similar to: plot within a loop

I suspect the weights argument is not having any effect. Package: Matrix Version: 0.995-2 Date: 2006-01-19 Beginning with this: Browse[1]> resp.lmer <- lmer(SensSSC ~ Block + Season + (1 | Plot) + (1 | Ma) + (1 | Pa) + + (1 | MaPa), weights = SensSSC.N, data = xx) I group the output into a table with my ran.eff function and get this:

Hello, I'm new here, and I hope I can get any help. At first I thought this is a support mailing list, but it looks more like one for developers. I'm using Nouveau in ubuntu bionic. My problem is, when I connect a second Monitor to my Laptop on DVI-D connector, the monitor is not detected, Nouveau reports it as "disconnected". The Hardware works, because the BIOS uses the

Problem: I am sorting through model selection process for first time and want to make sure that I have used glm, stepAIC, and update correctly. Something is strange because I get a different result between: 1) a glm of 12 predictor variables followed by a stepAIC where all interactions are considered and then an update to remove one specific interaction. vs. 2) entering all the terms

R Help - I'm trying to run a correlation matrix with a covariate of "age" and will at some point will also want to covary other variables concurrently. I'm using the "psych" package and have tried other methods such as writing a loop to extract semi-partial correlations, but it does not seem to be working. How can I accomplish this? library(psych) > set.cor(y =

I am having a couple of problems someone may be able to cast some light on. Question 1: I am making a logistic model but when i do this: glm.model = glm(as.factor(form$finished) ~ ., family=binomial, data=form[1:150000,]) I get this: Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : variable lengths differ (found for 'barrier') which is

Dear R users, I am using lmer combined with AIC model selection and averaging (in the MuMIn package) to try and assess how isotope values (which indicate diet) vary within a population of animals. I have multiple measures from individuals (variable 'Tattoo') and multiple individuals within social groups within 4 locations (A, B, C ,D) crucially I am interested if there are

(Apologies if this is better suited for R-help.) On my system (macOS Sierra, late 2014 MacBook Pro; R 3.4.1, Homebrew build), I found that it is faster to construct a function using eval(call("function", ...)) than using as.function(list(...)). Example: make_fn_1 <- function(a, b) eval(call("function", a, b), env = parent.frame()) make_fn_2 <- function(a, b)

Dear Thomas, This looks like a really interesting project, and I don't think that anyone responded to your message, though I may be mistaken. I took at a look at implementing parallel assignment, and came up with: passign <- function(..., envir=parent.frame()){ exprs <- list(...) vars <- names(exprs) exprs <- lapply(exprs, FUN=eval, envir=envir) for (i in

Hello to the whole group. I am a newbie to R, but I got my way through and think it is a lot easier to handle than other software packages (far less clicks necessary). However, I have a problem with respect to the summary of regression results. The summary function gives sth like: Residuals: Min 1Q Median 3Q Max -0.46743 -0.09772 0.01810 0.11175 0.42252

Hi!! Can anyone help me, i have problems to converge the following data with 5 nonlinears models that i evaluated. Firtly, i send my data (totalsinatipicos) that i just try to fit with the nonlinear models. Next, i have the following script where i called the data as totalsinatipicos. I made selfstarting each nonlinear model. ###Library library(NRAIA) ###Data d<-totalsinatipicos

Hello! I am optimizing my code in R and for this I need to know a bit more about the internals. It would help tremendously if someone could link me to a page with O()-complexities of all the operations. In this particular case, I need something like a linked list with O(1) insertLast/First ability. I can't preallocate a vector since I do not know the final size of the list ahead of time. The

Hello R users, For the last few days I am struggling with the following task: my data.frame: A1 A2 A3 B1 B2 B3 58.81 53.292 54.501 13.013 17.39 19.407 56.02 56.251 54.033 20.099 13.15 10.411 55.376 53.099 57.625 13.396 21.031 13.22 58.584 53.194 54.218 13.038 16.854 19.289 55.7 55.921 53.847 19.942 13.153 9.828 55.093 52.934