to trigger any answers. So I try again: Is there a way to produce boxplots (or something similar) that uses the estimated fixed effects of an lme{nlme} object? When I want to know the mean, I use estimable{gmodels} together with an appropriate matrix. I did look for a similar tool to estimable in

Hi, In the following lines of code are inside of a function, where "TRAIT1" is a function variable calling a column-name inside of the data.frame "new3". This works just fine: m2 <- lmer(get(TRAIT1) ~ perm.score + (1|site), data=new3) but this will not work, m3 <- lme(get(TRAIT1) ~ perm.score , random= ~1|site, data=new3) I get the following error: Error in

Hello all! R2.0.1, W2k I posted this question to the list last Sunday without getting any replies on the list. I got two off the list though, suggesting me to plot "manually" as a second step, from estimable() or intervals() objects respectively. As this was not really what I wished for, I take the risk to upset somebody with a trivial question, and re-post it (just a little

hello, i'd like to evaluate the 2 factors (f1 with 4, f2 with 2 levels) and its interactions in my model for writing up a summary table with likelihood-ratio tests for publication purpose. now i'm unsure about which terms of my model may be dropped from the full model and which not. mfull<-lme(resp~f1*f2,random=...) m1<-lme(resp~f1+f2,..) m2<-lme(resp~f1,..)

hi I'm puzzled as to the relation between the REML score computed by gam and the formula (4) on p.4 here: http://opus.bath.ac.uk/22707/1/Wood_JRSSB_2011_73_1_3.pdf I'm ok with this for poisson, or for quasipoisson when phi=1. However, when phi differs from 1, I'm stuck. #simulate some data library(mgcv) set.seed(1) x1<-runif(500) x2<-rnorm(500)

I would like to merge two parts of words to get a name of the data. First M3$N (invariable) and second is a number from 0001 to 3003 - M3$N0001,M3$N0002,...,M3$N3003. For example if I do it like this: my.data <- paste("M3$N",2456,sep="") I get > my.data [1] "M3$N2456" But I want to get something equivalent to my.data<- M3$N2456 Is there any way to do it?

Dear all, W.XP I recently upgraded to R2.10.0 and did what I usually do: copied all "special" libraries from the old installation and then used "update.packages()" on the command line. Nothing happened. Then I clicked on the same command on the droplist which generated "update.packages(ask='graphics')", but still with no effect. When I tried to load the

Kevin Wright wrote: > library(nlme) > m2 <- gnls(conc ~ t1*(1-t2*exp(-k*time)), > data = df.Chloride, > start = list( > t1 = 35, > t2 = 0.91, > k = 0.22)) So my error was to use nls instead that gnls. Thanks a lot, Kevin. > summary(m2) > plot(m2) > lag.plot(resid(m2), do.lines=FALSE) >

Dear all, W2k, R 2.5.1 I am trying out the AMMI function in the agricolae package, to analyse the dependence of environment for a certain cultivar. The function responds to four basic variables: ENV Environment GEN Genotype REP Replication Y Response My question concerns the 'REP' term. When I normally do an analysis of variance, the replication would refer to repeated observations

I would like to perform partial mantel tests on only within group values, with "between group" values assigned to NA. This is possible in package ncf partial.mantel.test, however this sues a different permutation to that used in ecodist.ecodist will not accept data with NA values, returning a "matrix is not square error. is it possible to perform this test in ecodist? many thanks

Dear R users, I have the following two problems, related to the function sub, grep, regexpr and similia. The header of the file(s) I have to import is like this. c("y (m)", "BD (g/cm3)", "PR (Mpa)", "Ks (m/s)", "SP g./g.", "P (m3/m3)", "theta1 (g/g)", "theta2 (g/g)", "AWC (g/g)") To get rid of spaces and

Dear All, I am new for R and SEM. I try to fit the model with Y (ordinal outcome), X (4 categorical data), M1-M3 (continuous), and 2 covariates (Age&sex) as a diagram. library(polycor) model.ly <-specify.model() 1: x -> m1, gam11, NA 2: x -> m2, gam12, NA 3: x -> m3, gam13, NA 4: age -> m1, gam14, NA 5: age -> m2, gam15, NA 6: age -> m3, gam16, NA 7: sex -> m1,

Hi, I have a data-matrix: > PID sato hrs fim health 214 3 4.376430 6.582958 5 193 6 4.361825 3.138525 6 8441 6 4.205771 3.835886 7 7525 6 4.284489 3.245139 6 6806 7 4.168926 2.821833 7 5682 7 1.788707 1.212653 7 5225 6 1.651463 1.436980 7 4845 6 1.692710 1.267359 4 4552 5 1.686448 1.220539 6

Hello everybody! I use R 1.6.2 in Windows, and have a problem controlling the na.action. In a dataset with twelve trials, one of the trials lack any readings of the variable "STS.SH" (standing power at harvest) Fitting an aov() object with the call: led1t7sts.aov <- aov(STS.SH ~ Trial/Block + Treatment + Treatment:Trial, data = led1t7, na.action=na.exclude) seems to work as it

Hi, I am trying to cross compile code for ARM Cortex m0, m3, and m4. For m0, I use: -target armv6--eabi -mcpu=cortex-m0 That seems to work. For m3 and m4, I use the following which does not work (fatal error: error in backend: CPU: 'cortex-m3' does not support ARM mode): -target armv7m--eabi -mcpu=cortex-m3 and -target armv7em--eabi -mcpu=cortex-m4 Who can help me with the

Hello all, R2.1.1, W2k I try to make a plot of a simple regression model in this way: > with(njfA_bcd, { + plot(TC_OS.G31,Prot,cex = 2, col = "red", xlab= "TC/OS at GS32", + ylab="Grain crude protein (CP)") + }) This part works well and produces the datapoints as red circles. When I try to add a line, using a fitted linear model in a way that works perfect