similar to: ANOVA with 3 factors

I am having trouble setting up a nested anova model. Here is a truncated version of my data: > data Ind Treatment PC1 1 PER14 SC 1.14105282 2 PER14 SH 1.45348615 3 PER14 AC 2.45096904 4 PER25 SC 1.23687887 5 PER25 SH 4.54797450 6 PER25 AC -0.73391131 7 PER30 SC 1.15899433 8 PER30 SH 2.83273736 9 PER30

Dear all, I am having a data for 2 different treatments with different time points. So, I used the following code to plot the boxplot and also to do anova. T11 <- c(280, 336, 249, 277, 429) T12 <- c(400, 397, 285, 407, 313) T13 <- c(725, 373, 364, 706, 249) T21 <- c(589, 257, 466, 248, 913) T22 <- c(519, 424, 512, 298, 907) T23 <- c(529, 479, 634, 354, 1015) obs <- c(T11,

I am really having trouble with getting the right syntax for my model. Here is a truncated version of my data: > data Ind Treatment Order Date PC1 1 PER14 SC 3rd 4-May-09 0.5704611 2 PER14 SH 1st 26-Apr-09 0.5329025 3 PER14 AC 2nd 29-Apr-09 2.1392279 4 PER25 SC 2nd 29-Apr-09 -0.2083382 5 PER25 SH 3rd 3-May-09 3.7818356 6

Dear R-Community! For example I have a study with 4 treatment groups (10 subjects per group) and 4 visits. Additionally, the gender is taken into account. I think - and hope this is a goog idea (!) - this data can be analysed using lme as below. In a balanced design everything is fine, but in an unbalanced design there are differences depending on fitting y~visit*treat*gender or

Dear list, I would like to run an ANCOVA using car::Anova with repeated measures factors, but I can't figure out how to do it. My (between-subjects) covariate always interacts with my within-subject factors. As far as I understand ANCOVA, covariates usually do not interact with the effects of interest but are simply additive (or am I wrong here?). More specifically, I can add a covariate as

Hi All, I have performed a 3-way ANOVA analysis for my experimental data using aov function. My simple R funtion for this is: 3aof <- function(x){ m <- data.frame(R,S,T, x); anova(aov(x ~ R+S+T+R*S+R*T+S*T+R*S*T, m) ) } Now, I am getting P values for all the main and interactions effects. If I want to perform postdoc test on one of my main effects, say T, what method I should use (i have

Hello, I am trying to perform a two-factor ANOVA analysis using a blocking design with "Vol" as the response variable. My intent is to have "Rater" treated as the treatment variable and the "Pipe" treated as the blocking variable. I am reading and preparing my dataset using the following three lines of code: values <- read.table("filename",

Dear R list: I modelled changes in a variable (mconc) over time (d) for individuals (replicate) given one of three treatments (treatment) using: mconc.lme <- lme(mconc~treatment*poly(d,2), random=~poly(d,2)|replicate, data=my.data) summary(mconc.lme) shows that the linear coefficient of one of the treatments is significantly different to zero, viz. Value Std.Error

I am now writing an R code to do the systematic permutation test and the random permutation test. And I encountered a couple of problems which I cannot figure them out. At first, whenever I type "permtest" in my R2.6.1, it always shows:"there is no such a function", I tried again and again and eventually I found I have to use "library (BHH2)" before I use

Hello everyone, I am trying to do within subjects repeated measures anova followed by the test of sphericity (sample dataset below). I am able to get either mixed model or linear model anova and TukeyHSD, but have no luck with Repeated-Measures Assuming Sphericity or Separate Sphericity Tests. I am trying to follow example from "car" package, but it seems that I am not getting something

Hi, every body! I am a new comer for R, so my question would unavoidablely sounds stupid. Sorry! In my experiment, there are two type of soil ( soil F and soil D), each half of them were subjected to steam sterilize (result in FS and DS soil). A equal volume of soil from two of the four soil types (F, D, FS, DS) were mixed as follows: F+F, F+D, F+FS, F+DS, D+F, D+FS, D+DS, FS+DS (eight

Hello all, I've become confused by the output produced by a call to anova(model1,model2). First a brief background. My model used to predict final tree height is summarised here: Df Sum Sq Mean Sq F value Pr(>F) Treatment 2 748.35 374.17 21.3096 7.123e-06 *** HeightInitial 1 0.31 0.31 0.0178 0.89519

Hallo! I have data of the following design: NSubj were measured at Baseline (visit 1) and at 3 following time points (visit 2, visit 3, visit 4). There is or is not a treatment. Most interesting is the question if there is a difference in treatment between the results of visit 4 and baseline. (The other time points are also of interest.) The level of significance is alpha=0.0179 (because of an

Hi, I have a dataset with a continuous response variable and, among other predictors, an ordinal variable. Here is what it could look like treatment <- factor(rep(c("AA", "AC", "AD","AE", "AB"), each = 10)) length <- c(75, 67, 70, 75, 65, 71, 67, 67, 76, 68, 57, 58, 60, 59, 62, 60, 60, 57, 59, 61, 58,

There may be a bug in the anova.glm function. deathstar[32] R R : Copyright 2004, The R Foundation for Statistical Computing Version 2.0.1 (2004-11-15), ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project

For some time I have been looking for a convenient way of performing post-hoc analysis to Repeated Measures ANOVA, that would be acceptable if sphericity is violated (i.e. leaving aside post-hoc to lme models). The best solution I found was John Fox's proposal to similar requests in R-help: http://tolstoy.newcastle.edu.au/R/e2/help/07/09/26518.html

Yesterday I posted the following question to the help list. Thanks to John Fox (copied below) who pointed out the solution. Original question: I have come across a result that I cannot explain, and am hopingthat someone else can provide an answer. A student fitted a mixed model usingthe lme function: out<- lme(fixed=Y~A+B+A:B, random=~1|Site). Y is a continuous variable while A and

Hello dear R members. I have been learning the Anova syntax in order to perform an SS type III Anova with repeated measures designs (thank you Prof. John Fox!) And another question came up: where/what are the (between/within) residuals for my model? ############ Play code: phase <- factor(rep(c("pretest", "posttest", "followup"), c(5, 5, 5)),

Hi folks, Where can I find document re "how to read anova output"? Google found many of them. But seemingly non of them can explain to me following output:- > tabA = c(5.67, 5.67, 5.55, 5.57) > tabB = c(5.75, 5.47, 5.43, 5.45) > tabC = c(4.74, 4.45, 4.65, 4.94) > tabs = data.frame(tabA, tabB, tabC) > tablets = stack(tabs) > anova(lm(values ~ ind, data =

I would like to perform multiple comparisons or post-hoc testing on the independent variable in an Anova.mlm object generated by the Anova function of the car package. I have defined a multivariate linear model and subsequently performed a repeated measures ANOVA as per the instructions in section #3 of the following comprehensive tutorial on the subject from the Gribble lab at UWO: