similar to: Nestad ANOVA with random Factors

Dear R-users, Does anybody know a good way to create and use several graphical devices at the same time in interactive mode? Ideally, I'd like to open 2 to 3 devices and assign names to them. This way, I could make any addition/modification/update to a particular device using its name. I did not see anything like a name argument in ?X11. Is there an alternative? Thanks in advance for your

Version of R: Windows Version 2.0.0 The experimental design contains two plant lines - a control (C) and a mutant (M) - grown out three separate times in plots A, B, C. The design is unbalanced: In plot A, 9 control plants were grown with 29 mutant plants. In plot B, 8 control plants were grown with 20 mutant plants. In plot C, 8 control plants were grown with 22 mutant plants. The

Dear forum members, What is the formula to calculate denominator degrees of freedom (den df) for nonlinear mixed-effect models with covariates? My model is similar to a CO2 uptake example from Pinheiro and Bates (2000, page 376). In this CO2 dataset, there are two treatments and two types (84 observations in total), but den df for each parameter of the model is 64. Isn’t it too high? Your

Hi, I hope some one can help. I need to compute Thurston's case 5 on a large set of data. I have gotten as far as computing the proportional preference matrix but the next math is beyond me. Here us my matrix 0.500 0.472 0.486 0.587 0.366 0.483 0.496 0.434 0.528 0.500 0.708 0.578 0.633 0.554 0.395 0.620 0.514 0.292 0.500 0.370 0.557 0.580 0.615 0.329 0.413 0.422 0.630 0.500 0.783 0.641 0.731

Dear R users, I'm trying to do betareg on my dataset. Dependent variable is not normally distributed and is proportion (of condom use (0,1)). But I'm having problems: gyl<-betareg(cond ~ alcoh + drug, data=results) Error in optim(par = start, fn = loglikfun, gr = gradfun, method = method, : initial value in 'vmmin' is not finite Why is R returning me error in optim()? What

I continue to struggle with mixed models. The square zero version of the problem that I am trying to deal with is as follows: A number (240) of students are measured (tested; for reading comprehension) on 6 separate occasions. Initially (square zero) I want to treat the test time as a factor (with 6 levels). The students are of course ``random effects''. Later I want to look at

Hello everybody. In my case, calculating multiple comparisons (Tukey) after lmer produced the following two errors: > sv.mc <- glht(model.sv,linfct=mcp(comp="Tukey")) Error in x$terms : $ operator not defined for this S4 class Error in factor_contrasts(model) : no 'model.matrix' method for 'model' found! What I have done before: > sv.growth <-

Hi, My objective is to calculate "Relative (Cumulative) Frequency of Event Occurrence" - something as follows: Sample.Number 1st.Fly 2nd.Fly Did.E.occur? Relative.Cum.Frequency.of.E 1 G B No 0.000 2 B B Yes 0.500 3 B G No 0.333 4 G B No 0.250 5 G G Yes 0.400 6 G B No 0.333 7 B B Yes 0.429 8 G G Yes 0.500 9 G B No 0.444 10 B B Yes 0.500 Please refer to the code below:

hello, i have been using ccf() to look at the correlation between lightning and electrogamnetic data. for the most part it has worked exactly as expected. however, i have come across something that puzzles me a bit: > x <- c(1, 0, 1, 0, 1, 0) > y <- c(0, 0, 0, 0, 0, 0) > ccf(x, x, plot = FALSE) Autocorrelations of series 'X', by lag -4 -3 -2 -1 0

Hola buenas. al final corri el siguiente código en mi máquina de casa. El problema es que ha habido algún cambio en la librería lme4, que hace incompatible los nuevos objetos lmer con la funcioón pamer.fnc. En este tipo de situaciones imagino que lo propio sería ponerme en contacto con el autor o intentar corregir yo mismo el código o incluso ambas. ¿Es decortes escribir al autor reportandole el

Hi Folks, I'm curious for an explanation of the following -- it's a matter of trying to understand how R parses it. I've written sundry little "helper" variants of functions, in particular plot(), to save repetitively typing the same options over and over again. For example: plotb <- function(x,...){plot(x,pch="+",col="blue",...)} This does exactly

I am performing a binary classification using a classification tree. Ironically, the data themselves are 2483 tree (real biological ones) locations as described by a suite of environmental variables (slope, soil moisture, radiation load, etc). I want to separate them from an equal number of random points. Doing eda on the data shows that there is substantial difference between the tree and random

Hi, When I look at the summary of an rpart object run on my data, I get 7 nodes but when I plot the rpart object, I get only 3 nodes. Should the number of nodes not match in the results of the 2 functions (summary and plot) or it is not always the same? Look forward to your reply, Carol -------------------------------------------- ?summary(rpart.res) Call: rpart(formula = mydata$class ~ ., data

Good Morning. Below I like statement like j<-grep(".r\\b",colnames(mydata),value=TRUE); j with the \\b option which I read long time ago which Ive found useful. Are there more or these options, other than ? grep? Thanks. dstat is just my own descriptive routine. > x ?[1] "age"????????? "sleep"??????? "primary"????? "middle" ?[5]

Hi, I've read up on readBin() and chapter 6 in the R Data Import/Export manual, but I still can't read a binary file. Here is how the creator of the file described the code that would be needed in Fortran: "Every record has a return in fortran. The length of each record is nx*ny*4. To read you would use the following: nlayx = nx*ny*4 do iz=1,nz,4 read(binary file) var(1:nlayx)

Hello: I have an two factorial random block design. It's a ecology experiment. My two factors are, guild removal and enfa removal. Both are two levels, 0 (no removal), 1 (removal). I have 5 blocks. But within each block, it's unbalanced at plot level because I have 5 plots instead of 4 in each block. Within each block, I have 1 plot with only guild removal, 1 plot with only enfa removal,

Javier, Creo que aquí aplica la ley de Linus que dice: "Dado un número suficientemente elevado de ojos, todos los errores se convierten en obvios". La persona que revisa y encuentra un error no necesariamente tiene que ser la misma que la que lo escribe. Una motivación muy importante al compartir un código es la de recibir los beneficios del control de calidad por parte de tus pares.

Dear helpers I constructed a data frame with this structure > str(dados1) `data.frame': 485 obs. of 16 variables: $ Emissor : int 1 1 1 1 1 1 1 1 1 1 ... $ Marisca.Rio : int 1 1 1 1 1 1 1 1 1 1 ... $ Per?odo : int 1 1 1 1 1 1 1 1 1 1 ... $ Reproducao : int 3 3 3 3 3 3 3 3 3 3 ... $ Estacao : int 2 2 2 2 2 2 2 2 2 2 ... $ X30cm : int

Dear friends, After running the lm() model, we can get summary resluts like the following: Coefficients: Estimate Std. Error t value Pr(>|t|) x1 0.11562 0.10994 1.052 0.2957 x2 -0.13879 0.09674 -1.435 0.1548 x3 0.01051 0.09862 0.107 0.9153 x4 0.14183 0.08471 1.674 0.0975 . x5 0.18995 0.10482 1.812 0.0732 . x6 0.24832 0.10059 2.469 0.0154 * x7

?s 02:10 de 02/08/2024, Steven Yen escreveu: > Good Morning. Below I like statement like > > j<-grep(".r\\b",colnames(mydata),value=TRUE); j > > with the \\b option which I read long time ago which Ive found useful. > > Are there more or these options, other than ? grep? Thanks. > > dstat is just my own descriptive routine. > > > x > ?[1]