search for: treatmente

Hello list, I'm trying to figure out how exactly the specification of nested random effects works in the lmer function of lme4. To give a concrete example, consider the rat-liver dataset from the R book (rats.txt from: http://www.bio.ic.ac.uk/research/mjcraw/therbook/data/ ). Crawley suggests to analyze this data in the following way: library(lme4) attach(rats) Treatment <-

Hi, I''m trying to understand the lme output and procedure. I''m using the Crawley''s book. I''m try to analyse the rats example take from Sokal and Rohlf (1995). I make a nested analysis using aov following the book. > summary(rats) Glycogen Treatment Rat Liver Min. :125.0 Min. :1 Min. :1.0 Min. :1 1st Qu.:135.8

Hi, I'm analysing a dataset in which the same 5 subjects (male.pair) were subjected to two treatments (treatment) and were measured for 12 successive days within each treatment (layingday). Overall 5*2*12=120 observations. I want to test the effect of treatment, time (layingday) and their interaction. I have done so through the ANOVA below: >

Dear all, I?m analyzing this dataset containing biodiversity indices, measured over time (Week), and at various contaminant concentrations (Treatment). We have two replicates (Replicate) per treatment. I?m looking for the effects of time (Week) and contaminant concentration (Treatment) on diversity indices (e.g. richness). Initial analysis with GAM models showed temporal autocorrelation of

Hello R experts, I have having a difficult time figuring out how to perform and interpret an ANCOVA of my nested experimental data and would love any suggestions that you might have. Here is the deal: 1) I have twelve tanks of fish (1-12), each with a bunch of fish in them 2) I have three treatments (1-3); 4 tanks per treatment. (each tank only has one treatment applied to it) 3) I sampled

I have a data frame that looks something like... Column 1 is an experiment_id, Column 2 is the type of treatment ("control", "full treatment", or "partial treatment"), and Column 3 is a value. Experiment_id Treament_type Value 12345 "control" 3 12345 "full treatment" 4 12345 "full treatment" 5 12345 "partial

Hello I am trying to fit a REML to some soil mineral data which has been collected over the time period 1999 - 2004. I want to know if the 19 different treatments imposed, differ in terms of their soil mineral content. A tree model of the data has shown differences between the treatments can be attributed to the Magnesium, Potassium and organic matter content of the soil, with Magnesium being the

Hi I''m not too sure how best to explain this but here goes! I am trying to write an appointment system. I have, through example, just about got the dynamics correct. Even tried to play with some table joins (and excuse me if I''ve used the incorrect terminlogy). But no matter what I try I can''t seem to get the following code to work. I have a cart filled with Treatment

Hi, Assuming that your dataset is similar to the one below: set.seed(25) dat1<- data.frame(Algae.Mass=sample(40:50,10,replace=TRUE),Seagrass.Mass=sample(30:70,10,replace=TRUE),Terrestrial.Mass=sample(80:100,10,replace=TRUE),Other.Mass=sample(40:60,10,replace=TRUE),Site.X.Treatment=rep(c("ALA1A","ALA1U"),each=5),stringsAsFactors=FALSE) library(reshape2)

Dear R users, I have a linear model of the kind outcome ~ treatment + covariate where 'treatment' is a factor with three levels ("0", "1", and "2"), and the covariate is continuous. Treatments "1" and "2" both have regression coefficients significantly different from 0 when using treatment contrasts with treatment "0" as the

Gday, This is a repost since I only had one direct reply and I remain mystified- This may be stupidity on my part but it may not be so simple. In brief, my problem is I'm not sure how to extract parameter values/effect sizes from a nonlinear regression model with a significant interaction term. My data sets are dose response curves (force and dose) for muscle that also have two

Hello fellow R users, I am having a problem finding the estimates for some overall treatment effects for my mixed models using 'lme' (package nlme). I hope someone can help. Firstly then, the model: The data: Plant biomass (log transformed) Fixed Factors: Treatment(x3 Dry, Wet, Control) Year(x8 2002-2009) Random Factors: 5 plots per treatment, 5 quadrats per plot (N=594 (3*5*5*8)

Hi, I performed an experiment where I raised different families coming from two different source populations, where each family was split up into a different treatments. After the experiment I measured several traits on each individual. To test for an effect of either treatment or source as well as their interaction, I used a linear mixed effect model with family as random factor, i.e.

It is frustrating to see the labels I want in the dimensions of a list but not be able to extract those labels into titles for plots generated from component objects. If someone could set me straight, I would appreciate it. For your amusement, I have provided an example of the Byzantine code I am currently using to avoid loops: # Simulate ANOVA type test data sex<-c(rep(1,8),rep(0,8))

Dear R-helpers, I am trying to construct a confidence interval on a prediction of a gam fit. I have the Wood (2006) book, and section 5.2.7 seems relevant but I am not able to apply that to this, different, problem. Any help is appreciated! Basically I have a function Y = f(X) for two different treatments A and B. I am interested in the treatment ratios : Y(treatment = B) / Y(treatment = A) as

Hi all, I am performing hundreds of kruskal wallis tests and trying to figure out how to create a file of the p.values I obtain. This is the code I use for the tests: A2<-kruskal.test(X2~treatment) A3<-kruskal.test(X3~treatment) A4<-kruskal.test(X4~treatment) A5<-kruskal.test(X5~treatment) A6<-kruskal.test(X6~treatment) A7<-kruskal.test(X7~treatment)

Dear R users I am trying to obtain p-values for (quasi)poisson lmer models, using Markov-chain Monte Carlo sampling and the command summary. > > My problems is that p values derived from both these methods are totally different. My question is (1) there a bug in my code and > (2) How can I proceed, left with these uncertainties in the estimations of > the p-values? > > Below is

Dear R users I am trying to obtain p-values for (quasi)poisson lmer models, including Markov-chain Monte Carlo sampling and the command summary. > > My problems is that p values derived from both these methods are totally different. My question is (1) there a bug in my code and > (2) How can I proceed, left with these uncertainties in the estimations of > the p-values? > > Below

Dear R users, I d like to assess the effect of "treatment" covariate on a disease relapse risk with the package cmprsk. However, the effect of this covariate on survival is time-dependent (assessed with cox.zph): no significant effect during the first year of follow-up, then after 1 year a favorable effect is observed on survival (step function might be the correct way to say that ?).

I haven't been able to fully make sense of the conflicting online information about whether and how to specify nesting structure for a nested, mixed model. I'll describe my experiment and hopefully somebody who knows lme4 well can help. We're measuring the fluorescence intensity of brain slices from frogs that have undergone various treatments. We want to use multcomp to look for