similar to: Mixed Anova

Dear all, I am having quite a hard time in trying to figure out how to correctly spell out a model in R (a repeated-measures anova with a within-subject covariate, I guess). Even though I have read in the posting guide that statistical advice may or may not get an answer on this list, I decided to try it anyway, hoping not to incur in somebody's ire for misusing the tool. For the sake of

Hi I'm having difficulty working out how to get what I think is the appropriate partitioning of variability in a repeated measures setup. I have G=5 treatment-groups, each containing n=6 subjects, and a response is measured on each subject on t=4 occasions. I think the anova degrees of freedom should partition as follows - Between-subjects: G*n-1=29 [ between-groups: g-1 = 4 ,

Dear r-help list, I would like to run a mixed design anova to compare the results from one population sample to another. Here my within subject variable (stiulusID) has 45 levels and my between subject variable (group) has two levels. In addition to my number of levels in the within subject variable being very large, one other 'feature' of my data is that it is not balanced on the between

Hi all, 1. I am doing some data analysis using both R and SPSS, to get used to both software packages. I had performed linear regression in R and SPSS for a number of times before this last one and the resulting coefficient values always matched. However, this last data set I was analyzing using simple linear regression and using the command lm(y~x), gave me different readings from R and

Hi everyone! I have some doubts about mixed effect models and I hope someone could help me. I´m trying to analyze a dataset coming from samples of dung beetles in the same forest fragments along 3 consecutive years (1994, 1995 and 1996) and 14 years after (2010). I sampled dung beetles in 18 different fragments with different sizes and different degrees of isolation. My aim is to determine

Dear r helpers, I would like to look at the interaction between two two-level factors, one between and one within participants, after accounting for any variance due to practice (31 trials in each of two blocks) in the task. It seems to require treating practice as a covariate. All the examples I noticed for handling covariates (i.e. ANCOVA, including the ones in Faraway's "Practical

Hi, I would like to run an ANOVA with repeated measures with both a between subjects variable and within subjects (repeated measures) variables. I know how to do this when I have equal numbers of subjects in both groups (i.e., balanced design). That has been very well documented by Baron, for example, see: http://cran.r-project.org/doc/contrib/Baron-rpsych.pdf On page 34 of this document,

Dear R-list members, I've been struggling with the proper setup for analysing my data. I've performed a route choice experiment, in which participants had to make a choice at each junction for the next road. During the experiment they received traffic information, but also encountered two different accidents. They also made trips without accidents. What I'm interested in is to

Hi list, I was searching how to properly write a command line for a mixed ANOVA. Well honestly, there are so many material on the older post of the list that just confused me. I have five factors. Season (fixed) Beach (fixed) Line (fixed) Block (random) Strata (random) nested in Block And for each of the tree strata per block I got 3 replicates. I saw lots of things about

Hi all, after browsing the archives for hours I'm still not sure about the proper analysis for my dataset. I subjected each of about 50 critters (about 10 each in 5 distinct populations) to 4 consecutive treatments (exposure to increasing concentrations), with one measurement per treatment and individual. I, of course, want to know, if there was a treatment and / or a population effect.

Dear Experts, I am new to R and unfortunately cannot start with a simply statistical analysis: I manually determined the volume of the right and left hippocampus in a group of meditators and in a group of controls. My data-sheet looks as follows: observation subject group age gender hemisphere volume 1 am04 m 25 f left 3.637 2 am04 m 25 f right 3.713 3 ao08 m 47 m left 3.715 4 ao08 m 47

Hi, I am not very familiar with complex ANOVA, so, I am not sure about the formula to use with aov() in this particular case. Could someone help me? Here is my data. I have an unbalanced plan with 4 factors: "Site", "Sex", "Age" and "Individual". "Site" is a 5-level fixed factor; "Sex" is of course a 2-levels fixed factor, as is

Hi, I am trying to do a repeated measures ANOVA to determine if there is a significant difference between two sets of timecourse data. Each individual was given a single treatment and then measured for one variable for 10 days. Here is made-up example of what my data would look like:

Hi, I am creating a mixed model based on a experiment where each subject has 2 repeats. In some instances though there is only data for one of a given subjects repeats for most there is data for both. Can I still justify having subject as a random effect? Thanks, Jonathan [X] [X] [X] [X] [X] [X] [X] [X] [[alternative HTML version deleted]]

Hi all, I've fit a mixed linear model to some longitudinal data. I'm interested in the differences in patterns of decrease in the dependent variable according to group status, and my hypothesis particularly predicts a difference between the groups in trajectory of change at between specific ages. The data shows a significant interaction between group and the linear and quadratic effects

Dear all, I am trying to use R to fit mixed models. Take the following example, where ind is a random effect and sample is fixed. I wanted to fit Model 1: values = ind + sample Model 2: values =ind * sample Model 3: values=ind(sample) + sample Tried to use the below for mod1, but it did not work. Can anyone give some help on this. Thanks so much. mod1 <- lme(values ~ sample +

Dear list, I'm quite confused when interpreting results from a mixed linear model. For example, working on Iris data frame, I want to know the effect of species on slope of the model "Petal.Length~Sepal.Length" I write this : data(iris) reg01 <- lm(Petal.Length~Sepal.Length + Sepal.Length:Species, data=iris) summary(reg01) It gives me a summary table with lm for the first

Dear R-help list, I am trying to do a mixed ANOVA on a 8960 x 5 dataframe. I have 3 factors for which I want to test all main effects and interactions : f1 (40 levels), f2 (7 levels), and f3 (4 levels). I also have a subject factor, subject, and a dependent variable, dv. Some more information about the factors: f2 is a between-subject factor. That is, for each level of f2 there are 8 nested

hi all - i'm having trouble using lme to specify a mixed effects model. i'm pretty sure this is quite easy for the experienced anova-er, which i unfortunately am not. i have a data frame with the following columns: col 1 : "Score1" (this is a continuous numeric measure between 0 and 1) col 2 : "Score2" (another continuous numeric measure, this time bounded between 0

hello R help I am trying to analyze a data set that has been collected from a hierarchical sampling design. The model should be a mixed model nested ANOVA. The purpose of my study is to analyze the variability at each spatial scale in my design (random factors, variance components), and say something about the variability between regions (fixed factor, contrast of means). The data is as