similar to: problems formulating arguments to lme()

Hello, I'm trying to analyse data from an incomplete design with four factor : - fr : number of the batch - op : ID of operator - meth : method used - mat : nature of the material used and one variable - mv : mesure - trmv : transformed mesure str(matvol) `data.frame': 120 obs. of 6 variables: $ fr : Factor w/ 30 levels "1","2","3","4",..:

On Thu, Dec 29, 2016 at 05:44:50PM +0100, Giulio Teslano wrote: > a. What other types of extended wild card(s) options are there ? > > or is this still currently limited to these two characters '*?' ? As I said, the branch "adds support for arbitrary glob-style wildcard patterns (where * matches 0 or more characters and ? a single

Dear list, I'm migrating a project from Matlab to R, and I'm facing a relatively complicated problem for nls. My objective function is below: >> objFun <- function(yEx,xEx,tEx,gamma,theta,kappa){ yTh <- pdfDY(xEx,tEx,gamma,theta,kappa) sum(log(yEx/yTh)^2) } The equation is yTh=P(xEx,tEx) + noise. I collect my data in: >> data <-

Hello, We have Xapian-Omega installed (Linux) and working in default mode and have browsed several interesting pages on the main site, at trac.xapian.org (the wiki) and in the mailing list. Having tested various search options (up to now only for Omega) we would like to ask a couple of questions. 1. Is it possible to search for Patterns of Text with Omega and/or Xapian Queries ?

To Olly Betts: Thank you very much for any feedback. I apologise for this belated reply and also for the fact that the text of the previous posting appeared fragmented, due to its fixed chars/line format. With reference to: > Can, or could, one construct a query so that Omega (Xapian) can handle > this ? > > ... perhaps with some type of Regex ? > > It would seem

Dear all, I am using the pls package of R to perform partial least square on a set of multivariate data. Instead of fitting a linear model, I want to fit my data with a quadratic function with interaction terms. But I am not sure how. I will use an example to illustrate my problem: Following the example in the PLS manual: ## Read data data(gasoline) gasTrain <- gasoline[1:50,] ## Perform

I'm trying to fit a nested mixed model using lmer and have some questions about the output and my model formulations. I have replicate measures on Lines which are strictly nested within Populations. (a) So if I want to fit a model where Line is a random effect and Populations are fixed and the random Line effect is constant across Populations, I have: measure_ijk = mu + P_i + L_ij +

Hello list reader, I am trying to form some constraints for an optimization I am working on. I think I have understand the use of the constraints in matrix form. I use them like: constr_mat<- -diag(2) constr_vec<- rep(-0.05,2) constr_mat<- rbind(constr_mat, diag(2)) constr_vec<- c(constr_vec, rep(-1, 2)) To get parameters in the interval [-1, 0.05]. (And this works so far) Now I

Hi all - R2.0.1 on OSX;MASS library;nlme library I am trying to emulate the solution to a problem set that has normally been run in Genstat, using R. The problem that I am having at the moment is with the following glmm question (using glmmPQL from the MASS library): "We have two different forest habitats (first rotation thicket, and high forest) which we want to survey for the

Hi, I would like to see if a matching variable is an effect-modifier in a conditional logistic regression. Naturally, the matching variable can't enter directly in the model but as an interaction with terms that are in. However, I have problems in formulating the correct model the term that's already in the model is a factor. I am using treatment contrasts and the problem is that if I

Hallo! I have run a EEG experiment and got the following data group: 1 2 1 2 1 2 1 2 ... as factor, 2 levels between subjects fixed effect (patient vs control) subj: 1 2 ... 14 1 2 ... 14 as factor 7 patients 7 control random effect condition: 1 1 ... 2 2 ... 1 1 ... 2 2 as factor, 2 levels within subjects, ie every subject worked on every cond fixed effect roi: 1 ... 2 ... 3 ... 4 ... as factor,

Below. -- Bert Bert Gunter On Thu, Jul 13, 2017 at 3:07 AM, Luigi Biagini <luigi.biagini at gmail.com> wrote: > I have two ideas about it. > > 1- > i) Entering variables in quadratic form is done with the command I > (variable ^ 2) - > plsr (octane ~ NIR + I (nir ^ 2), ncomp = 10, data = gasTrain, validation = > "LOO" > You could also use a new variable

I have two ideas about it. 1- i) Entering variables in quadratic form is done with the command I (variable ^ 2) - plsr (octane ~ NIR + I (nir ^ 2), ncomp = 10, data = gasTrain, validation = "LOO" You could also use a new variable NIR_sq <- (NIR) ^ 2 ii) To insert a square variable, use syntax I (x ^ 2) - it is very important to insert I before the parentheses. iii) If you want to

?? If I haven't misunderstood, they are completely different! 1) NIR must be a matrix, or poly(NIR,...) will fail. 2) Due to the previously identified bug in poly, degree must be explicitly given as poly(NIR, degree =2,raw = TRUE). Now consider the following example: > df <-matrix(runif(60),ncol=3) > y <- runif(20) > mdl1 <-lm(y~df*I(df^2)) > mdl2

> On Jul 13, 2017, at 7:43 AM, Bert Gunter <bgunter.4567 at gmail.com> wrote: > > Below. > > -- Bert > Bert Gunter > > > > On Thu, Jul 13, 2017 at 3:07 AM, Luigi Biagini <luigi.biagini at gmail.com> wrote: >> I have two ideas about it. >> >> 1- >> i) Entering variables in quadratic form is done with the command I >>