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Dear R user, I am trying to reproduce the results in Montgomery D.C (2001, chap 13, example 13-1). Briefly, there are three suppliers, four batches nested within suppliers and three determinations of purity (response variable) on each batch. It is a two stage nested design, where suppliers are fixed and batches are random. y_ijk=mu+tau_i+beta_j(nested in tau_i)+epsilon_ijk Here are the

Dear R gurus, I am trying to work out the problem given in Nested design - Montgomery - Design of Experiments p.561 I have attached a pdf of the data as well the anova table. It is a mixed model with Supplier as fixed effect and batches within the supplier as random effects. I am able to work out the error stratums as below using aov. Which agrees perfectly with the book example

Dear Prof. Ripley and Christoph, thank you very much for your comments. You have helped me a lot. Thanks, Tomas Goicoa >Dear Prof. Ripley > >Thank you for your email. Yes, this is of course the correct >syntax to save us the extra calculation. And I forgot the >"lower.tail = FALSE" for pf() in my example to obtain the >p-value. > >Thank you for the

Hi, I have a vector with 4 elements, e.g., tau_i=c(100,200,300,400), but potentially tau_i[0]=0. In a "for" loop, tau_i=c(100,200,300,400) m=4 tau_i[0]=0 # <------- ? P_i=1 for(i in 2:m) { P_i = P_i*(tau_i[i-1]-tau_i[i-2]) } Error in P_i = P_i * (tau_i[k - 1] - tau_i[k - 2]): replacement has length zero Unfortunately, I can add this potential element into

Hi all, I'm trying to define and log-likelihood function to work with MLE. There will be parameters like mu_i, sigma_i, tau_i, ro_i, for i between 1 to 24. Instead of listing all the parameters, one by one in the function definition, is there a neat way to do it in R ? The example is as follows: ll<- function(mu1=-0.5,b=1.2,tau_1=0.5,sigma_1=0.5,ro_1=0.7) { if (tau1>0 &&

All, How does one specify a model in lmer such that say the random effect for the intercept has a different variance per treatment group? Thus, in the model equation, we'd have say b_ij represent the random effect for patient j in treatment group i, with variance depending on i, i.e, var(b_ij) = tau_i. Didn't see this in the docs or Pinherio & Bates (section 5.2 is specific for

Dear R-gurus, I have an interpretation problem regarding lme models. I am currently working on dog locomotion, particularly on some variation factors. I try to figure out which limb out of 2 generated more dispersed data. I record a value called Peak, around 20 times for each limb with a record. I repeat the records during a single day, and on several days. I tried to build two models, one

Hi All, I would like to ask a question on fixed and random effecti in lme. I am fiddlying around Mick Crawley dataset "rats" : http://www.bio.ic.ac.uk/research/mjcraw/statcomp/data/ The advantage is that most work is already done in Crawley's book (page 361 onwards) so I can check what I am doing. I am tryg to reproduce the nested analysis on page 368:

Hi, I've been doing an experiment, measuring the dead-zone-diameters of bacteria, when they've been grown with paper diffusion disks of antimicrobial. There are two groups, or treatments - one is bacteria that have been cultured in said antimicrobial for the past year, the other group is of the same species, but lab stock and has not gone had any prior contact with the antimicrobial.

I have an interesting lme - problem. The data is part of the Master Thesis of my friend, and she had some problems analysing this data, until one of her Jurors proposed to use linear mixed-effect models. I'm trying to help her since she has no experience with R. I'm very used to R but have very few experience with lme. The group calls of one species of parrot were recorded at many

Dear members, I would like to switch from nlme to lme4 and try to translate some of my models that worked fine with lme. I have problems with the pdMat classes. Below a toy dataset with a fixed effect F and a random effect R. I gave also 2 similar lme models. The one containing pdLogChol (lme1) is easy to translate (as it is an explicit notation of the default model) The more parsimonious

The point is, that there are several possible problems. But. One the one hand they are not really problematic in my opinion (I do not care if my function has potential access to objects outside of its environment because this access is read-only at worst and it's not common practice to use this potential anyways). On the other hand I am not sure what the main()-idiom would actually add to

Why are the results not reliable? ________________________________ From: ESCHEN Rene [mailto:rene.eschen@unifr.ch] Sent: Wednesday, August 23, 2006 3:48 AM To: Spencer Graves; r-help@stat.math.ethz.ch Cc: Doran, Harold Subject: RE: [R] Random structure of nested design in lme The output of the suggested lmer model looks very similar to the output of aov, also when I ran the model

I am trying to use the survfit() function with the newdata argument to produce predicted survivor curves for a particular covariate profile. The main purpose of the plot will be to visualise the effect of snp1, coded 0 and 1. In my Cox model I have stratified by one variable, edu, and so I know I will automatically get a separate curve for each strata. My problem is how to deal with the

Hi R users: Is there a function to obtain the correlation within groups from this very simple lme model? > modeloMx1 Linear mixed-effects model fit by REML Data: barrag Log-restricted-likelihood: -70.92739 Fixed: fza_tension ~ 1 (Intercept) 90.86667 Random effects: Formula: ~1 | molde (Intercept) Residual StdDev: 2.610052 2.412176 Number of Observations: 30 Number

I appreciate the well-thought-out comments. To your first point, I am not sure what "glattering" means precisely (a Google search revealed nothing useful), but I assume it means something to the effect of overfilling the main namespace with too many names. Per Norm Matloff's counterpoint in The Art of R Programming regarding this issue, this is mostly avoided by well-defined,

Hi all, I am wondering if anyone has implemented (or at least tried to) an automatic reparametrization in order to satisfy "trivial" constraints (in the sense of Dennis & Schnabel, 1983) in optimization problems. To be perhaps clearer let us consider a simple bi-exponential model for some recorded signal (sorry for the LaTex notations I hope they aren't too confusing): $s(t) = A

Behavioral science data is often collected from nested structures (students in schools, in districts, etc.). This can produce nonindependence among responses from individuals in the same groups. Consequently, researchers are advised to model the nested nature of the data to avoid biases in SE estimates. Failing to account for nonindependence can lead to SE estimates that are too large or too

Am Dienstag, 25. April 2017, 11:21:31 schrieb Dirk Eddelbuettel: > On 25 April 2017 at 16:11, Johannes Ranke wrote: > | This looks similar to what I got this morning when I tested my > | (unreleased) > | backport of R 3.4.0 to Debian jessie. My test was > | > | library(MASS) > | example(rlm) > | > | and there was an object that was not found. I am on a train on the way

Something nice would be a script/rename or script/refactor which would allow for easy renaming of some model/view/controller or combination, including a db migration if necessary. One does not always start out with a complete understanding of the domain or with the greatest Rails purity and often it is desirable to go back and rename something. Maybe it does not even count as refactoring. I