search for: coursenotes

...al axis] against y [vertical axis]" Google tells me that "plot y against x" throws up about 147 hits, while "plot x against y" throws up about 54 hits. One of the latter is unequivocal and comes from a respected department of mathematics: http://www.maths.lancs.ac.uk/dept/coursenotes/lab100/pdffiles/a12.pdf Q 12.1 A simple plot. Invoke Matlab in an Xterm window and position the Figure window so that you can see it properly. x = -3:5 % plotting values (range) y = 2*x + 3 % a linear function of x plot(x,y) % plot x against y and at least two refer to "Stat...

Hi All, The data set that I have is a cluster data, and I want to run a HLM mixed model with multi-level response. Here is my data set: response: - Level (num: 1, 2, 3, 4, 5 - 5 levels) Covariates: - Type (Factor: A, B, C - 3 levels) - yr (num: 2006, 2007, ...) - Male (num: 0=not Male, 1=Male - 2 levels) - Ethnicity (Factor: A, B, H, ..., - 7 levels) - ELL (num: 0, 1, - 2

...h answers from the owner of the library - all incipient) how to interpret the output the function MCMCglmm, come to enlist the help of you, if someone has already worked with MCMCglmm function in the case of variables ordinal dependent. I've read and reread all the pdf's of the package, the coursenotes Jarrod, finally, I'm exhausted. To clarify the database, the treatment (called fases) consist of three levels (1-pre, 2-propolis and 3-vincris) and the ordinal variable response has three categories (1-normal, 2-agudo, 3 - cronico). See table! du <- transform(read.table('http://dl.dropb...

Hi, I have a dataset where the residual variance decreases with on one of the predictors (population size). Currently, the full model looks like this: prior<-list(R=list(V=1e-16, nu=-2),G1=list(V=diag(2), nu=2)) m<-MCMCglmm(response~poly(population size,2)*poly(other predictor,2)+time, random=~us(1+time):population, data=data, prior=prior) Basically, it's a random regression with

Hi to all the people, I'm working with abundance data of some species, but containing too zero values, and the factors are the ones typical in a BACI experiment (Before-and-After-Control-Impact). Thus, these are two fixed factors. As the data does not holds the normality and homogeneity of variances assumptions of clasiccal ANOVA, I'm trying to fit a zero-altered model using the MCMC glmm

Hello, I am trying to build a generalised linear mixed model.? My dependent variable is ordinal.? I have a random factor (7 individuals), and a repeated measure where the dependent variable was measured three times for each of four attempts (so the repeats are nested).? I also have a few covariates.? I am a complete novice in R, being used to using SPSS.? SPSS lets me build an ordinal model